. clear all
. cap noi which tabout
c:\ado\plus\t\tabout.ado
*! 2.0.8 Ian Watson 15mar2019
*! tabout version 3 (beta) available at: http://tabout.net.au
. if _rc==111 {
. cap noi ssc install tabout
. }
. cap noi which pathutil
c:\ado\plus\p\pathutil.ado
*! version 2.2.0 19nov2020 daniel klein
. if _rc==111 {
. cap noi net install pathutil, from("http://fmwww.bc.edu/repec/bocode/p/")
. }
. cap noi which pathutil
c:\ado\plus\p\pathutil.ado
*! version 2.2.0 19nov2020 daniel klein
. if _rc==111 {
. ssc install dirtools
. }
. cap noi which project
c:\ado\plus\p\project.ado
*! version 1.3.1 22dec2013 picard@netbox.com
. if _rc==111 {
. ssc install project
. }
. cap noi which stipw
c:\ado\plus\s\stipw.ado
*! Version 1.0.0 17Jan2022
. if _rc==111 {
. ssc install stipw
. }
. cap noi which stpm2
c:\ado\plus\s\stpm2.ado
*! version 1.7.5 May2021
. if _rc==111 {
. ssc install stpm2
. }
. cap noi which rcsgen
c:\ado\plus\r\rcsgen.ado
*! version 1.5.9 13FEB2022
. if _rc==111 {
. ssc install rcsgen
. }
. cap noi which matselrc
c:\ado\plus\m\matselrc.ado
*! NJC 1.1.0 20 Apr 2000 (STB-56: dm79)
. if _rc==111 {
. cap noi net install dm79, from(http://www.stata.com/stb/stb56)
. }
. cap noi which stpm2_standsurv
c:\ado\plus\s\stpm2_standsurv.ado
*! version 1.1.2 12Jun2018
. if _rc==111 {
. cap noi net install stpm2_standsurv.pkg, from(http://fmwww.bc.edu/RePEc/bocode/s)
. }
. cap noi which fs
c:\ado\plus\f\fs.ado
*! NJC 1.0.5 23 November 2006
. if _rc==111 {
. ssc install fs
. }
. cap noi which mkspline2
c:\ado\plus\m\mkspline2.ado
*! version 1.0.0 MLB 04Apr2009
. if _rc==111 {
. ssc install postrcspline
. }
.
. cap noi ssc install moremata
checking moremata consistency and verifying not already installed...
the following files already exist and are different:
c:\ado\plus\l\lmoremata.mlib
c:\ado\plus\l\lmoremata10.mlib
c:\ado\plus\l\lmoremata11.mlib
c:\ado\plus\l\lmoremata14.mlib
c:\ado\plus\m\moremata.hlp
c:\ado\plus\m\moremata_source.hlp
c:\ado\plus\m\moremata11_source.hlp
c:\ado\plus\m\mf_mm_quantile.hlp
c:\ado\plus\m\mf_mm_ipolate.hlp
c:\ado\plus\m\mf_mm_collapse.hlp
c:\ado\plus\m\mf_mm_ebal.sthlp
c:\ado\plus\m\mf_mm_density.sthlp
c:\ado\plus\m\mf_mm_hl.hlp
c:\ado\plus\m\mf_mm_mloc.hlp
c:\ado\plus\m\mf_mm_ls.hlp
c:\ado\plus\m\mf_mm_qr.sthlp
no files installed or copied
(no action taken)
Date created: 16:30:41 5 Apr 2023.
Get the folder
E:\Mi unidad\Alvacast\SISTRAT 2022 (github)
Fecha: 5 Apr 2023, considerando un SO Windows para el usuario: andre
Path data= ;
Tiempo: 5 Apr 2023, considerando un SO Windows
The file is located and named as: E:\Mi unidad\Alvacast\SISTRAT 2022 (github)fiscalia_mariel_oct_2022_match_SENDA.dta
=============================================================================
=============================================================================
We open the files
. use "fiscalia_mariel_feb_2023_match_SENDA_pris.dta", clear
.
. *b) select 10% of the data
. /*
> set seed 2125
> sample 10
> */
.
.
. fs mariel_ags_*.do
mariel_ags_b.do mariel_ags_b_m2.do mariel_ags_b_m1.do mariel_ags_b_m3.do
. di "`r(dofile)'"
.
. *tostring tr_modality, gen(tr_modality_str)
.
. cap noi encode tr_modality_str, gen(newtr_modality)
variable tr_modality_str not found
. cap confirm variable newtr_modality
. if !_rc {
. cap noi drop tr_modality
. cap noi rename newtr_modality tr_modality
. }
.
. cap noi encode condicion_ocupacional_cor, gen(newcondicion_ocupacional_cor)
not possible with numeric variable
. cap confirm variable newcondicion_ocupacional_cor
. if !_rc {
. cap noi drop condicion_ocupacional_cor
. cap noi rename newcondicion_ocupacional_cor condicion_ocupacional_cor
. }
.
. cap noi encode tipo_centro, gen(newtipo_centro)
variable tipo_centro not found
. cap confirm variable newtipo_centro
. if !_rc {
. cap noi drop tipo_centro
. cap noi rename newtipo_centro tipo_centro
. }
.
. cap noi encode sus_ini_mod_mvv, gen(newsus_ini_mod_mvv)
. cap confirm variable newsus_ini_mod_mvv
. if !_rc {
. cap noi drop sus_ini_mod_mvv
. cap noi rename newsus_ini_mod_mvv sus_ini_mod_mvv
. }
.
. cap noi encode dg_trs_cons_sus_or, gen(newdg_trs_cons_sus_or)
. cap confirm variable newdg_trs_cons_sus_or
. if !_rc {
. cap noi drop dg_trs_cons_sus_or
. cap noi rename newdg_trs_cons_sus_or dg_trs_cons_sus_or
. }
.
. cap noi encode con_quien_vive_joel, gen(newcon_quien_vive_joel)
. cap confirm variable newcon_quien_vive_joel
. if !_rc {
. cap noi drop con_quien_vive_joel
. cap noi rename newcon_quien_vive_joel con_quien_vive_joel
. }
.
.
. *order and encode
. cap noi decode freq_cons_sus_prin, gen(str_freq_cons_sus_prin)
. cap confirm variable str_freq_cons_sus_prin
. if !_rc {
. cap noi drop freq_cons_sus_prin
. label def freq_cons_sus_prin2 1 "Less than 1 day a week" 2 "1 day a week or more" 3 "2 to 3 days a week" 4 "4 to 6 days a week" 5 "Daily"
. encode str_freq_cons_sus_prin, gen(freq_cons_sus_prin) label (freq_cons_sus_prin2)
. }
. cap noi decode dg_trs_cons_sus_or, gen(str_dg_trs_cons_sus_or)
. cap confirm variable str_dg_trs_cons_sus_or
. if !_rc {
. cap noi drop dg_trs_cons_sus_or
. cap label def dg_trs_cons_sus_or2 1 "Hazardous consumption" 2 "Drug dependence"
. encode str_dg_trs_cons_sus_or, gen(dg_trs_cons_sus_or) label (dg_trs_cons_sus_or2)
. }
.
.
. cap noi encode escolaridad_rec, gen(esc_rec)
not possible with numeric variable
. cap noi encode sex, generate(sex_enc)
. cap noi encode sus_principal_mod, gen(sus_prin_mod)
not possible with numeric variable
. cap noi encode freq_cons_sus_prin, gen(fr_sus_prin)
not possible with numeric variable
. cap noi encode compromiso_biopsicosocial, gen(comp_biosoc)
variable compromiso_biopsicosocial not found
. cap noi encode tenencia_de_la_vivienda_mod, gen(ten_viv)
not possible with numeric variable
. *encode dg_cie_10_rec, generate(dg_cie_10_mental_h) *already numeric
. cap noi encode dg_trs_cons_sus_or, gen(sud_severity_icd10)
not possible with numeric variable
. cap noi encode macrozona, gen(macrozone)
not possible with numeric variable
.
. /*
> *2023-02-28, not done in R
> cap noi recode numero_de_hijos_mod (0=0 "No children") (1/10=1 "Children"), gen(newnumero_de_hijos_mod)
> cap confirm variable newnumero_de_hijos_mod
> if !_rc {
> drop numero_de_hijos_mod
> cap noi rename newnumero_de_hijos_mod numero_de_hijos_mod
> }
> */
.
. *same for condemnatory sentence
. mkspline2 rc_x = edad_al_ing_1, cubic nknots(4) displayknots
| knot1 knot2 knot3 knot4
-------------+--------------------------------------------
edad_al_in~1 | 21.18685 29.99178 38.92615 56.32477
.
. *not necessary: 2023-02-28
. *gen motivodeegreso_mod_imp_rec3 = 1
. *replace motivodeegreso_mod_imp_rec3 = 2 if strpos(motivodeegreso_mod_imp_rec,"Early")>0
. *replace motivodeegreso_mod_imp_rec3 = 3 if strpos(motivodeegreso_mod_imp_rec,"Late")>0
.
. *encode policonsumo, generate(policon) *already numeric
. // Generate a restricted cubic spline variable for a variable "x" with 4 knots
. *https://chat.openai.com/chat/4a9396cd-2caa-4a2e-b5f4-ed2c2d0779b3
. *https://www.stata.com/meeting/nordic-and-baltic15/abstracts/materials/sweden15_oskarsson.pdf
. *mkspline xspline = edad_al_ing_1, cubic nknots(4)
. *gen rcs_x = xspline1*xspline2 xspline3 xspline4
.
. *https://www.statalist.org/forums/forum/general-stata-discussion/general/1638622-comparing-cox-proportional-hazard-linear-and-non-linear-restricted-cubic-spline-models-using-likelihood-ratio-test
.
We show a table of missing values
. /*
>
> vars_cov<-c("motivodeegreso_mod_imp_rec", "tr_modality", "edad_al_ing_1", "sex", "edad_ini_cons", "escolaridad_rec", "sus_principal_mod", "freq_cons_sus_prin", "condicion_ocupacional_corr", "policonsumo", "num_hij
> os_mod_joel_bin", "tenencia_de_la_vivienda_mod", "macrozona", "n_off_vio", "n_off_acq", "n_off_sud", "n_off_oth", "dg_cie_10_rec", "dg_trs_cons_sus_or", "clas_r", "porc_pobr", "sus_ini_mod_mvv", "ano_nac_corr", "c
> on_quien_vive_joel", "fis_comorbidity_icd_10")
>
> */
.
. misstable sum motivodeegreso_mod_imp_rec tr_modality edad_al_ing_1 sex_enc edad_ini_cons escolaridad_rec sus_principal_mod freq_cons_sus_prin condicion_ocupacional_cor policonsumo num_hijos_mod_joel_bin tenencia_d
> e_la_vivienda_mod macrozona n_off_vio n_off_acq n_off_sud n_off_oth dg_cie_10_rec dg_trs_cons_sus_or clas_r porc_pobr sus_ini_mod_mvv ano_nac_corr con_quien_vive_joel fis_comorbidity_icd_10
Obs<.
+------------------------------
| | Unique
Variable | Obs=. Obs>. Obs<. | values Min Max
-------------+--------------------------------+------------------------------
motivodeeg~c | 9 70,854 | 3 1 3
tr_modality | 68 70,795 | 2 1 2
edad_ini_c~s | 5,924 64,939 | 68 5 74
escolarida~c | 317 70,546 | 3 1 3
sus_princi~d | 1 70,862 | 5 1 5
freq_cons_~n | 355 70,508 | 5 1 5
condicion_~r | 1 70,862 | 6 1 6
num_hijos_~n | 604 70,259 | 2 0 1
tenencia_d~d | 4,058 66,805 | 5 1 5
macrozona | 16 70,847 | 3 1 3
dg_trs_con~r | 1 70,862 | 2 1 2
clas_r | 2 70,861 | 3 1 3
porc_pobr | 2 70,861 | >500 .0003295 .6305783
sus_ini_mo~v | 5,787 65,076 | 5 1 5
con_quien_~l | 1 70,862 | 4 1 4
-----------------------------------------------------------------------------
And missing patterns
. misstable pat motivodeegreso_mod_imp_rec tr_modality edad_al_ing_1 sex_enc edad_ini_cons escolaridad_rec sus_principal_mod freq_cons_sus_prin condicion_ocupacional_cor policonsumo num_hijos_mod_joel_bin tenencia_d
> e_la_vivienda_mod macrozona n_off_vio n_off_acq n_off_sud n_off_oth dg_cie_10_rec dg_trs_cons_sus_or clas_r porc_pobr sus_ini_mod_mvv ano_nac_corr con_quien_vive_joel fis_comorbidity_icd_10
Missing-value patterns
(1 means complete)
| Pattern
Percent | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
------------+----------------------------------------------------
85% | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
|
7 | 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
5 | 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
<1 | 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1
<1 | 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0
<1 | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
<1 | 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
<1 | 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
<1 | 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
<1 | 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1
<1 | 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1
<1 | 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0
<1 | 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0
<1 | 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1
<1 | 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0
<1 | 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0
<1 | 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0
<1 | 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1
<1 | 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1
<1 | 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0
<1 | 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1
<1 | 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1
<1 | 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1
<1 | 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0
<1 | 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1
<1 | 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0
<1 | 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0
<1 | 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0
<1 | 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1
<1 | 0 0 0 0 1 1 1 1 1 0 0 1 0 0 0
<1 | 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1
<1 | 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1
<1 | 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1
<1 | 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1
<1 | 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0
<1 | 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0
<1 | 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0
<1 | 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0
<1 | 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1
<1 | 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0
<1 | 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0
<1 | 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1
<1 | 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1
<1 | 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1
<1 | 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1
<1 | 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0
<1 | 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1
<1 | 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0
<1 | 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1
<1 | 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0
------------+----------------------------------------------------
100% |
Variables are (1) con_quien_vive_joel (2) condicion_ocupacional_corr (3) dg_trs_cons_sus_or (4) sus_principal_mod (5) clas_r (6) porc_pobr (7) motivodeegreso_mod_imp_rec (8) macrozona (9) tr_modality
(10) escolaridad_rec (11) freq_cons_sus_prin (12) num_hijos_mod_joel_bin (13) tenencia_de_la_vivienda_mod (14) sus_ini_mod_mvv (15) edad_ini_cons
=============================================================================
=============================================================================
Reset-time
. *if missing offender_d (status) , means that there was a record and the time is the time of offense
.
. *set the indicator
. gen event=0
. replace event=1 if !missing(offender_d)
(5,144 real changes made)
. *replace event=1 if !missing(sex)
.
. *correct time to event if _st=0
. gen diff= age_offending_imp-edad_al_egres_imp
. gen diffc= cond(diff<0.001, 0.001, diff)
. drop diff
. rename diffc diff
. lab var diff "Time to offense leading to condemnatory sentence"
.
. *age time
. *stset age_offending_imp, fail(event ==1) enter(edad_al_egres_imp)
. *reset time
. stset diff, failure(event ==1)
failure event: event == 1
obs. time interval: (0, diff]
exit on or before: failure
------------------------------------------------------------------------------
70,863 total observations
0 exclusions
------------------------------------------------------------------------------
70,863 observations remaining, representing
5,144 failures in single-record/single-failure data
302,812.79 total analysis time at risk and under observation
at risk from t = 0
earliest observed entry t = 0
last observed exit t = 10.75828
.
. stdescribe, weight
failure _d: event == 1
analysis time _t: diff
|-------------- per subject --------------|
Category total mean min median max
------------------------------------------------------------------------------
no. of subjects 70863
no. of records 70863 1 1 1 1
(first) entry time 0 0 0 0
(final) exit time 4.273214 .001 3.964384 10.75828
subjects with gap 0
time on gap if gap 0
time at risk 302812.79 4.273214 .001 3.964384 10.75828
failures 5144 .0725908 0 0 1
------------------------------------------------------------------------------
We calculate the incidence rate.
. stsum, by (motivodeegreso_mod_imp_rec)
failure _d: event == 1
analysis time _t: diff
| Incidence Number of |------ Survival time -----|
motivo~c | Time at risk rate subjects 25% 50% 75%
---------+---------------------------------------------------------------------
Treatmen | 76,631.0368 .0086649 19276 . . .
Treatmen | 65,879.5092 .0259717 15797 . . .
Treatmen | 160,259.189 .0172595 35781 . . .
---------+---------------------------------------------------------------------
Total | 302,769.735 .0169799 70854 . . .
. *Micki Hill & Paul C Lambert & Michael J Crowther, 2021. "Introducing stipw: inverse probability weighted parametric survival models," London Stata Conference 2021 15, Stata Users Group.
. *https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Ffmwww.bc.edu%2Frepec%2Fusug2021%2Fusug21_hill.pptx&wdOrigin=BROWSELINK
.
. *Treatment variable should be a binary variable with values 0 and 1.
. gen motivodeegreso_mod_imp_rec2 = 0
. replace motivodeegreso_mod_imp_rec2 = 1 if motivodeegreso_mod_imp_rec==2
(15,797 real changes made)
. replace motivodeegreso_mod_imp_rec2 = 1 if motivodeegreso_mod_imp_rec==3
(35,781 real changes made)
.
. recode motivodeegreso_mod_imp_rec2 (0=1 "Tr Completion") (1=0 "Tr Non-completion (Late & Early)"), gen(caus_disch_mod_imp_rec)
(70863 differences between motivodeegreso_mod_imp_rec2 and caus_disch_mod_imp_rec)
.
. cap noi gen motegr_dum3= motivodeegreso_mod_imp_rec2
. replace motegr_dum3 = 0 if motivodeegreso_mod_imp_rec==2
(15,797 real changes made)
. cap noi gen motegr_dum2= motivodeegreso_mod_imp_rec2
. replace motegr_dum2 = 0 if motivodeegreso_mod_imp_rec==3
(35,781 real changes made)
. lab var motegr_dum3 "Baseline treatment outcome(dich, 1= Late Dropout)"
. lab var motegr_dum2 "Baseline treatment outcome(dich, 1= Early Dropout)"
. lab var caus_disch_mod_imp_rec "Baseline treatment outcome(dich)"
.
.
. *Factor variables not allowed for tvc() option. Create your own dummy varibles.
. gen motivodeegreso_mod_imp_rec_earl = 1
. replace motivodeegreso_mod_imp_rec_earl = 0 if motivodeegreso_mod_imp_rec==1
(19,276 real changes made)
. replace motivodeegreso_mod_imp_rec_earl = 0 if motivodeegreso_mod_imp_rec==3
(35,781 real changes made)
.
. gen motivodeegreso_mod_imp_rec_late = 1
. replace motivodeegreso_mod_imp_rec_late = 0 if motivodeegreso_mod_imp_rec==1
(19,276 real changes made)
. replace motivodeegreso_mod_imp_rec_late = 0 if motivodeegreso_mod_imp_rec==2
(15,797 real changes made)
.
. *recode motivodeegreso_mod_imp_rec_earl (1=1 "Early dropout") (0=0 "Tr. comp & Late dropout"), gen(newmotivodeegreso_mod_imp_rec_e)
. *recode motivodeegreso_mod_imp_rec_late (1=1 "Late dropout") (0=0 "Tr. comp & Early dropout"), gen(newmotivodeegreso_mod_imp_rec_l)
.
. lab var motivodeegreso_mod_imp_rec_earl "Baseline treatment outcome- Early dropout(dich)"
. lab var motivodeegreso_mod_imp_rec_late "Baseline treatment outcome- Late dropout(dich)"
.
. cap noi rename motivodeegreso_mod_imp_rec_late mot_egr_late
. cap noi rename motivodeegreso_mod_imp_rec_earl mot_egr_early
=============================================================================
=============================================================================
We generated a graph with every type of treatment and the Nelson-Aalen estimate.
. sts graph, na by (motivodeegreso_mod_imp_rec) ci ///
> title("Comission of an offense (impprisonment)") ///
> subtitle("Nelson-Aalen Cum Hazards w/ Confidence Intervals 95%") ///
> risktable(, size(*.5) order(1 "Tr Completion" 2 "Early Disch" 3 "Late Disch")) ///
> ytitle("Cum. Hazards") ylabel(#8) ///
> xtitle("Years since tr. outcome") xlabel(#8) ///
> note("Source: nDP, SENDA's SUD Treatments & POs Office Data period 2010-2019 ") ///
> legend(rows(3)) ///
> legend(cols(4)) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> legend(order(1 "95CI Tr Completion" 2 "Tr Completion" 3 "95CI Early Tr Disch" 4 "Early Tr Disch " 5 "95CI Late Tr Disch" 6 "Late Tr Disch" )size(*.5)region(lstyle(none)) region(c(none)) nobox)
failure _d: event == 1
analysis time _t: diff
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\tto_2023_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\tto_2023_pris.gph saved)
=============================================================================
=============================================================================
. /*
> vars_cov<-c("motivodeegreso_mod_imp_rec", "tr_modality", "edad_al_ing_1", "sex", "edad_ini_cons", "escolaridad_rec", "sus_principal_mod", "freq_cons_sus_prin", "condicion_ocupacional_corr", "policonsumo", "num_hij
> os_mod_joel_bin", "tenencia_de_la_vivienda_mod", "macrozona", "n_off_vio", "n_off_acq", "n_off_sud", "n_off_oth", "dg_cie_10_rec", "dg_trs_cons_sus_or", "clas_r", "porc_pobr", "sus_ini_mod_mvv", "ano_nac_corr", "
> con_quien_vive_joel", "fis_comorbidity_icd_10")
> */
.
. global covs "i.motivodeegreso_mod_imp_rec i.tr_modality i.sex_enc edad_ini_cons i.escolaridad_rec i.sus_principal_mod i.freq_cons_sus_prin i.condicion_ocupacional_cor i.policonsumo i.num_hijos_mod_joel_bin i.tenen
> cia_de_la_vivienda_mod i.macrozona i.n_off_vio i.n_off_acq i.n_off_sud i.n_off_oth i.dg_cie_10_rec i.dg_trs_cons_sus_or i.clas_r porc_pobr i.sus_ini_mod_mvv ano_nac_corr i.con_quien_vive_joel i.fis_comorbidity_icd
> _10"
.
.
. qui noi stcox $covs edad_al_ing_1, efron robust nolog schoenfeld(sch_a*) scaledsch(sca_a*) //change _a
failure _d: event == 1
analysis time _t: diff
Cox regression -- Efron method for ties
No. of subjects = 60,247 Number of obs = 60,247
No. of failures = 3,971
Time at risk = 235636.9084
Wald chi2(49) = 4899.58
Log pseudolikelihood = -39767.557 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
| Robust
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.893343 .1155171 10.46 0.000 1.679947 2.133846
Treatment non-completion (Late) | 1.615254 .0830985 9.32 0.000 1.460326 1.786618
|
tr_modality |
Residential | 1.213021 .0535177 4.38 0.000 1.112536 1.322583
|
sex_enc |
Women | .6047681 .0294192 -10.34 0.000 .5497709 .665267
edad_ini_cons | .9716051 .0047457 -5.90 0.000 .9623482 .9809511
|
escolaridad_rec |
2-Completed high school or less | .8988936 .0324129 -2.96 0.003 .8375583 .9647204
1-More than high school | .724406 .0449833 -5.19 0.000 .6413945 .8181612
|
sus_principal_mod |
Cocaine hydrochloride | 1.188918 .0804801 2.56 0.011 1.041195 1.357598
Cocaine paste | 1.742265 .0964721 10.03 0.000 1.563082 1.941989
Marijuana | 1.1774 .0960346 2.00 0.045 1.003451 1.381505
Other | 1.602489 .2500445 3.02 0.003 1.18026 2.175767
|
freq_cons_sus_prin |
1 day a week or more | .965081 .1095067 -0.31 0.754 .7726429 1.205449
2 to 3 days a week | .9788317 .0909806 -0.23 0.818 .8158127 1.174426
4 to 6 days a week | 1.000066 .0962824 0.00 0.999 .828091 1.207755
Daily | 1.027076 .0952177 0.29 0.773 .8564254 1.231729
|
condicion_ocupacional_corr |
Inactive | 1.027334 .0729764 0.38 0.704 .8938132 1.1808
Looking for a job for the first time | 1.100779 .2864594 0.37 0.712 .6609815 1.833206
No activity | 1.193664 .0879715 2.40 0.016 1.033117 1.37916
Not seeking for work | 1.025036 .1567036 0.16 0.872 .7596449 1.383144
Unemployed | 1.183684 .0465496 4.29 0.000 1.095876 1.278528
|
1.policonsumo | 1.005346 .0495512 0.11 0.914 .9127714 1.107311
1.num_hijos_mod_joel_bin | 1.159625 .0465399 3.69 0.000 1.071904 1.254525
|
tenencia_de_la_vivienda_mod |
Others | 1.049513 .1617296 0.31 0.754 .7759217 1.419573
Owner/Transferred dwellings/Pays Dividends | .9233792 .1253519 -0.59 0.557 .7076632 1.204852
Renting | .9750389 .1335778 -0.18 0.854 .7454346 1.275365
Stays temporarily with a relative | .9454354 .1281242 -0.41 0.679 .7249 1.233064
|
macrozona |
North | 1.436985 .0607238 8.58 0.000 1.322764 1.561069
South | 1.520352 .0990933 6.43 0.000 1.338026 1.727522
|
n_off_vio |
1 | 1.462779 .0578913 9.61 0.000 1.353604 1.580761
|
n_off_acq |
1 | 2.79371 .1008959 28.45 0.000 2.602794 2.998629
|
n_off_sud |
1 | 1.398199 .0532569 8.80 0.000 1.297619 1.506576
|
n_off_oth |
1 | 1.742425 .0661359 14.63 0.000 1.617505 1.876993
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.1229 .0563433 2.31 0.021 1.017726 1.238944
With psychiatric comorbidity | 1.103991 .0434116 2.52 0.012 1.022102 1.192441
|
dg_trs_cons_sus_or |
Drug dependence | 1.042081 .0448559 0.96 0.338 .9577718 1.133812
|
clas_r |
Mixta | .9031561 .0581037 -1.58 0.113 .7961622 1.024528
Rural | .8685267 .0605688 -2.02 0.043 .7575697 .9957349
|
porc_pobr | 1.543461 .387863 1.73 0.084 .9431779 2.525793
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.189748 .1078452 1.92 0.055 .9960875 1.42106
Cocaine paste | 1.276789 .0840972 3.71 0.000 1.122158 1.452729
Marijuana | 1.171154 .0444812 4.16 0.000 1.087139 1.261663
Other | 1.428192 .1388473 3.67 0.000 1.180413 1.727984
|
ano_nac_corr | .8490027 .0077506 -17.93 0.000 .8339469 .8643303
|
con_quien_vive_joel |
Family of origin | .8832343 .0610389 -1.80 0.072 .7713487 1.011349
Others | 1.078343 .0883292 0.92 0.357 .9184035 1.266137
With couple/children | .9794422 .0669097 -0.30 0.761 .8567018 1.119768
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.056193 .0368851 1.57 0.117 .9863185 1.131018
One or more | .8059857 .0714058 -2.43 0.015 .6775099 .9588243
|
edad_al_ing_1 | .8226295 .0077362 -20.76 0.000 .8076057 .8379329
-------------------------------------------------------------------------------------------------------------
. qui noi estat phtest, log detail
Test of proportional-hazards assumption
Time: Log(t)
----------------------------------------------------------------
| rho chi2 df Prob>chi2
------------+---------------------------------------------------
1b.motivod~c| . . 1 .
2.motivode~c| -0.05059 10.53 1 0.0012
3.motivode~c| -0.03586 5.26 1 0.0218
1b.tr_moda~y| . . 1 .
2.tr_modal~y| 0.01507 1.06 1 0.3037
1b.sex_enc | . . 1 .
2.sex_enc | -0.04339 7.62 1 0.0058
edad_ini_c~s| 0.03986 6.91 1 0.0085
1b.escolar~c| . . 1 .
2.escolari~c| -0.01171 0.59 1 0.4431
3.escolari~c| 0.02373 2.35 1 0.1249
1b.sus_pri~d| . . 1 .
2.sus_prin~d| 0.00409 0.07 1 0.7936
3.sus_prin~d| -0.00610 0.16 1 0.6875
4.sus_prin~d| 0.01437 0.89 1 0.3447
5.sus_prin~d| -0.03449 5.40 1 0.0201
1b.freq_co~n| . . 1 .
2.freq_con~n| 0.01951 1.58 1 0.2086
3.freq_con~n| -0.00189 0.02 1 0.9025
4.freq_con~n| -0.01001 0.42 1 0.5158
5.freq_con~n| -0.00734 0.23 1 0.6305
1b.condici~r| . . 1 .
2.condicio~r| 0.02791 3.08 1 0.0793
3.condicio~r| 0.00173 0.01 1 0.9149
4.condicio~r| -0.00312 0.04 1 0.8393
5.condicio~r| 0.01235 0.60 1 0.4369
6.condicio~r| -0.01120 0.51 1 0.4754
0b.policon~o| . . 1 .
1.policons~o| -0.03022 3.84 1 0.0500
0b.num_hij~n| . . 1 .
1.num_hijo~n| -0.00038 0.00 1 0.9803
1b.tenenci~d| . . 1 .
2.tenencia~d| 0.01153 0.60 1 0.4371
3.tenencia~d| 0.00626 0.19 1 0.6666
4.tenencia~d| 0.00253 0.03 1 0.8619
5.tenencia~d| 0.01017 0.49 1 0.4841
1b.macrozona| . . 1 .
2.macrozona | 0.03208 4.26 1 0.0391
3.macrozona | -0.01009 0.45 1 0.5024
1b.n_off_vio| . . 1 .
2.n_off_vio | -0.00915 0.39 1 0.5303
1b.n_off_acq| . . 1 .
2.n_off_acq | -0.06145 18.23 1 0.0000
1b.n_off_sud| . . 1 .
2.n_off_sud | 0.00104 0.01 1 0.9430
1b.n_off_oth| . . 1 .
2.n_off_oth | -0.03930 7.12 1 0.0076
1b.dg_cie_~c| . . 1 .
2.dg_cie_1~c| 0.01661 1.16 1 0.2813
3.dg_cie_1~c| -0.02129 1.95 1 0.1624
1b.dg_trs_~r| . . 1 .
2.dg_trs_c~r| 0.00988 0.41 1 0.5215
1b.clas_r | . . 1 .
2.clas_r | 0.00879 0.35 1 0.5546
3.clas_r | 0.02052 1.77 1 0.1833
porc_pobr | -0.01220 0.61 1 0.4341
1b.sus_ini~v| . . 1 .
2.sus_ini_~v| 0.01219 0.58 1 0.4457
3.sus_ini_~v| -0.00555 0.13 1 0.7173
4.sus_ini_~v| -0.00124 0.01 1 0.9350
5.sus_ini_~v| -0.01537 1.08 1 0.2986
ano_nac_corr| -0.04172 5.99 1 0.0144
1b.con_qui~l| . . 1 .
2.con_quie~l| -0.01158 0.59 1 0.4437
3.con_quie~l| -0.01958 1.65 1 0.1986
4.con_quie~l| 0.01517 1.01 1 0.3152
1b.fis_co~10| . . 1 .
2.fis_com~10| 0.00406 0.07 1 0.7934
3.fis_com~10| -0.01001 0.43 1 0.5135
edad_al_in~1| -0.05740 11.94 1 0.0005
------------+---------------------------------------------------
global test | 158.90 49 0.0000
----------------------------------------------------------------
note: robust variance-covariance matrix used.
. mat mat_scho_test = r(phtest)
. scalar chi2_scho_test = r(chi2)
. scalar chi2_scho_test_df = r(df)
. scalar chi2_scho_test_p = r(p)
.
. esttab matrix(mat_scho_test) using "mat_scho_test_02_2023_1_pris.csv", replace
(output written to mat_scho_test_02_2023_1_pris.csv)
. esttab matrix(mat_scho_test) using "mat_scho_test_02_2023_1_pris.html", replace
(output written to mat_scho_test_02_2023_1_pris.html)
.
Chi^2(49)= 158.9, p= 0
| mat_scho_test | ||||
| rho | chi2 | df | p | |
| 1b.motivodeegreso_mod_imp_rec | . | . | 1 | . |
| 2.motivodeegreso_mod_imp_rec | -.0505855 | 10.53056 | 1 | .0011742 |
| 3.motivodeegreso_mod_imp_rec | -.0358612 | 5.261426 | 1 | .0218032 |
| 1b.tr_modality | . | . | 1 | . |
| 2.tr_modality | .015071 | 1.057964 | 1 | .30368 |
| 1b.sex_enc | . | . | 1 | . |
| 2.sex_enc | -.0433908 | 7.618497 | 1 | .0057773 |
| edad_ini_cons | .0398606 | 6.914851 | 1 | .0085483 |
| 1b.escolaridad_rec | . | . | 1 | . |
| 2.escolaridad_rec | -.0117144 | .5883052 | 1 | .4430753 |
| 3.escolaridad_rec | .0237257 | 2.354241 | 1 | .1249427 |
| 1b.sus_principal_mod | . | . | 1 | . |
| 2.sus_principal_mod | .0040949 | .0684282 | 1 | .7936393 |
| 3.sus_principal_mod | -.0060988 | .1617882 | 1 | .6875155 |
| 4.sus_principal_mod | .0143697 | .8929738 | 1 | .3446727 |
| 5.sus_principal_mod | -.0344916 | 5.40097 | 1 | .0201256 |
| 1b.freq_cons_sus_prin | . | . | 1 | . |
| 2.freq_cons_sus_prin | .0195059 | 1.581007 | 1 | .2086158 |
| 3.freq_cons_sus_prin | -.0018923 | .0150174 | 1 | .9024671 |
| 4.freq_cons_sus_prin | -.0100098 | .4223019 | 1 | .5157907 |
| 5.freq_cons_sus_prin | -.0073376 | .2313167 | 1 | .6305493 |
| 1b.condicion_ocupacional_corr | . | . | 1 | . |
| 2.condicion_ocupacional_corr | .0279073 | 3.07992 | 1 | .0792644 |
| 3.condicion_ocupacional_corr | .0017338 | .0114057 | 1 | .9149497 |
| 4.condicion_ocupacional_corr | -.0031215 | .0411386 | 1 | .8392706 |
| 5.condicion_ocupacional_corr | .0123507 | .6045029 | 1 | .4368651 |
| 6.condicion_ocupacional_corr | -.0111956 | .5093345 | 1 | .4754271 |
| 0b.policonsumo | . | . | 1 | . |
| 1.policonsumo | -.0302215 | 3.842904 | 1 | .0499569 |
| 0b.num_hijos_mod_joel_bin | . | . | 1 | . |
| 1.num_hijos_mod_joel_bin | -.0003791 | .0006091 | 1 | .9803103 |
| 1b.tenencia_de_la_vivienda_mod | . | . | 1 | . |
| 2.tenencia_de_la_vivienda_mod | .0115322 | .6038898 | 1 | .4370977 |
| 3.tenencia_de_la_vivienda_mod | .0062635 | .1855696 | 1 | .6666299 |
| 4.tenencia_de_la_vivienda_mod | .0025333 | .0302815 | 1 | .8618531 |
| 5.tenencia_de_la_vivienda_mod | .0101733 | .489641 | 1 | .4840875 |
| 1b.macrozona | . | . | 1 | . |
| 2.macrozona | .0320817 | 4.258089 | 1 | .0390638 |
| 3.macrozona | -.0100901 | .4497977 | 1 | .5024311 |
| 1b.n_off_vio | . | . | 1 | . |
| 2.n_off_vio | -.0091515 | .3938548 | 1 | .5302801 |
| 1b.n_off_acq | . | . | 1 | . |
| 2.n_off_acq | -.0614513 | 18.2312 | 1 | .0000196 |
| 1b.n_off_sud | . | . | 1 | . |
| 2.n_off_sud | .001039 | .0051103 | 1 | .9430109 |
| 1b.n_off_oth | . | . | 1 | . |
| 2.n_off_oth | -.0392972 | 7.119297 | 1 | .0076259 |
| 1b.dg_cie_10_rec | . | . | 1 | . |
| 2.dg_cie_10_rec | .0166141 | 1.16072 | 1 | .2813163 |
| 3.dg_cie_10_rec | -.0212859 | 1.951763 | 1 | .162397 |
| 1b.dg_trs_cons_sus_or | . | . | 1 | . |
| 2.dg_trs_cons_sus_or | .0098824 | .4109435 | 1 | .521491 |
| 1b.clas_r | . | . | 1 | . |
| 2.clas_r | .0087935 | .3490787 | 1 | .5546351 |
| 3.clas_r | .0205235 | 1.770267 | 1 | .1833491 |
| porc_pobr | -.0121971 | .6117374 | 1 | .4341344 |
| 1b.sus_ini_mod_mvv | . | . | 1 | . |
| 2.sus_ini_mod_mvv | .0121904 | .581623 | 1 | .4456769 |
| 3.sus_ini_mod_mvv | -.0055531 | .1310749 | 1 | .7173202 |
| 4.sus_ini_mod_mvv | -.0012447 | .0066507 | 1 | .9350033 |
| 5.sus_ini_mod_mvv | -.015372 | 1.080382 | 1 | .2986122 |
| ano_nac_corr | -.041718 | 5.989176 | 1 | .0143939 |
| 1b.con_quien_vive_joel | . | . | 1 | . |
| 2.con_quien_vive_joel | -.0115847 | .5865831 | 1 | .4437435 |
| 3.con_quien_vive_joel | -.0195801 | 1.65292 | 1 | .198562 |
| 4.con_quien_vive_joel | .0151687 | 1.008687 | 1 | .3152175 |
| 1b.fis_comorbidity_icd_10 | . | . | 1 | . |
| 2.fis_comorbidity_icd_10 | .0040638 | .0685903 | 1 | .7934005 |
| 3.fis_comorbidity_icd_10 | -.0100107 | .4268851 | 1 | .5135213 |
| edad_al_ing_1 | -.0574039 | 11.93863 | 1 | .0005498 |
. // VERIFY FIRST SPLINE VARIABLE IS THE ORIGINAL VARIABLE
. assert float(edad_al_ing_1) == float(rc_x1)
.
. // MODEL WITH FULL SPLINE
. qui noi stcox $covs rc*
failure _d: event == 1
analysis time _t: diff
Iteration 0: log likelihood = -42140.282
Iteration 1: log likelihood = -40144.884
Iteration 2: log likelihood = -39754.335
Iteration 3: log likelihood = -39752.062
Iteration 4: log likelihood = -39752.058
Refining estimates:
Iteration 0: log likelihood = -39752.058
Cox regression -- Breslow method for ties
No. of subjects = 60,247 Number of obs = 60,247
No. of failures = 3,971
Time at risk = 235636.9084
LR chi2(51) = 4776.45
Log likelihood = -39752.058 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.893387 .114328 10.57 0.000 1.68206 2.131264
Treatment non-completion (Late) | 1.613756 .082803 9.33 0.000 1.459359 1.784488
|
tr_modality |
Residential | 1.219599 .0518923 4.67 0.000 1.122017 1.325667
|
sex_enc |
Women | .6063769 .0294939 -10.28 0.000 .5512397 .6670291
edad_ini_cons | .9713696 .0047141 -5.99 0.000 .9621738 .9806532
|
escolaridad_rec |
2-Completed high school or less | .8823829 .0313629 -3.52 0.000 .823005 .9460449
1-More than high school | .6983125 .0432768 -5.79 0.000 .6184407 .7884999
|
sus_principal_mod |
Cocaine hydrochloride | 1.160171 .0785268 2.19 0.028 1.016033 1.324756
Cocaine paste | 1.686672 .0920129 9.58 0.000 1.515637 1.877008
Marijuana | 1.174471 .0936743 2.02 0.044 1.004504 1.373198
Other | 1.60089 .2406791 3.13 0.002 1.192315 2.149472
|
freq_cons_sus_prin |
1 day a week or more | .9665919 .1087656 -0.30 0.763 .7752858 1.205104
2 to 3 days a week | .978547 .089432 -0.24 0.812 .8180655 1.17051
4 to 6 days a week | 1.003253 .0951207 0.03 0.973 .8331169 1.208133
Daily | 1.028611 .0933393 0.31 0.756 .861015 1.22883
|
condicion_ocupacional_corr |
Inactive | 1.051563 .0747472 0.71 0.479 .9148085 1.208761
Looking for a job for the first time | 1.155319 .3116902 0.54 0.593 .6808613 1.960402
No activity | 1.222687 .0891848 2.76 0.006 1.059808 1.410598
Not seeking for work | 1.060001 .164504 0.38 0.707 .7819993 1.436833
Unemployed | 1.192953 .0466641 4.51 0.000 1.104911 1.28801
|
1.policonsumo | .9911901 .0486007 -0.18 0.857 .9003685 1.091173
1.num_hijos_mod_joel_bin | 1.124615 .0447498 2.95 0.003 1.04024 1.215834
|
tenencia_de_la_vivienda_mod |
Others | 1.053017 .1531197 0.36 0.722 .7918847 1.400261
Owner/Transferred dwellings/Pays Dividends | .9354223 .1183719 -0.53 0.598 .7299505 1.198732
Renting | .9714143 .1240264 -0.23 0.820 .7563562 1.247621
Stays temporarily with a relative | .9457054 .1194975 -0.44 0.659 .7382437 1.211468
|
macrozona |
North | 1.45097 .0608843 8.87 0.000 1.336415 1.575346
South | 1.519347 .0962216 6.60 0.000 1.341991 1.720142
|
n_off_vio |
1 | 1.467445 .0554534 10.15 0.000 1.362686 1.580258
|
n_off_acq |
1 | 2.798207 .097208 29.62 0.000 2.614025 2.995368
|
n_off_sud |
1 | 1.389128 .0506389 9.02 0.000 1.293341 1.49201
|
n_off_oth |
1 | 1.736869 .0634168 15.12 0.000 1.616918 1.865719
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.120116 .0551691 2.30 0.021 1.017042 1.233637
With psychiatric comorbidity | 1.098108 .0423432 2.43 0.015 1.018175 1.184315
|
dg_trs_cons_sus_or |
Drug dependence | 1.036542 .0441344 0.84 0.399 .9535508 1.126755
|
clas_r |
Mixta | .9001307 .0560762 -1.69 0.091 .7966684 1.01703
Rural | .8620275 .0596641 -2.15 0.032 .7526729 .9872701
|
porc_pobr | 1.553654 .3891829 1.76 0.079 .950895 2.538495
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.186979 .1082062 1.88 0.060 .9927655 1.419186
Cocaine paste | 1.269512 .0818297 3.70 0.000 1.118847 1.440467
Marijuana | 1.17805 .0439385 4.39 0.000 1.095004 1.267393
Other | 1.421008 .1319288 3.78 0.000 1.184594 1.704604
|
ano_nac_corr | .849161 .0080211 -17.31 0.000 .8335846 .8650284
|
con_quien_vive_joel |
Family of origin | .8820258 .0593114 -1.87 0.062 .7731124 1.006282
Others | 1.078223 .0862885 0.94 0.347 .9216974 1.261331
With couple/children | .9674378 .0644946 -0.50 0.619 .8489407 1.102475
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.0583 .0364898 1.64 0.100 .9891445 1.132291
One or more | .8195873 .0710186 -2.30 0.022 .6915716 .9712998
|
rc_x1 | .8497888 .0101842 -13.58 0.000 .8300608 .8699857
rc_x2 | .8799622 .0351027 -3.21 0.001 .8137829 .9515233
rc_x3 | 1.28374 .1365716 2.35 0.019 1.042129 1.581367
-------------------------------------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 60,247 -42140.28 -39752.06 51 79606.12 80065.43
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.
. estimates store full_spline
. scalar ll_1= e(ll)
. // MODEL WITH ONLY LINEAR TERM
. qui noi stcox $covs rc_x1
failure _d: event == 1
analysis time _t: diff
Iteration 0: log likelihood = -42140.282
Iteration 1: log likelihood = -40130.013
Iteration 2: log likelihood = -39768.772
Iteration 3: log likelihood = -39767.558
Iteration 4: log likelihood = -39767.558
Refining estimates:
Iteration 0: log likelihood = -39767.558
Cox regression -- Breslow method for ties
No. of subjects = 60,247 Number of obs = 60,247
No. of failures = 3,971
Time at risk = 235636.9084
LR chi2(49) = 4745.45
Log likelihood = -39767.558 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.893343 .1143877 10.57 0.000 1.681912 2.131352
Treatment non-completion (Late) | 1.615254 .0828933 9.34 0.000 1.46069 1.786174
|
tr_modality |
Residential | 1.213022 .0515964 4.54 0.000 1.115995 1.318484
|
sex_enc |
Women | .604768 .0293792 -10.35 0.000 .5498422 .6651806
edad_ini_cons | .9716051 .004664 -6.00 0.000 .9625068 .9807895
|
escolaridad_rec |
2-Completed high school or less | .8988937 .0318097 -3.01 0.003 .8386607 .9634526
1-More than high school | .7244061 .0446441 -5.23 0.000 .6419833 .8174109
|
sus_principal_mod |
Cocaine hydrochloride | 1.188917 .0804929 2.56 0.011 1.041173 1.357626
Cocaine paste | 1.742265 .0949108 10.19 0.000 1.56583 1.93858
Marijuana | 1.1774 .0940979 2.04 0.041 1.006691 1.377058
Other | 1.602489 .2412605 3.13 0.002 1.193008 2.152516
|
freq_cons_sus_prin |
1 day a week or more | .9650809 .1085998 -0.32 0.752 .7740672 1.20323
2 to 3 days a week | .9788315 .0894647 -0.23 0.815 .8182925 1.170866
4 to 6 days a week | 1.000065 .0948307 0.00 0.999 .8304503 1.204323
Daily | 1.027075 .0932082 0.29 0.768 .8597157 1.227015
|
condicion_ocupacional_corr |
Inactive | 1.027334 .0727596 0.38 0.703 .8941831 1.180312
Looking for a job for the first time | 1.10078 .2968384 0.36 0.722 .648879 1.867399
No activity | 1.193664 .0869341 2.43 0.015 1.034879 1.376813
Not seeking for work | 1.025036 .1590235 0.16 0.873 .7562831 1.389294
Unemployed | 1.183684 .0462946 4.31 0.000 1.096339 1.277988
|
1.policonsumo | 1.005346 .0493259 0.11 0.913 .9131721 1.106824
1.num_hijos_mod_joel_bin | 1.159626 .0457771 3.75 0.000 1.073287 1.25291
|
tenencia_de_la_vivienda_mod |
Others | 1.049511 .1526907 0.33 0.740 .789129 1.39581
Owner/Transferred dwellings/Pays Dividends | .923379 .1168685 -0.63 0.529 .7205211 1.18335
Renting | .9750384 .1245086 -0.20 0.843 .7591482 1.252325
Stays temporarily with a relative | .9454351 .1195051 -0.44 0.657 .7379688 1.211227
|
macrozona |
North | 1.436985 .060249 8.65 0.000 1.323621 1.560058
South | 1.520351 .0962445 6.62 0.000 1.342948 1.721188
|
n_off_vio |
1 | 1.46278 .0552954 10.06 0.000 1.35832 1.575273
|
n_off_acq |
1 | 2.793709 .097193 29.53 0.000 2.609564 2.990849
|
n_off_sud |
1 | 1.398199 .0509371 9.20 0.000 1.301845 1.501684
|
n_off_oth |
1 | 1.742425 .0636013 15.21 0.000 1.622124 1.871649
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.122901 .0553074 2.35 0.019 1.019568 1.236706
With psychiatric comorbidity | 1.103992 .0425498 2.57 0.010 1.023668 1.190619
|
dg_trs_cons_sus_or |
Drug dependence | 1.042081 .0443403 0.97 0.333 .9587013 1.132713
|
clas_r |
Mixta | .9031563 .0562495 -1.64 0.102 .7993725 1.020415
Rural | .8685269 .0601068 -2.04 0.042 .7583602 .9946976
|
porc_pobr | 1.54346 .3861859 1.73 0.083 .945188 2.52042
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.189748 .1084572 1.91 0.057 .9950837 1.422494
Cocaine paste | 1.27679 .0823036 3.79 0.000 1.125252 1.448735
Marijuana | 1.171154 .0437164 4.23 0.000 1.088531 1.260049
Other | 1.428193 .1326884 3.84 0.000 1.190432 1.71344
|
ano_nac_corr | .8490027 .0080164 -17.34 0.000 .8334352 .8648609
|
con_quien_vive_joel |
Family of origin | .883235 .0595004 -1.84 0.065 .7739873 1.007903
Others | 1.078344 .086368 0.94 0.346 .9216835 1.261632
With couple/children | .9794429 .0652737 -0.31 0.755 .8595119 1.116108
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.056193 .0364207 1.59 0.113 .9871687 1.130044
One or more | .8059858 .0698305 -2.49 0.013 .6801103 .9551584
|
rc_x1 | .8226295 .0079032 -20.32 0.000 .8072845 .8382662
-------------------------------------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 60,247 -42140.28 -39767.56 49 79633.12 80074.42
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.
. scalar ll_2= e(ll)
. estimates store linear_term
.
. lrtest full_spline linear_term
Likelihood-ratio test LR chi2(2) = 31.00
(Assumption: linear_term nested in full_spline) Prob > chi2 = 0.0000
.
. scalar ll_diff= round(`=scalar(ll_1)'-`=scalar(ll_2)',.01)
. di "Log-likelihood difference (spline - linear): `=scalar(ll_diff)'"
Log-likelihood difference (spline - linear): 15.5
.
. * the presence of censored observations makes it difficult to decide further among them. (This is partly due to the fact that both the Cox model and the parametric survival models assume that censoring is orthogon
> al to survival time, a mathematically handy assumption that is often demonstrably and seriously in error, and the actual data generation process for survival is often too unknown or too messy to simulate.) So in t
> his context, reliance on LR tests or IC statistics is a fallback position.
Log-likelihood difference (spline - linear): 15.5
Nevetheless, we chose the model with spline terms due to linearity over a better fit.
. *Micki Hill & Paul C Lambert & Michael J Crowther, 2021. "Introducing stipw: inverse probability weighted parametric survival models," London Stata Conference 2021 15, Stata Users Group.
. *https://view.officeapps.live.com/op/view.aspx?src=http%3A%2F%2Ffmwww.bc.edu%2Frepec%2Fusug2021%2Fusug21_hill.pptx&wdOrigin=BROWSELINK
.
. *Treatment variable should be a binary variable with values 0 and 1.
.
. qui noi stcox $covs rc_x*, efron robust nolog schoenfeld(sch_b*) scaledsch(sca_b*) //change _b
failure _d: event == 1
analysis time _t: diff
Cox regression -- Efron method for ties
No. of subjects = 60,247 Number of obs = 60,247
No. of failures = 3,971
Time at risk = 235636.9084
Wald chi2(51) = 4759.03
Log pseudolikelihood = -39752.057 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
| Robust
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.893387 .1153755 10.48 0.000 1.680237 2.133577
Treatment non-completion (Late) | 1.613756 .0829994 9.30 0.000 1.459011 1.784914
|
tr_modality |
Residential | 1.219598 .0537778 4.50 0.000 1.118622 1.32969
|
sex_enc |
Women | .606377 .0295194 -10.28 0.000 .5511944 .667084
edad_ini_cons | .9713696 .0048511 -5.82 0.000 .9619079 .9809242
|
escolaridad_rec |
2-Completed high school or less | .8823828 .03187 -3.46 0.001 .8220785 .9471109
1-More than high school | .6983125 .0435408 -5.76 0.000 .6179825 .7890843
|
sus_principal_mod |
Cocaine hydrochloride | 1.160171 .0783958 2.20 0.028 1.016258 1.324463
Cocaine paste | 1.686673 .0932598 9.45 0.000 1.513443 1.87973
Marijuana | 1.174471 .0953522 1.98 0.048 1.001695 1.377048
Other | 1.60089 .2494801 3.02 0.003 1.179537 2.172758
|
freq_cons_sus_prin |
1 day a week or more | .966592 .1096613 -0.30 0.765 .7738792 1.207295
2 to 3 days a week | .9785472 .0909173 -0.23 0.815 .8156357 1.173998
4 to 6 days a week | 1.003253 .0965246 0.03 0.973 .8308353 1.211451
Daily | 1.028612 .0953241 0.30 0.761 .8577651 1.233487
|
condicion_ocupacional_corr |
Inactive | 1.051563 .0750485 0.70 0.481 .9142947 1.20944
Looking for a job for the first time | 1.155319 .2988474 0.56 0.577 .6958581 1.918152
No activity | 1.222686 .0902503 2.72 0.006 1.057999 1.413009
Not seeking for work | 1.060001 .1616386 0.38 0.702 .7861531 1.42924
Unemployed | 1.192953 .0469334 4.48 0.000 1.104422 1.28858
|
1.policonsumo | .9911903 .0487254 -0.18 0.857 .9001467 1.091442
1.num_hijos_mod_joel_bin | 1.124615 .0453907 2.91 0.004 1.039078 1.217192
|
tenencia_de_la_vivienda_mod |
Others | 1.053019 .1611484 0.34 0.736 .7801405 1.421345
Owner/Transferred dwellings/Pays Dividends | .9354226 .1259764 -0.50 0.620 .7184123 1.217985
Renting | .9714148 .132006 -0.21 0.831 .7442769 1.26787
Stays temporarily with a relative | .9457056 .1271121 -0.42 0.678 .7266849 1.230739
|
macrozona |
North | 1.45097 .0613192 8.81 0.000 1.335629 1.576271
South | 1.519348 .0989633 6.42 0.000 1.337254 1.726238
|
n_off_vio |
1 | 1.467445 .0579478 9.71 0.000 1.358153 1.585531
|
n_off_acq |
1 | 2.798208 .1007418 28.58 0.000 2.607563 3.002791
|
n_off_sud |
1 | 1.389129 .0528773 8.63 0.000 1.289263 1.496731
|
n_off_oth |
1 | 1.736869 .0658694 14.56 0.000 1.612449 1.87089
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.120116 .0561053 2.26 0.024 1.015377 1.235659
With psychiatric comorbidity | 1.098107 .0432098 2.38 0.017 1.016601 1.186148
|
dg_trs_cons_sus_or |
Drug dependence | 1.036541 .04462 0.83 0.404 .9526756 1.12779
|
clas_r |
Mixta | .9001304 .0578332 -1.64 0.102 .793626 1.020928
Rural | .8620272 .0601237 -2.13 0.033 .7518866 .9883019
|
porc_pobr | 1.553655 .3909832 1.75 0.080 .9487385 2.544267
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.186979 .1074285 1.89 0.058 .9940411 1.417365
Cocaine paste | 1.269512 .0833532 3.63 0.000 1.116218 1.443859
Marijuana | 1.17805 .0446242 4.33 0.000 1.093756 1.26884
Other | 1.421008 .1378702 3.62 0.000 1.174926 1.71863
|
ano_nac_corr | .849161 .0077491 -17.92 0.000 .8341081 .8644856
|
con_quien_vive_joel |
Family of origin | .8820251 .060847 -1.82 0.069 .7704781 1.009722
Others | 1.078223 .0882911 0.92 0.358 .9183479 1.26593
With couple/children | .9674371 .0661817 -0.48 0.628 .8460433 1.106249
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.0583 .0369221 1.62 0.104 .9883529 1.133198
One or more | .8195872 .0724765 -2.25 0.024 .6891647 .9746919
|
rc_x1 | .8497889 .0101321 -13.65 0.000 .8301605 .8698814
rc_x2 | .8799619 .0356904 -3.15 0.002 .8127183 .9527693
rc_x3 | 1.283741 .1382809 2.32 0.020 1.039413 1.5855
-------------------------------------------------------------------------------------------------------------
. qui noi estat phtest, log detail
Test of proportional-hazards assumption
Time: Log(t)
----------------------------------------------------------------
| rho chi2 df Prob>chi2
------------+---------------------------------------------------
1b.motivod~c| . . 1 .
2.motivode~c| -0.05005 10.28 1 0.0013
3.motivode~c| -0.03528 5.10 1 0.0239
1b.tr_moda~y| . . 1 .
2.tr_modal~y| 0.01373 0.88 1 0.3491
1b.sex_enc | . . 1 .
2.sex_enc | -0.04371 7.74 1 0.0054
edad_ini_c~s| 0.03926 7.02 1 0.0081
1b.escolar~c| . . 1 .
2.escolari~c| -0.00937 0.37 1 0.5414
3.escolari~c| 0.02602 2.82 1 0.0934
1b.sus_pri~d| . . 1 .
2.sus_prin~d| 0.00558 0.13 1 0.7225
3.sus_prin~d| -0.00354 0.05 1 0.8165
4.sus_prin~d| 0.01438 0.89 1 0.3465
5.sus_prin~d| -0.03383 5.17 1 0.0230
1b.freq_co~n| . . 1 .
2.freq_con~n| 0.01849 1.43 1 0.2317
3.freq_con~n| -0.00275 0.03 1 0.8583
4.freq_con~n| -0.01090 0.50 1 0.4778
5.freq_con~n| -0.00835 0.30 1 0.5825
1b.condici~r| . . 1 .
2.condicio~r| 0.02585 2.67 1 0.1024
3.condicio~r| 0.00033 0.00 1 0.9840
4.condicio~r| -0.00472 0.09 1 0.7580
5.condicio~r| 0.01129 0.50 1 0.4788
6.condicio~r| -0.01198 0.58 1 0.4446
0b.policon~o| . . 1 .
1.policons~o| -0.02873 3.44 1 0.0638
0b.num_hij~n| . . 1 .
1.num_hijo~n| 0.00372 0.06 1 0.8093
1b.tenenci~d| . . 1 .
2.tenencia~d| 0.01112 0.55 1 0.4569
3.tenencia~d| 0.00521 0.13 1 0.7222
4.tenencia~d| 0.00190 0.02 1 0.8969
5.tenencia~d| 0.00985 0.45 1 0.5017
1b.macrozona| . . 1 .
2.macrozona | 0.03079 3.92 1 0.0478
3.macrozona | -0.00960 0.41 1 0.5233
1b.n_off_vio| . . 1 .
2.n_off_vio | -0.01015 0.48 1 0.4868
1b.n_off_acq| . . 1 .
2.n_off_acq | -0.06123 18.01 1 0.0000
1b.n_off_sud| . . 1 .
2.n_off_sud | 0.00293 0.04 1 0.8400
1b.n_off_oth| . . 1 .
2.n_off_oth | -0.03847 6.81 1 0.0091
1b.dg_cie_~c| . . 1 .
2.dg_cie_1~c| 0.01716 1.23 1 0.2666
3.dg_cie_1~c| -0.02002 1.74 1 0.1871
1b.dg_trs_~r| . . 1 .
2.dg_trs_c~r| 0.01024 0.44 1 0.5065
1b.clas_r | . . 1 .
2.clas_r | 0.00911 0.37 1 0.5411
3.clas_r | 0.02121 1.90 1 0.1686
porc_pobr | -0.01235 0.63 1 0.4273
1b.sus_ini~v| . . 1 .
2.sus_ini_~v| 0.01152 0.52 1 0.4717
3.sus_ini_~v| -0.00471 0.09 1 0.7594
4.sus_ini_~v| -0.00214 0.02 1 0.8886
5.sus_ini_~v| -0.01540 1.08 1 0.2992
ano_nac_corr| -0.04188 6.04 1 0.0140
1b.con_qui~l| . . 1 .
2.con_quie~l| -0.01141 0.57 1 0.4511
3.con_quie~l| -0.01955 1.65 1 0.1988
4.con_quie~l| 0.01553 1.06 1 0.3034
1b.fis_co~10| . . 1 .
2.fis_com~10| 0.00429 0.08 1 0.7826
3.fis_com~10| -0.01062 0.48 1 0.4889
rc_x1 | -0.05720 12.94 1 0.0003
rc_x2 | 0.01569 1.05 1 0.3048
rc_x3 | -0.01308 0.72 1 0.3964
------------+---------------------------------------------------
global test | 160.56 51 0.0000
----------------------------------------------------------------
note: robust variance-covariance matrix used.
. mat mat_scho_test2 = r(phtest)
. scalar chi2_scho_test2 = r(chi2)
. scalar chi2_scho_test_df2 = r(df)
. scalar chi2_scho_test_p2 = r(p)
.
. esttab matrix(mat_scho_test2) using "mat_scho_test_02_2023_2_pris.csv", replace
(output written to mat_scho_test_02_2023_2_pris.csv)
. esttab matrix(mat_scho_test2) using "mat_scho_test_02_2023_2_pris.html", replace
(output written to mat_scho_test_02_2023_2_pris.html)
.
Chi^2(51)= 160.56, p= 0
| mat_scho_test2 | ||||
| rho | chi2 | df | p | |
| 1b.motivodeegreso_mod_imp_rec | . | . | 1 | . |
| 2.motivodeegreso_mod_imp_rec | -.0500499 | 10.28204 | 1 | .0013433 |
| 3.motivodeegreso_mod_imp_rec | -.0352819 | 5.099542 | 1 | .0239322 |
| 1b.tr_modality | . | . | 1 | . |
| 2.tr_modality | .0137262 | .8765907 | 1 | .3491372 |
| 1b.sex_enc | . | . | 1 | . |
| 2.sex_enc | -.0437137 | 7.737951 | 1 | .0054072 |
| edad_ini_cons | .039264 | 7.02219 | 1 | .0080506 |
| 1b.escolaridad_rec | . | . | 1 | . |
| 2.escolaridad_rec | -.0093679 | .372936 | 1 | .5414082 |
| 3.escolaridad_rec | .0260203 | 2.81527 | 1 | .0933712 |
| 1b.sus_principal_mod | . | . | 1 | . |
| 2.sus_principal_mod | .0055767 | .1261355 | 1 | .722473 |
| 3.sus_principal_mod | -.0035439 | .0538571 | 1 | .8164826 |
| 4.sus_principal_mod | .014379 | .8861613 | 1 | .3465197 |
| 5.sus_principal_mod | -.0338319 | 5.165039 | 1 | .0230459 |
| 1b.freq_cons_sus_prin | . | . | 1 | . |
| 2.freq_cons_sus_prin | .0184913 | 1.430093 | 1 | .2317492 |
| 3.freq_cons_sus_prin | -.0027462 | .031863 | 1 | .8583286 |
| 4.freq_cons_sus_prin | -.0109016 | .5039663 | 1 | .4777625 |
| 5.freq_cons_sus_prin | -.0083538 | .3021703 | 1 | .582525 |
| 1b.condicion_ocupacional_corr | . | . | 1 | . |
| 2.condicion_ocupacional_corr | .0258495 | 2.668538 | 1 | .10235 |
| 3.condicion_ocupacional_corr | .0003277 | .0004034 | 1 | .9839755 |
| 4.condicion_ocupacional_corr | -.0047248 | .0949142 | 1 | .7580204 |
| 5.condicion_ocupacional_corr | .0112913 | .5015134 | 1 | .4788359 |
| 6.condicion_ocupacional_corr | -.0119765 | .5844395 | 1 | .4445774 |
| 0b.policonsumo | . | . | 1 | . |
| 1.policonsumo | -.0287281 | 3.436055 | 1 | .0637878 |
| 0b.num_hijos_mod_joel_bin | . | . | 1 | . |
| 1.num_hijos_mod_joel_bin | .0037205 | .0582206 | 1 | .8093308 |
| 1b.tenencia_de_la_vivienda_mod | . | . | 1 | . |
| 2.tenencia_de_la_vivienda_mod | .0111188 | .5534841 | 1 | .4568976 |
| 3.tenencia_de_la_vivienda_mod | .005211 | .1263908 | 1 | .722204 |
| 4.tenencia_de_la_vivienda_mod | .0019021 | .0167999 | 1 | .8968716 |
| 5.tenencia_de_la_vivienda_mod | .0098461 | .451242 | 1 | .5017457 |
| 1b.macrozona | . | . | 1 | . |
| 2.macrozona | .0307854 | 3.917798 | 1 | .0477774 |
| 3.macrozona | -.0095954 | .4074077 | 1 | .5232882 |
| 1b.n_off_vio | . | . | 1 | . |
| 2.n_off_vio | -.0101528 | .4835621 | 1 | .4868132 |
| 1b.n_off_acq | . | . | 1 | . |
| 2.n_off_acq | -.0612278 | 18.01141 | 1 | .000022 |
| 1b.n_off_sud | . | . | 1 | . |
| 2.n_off_sud | .0029325 | .0407689 | 1 | .8399847 |
| 1b.n_off_oth | . | . | 1 | . |
| 2.n_off_oth | -.0384719 | 6.809868 | 1 | .0090655 |
| 1b.dg_cie_10_rec | . | . | 1 | . |
| 2.dg_cie_10_rec | .0171607 | 1.233982 | 1 | .266634 |
| 3.dg_cie_10_rec | -.0200249 | 1.740406 | 1 | .1870874 |
| 1b.dg_trs_cons_sus_or | . | . | 1 | . |
| 2.dg_trs_cons_sus_or | .0102361 | .4412358 | 1 | .5065266 |
| 1b.clas_r | . | . | 1 | . |
| 2.clas_r | .0091065 | .3734673 | 1 | .5411203 |
| 3.clas_r | .0212135 | 1.895354 | 1 | .1685993 |
| porc_pobr | -.0123476 | .6301175 | 1 | .4273122 |
| 1b.sus_ini_mod_mvv | . | . | 1 | . |
| 2.sus_ini_mod_mvv | .0115191 | .5180372 | 1 | .4716801 |
| 3.sus_ini_mod_mvv | -.0047116 | .0937702 | 1 | .7594377 |
| 4.sus_ini_mod_mvv | -.0021437 | .0196365 | 1 | .888557 |
| 5.sus_ini_mod_mvv | -.015405 | 1.077809 | 1 | .2991882 |
| ano_nac_corr | -.0418808 | 6.042403 | 1 | .0139663 |
| 1b.con_quien_vive_joel | . | . | 1 | . |
| 2.con_quien_vive_joel | -.011411 | .5678431 | 1 | .4511173 |
| 3.con_quien_vive_joel | -.0195452 | 1.65145 | 1 | .1987617 |
| 4.con_quien_vive_joel | .0155289 | 1.059129 | 1 | .303414 |
| 1b.fis_comorbidity_icd_10 | . | . | 1 | . |
| 2.fis_comorbidity_icd_10 | .0042862 | .0761292 | 1 | .7826132 |
| 3.fis_comorbidity_icd_10 | -.0106249 | .4790314 | 1 | .4888614 |
| rc_x1 | -.0572021 | 12.93647 | 1 | .0003222 |
| rc_x2 | .0156938 | 1.053289 | 1 | .3047509 |
| rc_x3 | -.0130798 | .7191633 | 1 | .3964185 |
=============================================================================
=============================================================================
In view of nonproportional hazards, we explored different shapes of time-dependent effects and baseline hazards.
. *______________________________________________
. *______________________________________________
. * ADJUSTED ROYSTON PARMAR - NO STAGGERED ENTRY, BINARY TREATMENT (1-DROPOUT VS. 0-COMPLETION)
.
. /*
> vars_cov<-c("motivodeegreso_mod_imp_rec", "tr_modality", "edad_al_ing_1", "sex", "edad_ini_cons", "escolaridad_rec", "sus_principal_mod", "freq_cons_sus_prin", "condicion_ocupacional_corr", "policonsumo", "num_hij
> os_mod_joel_bin", "tenencia_de_la_vivienda_mod", "macrozona", "n_off_vio", "n_off_acq", "n_off_sud", "n_off_oth", "dg_cie_10_rec", "dg_trs_cons_sus_or", "clas_r", "porc_pobr", "sus_ini_mod_mvv", "ano_nac_corr", "
> con_quien_vive_joel", "fis_comorbidity_icd_10")
> */
.
. cap noi tab tr_modality, gen(tr_mod)
Treatment |
Modality | Freq. Percent Cum.
------------+-----------------------------------
Ambulatory | 60,398 85.31 85.31
Residential | 10,397 14.69 100.00
------------+-----------------------------------
Total | 70,795 100.00
. cap noi tab sex_enc, gen(sex_dum)
Sex | Freq. Percent Cum.
------------+-----------------------------------
Men | 54,048 76.27 76.27
Women | 16,815 23.73 100.00
------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab escolaridad_rec, gen(esc)
Educational Attainment | Freq. Percent Cum.
-----------------------------------+-----------------------------------
3-Completed primary school or less | 20,249 28.70 28.70
2-Completed high school or less | 39,038 55.34 84.04
1-More than high school | 11,259 15.96 100.00
-----------------------------------+-----------------------------------
Total | 70,546 100.00
. cap noi tab sus_principal_mod, gen(sus_prin)
Primary Substance |
(admission to |
treatment) | Freq. Percent Cum.
----------------------+-----------------------------------
Alcohol | 23,863 33.68 33.68
Cocaine hydrochloride | 13,243 18.69 52.36
Cocaine paste | 27,791 39.22 91.58
Marijuana | 4,748 6.70 98.28
Other | 1,217 1.72 100.00
----------------------+-----------------------------------
Total | 70,862 100.00
. cap noi tab freq_cons_sus_prin, gen(fr_cons_sus_prin)
Frequency of Substance |
Use (Primary |
Substance) | Freq. Percent Cum.
-----------------------+-----------------------------------
Less than 1 day a week | 3,495 4.96 4.96
1 day a week or more | 4,780 6.78 11.74
2 to 3 days a week | 20,061 28.45 40.19
4 to 6 days a week | 11,612 16.47 56.66
Daily | 30,560 43.34 100.00
-----------------------+-----------------------------------
Total | 70,508 100.00
. cap noi tab condicion_ocupacional_cor, gen(cond_ocu)
Corrected Occupational Status (f) | Freq. Percent Cum.
-------------------------------------+-----------------------------------
Employed | 35,367 49.91 49.91
Inactive | 7,169 10.12 60.03
Looking for a job for the first time | 159 0.22 60.25
No activity | 3,558 5.02 65.27
Not seeking for work | 713 1.01 66.28
Unemployed | 23,896 33.72 100.00
-------------------------------------+-----------------------------------
Total | 70,862 100.00
. cap noi tab num_hijos_mod_joel_bin, gen(num_hij)
Number of |
Children |
(dichotomiz |
ed) | Freq. Percent Cum.
------------+-----------------------------------
0 | 16,428 23.38 23.38
1 | 53,831 76.62 100.00
------------+-----------------------------------
Total | 70,259 100.00
. cap noi tab tenencia_de_la_vivienda_mod, gen(tenviv)
Housing Situation (Tenure Status) | Freq. Percent Cum.
----------------------------------------+-----------------------------------
Illegal Settlement | 749 1.12 1.12
Others | 2,003 3.00 4.12
Owner/Transferred dwellings/Pays Divide | 24,816 37.15 41.27
Renting | 12,095 18.10 59.37
Stays temporarily with a relative | 27,142 40.63 100.00
----------------------------------------+-----------------------------------
Total | 66,805 100.00
. cap noi tab macrozona, gen(mzone)
Macro |
Administrat |
ive Zone in |
Chile | Freq. Percent Cum.
------------+-----------------------------------
Center | 53,683 75.77 75.77
North | 10,486 14.80 90.57
South | 6,678 9.43 100.00
------------+-----------------------------------
Total | 70,847 100.00
. cap noi tab clas_r, gen(rural)
Socioeconom |
ic |
Classificat |
ion | Freq. Percent Cum.
------------+-----------------------------------
Urbana | 58,276 82.24 82.24
Mixta | 6,835 9.65 91.89
Rural | 5,750 8.11 100.00
------------+-----------------------------------
Total | 70,861 100.00
. cap noi tab sus_ini_mod_mvv, gen(susini)
Primary Substance |
(initial diagnosis) | Freq. Percent Cum.
----------------------+-----------------------------------
Alcohol | 38,412 59.03 59.03
Cocaine hydrochloride | 2,605 4.00 63.03
Cocaine paste | 3,311 5.09 68.12
Marijuana | 19,142 29.41 97.53
Other | 1,606 2.47 100.00
----------------------+-----------------------------------
Total | 65,076 100.00
. cap noi tab con_quien_vive_joel, gen(cohab)
Cohabitation status |
(Recoded) (f) | Freq. Percent Cum.
---------------------+-----------------------------------
Alone | 6,688 9.44 9.44
Family of origin | 29,340 41.40 50.84
Others | 6,109 8.62 59.46
With couple/children | 28,725 40.54 100.00
---------------------+-----------------------------------
Total | 70,862 100.00
. cap noi tab fis_comorbidity_icd_10, gen(fis_com)
Physical Comorbidity (ICD-10) | Freq. Percent Cum.
--------------------------------+-----------------------------------
Without physical comorbidity | 28,053 39.59 39.59
Diagnosis unknown (under study) | 38,395 54.18 93.77
One or more | 4,415 6.23 100.00
--------------------------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab dg_cie_10_rec, gen(psy_com)
Psychiatric Comorbidity |
(ICD-10) | Freq. Percent Cum.
--------------------------------+-----------------------------------
Without psychiatric comorbidity | 27,922 39.40 39.40
Diagnosis unknown (under study) | 13,273 18.73 58.13
With psychiatric comorbidity | 29,668 41.87 100.00
--------------------------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab dg_trs_cons_sus_or, gen(dep)
SUD Severity |
(Dependence status) | Freq. Percent Cum.
----------------------+-----------------------------------
Hazardous consumption | 19,696 27.79 27.79
Drug dependence | 51,166 72.21 100.00
----------------------+-----------------------------------
Total | 70,862 100.00
.
. /*
> *NO LONGER USEFUL
> local varslab "dg_fis_anemia dg_fis_card dg_fis_in_study dg_fis_enf_som dg_fis_ets dg_fis_hep_alc dg_fis_hep_b dg_fis_hep_cro dg_fis_inf dg_fis_otr_cond_fis_ries_vit dg_fis_otr_cond_fis dg_fis_pat_buc dg_fis_pat_g
> es_intrau dg_fis_trau_sec"
> forvalues i = 1/14 {
> local v : word `i' of `varslab'
> di "`v'"
> gen `v'2= 0
> replace `v'2 =1 if `v'==2
> }
> */
.
. global covs_3b "mot_egr_early mot_egr_late i.tr_modality i.sex_enc edad_ini_cons i.escolaridad_rec i.sus_principal_mod i.freq_cons_sus_prin i.condicion_ocupacional_cor i.policonsumo i.num_hijos_mod_joel_bin i.tene
> ncia_de_la_vivienda_mod i.macrozona i.n_off_vio i.n_off_acq i.n_off_sud i.n_off_oth i.dg_cie_10_rec i.dg_trs_cons_sus_or i.clas_r porc_pobr i.sus_ini_mod_mvv ano_nac_corr i.con_quien_vive_joel i.fis_comorbidity_ic
> d_10 rc_x1 rc_x2 rc_x3"
.
. *REALLY NEEDS DUMMY VARS
. global covs_3b_dum_pre "mot_egr_early mot_egr_late tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2
> cond_ocu3 cond_ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 su
> sini4 susini5 ano_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3"
.
. forvalues i=1/10 {
2. forvalues j=1/7 {
3. qui noi stpm2 $covs_3b_dum_pre , scale(hazard) df(`i') eform tvc(mot_egr_early mot_egr_late) dftvc(`j')
4. estimates store m_nostag_rp`i'_tvc_`j'
5. }
6. }
Iteration 0: log likelihood = -17115.278
Iteration 1: log likelihood = -17042.637
Iteration 2: log likelihood = -17041.827
Iteration 3: log likelihood = -17041.826
Log likelihood = -17041.826 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.969066 .1241398 10.75 0.000 1.740188 2.228046
mot_egr_late | 1.677501 .0911885 9.52 0.000 1.507966 1.866095
tr_mod2 | 1.217145 .0517899 4.62 0.000 1.119756 1.323004
sex_dum2 | .6053003 .0294243 -10.33 0.000 .5502918 .6658077
edad_ini_cons | .9714869 .0047128 -5.96 0.000 .9622937 .980768
esc1 | 1.433098 .0888005 5.81 0.000 1.269205 1.618153
esc2 | 1.264851 .0732796 4.06 0.000 1.12908 1.416948
sus_prin2 | 1.15406 .0780163 2.12 0.034 1.010847 1.317561
sus_prin3 | 1.679612 .0915314 9.52 0.000 1.509462 1.868942
sus_prin4 | 1.166705 .0930059 1.93 0.053 .997944 1.364005
sus_prin5 | 1.58259 .2379059 3.05 0.002 1.178718 2.124844
fr_cons_sus_prin2 | .9688799 .1090278 -0.28 0.779 .7771135 1.207968
fr_cons_sus_prin3 | .978162 .0893999 -0.24 0.809 .8177386 1.170057
fr_cons_sus_prin4 | 1.003153 .0951114 0.03 0.974 .833034 1.208013
fr_cons_sus_prin5 | 1.030034 .0934658 0.33 0.744 .8622104 1.230524
cond_ocu2 | 1.048707 .0745268 0.67 0.503 .9123539 1.205439
cond_ocu3 | 1.14806 .3097316 0.51 0.609 .6765839 1.948084
cond_ocu4 | 1.22697 .0894862 2.80 0.005 1.063539 1.415515
cond_ocu5 | 1.063178 .1649767 0.39 0.693 .7843724 1.441085
cond_ocu6 | 1.189747 .0465116 4.44 0.000 1.10199 1.284491
policonsumo | .987923 .0484199 -0.25 0.804 .8974375 1.087532
num_hij2 | 1.126344 .0448123 2.99 0.003 1.041851 1.21769
tenviv1 | 1.060797 .13425 0.47 0.641 .827766 1.359429
tenviv2 | 1.120982 .0965556 1.33 0.185 .9468483 1.32714
tenviv4 | 1.036973 .0509562 0.74 0.460 .9417594 1.141813
tenviv5 | 1.009222 .0382661 0.24 0.809 .9369411 1.08708
mzone2 | 1.450618 .0608413 8.87 0.000 1.336141 1.574903
mzone3 | 1.533408 .0968356 6.77 0.000 1.354889 1.735447
n_off_vio | 1.469345 .0555828 10.17 0.000 1.364345 1.582426
n_off_acq | 2.818076 .0980209 29.79 0.000 2.632361 3.016893
n_off_sud | 1.394014 .05085 9.11 0.000 1.297829 1.497328
n_off_oth | 1.742712 .063707 15.19 0.000 1.622217 1.872157
psy_com2 | 1.117061 .0549582 2.25 0.024 1.014376 1.230142
psy_com3 | 1.100806 .0424376 2.49 0.013 1.020694 1.187205
dep2 | 1.036604 .0441273 0.84 0.398 .9536257 1.126802
rural2 | .8989648 .0559922 -1.71 0.087 .7956561 1.015687
rural3 | .8631168 .0597132 -2.13 0.033 .7536691 .9884585
porc_pobr | 1.507292 .377367 1.64 0.101 .9227625 2.462096
susini2 | 1.190024 .108465 1.91 0.056 .9953426 1.422782
susini3 | 1.271806 .081976 3.73 0.000 1.12087 1.443066
susini4 | 1.181872 .0440662 4.48 0.000 1.098584 1.271474
susini5 | 1.422922 .1321055 3.80 0.000 1.186191 1.706898
ano_nac_corr | .8692351 .0080656 -15.10 0.000 .8535697 .885188
cohab2 | .8785121 .0590111 -1.93 0.054 .7701426 1.002131
cohab3 | 1.073444 .0858425 0.89 0.375 .9177185 1.255595
cohab4 | .9633544 .064143 -0.56 0.575 .845494 1.097644
fis_com2 | 1.061096 .0365717 1.72 0.085 .9917846 1.135252
fis_com3 | .8203809 .0710755 -2.29 0.022 .6922609 .9722126
rc_x1 | .869338 .0103092 -11.81 0.000 .8493654 .8897802
rc_x2 | .8813251 .0351478 -3.17 0.002 .8150602 .9529775
rc_x3 | 1.280426 .1361889 2.32 0.020 1.039486 1.577212
_rcs1 | 2.156674 .0681956 24.31 0.000 2.027071 2.294564
_rcs_mot_egr_early1 | .9041276 .0320796 -2.84 0.005 .8433891 .9692403
_rcs_mot_egr_late1 | .9210335 .0314665 -2.41 0.016 .8613798 .9848183
_cons | 4.9e+119 9.2e+120 14.75 0.000 6.1e+103 3.9e+135
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -17041.793
Iteration 1: log likelihood = -16999.464
Iteration 2: log likelihood = -16998.982
Iteration 3: log likelihood = -16998.981
Log likelihood = -16998.981 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010604 .1270502 11.05 0.000 1.776393 2.275695
mot_egr_late | 1.704732 .0927892 9.80 0.000 1.532234 1.896651
tr_mod2 | 1.216519 .0517611 4.61 0.000 1.119184 1.32232
sex_dum2 | .6063496 .0294762 -10.29 0.000 .5512442 .6669637
edad_ini_cons | .9715432 .0047122 -5.95 0.000 .9623511 .980823
esc1 | 1.431434 .0887045 5.79 0.000 1.26772 1.61629
esc2 | 1.264569 .073265 4.05 0.000 1.128825 1.416636
sus_prin2 | 1.152504 .0779095 2.10 0.036 1.009488 1.315782
sus_prin3 | 1.677393 .091411 9.49 0.000 1.507467 1.866473
sus_prin4 | 1.167488 .0930673 1.94 0.052 .9986153 1.364919
sus_prin5 | 1.581021 .2376742 3.05 0.002 1.177543 2.122747
fr_cons_sus_prin2 | .9685158 .1089847 -0.28 0.776 .7768249 1.207509
fr_cons_sus_prin3 | .9787468 .0894488 -0.24 0.814 .8182349 1.170746
fr_cons_sus_prin4 | 1.003107 .0951028 0.03 0.974 .8330025 1.207948
fr_cons_sus_prin5 | 1.030035 .0934567 0.33 0.744 .8622263 1.230504
cond_ocu2 | 1.049829 .0746102 0.68 0.494 .913323 1.206736
cond_ocu3 | 1.140441 .3076817 0.49 0.626 .6720868 1.935174
cond_ocu4 | 1.226214 .0894207 2.80 0.005 1.062902 1.414619
cond_ocu5 | 1.06028 .1645235 0.38 0.706 .7822396 1.437148
cond_ocu6 | 1.188851 .0464774 4.42 0.000 1.10116 1.283526
policonsumo | .9891671 .048481 -0.22 0.824 .8985673 1.088902
num_hij2 | 1.126124 .0448033 2.99 0.003 1.041647 1.217451
tenviv1 | 1.062929 .1345095 0.48 0.630 .8294459 1.362136
tenviv2 | 1.120538 .0965259 1.32 0.186 .9464593 1.326634
tenviv4 | 1.037489 .0509809 0.75 0.454 .9422295 1.14238
tenviv5 | 1.009596 .0382813 0.25 0.801 .9372865 1.087485
mzone2 | 1.447768 .0607255 8.82 0.000 1.333509 1.571817
mzone3 | 1.530456 .0966259 6.74 0.000 1.352322 1.732056
n_off_vio | 1.466806 .0554857 10.13 0.000 1.361989 1.579688
n_off_acq | 2.806718 .0976317 29.67 0.000 2.621741 3.004747
n_off_sud | 1.393429 .0508207 9.10 0.000 1.297299 1.496682
n_off_oth | 1.738673 .0635615 15.13 0.000 1.618453 1.867823
psy_com2 | 1.117858 .0550277 2.26 0.024 1.015045 1.231084
psy_com3 | 1.100078 .0424056 2.47 0.013 1.020027 1.186412
dep2 | 1.036067 .0441093 0.83 0.405 .9531226 1.126229
rural2 | .898991 .0559924 -1.71 0.087 .7956818 1.015714
rural3 | .8613534 .0596092 -2.16 0.031 .7520985 .9864794
porc_pobr | 1.527958 .3824717 1.69 0.090 .9354974 2.495632
susini2 | 1.188859 .108369 1.90 0.058 .9943522 1.421414
susini3 | 1.270259 .0818722 3.71 0.000 1.119515 1.441302
susini4 | 1.181133 .0440365 4.47 0.000 1.097901 1.270675
susini5 | 1.420481 .1318648 3.78 0.000 1.184179 1.703936
ano_nac_corr | .8573936 .0080228 -16.44 0.000 .8418124 .8732631
cohab2 | .8793305 .0590609 -1.91 0.056 .770869 1.003053
cohab3 | 1.074381 .0859166 0.90 0.370 .9185209 1.256689
cohab4 | .9637543 .0641648 -0.55 0.579 .8458534 1.098089
fis_com2 | 1.060861 .0365681 1.71 0.087 .9915565 1.13501
fis_com3 | .8201104 .0710525 -2.29 0.022 .692032 .971893
rc_x1 | .8575913 .0102199 -12.89 0.000 .8377928 .8778577
rc_x2 | .8817014 .0351611 -3.16 0.002 .8154113 .9533808
rc_x3 | 1.278546 .1359844 2.31 0.021 1.037967 1.574885
_rcs1 | 2.137858 .0669425 24.26 0.000 2.010599 2.273173
_rcs_mot_egr_early1 | .9130403 .0322641 -2.57 0.010 .8519439 .9785182
_rcs_mot_egr_early2 | 1.064471 .0137549 4.84 0.000 1.03785 1.091774
_rcs_mot_egr_late1 | .9425627 .0322176 -1.73 0.084 .8814861 1.007871
_rcs_mot_egr_late2 | 1.08894 .0124752 7.44 0.000 1.064762 1.113668
_cons | 4.8e+131 9.1e+132 16.09 0.000 4.5e+115 5.3e+147
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16994.254
Iteration 1: log likelihood = -16987.45
Iteration 2: log likelihood = -16987.432
Iteration 3: log likelihood = -16987.432
Log likelihood = -16987.432 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.020219 .1277436 11.12 0.000 1.784739 2.286768
mot_egr_late | 1.707737 .0930118 9.83 0.000 1.534831 1.900123
tr_mod2 | 1.216426 .0517531 4.60 0.000 1.119106 1.322209
sex_dum2 | .6067736 .029495 -10.28 0.000 .5516329 .6674261
edad_ini_cons | .9715205 .0047122 -5.96 0.000 .9623285 .9808002
esc1 | 1.430753 .0886676 5.78 0.000 1.267107 1.615533
esc2 | 1.264284 .07325 4.05 0.000 1.128568 1.41632
sus_prin2 | 1.154237 .0780316 2.12 0.034 1.010997 1.317771
sus_prin3 | 1.678764 .0914944 9.51 0.000 1.508684 1.868018
sus_prin4 | 1.169131 .0932028 1.96 0.050 1.000013 1.366849
sus_prin5 | 1.584144 .2381546 3.06 0.002 1.179853 2.126969
fr_cons_sus_prin2 | .9679343 .1089186 -0.29 0.772 .7763595 1.206782
fr_cons_sus_prin3 | .9786478 .0894387 -0.24 0.813 .8181539 1.170625
fr_cons_sus_prin4 | 1.00316 .0951066 0.03 0.973 .8330482 1.208008
fr_cons_sus_prin5 | 1.030119 .0934631 0.33 0.744 .8622985 1.230601
cond_ocu2 | 1.049865 .0746103 0.68 0.494 .9133591 1.206773
cond_ocu3 | 1.139451 .3074176 0.48 0.628 .6715006 1.933505
cond_ocu4 | 1.22455 .089302 2.78 0.005 1.061455 1.412705
cond_ocu5 | 1.059233 .1643603 0.37 0.711 .7814681 1.435727
cond_ocu6 | 1.188996 .0464823 4.43 0.000 1.101295 1.283681
policonsumo | .990555 .0485523 -0.19 0.846 .8998222 1.090437
num_hij2 | 1.125709 .0447882 2.98 0.003 1.041261 1.217006
tenviv1 | 1.064324 .1346751 0.49 0.622 .8305514 1.363896
tenviv2 | 1.121826 .0966409 1.33 0.182 .9475409 1.328169
tenviv4 | 1.037773 .0509939 0.75 0.451 .9424883 1.14269
tenviv5 | 1.01031 .0383111 0.27 0.787 .9379446 1.08826
mzone2 | 1.449048 .0607871 8.84 0.000 1.334674 1.573223
mzone3 | 1.530237 .0966216 6.74 0.000 1.352112 1.731829
n_off_vio | 1.466658 .0554654 10.13 0.000 1.361879 1.579498
n_off_acq | 2.803357 .0974871 29.64 0.000 2.618652 3.00109
n_off_sud | 1.392611 .0507834 9.08 0.000 1.296551 1.495788
n_off_oth | 1.737295 .0634935 15.11 0.000 1.617203 1.866306
psy_com2 | 1.118367 .0550616 2.27 0.023 1.015492 1.231664
psy_com3 | 1.100255 .0424101 2.48 0.013 1.020195 1.186598
dep2 | 1.036139 .0441141 0.83 0.404 .953186 1.126311
rural2 | .8986624 .0559746 -1.72 0.086 .7953864 1.015348
rural3 | .8601765 .0595419 -2.18 0.030 .7510467 .9851632
porc_pobr | 1.557447 .3898323 1.77 0.077 .953577 2.543729
susini2 | 1.188333 .1083244 1.89 0.058 .9939062 1.420793
susini3 | 1.269514 .0818214 3.70 0.000 1.118862 1.44045
susini4 | 1.180727 .0440221 4.46 0.000 1.097523 1.27024
susini5 | 1.42107 .1319218 3.79 0.000 1.184667 1.704648
ano_nac_corr | .853482 .0080231 -16.85 0.000 .8379011 .8693528
cohab2 | .8797482 .0590864 -1.91 0.056 .7712395 1.003523
cohab3 | 1.075006 .0859639 0.90 0.366 .9190593 1.257413
cohab4 | .9641432 .0641885 -0.55 0.583 .8461984 1.098527
fis_com2 | 1.059775 .0365336 1.68 0.092 .9905356 1.133854
fis_com3 | .8195946 .0710097 -2.30 0.022 .6915935 .9712862
rc_x1 | .8537155 .0102011 -13.24 0.000 .833954 .8739453
rc_x2 | .8817057 .0351634 -3.16 0.002 .8154115 .9533898
rc_x3 | 1.278457 .1359813 2.31 0.021 1.037885 1.574791
_rcs1 | 2.132191 .0666137 24.24 0.000 2.005547 2.266831
_rcs_mot_egr_early1 | .9186112 .032518 -2.40 0.016 .8570378 .9846084
_rcs_mot_egr_early2 | 1.060395 .01293 4.81 0.000 1.035353 1.086042
_rcs_mot_egr_early3 | 1.029227 .0096442 3.07 0.002 1.010497 1.048304
_rcs_mot_egr_late1 | .9480564 .0323978 -1.56 0.119 .8866377 1.01373
_rcs_mot_egr_late2 | 1.077642 .011729 6.87 0.000 1.054897 1.100877
_rcs_mot_egr_late3 | 1.036792 .008016 4.67 0.000 1.021199 1.052622
_cons | 4.8e+135 9.1e+136 16.51 0.000 3.7e+119 6.2e+151
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16999.057
Iteration 1: log likelihood = -16985.992
Iteration 2: log likelihood = -16985.872
Iteration 3: log likelihood = -16985.872
Log likelihood = -16985.872 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.022425 .1279181 11.14 0.000 1.786628 2.289342
mot_egr_late | 1.708639 .0930864 9.83 0.000 1.535596 1.901182
tr_mod2 | 1.216388 .05175 4.60 0.000 1.119074 1.322165
sex_dum2 | .6069324 .0295028 -10.27 0.000 .551777 .667601
edad_ini_cons | .9715132 .0047122 -5.96 0.000 .9623212 .9807931
esc1 | 1.430609 .0886596 5.78 0.000 1.266979 1.615373
esc2 | 1.264185 .0732446 4.05 0.000 1.128479 1.41621
sus_prin2 | 1.154886 .0780779 2.13 0.033 1.011562 1.318518
sus_prin3 | 1.679292 .0915277 9.51 0.000 1.50915 1.868615
sus_prin4 | 1.16956 .0932391 1.96 0.049 1.000377 1.367356
sus_prin5 | 1.585141 .2383134 3.06 0.002 1.180583 2.128332
fr_cons_sus_prin2 | .967811 .1089047 -0.29 0.771 .7762606 1.206628
fr_cons_sus_prin3 | .9786437 .0894383 -0.24 0.813 .8181506 1.17062
fr_cons_sus_prin4 | 1.003094 .0951003 0.03 0.974 .832994 1.207929
fr_cons_sus_prin5 | 1.030138 .0934648 0.33 0.743 .8623144 1.230624
cond_ocu2 | 1.049687 .0745971 0.68 0.495 .9132049 1.206567
cond_ocu3 | 1.140262 .3076365 0.49 0.627 .6719779 1.934881
cond_ocu4 | 1.22388 .0892541 2.77 0.006 1.060872 1.411935
cond_ocu5 | 1.059211 .1643571 0.37 0.711 .781452 1.435697
cond_ocu6 | 1.189118 .0464871 4.43 0.000 1.101408 1.283812
policonsumo | .9908439 .0485673 -0.19 0.851 .9000832 1.090757
num_hij2 | 1.125717 .0447885 2.98 0.003 1.041268 1.217014
tenviv1 | 1.064954 .1347529 0.50 0.619 .8310452 1.364698
tenviv2 | 1.122442 .0966966 1.34 0.180 .9480567 1.328904
tenviv4 | 1.037966 .0510032 0.76 0.448 .9426642 1.142902
tenviv5 | 1.010587 .0383223 0.28 0.781 .9381998 1.088559
mzone2 | 1.44943 .0608059 8.85 0.000 1.335021 1.573644
mzone3 | 1.530478 .0966424 6.74 0.000 1.352314 1.732113
n_off_vio | 1.466602 .0554591 10.13 0.000 1.361835 1.57943
n_off_acq | 2.802449 .0974482 29.64 0.000 2.617817 3.000103
n_off_sud | 1.392287 .0507701 9.08 0.000 1.296252 1.495436
n_off_oth | 1.73693 .0634746 15.11 0.000 1.616873 1.865902
psy_com2 | 1.118309 .0550591 2.27 0.023 1.015438 1.231601
psy_com3 | 1.100193 .0424075 2.48 0.013 1.020138 1.18653
dep2 | 1.036149 .0441148 0.83 0.404 .9531953 1.126323
rural2 | .8986725 .0559761 -1.72 0.086 .7953938 1.015361
rural3 | .8601944 .0595456 -2.18 0.030 .7510581 .9851893
porc_pobr | 1.562341 .3910473 1.78 0.075 .9565856 2.551691
susini2 | 1.188253 .1083174 1.89 0.058 .9938388 1.420698
susini3 | 1.269715 .0818343 3.71 0.000 1.11904 1.440678
susini4 | 1.180633 .0440192 4.45 0.000 1.097434 1.27014
susini5 | 1.421288 .1319462 3.79 0.000 1.184841 1.704919
ano_nac_corr | .8529212 .0080249 -16.91 0.000 .8373369 .8687956
cohab2 | .8797577 .0590858 -1.91 0.056 .77125 1.003531
cohab3 | 1.07485 .0859502 0.90 0.367 .9189288 1.257229
cohab4 | .964108 .0641851 -0.55 0.583 .8461693 1.098485
fis_com2 | 1.059469 .0365238 1.68 0.094 .9902481 1.133528
fis_com3 | .8195036 .0710023 -2.30 0.022 .691516 .9711795
rc_x1 | .8531643 .0101998 -13.28 0.000 .8334054 .8733916
rc_x2 | .8816427 .0351615 -3.16 0.002 .815352 .9533231
rc_x3 | 1.278705 .1360098 2.31 0.021 1.038083 1.575102
_rcs1 | 2.132056 .0666556 24.22 0.000 2.005336 2.266785
_rcs_mot_egr_early1 | .9186504 .0325501 -2.39 0.017 .8570183 .9847147
_rcs_mot_egr_early2 | 1.05912 .0129405 4.70 0.000 1.034058 1.084789
_rcs_mot_egr_early3 | 1.03069 .0098905 3.15 0.002 1.011486 1.050258
_rcs_mot_egr_early4 | 1.009656 .0070266 1.38 0.167 .9959779 1.023523
_rcs_mot_egr_late1 | .9478227 .0324138 -1.57 0.117 .8863752 1.01353
_rcs_mot_egr_late2 | 1.076282 .0118613 6.67 0.000 1.053284 1.099783
_rcs_mot_egr_late3 | 1.036695 .0084374 4.43 0.000 1.020289 1.053365
_rcs_mot_egr_late4 | 1.013596 .0055912 2.45 0.014 1.002697 1.024614
_cons | 1.8e+136 3.4e+137 16.56 0.000 1.4e+120 2.4e+152
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16990.787
Iteration 1: log likelihood = -16983.45
Iteration 2: log likelihood = -16983.426
Iteration 3: log likelihood = -16983.426
Log likelihood = -16983.426 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.024744 .1280929 11.15 0.000 1.788627 2.29203
mot_egr_late | 1.709328 .0931522 9.84 0.000 1.536166 1.90201
tr_mod2 | 1.216361 .0517462 4.60 0.000 1.119053 1.322129
sex_dum2 | .6071629 .0295141 -10.26 0.000 .5519864 .6678548
edad_ini_cons | .9715 .0047123 -5.96 0.000 .9623078 .9807801
esc1 | 1.430479 .0886519 5.78 0.000 1.266862 1.615227
esc2 | 1.264073 .0732382 4.04 0.000 1.128379 1.416085
sus_prin2 | 1.155395 .0781131 2.14 0.033 1.012006 1.319101
sus_prin3 | 1.679599 .0915472 9.51 0.000 1.509422 1.868963
sus_prin4 | 1.169939 .0932704 1.97 0.049 1.000699 1.367801
sus_prin5 | 1.585245 .2383377 3.06 0.002 1.180647 2.128494
fr_cons_sus_prin2 | .9678202 .1089059 -0.29 0.771 .7762678 1.20664
fr_cons_sus_prin3 | .9787218 .0894453 -0.24 0.814 .8182162 1.170713
fr_cons_sus_prin4 | 1.003105 .0951011 0.03 0.974 .8330037 1.207942
fr_cons_sus_prin5 | 1.03023 .0934732 0.33 0.743 .8623911 1.230734
cond_ocu2 | 1.049386 .0745757 0.68 0.498 .9129429 1.20622
cond_ocu3 | 1.141414 .3079462 0.49 0.624 .6726578 1.936832
cond_ocu4 | 1.223034 .0891913 2.76 0.006 1.060141 1.410956
cond_ocu5 | 1.059352 .1643777 0.37 0.710 .7815571 1.435885
cond_ocu6 | 1.189292 .0464938 4.43 0.000 1.101569 1.284
policonsumo | .9909095 .0485702 -0.19 0.852 .9001434 1.090828
num_hij2 | 1.125774 .0447912 2.98 0.003 1.04132 1.217076
tenviv1 | 1.065741 .1348501 0.50 0.615 .831664 1.365702
tenviv2 | 1.123139 .09676 1.35 0.178 .9486398 1.329737
tenviv4 | 1.03839 .0510242 0.77 0.443 .9430488 1.143369
tenviv5 | 1.010888 .038334 0.29 0.775 .9384792 1.088884
mzone2 | 1.44964 .0608161 8.85 0.000 1.335211 1.573875
mzone3 | 1.530626 .0966565 6.74 0.000 1.352438 1.732292
n_off_vio | 1.466494 .0554499 10.13 0.000 1.361744 1.579302
n_off_acq | 2.801355 .0974006 29.63 0.000 2.616813 2.998912
n_off_sud | 1.391949 .050755 9.07 0.000 1.295943 1.495068
n_off_oth | 1.736626 .0634558 15.11 0.000 1.616605 1.865559
psy_com2 | 1.117998 .0550451 2.27 0.023 1.015154 1.231262
psy_com3 | 1.100097 .0424035 2.47 0.013 1.020049 1.186426
dep2 | 1.036116 .0441137 0.83 0.405 .9531636 1.126287
rural2 | .8986297 .0559738 -1.72 0.086 .7953552 1.015314
rural3 | .8604255 .059564 -2.17 0.030 .7512558 .9854593
porc_pobr | 1.567027 .3921911 1.79 0.073 .9594889 2.55925
susini2 | 1.188106 .1083037 1.89 0.059 .993717 1.420522
susini3 | 1.270352 .0818748 3.71 0.000 1.119602 1.441399
susini4 | 1.180544 .0440162 4.45 0.000 1.09735 1.270044
susini5 | 1.421866 .1320044 3.79 0.000 1.185316 1.705624
ano_nac_corr | .8524396 .0080232 -16.96 0.000 .8368587 .8683107
cohab2 | .8797739 .0590849 -1.91 0.056 .7712676 1.003545
cohab3 | 1.074572 .085926 0.90 0.368 .9186946 1.256899
cohab4 | .9639718 .0641739 -0.55 0.582 .8460535 1.098325
fis_com2 | 1.059299 .0365175 1.67 0.095 .9900905 1.133345
fis_com3 | .8194108 .0709945 -2.30 0.022 .6914374 .97107
rc_x1 | .8526854 .0101962 -13.33 0.000 .8329336 .8729056
rc_x2 | .8815919 .0351605 -3.16 0.002 .8153033 .9532703
rc_x3 | 1.278916 .1360367 2.31 0.021 1.038248 1.575373
_rcs1 | 2.132264 .0667299 24.19 0.000 2.005406 2.267146
_rcs_mot_egr_early1 | .9191949 .0326173 -2.37 0.018 .8574386 .9853992
_rcs_mot_egr_early2 | 1.058373 .0128409 4.68 0.000 1.033502 1.083843
_rcs_mot_egr_early3 | 1.032794 .0099414 3.35 0.001 1.013492 1.052464
_rcs_mot_egr_early4 | 1.010137 .0072199 1.41 0.158 .996085 1.024387
_rcs_mot_egr_early5 | 1.010535 .0052601 2.01 0.044 1.000277 1.020897
_rcs_mot_egr_late1 | .9477467 .0324489 -1.57 0.117 .886235 1.013528
_rcs_mot_egr_late2 | 1.075121 .0118402 6.58 0.000 1.052163 1.09858
_rcs_mot_egr_late3 | 1.037619 .0086766 4.42 0.000 1.020752 1.054765
_rcs_mot_egr_late4 | 1.015927 .0058939 2.72 0.006 1.004441 1.027545
_rcs_mot_egr_late5 | 1.009197 .004127 2.24 0.025 1.00114 1.017318
_cons | 5.6e+136 1.1e+138 16.62 0.000 4.2e+120 7.6e+152
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16991.826
Iteration 1: log likelihood = -16981.954
Iteration 2: log likelihood = -16981.89
Iteration 3: log likelihood = -16981.89
Log likelihood = -16981.89 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.025404 .1281428 11.16 0.000 1.789197 2.292796
mot_egr_late | 1.709551 .0931727 9.84 0.000 1.536351 1.902277
tr_mod2 | 1.216358 .0517442 4.60 0.000 1.119055 1.322123
sex_dum2 | .6073262 .0295219 -10.26 0.000 .5521351 .6680342
edad_ini_cons | .9714874 .0047124 -5.96 0.000 .9622951 .9807675
esc1 | 1.430387 .0886465 5.78 0.000 1.26678 1.615123
esc2 | 1.264019 .073235 4.04 0.000 1.128331 1.416024
sus_prin2 | 1.155668 .0781322 2.14 0.032 1.012244 1.319414
sus_prin3 | 1.679716 .0915542 9.52 0.000 1.509526 1.869094
sus_prin4 | 1.170021 .0932773 1.97 0.049 1.000768 1.367898
sus_prin5 | 1.585252 .2383395 3.06 0.002 1.180652 2.128506
fr_cons_sus_prin2 | .9678691 .1089116 -0.29 0.772 .7763066 1.206702
fr_cons_sus_prin3 | .9787435 .0894471 -0.24 0.814 .8182345 1.170739
fr_cons_sus_prin4 | 1.003149 .0951051 0.03 0.974 .8330403 1.207994
fr_cons_sus_prin5 | 1.030258 .093476 0.33 0.742 .8624143 1.230768
cond_ocu2 | 1.049117 .0745566 0.67 0.500 .9127093 1.205912
cond_ocu3 | 1.14219 .3081552 0.49 0.622 .6731158 1.938147
cond_ocu4 | 1.222667 .0891622 2.76 0.006 1.059826 1.410527
cond_ocu5 | 1.059013 .1643253 0.37 0.712 .7813073 1.435426
cond_ocu6 | 1.189386 .046497 4.44 0.000 1.101657 1.284101
policonsumo | .9908988 .0485688 -0.19 0.852 .9001352 1.090814
num_hij2 | 1.125781 .0447918 2.98 0.003 1.041326 1.217085
tenviv1 | 1.06613 .1348974 0.51 0.613 .8319701 1.366195
tenviv2 | 1.123733 .0968127 1.35 0.176 .9491387 1.330444
tenviv4 | 1.038578 .0510336 0.77 0.441 .9432195 1.143577
tenviv5 | 1.011096 .0383421 0.29 0.771 .9386716 1.089108
mzone2 | 1.449792 .0608236 8.85 0.000 1.33535 1.574043
mzone3 | 1.530831 .0966718 6.74 0.000 1.352614 1.732529
n_off_vio | 1.466437 .0554444 10.13 0.000 1.361697 1.579234
n_off_acq | 2.800822 .0973755 29.62 0.000 2.616327 2.998328
n_off_sud | 1.391814 .0507483 9.07 0.000 1.29582 1.494919
n_off_oth | 1.736477 .0634452 15.10 0.000 1.616475 1.865388
psy_com2 | 1.117968 .0550442 2.26 0.024 1.015125 1.23123
psy_com3 | 1.100099 .0424035 2.48 0.013 1.020051 1.186428
dep2 | 1.036115 .0441139 0.83 0.405 .9531622 1.126286
rural2 | .898515 .0559663 -1.72 0.086 .7952544 1.015184
rural3 | .86047 .0595685 -2.17 0.030 .7512921 .9855137
porc_pobr | 1.57009 .3929532 1.80 0.071 .96137 2.564238
susini2 | 1.187961 .1082899 1.89 0.059 .9935958 1.420346
susini3 | 1.270986 .0819152 3.72 0.000 1.120162 1.442118
susini4 | 1.180533 .0440157 4.45 0.000 1.09734 1.270032
susini5 | 1.422082 .1320247 3.79 0.000 1.185496 1.705884
ano_nac_corr | .8522663 .0080229 -16.98 0.000 .8366859 .8681369
cohab2 | .8798443 .0590891 -1.91 0.057 .7713302 1.003625
cohab3 | 1.074514 .0859205 0.90 0.369 .9186456 1.256828
cohab4 | .9639527 .0641722 -0.55 0.581 .8460374 1.098302
fis_com2 | 1.059292 .0365168 1.67 0.095 .990085 1.133337
fis_com3 | .81934 .0709887 -2.30 0.021 .691377 .9709869
rc_x1 | .8525151 .0101951 -13.34 0.000 .8327654 .8727332
rc_x2 | .8815563 .0351594 -3.16 0.002 .8152697 .9532324
rc_x3 | 1.279056 .1360543 2.31 0.021 1.038357 1.575552
_rcs1 | 2.13216 .0667353 24.19 0.000 2.005293 2.267054
_rcs_mot_egr_early1 | .9191522 .0326208 -2.38 0.018 .8573897 .9853638
_rcs_mot_egr_early2 | 1.057384 .0128349 4.60 0.000 1.032525 1.082842
_rcs_mot_egr_early3 | 1.032453 .0100692 3.27 0.001 1.012905 1.052378
_rcs_mot_egr_early4 | 1.011849 .0072006 1.66 0.098 .9978341 1.026061
_rcs_mot_egr_early5 | 1.008916 .0053858 1.66 0.096 .9984151 1.019527
_rcs_mot_egr_early6 | 1.009678 .0043271 2.25 0.025 1.001232 1.018194
_rcs_mot_egr_late1 | .9475975 .0324476 -1.57 0.116 .8860884 1.013376
_rcs_mot_egr_late2 | 1.074536 .011899 6.49 0.000 1.051466 1.098112
_rcs_mot_egr_late3 | 1.036327 .0089015 4.15 0.000 1.019026 1.053921
_rcs_mot_egr_late4 | 1.018814 .006086 3.12 0.002 1.006955 1.030812
_rcs_mot_egr_late5 | 1.009576 .0043401 2.22 0.027 1.001105 1.018118
_rcs_mot_egr_late6 | 1.007248 .0033809 2.15 0.031 1.000643 1.013896
_cons | 8.5e+136 1.6e+138 16.64 0.000 6.2e+120 1.1e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16991.588
Iteration 1: log likelihood = -16981.922
Iteration 2: log likelihood = -16981.86
Iteration 3: log likelihood = -16981.86
Log likelihood = -16981.86 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.025364 .1281365 11.16 0.000 1.789168 2.292742
mot_egr_late | 1.709485 .0931659 9.84 0.000 1.536298 1.902196
tr_mod2 | 1.216344 .0517436 4.60 0.000 1.119042 1.322108
sex_dum2 | .6073537 .0295234 -10.26 0.000 .5521599 .6680646
edad_ini_cons | .9714854 .0047124 -5.96 0.000 .962293 .9807656
esc1 | 1.430456 .0886505 5.78 0.000 1.266842 1.615201
esc2 | 1.264057 .0732371 4.04 0.000 1.128365 1.416067
sus_prin2 | 1.155727 .0781363 2.14 0.032 1.012295 1.319481
sus_prin3 | 1.67979 .091559 9.52 0.000 1.509591 1.869178
sus_prin4 | 1.17008 .0932821 1.97 0.049 1.000819 1.367968
sus_prin5 | 1.585378 .2383587 3.07 0.002 1.180746 2.128676
fr_cons_sus_prin2 | .9678317 .1089074 -0.29 0.771 .7762767 1.206655
fr_cons_sus_prin3 | .9787281 .0894456 -0.24 0.814 .8182217 1.17072
fr_cons_sus_prin4 | 1.003131 .0951033 0.03 0.974 .833025 1.207972
fr_cons_sus_prin5 | 1.030215 .0934722 0.33 0.743 .8623784 1.230717
cond_ocu2 | 1.049092 .0745548 0.67 0.500 .9126876 1.205883
cond_ocu3 | 1.142424 .3082191 0.49 0.622 .6732534 1.938547
cond_ocu4 | 1.222568 .0891546 2.76 0.006 1.059741 1.410412
cond_ocu5 | 1.059023 .1643267 0.37 0.712 .7813146 1.435439
cond_ocu6 | 1.189447 .0464996 4.44 0.000 1.101714 1.284167
policonsumo | .9908883 .0485681 -0.19 0.852 .900126 1.090803
num_hij2 | 1.125804 .0447929 2.98 0.003 1.041347 1.21711
tenviv1 | 1.06613 .1348972 0.51 0.613 .8319705 1.366195
tenviv2 | 1.123831 .096822 1.36 0.175 .9492203 1.330562
tenviv4 | 1.038612 .0510353 0.77 0.441 .9432506 1.143615
tenviv5 | 1.011127 .0383433 0.29 0.770 .9387002 1.089142
mzone2 | 1.449837 .0608259 8.85 0.000 1.33539 1.574093
mzone3 | 1.530908 .0966777 6.74 0.000 1.35268 1.732618
n_off_vio | 1.466388 .055442 10.12 0.000 1.361652 1.57918
n_off_acq | 2.800715 .0973704 29.62 0.000 2.616229 2.99821
n_off_sud | 1.391776 .0507465 9.07 0.000 1.295786 1.494878
n_off_oth | 1.736416 .0634419 15.10 0.000 1.61642 1.86532
psy_com2 | 1.117995 .0550461 2.27 0.023 1.015149 1.231261
psy_com3 | 1.100119 .0424043 2.48 0.013 1.02007 1.18645
dep2 | 1.036074 .0441121 0.83 0.405 .9531252 1.126242
rural2 | .8985228 .0559668 -1.72 0.086 .7952613 1.015192
rural3 | .860464 .0595683 -2.17 0.030 .7512866 .9855072
porc_pobr | 1.570314 .3930058 1.80 0.071 .9615118 2.564594
susini2 | 1.187963 .10829 1.89 0.059 .9935981 1.420349
susini3 | 1.270983 .0819159 3.72 0.000 1.120158 1.442117
susini4 | 1.180526 .0440157 4.45 0.000 1.097333 1.270025
susini5 | 1.422141 .1320304 3.79 0.000 1.185544 1.705954
ano_nac_corr | .852207 .008023 -16.99 0.000 .8366263 .8680778
cohab2 | .8798269 .0590878 -1.91 0.057 .7713152 1.003604
cohab3 | 1.074516 .0859204 0.90 0.369 .918648 1.25683
cohab4 | .9639146 .0641695 -0.55 0.581 .8460042 1.098259
fis_com2 | 1.059265 .0365159 1.67 0.095 .9900594 1.133308
fis_com3 | .8193085 .0709861 -2.30 0.021 .6913503 .9709498
rc_x1 | .8524591 .0101949 -13.35 0.000 .8327097 .8726768
rc_x2 | .8815382 .0351588 -3.16 0.002 .8152527 .9532131
rc_x3 | 1.279121 .1360617 2.31 0.021 1.038409 1.575633
_rcs1 | 2.131829 .066701 24.19 0.000 2.005025 2.266653
_rcs_mot_egr_early1 | .9195035 .0326257 -2.37 0.018 .857731 .9857247
_rcs_mot_egr_early2 | 1.056823 .0127783 4.57 0.000 1.032072 1.082167
_rcs_mot_egr_early3 | 1.03381 .0100594 3.42 0.001 1.014281 1.053715
_rcs_mot_egr_early4 | 1.011575 .0073735 1.58 0.114 .9972257 1.02613
_rcs_mot_egr_early5 | 1.009206 .0055503 1.67 0.096 .9983857 1.020143
_rcs_mot_egr_early6 | 1.009895 .0045054 2.21 0.027 1.001103 1.018764
_rcs_mot_egr_early7 | 1.005717 .0037311 1.54 0.124 .9984303 1.013056
_rcs_mot_egr_late1 | .9477029 .0324395 -1.57 0.117 .8862085 1.013464
_rcs_mot_egr_late2 | 1.07381 .0119551 6.40 0.000 1.050632 1.097499
_rcs_mot_egr_late3 | 1.035579 .0091072 3.98 0.000 1.017882 1.053584
_rcs_mot_egr_late4 | 1.021454 .0063219 3.43 0.001 1.009138 1.03392
_rcs_mot_egr_late5 | 1.009691 .0044504 2.19 0.029 1.001006 1.018452
_rcs_mot_egr_late6 | 1.008894 .0034944 2.56 0.011 1.002069 1.015766
_rcs_mot_egr_late7 | 1.004284 .0028853 1.49 0.137 .9986443 1.009955
_cons | 9.7e+136 1.8e+138 16.64 0.000 7.2e+120 1.3e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -17035.91
Iteration 1: log likelihood = -16997.76
Iteration 2: log likelihood = -16997.379
Iteration 3: log likelihood = -16997.379
Log likelihood = -16997.379 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.001991 .1262213 11.01 0.000 1.769276 2.265315
mot_egr_late | 1.692536 .0919399 9.69 0.000 1.521598 1.882677
tr_mod2 | 1.218067 .0518206 4.64 0.000 1.120619 1.323988
sex_dum2 | .6062319 .0294702 -10.30 0.000 .5511376 .6668336
edad_ini_cons | .9715216 .0047124 -5.96 0.000 .9623291 .9808019
esc1 | 1.431395 .0887021 5.79 0.000 1.267685 1.616246
esc2 | 1.264718 .0732737 4.05 0.000 1.128958 1.416803
sus_prin2 | 1.152902 .0779408 2.10 0.035 1.009828 1.316246
sus_prin3 | 1.678019 .0914555 9.50 0.000 1.508011 1.867192
sus_prin4 | 1.167847 .093101 1.95 0.052 .9989141 1.36535
sus_prin5 | 1.582848 .2379334 3.05 0.002 1.178927 2.125161
fr_cons_sus_prin2 | .9683242 .1089623 -0.29 0.775 .7766724 1.207268
fr_cons_sus_prin3 | .9787665 .0894512 -0.23 0.814 .8182504 1.170771
fr_cons_sus_prin4 | 1.003167 .0951102 0.03 0.973 .8330499 1.208024
fr_cons_sus_prin5 | 1.029986 .0934551 0.33 0.745 .8621803 1.230452
cond_ocu2 | 1.049919 .0746173 0.69 0.493 .9134001 1.206841
cond_ocu3 | 1.14271 .3082928 0.49 0.621 .6734257 1.939021
cond_ocu4 | 1.225575 .0893789 2.79 0.005 1.062339 1.413893
cond_ocu5 | 1.058959 .1643187 0.37 0.712 .7812648 1.435358
cond_ocu6 | 1.188822 .0464788 4.42 0.000 1.101128 1.2835
policonsumo | .9894097 .0484983 -0.22 0.828 .898778 1.08918
num_hij2 | 1.126015 .0447992 2.98 0.003 1.041546 1.217334
tenviv1 | 1.063465 .1345786 0.49 0.627 .829862 1.362826
tenviv2 | 1.120909 .0965561 1.33 0.185 .946776 1.32707
tenviv4 | 1.037144 .0509646 0.74 0.458 .9419145 1.142001
tenviv5 | 1.009231 .0382667 0.24 0.809 .936949 1.08709
mzone2 | 1.447673 .0607234 8.82 0.000 1.333418 1.571717
mzone3 | 1.528879 .0965329 6.72 0.000 1.350917 1.730285
n_off_vio | 1.466947 .0554867 10.13 0.000 1.362129 1.579832
n_off_acq | 2.805647 .0975792 29.66 0.000 2.620768 3.003568
n_off_sud | 1.393059 .0508062 9.09 0.000 1.296957 1.496283
n_off_oth | 1.738541 .0635522 15.13 0.000 1.618339 1.867672
psy_com2 | 1.117496 .0550039 2.26 0.024 1.014727 1.230673
psy_com3 | 1.1003 .0424142 2.48 0.013 1.020233 1.186651
dep2 | 1.036189 .0441138 0.84 0.404 .9532361 1.12636
rural2 | .8989694 .055993 -1.71 0.087 .7956593 1.015694
rural3 | .8616066 .0596188 -2.15 0.031 .7523332 .9867515
porc_pobr | 1.527672 .3824373 1.69 0.091 .9352775 2.495283
susini2 | 1.189207 .1083985 1.90 0.057 .9946467 1.421825
susini3 | 1.269589 .0818301 3.70 0.000 1.118922 1.440544
susini4 | 1.181285 .0440432 4.47 0.000 1.098041 1.270841
susini5 | 1.419889 .1318044 3.78 0.000 1.183695 1.703214
ano_nac_corr | .8561298 .0080237 -16.57 0.000 .8405472 .8720013
cohab2 | .8797116 .0590842 -1.91 0.056 .771207 1.003482
cohab3 | 1.075104 .0859724 0.91 0.365 .9191424 1.25753
cohab4 | .9639892 .0641809 -0.55 0.582 .8460587 1.098358
fis_com2 | 1.060525 .0365541 1.70 0.088 .9912467 1.134645
fis_com3 | .8201311 .0710544 -2.29 0.022 .6920492 .9719178
rc_x1 | .8563474 .0102147 -13.00 0.000 .8365591 .8766037
rc_x2 | .8818353 .0351653 -3.15 0.002 .8155372 .9535231
rc_x3 | 1.277678 .1358876 2.30 0.021 1.03727 1.573806
_rcs1 | 2.1945 .0693295 24.88 0.000 2.062738 2.334678
_rcs2 | 1.077013 .008878 9.00 0.000 1.059753 1.094555
_rcs_mot_egr_early1 | .8917197 .0314794 -3.25 0.001 .8321073 .9556027
_rcs_mot_egr_late1 | .913787 .0310468 -2.65 0.008 .8549183 .9767094
_cons | 9.5e+132 1.8e+134 16.23 0.000 8.2e+116 1.1e+149
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -17037.151
Iteration 1: log likelihood = -16996.776
Iteration 2: log likelihood = -16996.302
Iteration 3: log likelihood = -16996.301
Log likelihood = -16996.301 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.999617 .1261939 10.98 0.000 1.766967 2.2629
mot_egr_late | 1.694693 .0921164 9.70 0.000 1.523433 1.885206
tr_mod2 | 1.217369 .0517951 4.62 0.000 1.11997 1.323238
sex_dum2 | .6063122 .0294742 -10.29 0.000 .5512106 .6669221
edad_ini_cons | .971529 .0047123 -5.95 0.000 .9623367 .980809
esc1 | 1.43139 .0887013 5.79 0.000 1.267681 1.61624
esc2 | 1.264634 .0732689 4.05 0.000 1.128883 1.416709
sus_prin2 | 1.15307 .0779521 2.11 0.035 1.009976 1.316438
sus_prin3 | 1.678124 .0914589 9.50 0.000 1.50811 1.867304
sus_prin4 | 1.167875 .0931025 1.95 0.052 .9989394 1.365381
sus_prin5 | 1.583741 .2380736 3.06 0.002 1.179583 2.126375
fr_cons_sus_prin2 | .9682334 .1089522 -0.29 0.774 .7765994 1.207155
fr_cons_sus_prin3 | .9787639 .0894506 -0.23 0.814 .8182488 1.170767
fr_cons_sus_prin4 | 1.003153 .0951083 0.03 0.974 .8330387 1.208006
fr_cons_sus_prin5 | 1.029998 .0934547 0.33 0.745 .862193 1.230463
cond_ocu2 | 1.049672 .0746 0.68 0.495 .913185 1.206558
cond_ocu3 | 1.142263 .3081726 0.49 0.622 .6731617 1.938264
cond_ocu4 | 1.225873 .0893965 2.79 0.005 1.062604 1.414227
cond_ocu5 | 1.059748 .1644448 0.37 0.708 .7818418 1.436437
cond_ocu6 | 1.188838 .0464786 4.42 0.000 1.101145 1.283516
policonsumo | .9895442 .0485046 -0.21 0.830 .8989008 1.089328
num_hij2 | 1.126092 .0448021 2.98 0.003 1.041618 1.217417
tenviv1 | 1.063375 .1345675 0.49 0.627 .8297915 1.362712
tenviv2 | 1.120496 .0965236 1.32 0.187 .9464215 1.326587
tenviv4 | 1.037296 .0509723 0.75 0.456 .9420521 1.142169
tenviv5 | 1.009439 .0382748 0.25 0.804 .9371412 1.087314
mzone2 | 1.447898 .0607316 8.82 0.000 1.333628 1.57196
mzone3 | 1.529488 .09657 6.73 0.000 1.351457 1.730971
n_off_vio | 1.466896 .0554853 10.13 0.000 1.362081 1.579778
n_off_acq | 2.805915 .0975881 29.66 0.000 2.621019 3.003854
n_off_sud | 1.393104 .050807 9.09 0.000 1.296999 1.496329
n_off_oth | 1.738598 .0635533 15.13 0.000 1.618393 1.867731
psy_com2 | 1.118168 .0550407 2.27 0.023 1.015331 1.231422
psy_com3 | 1.100044 .0424045 2.47 0.013 1.019994 1.186375
dep2 | 1.036176 .0441142 0.83 0.404 .9532226 1.126348
rural2 | .898867 .0559866 -1.71 0.087 .7955687 1.015578
rural3 | .8611517 .0595925 -2.16 0.031 .751927 .9862423
porc_pobr | 1.527407 .3823693 1.69 0.091 .9351167 2.494845
susini2 | 1.188764 .1083595 1.90 0.058 .9942739 1.421299
susini3 | 1.270017 .0818588 3.71 0.000 1.119297 1.441032
susini4 | 1.181156 .0440384 4.47 0.000 1.09792 1.270701
susini5 | 1.419982 .1318144 3.78 0.000 1.18377 1.703328
ano_nac_corr | .8561605 .008026 -16.57 0.000 .8405734 .8720366
cohab2 | .8794347 .0590673 -1.91 0.056 .7709613 1.00317
cohab3 | 1.074791 .0859492 0.90 0.367 .9188721 1.257168
cohab4 | .9638188 .0641696 -0.55 0.580 .8459091 1.098164
fis_com2 | 1.060436 .0365521 1.70 0.089 .991162 1.134552
fis_com3 | .8199747 .0710413 -2.29 0.022 .6919166 .9717335
rc_x1 | .8563632 .0102164 -13.00 0.000 .8365717 .876623
rc_x2 | .8818456 .0351656 -3.15 0.002 .8155469 .9535339
rc_x3 | 1.277721 .1358924 2.30 0.021 1.037304 1.573859
_rcs1 | 2.178327 .0727043 23.33 0.000 2.04039 2.325589
_rcs2 | 1.059874 .0277596 2.22 0.026 1.006839 1.115702
_rcs_mot_egr_early1 | .8955463 .0333604 -2.96 0.003 .832491 .9633776
_rcs_mot_egr_early2 | 1.004687 .0292204 0.16 0.872 .9490178 1.063622
_rcs_mot_egr_late1 | .9246 .0334173 -2.17 0.030 .8613694 .9924723
_rcs_mot_egr_late2 | 1.027772 .0292567 0.96 0.336 .9720001 1.086744
_cons | 8.8e+132 1.7e+134 16.22 0.000 7.6e+116 1.0e+149
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16990.029
Iteration 1: log likelihood = -16984.756
Iteration 2: log likelihood = -16984.739
Iteration 3: log likelihood = -16984.739
Log likelihood = -16984.739 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.008592 .1268451 11.04 0.000 1.77475 2.273244
mot_egr_late | 1.697232 .0923133 9.73 0.000 1.525611 1.888158
tr_mod2 | 1.217279 .0517874 4.62 0.000 1.119895 1.323133
sex_dum2 | .6067312 .0294927 -10.28 0.000 .5515947 .667379
edad_ini_cons | .9715063 .0047123 -5.96 0.000 .9623141 .9807863
esc1 | 1.430718 .0886649 5.78 0.000 1.267077 1.615492
esc2 | 1.264357 .0732544 4.05 0.000 1.128633 1.416403
sus_prin2 | 1.154763 .0780714 2.13 0.033 1.011451 1.318382
sus_prin3 | 1.679463 .0915402 9.51 0.000 1.509298 1.868812
sus_prin4 | 1.169487 .0932353 1.96 0.050 1.00031 1.367275
sus_prin5 | 1.58681 .2385451 3.07 0.002 1.181854 2.130522
fr_cons_sus_prin2 | .9676609 .1088872 -0.29 0.770 .7761412 1.20644
fr_cons_sus_prin3 | .9786689 .0894409 -0.24 0.813 .8181711 1.170651
fr_cons_sus_prin4 | 1.003212 .0951127 0.03 0.973 .8330898 1.208074
fr_cons_sus_prin5 | 1.030079 .0934607 0.33 0.744 .8622625 1.230556
cond_ocu2 | 1.049719 .0746009 0.68 0.495 .9132302 1.206607
cond_ocu3 | 1.141235 .3078981 0.49 0.624 .6725529 1.936529
cond_ocu4 | 1.224255 .0892812 2.77 0.006 1.061198 1.412367
cond_ocu5 | 1.058679 .1642782 0.37 0.713 .7810534 1.434986
cond_ocu6 | 1.188975 .0464831 4.43 0.000 1.101272 1.283661
policonsumo | .9909081 .0485747 -0.19 0.852 .9001341 1.090836
num_hij2 | 1.125673 .0447867 2.98 0.003 1.041228 1.216966
tenviv1 | 1.064718 .1347267 0.50 0.620 .8308563 1.364406
tenviv2 | 1.121746 .0966353 1.33 0.182 .9474708 1.328077
tenviv4 | 1.037568 .0509848 0.75 0.453 .9423008 1.142467
tenviv5 | 1.010137 .038304 0.27 0.790 .9377844 1.088072
mzone2 | 1.449153 .0607921 8.84 0.000 1.33477 1.573339
mzone3 | 1.529264 .096565 6.73 0.000 1.351243 1.730738
n_off_vio | 1.466748 .0554652 10.13 0.000 1.361969 1.579587
n_off_acq | 2.802601 .0974455 29.64 0.000 2.617974 3.000249
n_off_sud | 1.392306 .0507706 9.08 0.000 1.296271 1.495457
n_off_oth | 1.737244 .0634865 15.11 0.000 1.617164 1.866239
psy_com2 | 1.118684 .055075 2.28 0.023 1.015784 1.232009
psy_com3 | 1.10022 .0424089 2.48 0.013 1.020162 1.18656
dep2 | 1.036247 .044119 0.84 0.403 .9532848 1.126429
rural2 | .898546 .0559692 -1.72 0.086 .7952801 1.015221
rural3 | .8599709 .0595248 -2.18 0.029 .7508721 .9849213
porc_pobr | 1.556479 .3896279 1.77 0.077 .9529383 2.542269
susini2 | 1.188242 .1083153 1.89 0.058 .9938314 1.420682
susini3 | 1.269264 .0818076 3.70 0.000 1.118639 1.440172
susini4 | 1.180754 .0440242 4.46 0.000 1.097546 1.270271
susini5 | 1.420559 .1318699 3.78 0.000 1.184248 1.704025
ano_nac_corr | .8522783 .0080253 -16.98 0.000 .8366933 .8681536
cohab2 | .8798547 .0590931 -1.91 0.057 .7713336 1.003644
cohab3 | 1.075428 .0859975 0.91 0.363 .9194203 1.257906
cohab4 | .9642121 .0641938 -0.55 0.584 .8462577 1.098607
fis_com2 | 1.059364 .036518 1.67 0.094 .9901545 1.133411
fis_com3 | .8194597 .0709986 -2.30 0.022 .6914788 .9711276
rc_x1 | .8525172 .010197 -13.34 0.000 .8327638 .8727391
rc_x2 | .88185 .0351678 -3.15 0.002 .8155474 .9535429
rc_x3 | 1.277629 .1358888 2.30 0.021 1.03722 1.57376
_rcs1 | 2.172347 .072301 23.31 0.000 2.035163 2.318778
_rcs2 | 1.059725 .0276192 2.23 0.026 1.006952 1.115264
_rcs_mot_egr_early1 | .901176 .0335909 -2.79 0.005 .8376865 .9694774
_rcs_mot_egr_early2 | 1.001052 .0286431 0.04 0.971 .9464571 1.058795
_rcs_mot_egr_early3 | 1.02571 .0097366 2.67 0.007 1.006803 1.044972
_rcs_mot_egr_late1 | .9301778 .0335746 -2.01 0.045 .8666465 .9983663
_rcs_mot_egr_late2 | 1.017244 .0285992 0.61 0.543 .9627069 1.074871
_rcs_mot_egr_late3 | 1.033329 .0081365 4.16 0.000 1.017504 1.0494
_cons | 8.3e+136 1.6e+138 16.63 0.000 6.0e+120 1.1e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16994.451
Iteration 1: log likelihood = -16983.164
Iteration 2: log likelihood = -16983.055
Iteration 3: log likelihood = -16983.055
Log likelihood = -16983.055 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010686 .1270018 11.06 0.000 1.776558 2.275669
mot_egr_late | 1.697973 .0923706 9.73 0.000 1.526248 1.88902
tr_mod2 | 1.217256 .0517848 4.62 0.000 1.119876 1.323104
sex_dum2 | .6069002 .029501 -10.27 0.000 .5517482 .6675652
edad_ini_cons | .9714983 .0047123 -5.96 0.000 .962306 .9807784
esc1 | 1.430566 .0886564 5.78 0.000 1.266941 1.615323
esc2 | 1.264252 .0732487 4.05 0.000 1.128539 1.416286
sus_prin2 | 1.155478 .0781224 2.14 0.033 1.012073 1.319204
sus_prin3 | 1.680052 .0915774 9.52 0.000 1.509819 1.869479
sus_prin4 | 1.169958 .0932752 1.97 0.049 1.000709 1.367831
sus_prin5 | 1.587964 .2387279 3.08 0.002 1.1827 2.132096
fr_cons_sus_prin2 | .9675199 .1088713 -0.29 0.769 .7760282 1.206264
fr_cons_sus_prin3 | .9786609 .0894401 -0.24 0.813 .8181646 1.170641
fr_cons_sus_prin4 | 1.003141 .0951059 0.03 0.974 .833031 1.207989
fr_cons_sus_prin5 | 1.030099 .0934627 0.33 0.744 .8622791 1.23058
cond_ocu2 | 1.049522 .0745864 0.68 0.496 .9130601 1.20638
cond_ocu3 | 1.142147 .3081442 0.49 0.622 .6730897 1.938076
cond_ocu4 | 1.223517 .0892284 2.77 0.006 1.060556 1.411517
cond_ocu5 | 1.058659 .1642754 0.37 0.713 .781039 1.43496
cond_ocu6 | 1.189105 .0464883 4.43 0.000 1.101393 1.283802
policonsumo | .991229 .0485914 -0.18 0.857 .9004238 1.091192
num_hij2 | 1.125683 .0447872 2.98 0.003 1.041237 1.216978
tenviv1 | 1.065407 .1348119 0.50 0.617 .8313967 1.365284
tenviv2 | 1.122408 .0966952 1.34 0.180 .9480254 1.328867
tenviv4 | 1.037767 .0509943 0.75 0.451 .942482 1.142685
tenviv5 | 1.010426 .0383157 0.27 0.784 .9380518 1.088385
mzone2 | 1.44957 .0608126 8.85 0.000 1.335149 1.573798
mzone3 | 1.529493 .0965856 6.73 0.000 1.351435 1.731011
n_off_vio | 1.466694 .0554585 10.13 0.000 1.361927 1.579519
n_off_acq | 2.801611 .0974028 29.63 0.000 2.617064 2.999171
n_off_sud | 1.391947 .0507558 9.07 0.000 1.295939 1.495067
n_off_oth | 1.736849 .063466 15.11 0.000 1.616808 1.865802
psy_com2 | 1.118627 .0550725 2.28 0.023 1.015731 1.231946
psy_com3 | 1.100154 .0424062 2.48 0.013 1.020102 1.186489
dep2 | 1.036261 .0441198 0.84 0.403 .9532973 1.126444
rural2 | .8985529 .0559706 -1.72 0.086 .7952846 1.015231
rural3 | .8599892 .0595286 -2.18 0.029 .7508837 .984948
porc_pobr | 1.561693 .3909213 1.78 0.075 .9561448 2.550748
susini2 | 1.188156 .1083078 1.89 0.059 .9937588 1.42058
susini3 | 1.269465 .0818205 3.70 0.000 1.118815 1.4404
susini4 | 1.180653 .0440211 4.45 0.000 1.09745 1.270163
susini5 | 1.42078 .1318949 3.78 0.000 1.184424 1.7043
ano_nac_corr | .8516547 .0080275 -17.04 0.000 .8360656 .8675344
cohab2 | .879863 .0590923 -1.91 0.057 .7713432 1.00365
cohab3 | 1.075263 .085983 0.91 0.364 .9192823 1.257711
cohab4 | .96417 .0641898 -0.55 0.584 .8462228 1.098557
fis_com2 | 1.059025 .036507 1.66 0.096 .9898358 1.13305
fis_com3 | .8193607 .0709905 -2.30 0.021 .6913945 .9710115
rc_x1 | .8519034 .0101957 -13.39 0.000 .8321527 .8721228
rc_x2 | .8817877 .035166 -3.15 0.002 .8154885 .9534769
rc_x3 | 1.27787 .1359165 2.31 0.021 1.037412 1.574062
_rcs1 | 2.173535 .0724948 23.28 0.000 2.035992 2.320369
_rcs2 | 1.06138 .0278007 2.27 0.023 1.008266 1.117291
_rcs_mot_egr_early1 | .9006311 .0336429 -2.80 0.005 .8370483 .9690438
_rcs_mot_egr_early2 | .9984552 .0286385 -0.05 0.957 .9438734 1.056193
_rcs_mot_egr_early3 | 1.02456 .010189 2.44 0.015 1.004783 1.044726
_rcs_mot_egr_early4 | 1.009683 .0070199 1.39 0.166 .9960174 1.023536
_rcs_mot_egr_late1 | .9293136 .0336174 -2.03 0.043 .8657063 .9975944
_rcs_mot_egr_late2 | 1.014595 .0286191 0.51 0.607 .9600252 1.072267
_rcs_mot_egr_late3 | 1.030513 .008813 3.51 0.000 1.013384 1.047932
_rcs_mot_egr_late4 | 1.01364 .0055867 2.46 0.014 1.002749 1.024649
_cons | 3.6e+137 6.9e+138 16.69 0.000 2.5e+121 5.2e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.356
Iteration 1: log likelihood = -16980.579
Iteration 2: log likelihood = -16980.555
Iteration 3: log likelihood = -16980.555
Log likelihood = -16980.555 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012805 .1271573 11.07 0.000 1.778393 2.278115
mot_egr_late | 1.698493 .0924213 9.74 0.000 1.526675 1.889647
tr_mod2 | 1.217237 .0517812 4.62 0.000 1.119864 1.323078
sex_dum2 | .6071316 .0295124 -10.27 0.000 .5519583 .6678199
edad_ini_cons | .9714848 .0047124 -5.96 0.000 .9622923 .9807651
esc1 | 1.430435 .0886487 5.78 0.000 1.266824 1.615176
esc2 | 1.26414 .0732422 4.05 0.000 1.128439 1.416161
sus_prin2 | 1.155997 .0781583 2.14 0.032 1.012526 1.319798
sus_prin3 | 1.68037 .0915976 9.52 0.000 1.5101 1.869839
sus_prin4 | 1.170341 .0933069 1.97 0.049 1.001035 1.368281
sus_prin5 | 1.5881 .2387569 3.08 0.002 1.182789 2.132301
fr_cons_sus_prin2 | .9675271 .1088722 -0.29 0.769 .7760338 1.206273
fr_cons_sus_prin3 | .9787394 .0894471 -0.24 0.814 .8182305 1.170735
fr_cons_sus_prin4 | 1.003153 .0951068 0.03 0.974 .8330415 1.208003
fr_cons_sus_prin5 | 1.03019 .093471 0.33 0.743 .8623554 1.230689
cond_ocu2 | 1.049218 .0745647 0.68 0.499 .9127951 1.206029
cond_ocu3 | 1.143333 .3084632 0.50 0.620 .6737899 1.940086
cond_ocu4 | 1.222666 .0891653 2.76 0.006 1.059821 1.410533
cond_ocu5 | 1.058788 .1642943 0.37 0.713 .7811355 1.435132
cond_ocu6 | 1.18928 .0464951 4.43 0.000 1.101555 1.283991
policonsumo | .9912975 .0485945 -0.18 0.858 .9004865 1.091267
num_hij2 | 1.125739 .0447899 2.98 0.003 1.041289 1.21704
tenviv1 | 1.066199 .1349096 0.51 0.612 .8320189 1.366292
tenviv2 | 1.123109 .0967589 1.35 0.178 .9486123 1.329705
tenviv4 | 1.038189 .0510153 0.76 0.446 .9428652 1.143151
tenviv5 | 1.010728 .0383274 0.28 0.778 .938331 1.08871
mzone2 | 1.449783 .0608229 8.85 0.000 1.335342 1.574032
mzone3 | 1.529636 .0965994 6.73 0.000 1.351553 1.731183
n_off_vio | 1.466587 .0554493 10.13 0.000 1.361837 1.579393
n_off_acq | 2.800505 .0973545 29.62 0.000 2.616049 2.997967
n_off_sud | 1.391604 .0507405 9.06 0.000 1.295625 1.494693
n_off_oth | 1.736543 .0634469 15.11 0.000 1.616537 1.865458
psy_com2 | 1.118321 .0550588 2.27 0.023 1.015451 1.231613
psy_com3 | 1.100058 .0424022 2.47 0.013 1.020013 1.186384
dep2 | 1.036229 .0441188 0.84 0.403 .9532674 1.126411
rural2 | .8985084 .0559682 -1.72 0.086 .7952445 1.015181
rural3 | .8602181 .0595468 -2.18 0.030 .7510796 .9852154
porc_pobr | 1.566357 .3920603 1.79 0.073 .9590346 2.558274
susini2 | 1.188007 .1082939 1.89 0.059 .9936349 1.420401
susini3 | 1.270103 .0818611 3.71 0.000 1.119378 1.441122
susini4 | 1.180563 .0440181 4.45 0.000 1.097366 1.270068
susini5 | 1.421354 .1319525 3.79 0.000 1.184896 1.704999
ano_nac_corr | .8511606 .0080256 -17.09 0.000 .8355751 .8670368
cohab2 | .8798794 .0590914 -1.91 0.057 .7713611 1.003665
cohab3 | 1.074988 .0859589 0.90 0.366 .91905 1.257384
cohab4 | .9640336 .0641786 -0.55 0.582 .8461067 1.098397
fis_com2 | 1.058849 .0365005 1.66 0.097 .9896728 1.132861
fis_com3 | .8192646 .0709824 -2.30 0.021 .6913131 .9708981
rc_x1 | .8514121 .0101919 -13.44 0.000 .8316688 .8716241
rc_x2 | .8817377 .0351649 -3.16 0.002 .8154405 .953425
rc_x3 | 1.278075 .1359427 2.31 0.021 1.037571 1.574325
_rcs1 | 2.174203 .0726021 23.26 0.000 2.036462 2.32126
_rcs2 | 1.062055 .0278584 2.30 0.022 1.008834 1.118085
_rcs_mot_egr_early1 | .9009703 .0337067 -2.79 0.005 .8372704 .9695166
_rcs_mot_egr_early2 | .9973151 .0285268 -0.09 0.925 .942942 1.054823
_rcs_mot_egr_early3 | 1.024766 .0104608 2.40 0.017 1.004467 1.045475
_rcs_mot_egr_early4 | 1.009504 .0072127 1.32 0.186 .995466 1.02374
_rcs_mot_egr_early5 | 1.010611 .0052563 2.03 0.042 1.000361 1.020965
_rcs_mot_egr_late1 | .929041 .0336495 -2.03 0.042 .8653757 .9973903
_rcs_mot_egr_late2 | 1.013066 .0285255 0.46 0.645 .9586717 1.070546
_rcs_mot_egr_late3 | 1.029538 .0092968 3.22 0.001 1.011477 1.047922
_rcs_mot_egr_late4 | 1.015304 .0058912 2.62 0.009 1.003823 1.026917
_rcs_mot_egr_late5 | 1.00929 .0041243 2.26 0.024 1.001239 1.017406
_cons | 1.2e+138 2.2e+139 16.75 0.000 8.1e+121 1.7e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.402
Iteration 1: log likelihood = -16979.133
Iteration 2: log likelihood = -16979.072
Iteration 3: log likelihood = -16979.072
Log likelihood = -16979.072 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.013443 .1272078 11.08 0.000 1.778939 2.27886
mot_egr_late | 1.698713 .0924437 9.74 0.000 1.526855 1.889915
tr_mod2 | 1.217228 .051779 4.62 0.000 1.119858 1.323063
sex_dum2 | .607294 .0295202 -10.26 0.000 .5521062 .6679982
edad_ini_cons | .9714724 .0047125 -5.97 0.000 .9622798 .9807527
esc1 | 1.430346 .0886435 5.78 0.000 1.266745 1.615076
esc2 | 1.264087 .0732391 4.04 0.000 1.128391 1.4161
sus_prin2 | 1.156264 .0781769 2.15 0.032 1.012758 1.320104
sus_prin3 | 1.680481 .0916042 9.52 0.000 1.510199 1.869964
sus_prin4 | 1.170419 .0933135 1.97 0.048 1.001101 1.368374
sus_prin5 | 1.588087 .2387556 3.08 0.002 1.182778 2.132285
fr_cons_sus_prin2 | .967577 .1088781 -0.29 0.770 .7760734 1.206336
fr_cons_sus_prin3 | .9787597 .0894488 -0.23 0.814 .8182477 1.170759
fr_cons_sus_prin4 | 1.003195 .0951106 0.03 0.973 .8330768 1.208053
fr_cons_sus_prin5 | 1.030217 .0934736 0.33 0.743 .8623773 1.230721
cond_ocu2 | 1.048954 .0745459 0.67 0.501 .9125656 1.205726
cond_ocu3 | 1.144092 .3086676 0.50 0.618 .6742379 1.941373
cond_ocu4 | 1.222305 .0891367 2.75 0.006 1.059511 1.410112
cond_ocu5 | 1.058458 .1642432 0.37 0.714 .7808916 1.434684
cond_ocu6 | 1.189375 .0464983 4.44 0.000 1.101644 1.284093
policonsumo | .9912834 .0485928 -0.18 0.858 .9004753 1.091249
num_hij2 | 1.125746 .0447904 2.98 0.003 1.041294 1.217047
tenviv1 | 1.066579 .1349558 0.51 0.610 .8323183 1.366775
tenviv2 | 1.123697 .0968112 1.35 0.176 .9491063 1.330405
tenviv4 | 1.038378 .0510247 0.77 0.443 .943036 1.143358
tenviv5 | 1.010934 .0383354 0.29 0.774 .9385227 1.088933
mzone2 | 1.449934 .0608303 8.86 0.000 1.335479 1.574198
mzone3 | 1.529847 .096615 6.73 0.000 1.351735 1.731427
n_off_vio | 1.466529 .0554438 10.13 0.000 1.361789 1.579324
n_off_acq | 2.799986 .09733 29.62 0.000 2.615576 2.997397
n_off_sud | 1.391473 .050734 9.06 0.000 1.295506 1.494549
n_off_oth | 1.736396 .0634365 15.10 0.000 1.61641 1.865289
psy_com2 | 1.118289 .0550578 2.27 0.023 1.015421 1.231578
psy_com3 | 1.10006 .0424022 2.47 0.013 1.020015 1.186386
dep2 | 1.036227 .0441189 0.84 0.403 .953265 1.126409
rural2 | .8983972 .0559609 -1.72 0.085 .7951468 1.015055
rural3 | .8602639 .0595515 -2.17 0.030 .751117 .9852712
porc_pobr | 1.569386 .3928137 1.80 0.072 .9608951 2.563206
susini2 | 1.187864 .1082804 1.89 0.059 .9935165 1.420229
susini3 | 1.270733 .0819012 3.72 0.000 1.119935 1.441836
susini4 | 1.180552 .0440176 4.45 0.000 1.097356 1.270055
susini5 | 1.421573 .1319732 3.79 0.000 1.185078 1.705263
ano_nac_corr | .8510003 .0080255 -17.11 0.000 .8354152 .8668762
cohab2 | .8799488 .0590955 -1.90 0.057 .7714228 1.003742
cohab3 | 1.074926 .0859532 0.90 0.366 .9189987 1.25731
cohab4 | .9640148 .0641769 -0.55 0.582 .846091 1.098374
fis_com2 | 1.058846 .0364999 1.66 0.097 .9896705 1.132856
fis_com3 | .819195 .0709767 -2.30 0.021 .6912536 .9708164
rc_x1 | .8512548 .010191 -13.45 0.000 .8315133 .871465
rc_x2 | .8817007 .0351638 -3.16 0.002 .8154057 .9533857
rc_x3 | 1.278222 .1359611 2.31 0.021 1.037687 1.574513
_rcs1 | 2.173505 .0725341 23.26 0.000 2.03589 2.320422
_rcs2 | 1.061419 .0278099 2.28 0.023 1.008289 1.117349
_rcs_mot_egr_early1 | .9011965 .0336972 -2.78 0.005 .8375134 .9697221
_rcs_mot_egr_early2 | .9971211 .0284372 -0.10 0.919 .9429144 1.054444
_rcs_mot_egr_early3 | 1.023525 .0107191 2.22 0.026 1.00273 1.044751
_rcs_mot_egr_early4 | 1.010518 .0072099 1.47 0.143 .9964854 1.024749
_rcs_mot_egr_early5 | 1.008923 .0053804 1.67 0.096 .9984322 1.019523
_rcs_mot_egr_early6 | 1.009662 .0043218 2.25 0.025 1.001227 1.018169
_rcs_mot_egr_late1 | .9291646 .0336345 -2.03 0.042 .8655264 .9974819
_rcs_mot_egr_late2 | 1.013259 .028468 0.47 0.639 .9589715 1.07062
_rcs_mot_egr_late3 | 1.027348 .0096604 2.87 0.004 1.008587 1.046458
_rcs_mot_egr_late4 | 1.017476 .0061031 2.89 0.004 1.005585 1.029509
_rcs_mot_egr_late5 | 1.009602 .0043363 2.22 0.026 1.001138 1.018137
_rcs_mot_egr_late6 | 1.007254 .0033772 2.16 0.031 1.000657 1.013895
_cons | 1.7e+138 3.2e+139 16.76 0.000 1.2e+122 2.5e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.219
Iteration 1: log likelihood = -16979.077
Iteration 2: log likelihood = -16979.013
Iteration 3: log likelihood = -16979.013
Log likelihood = -16979.013 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.013402 .1272009 11.08 0.000 1.77891 2.278803
mot_egr_late | 1.698639 .0924358 9.74 0.000 1.526795 1.889825
tr_mod2 | 1.217217 .0517785 4.62 0.000 1.119849 1.323052
sex_dum2 | .6073216 .0295216 -10.26 0.000 .5521311 .6680288
edad_ini_cons | .9714703 .0047125 -5.97 0.000 .9622777 .9807507
esc1 | 1.430415 .0886474 5.78 0.000 1.266806 1.615153
esc2 | 1.264125 .0732412 4.05 0.000 1.128425 1.416143
sus_prin2 | 1.156325 .0781812 2.15 0.032 1.012811 1.320174
sus_prin3 | 1.680558 .0916093 9.52 0.000 1.510266 1.870052
sus_prin4 | 1.17048 .0933184 1.97 0.048 1.001154 1.368445
sus_prin5 | 1.588225 .2387766 3.08 0.002 1.18288 2.132471
fr_cons_sus_prin2 | .9675393 .1088738 -0.29 0.769 .7760432 1.206289
fr_cons_sus_prin3 | .9787449 .0894474 -0.24 0.814 .8182354 1.170741
fr_cons_sus_prin4 | 1.003177 .0951089 0.03 0.973 .8330619 1.208031
fr_cons_sus_prin5 | 1.030174 .0934698 0.33 0.743 .8623418 1.230671
cond_ocu2 | 1.048928 .0745441 0.67 0.501 .9125429 1.205696
cond_ocu3 | 1.144337 .3087343 0.50 0.617 .6743817 1.941791
cond_ocu4 | 1.222203 .0891289 2.75 0.006 1.059424 1.409994
cond_ocu5 | 1.058463 .1642439 0.37 0.714 .780896 1.434692
cond_ocu6 | 1.189436 .0465009 4.44 0.000 1.1017 1.284159
policonsumo | .9912743 .0485923 -0.18 0.858 .9004673 1.091239
num_hij2 | 1.12577 .0447915 2.98 0.003 1.041316 1.217073
tenviv1 | 1.066583 .1349561 0.51 0.610 .8323216 1.366779
tenviv2 | 1.123797 .0968206 1.35 0.176 .9491892 1.330525
tenviv4 | 1.038411 .0510264 0.77 0.443 .9430665 1.143396
tenviv5 | 1.010965 .0383366 0.29 0.774 .9385508 1.088966
mzone2 | 1.449979 .0608327 8.86 0.000 1.33552 1.574248
mzone3 | 1.52992 .0966207 6.73 0.000 1.351798 1.731512
n_off_vio | 1.46648 .0554414 10.13 0.000 1.361745 1.57927
n_off_acq | 2.799872 .0973247 29.62 0.000 2.615472 2.997273
n_off_sud | 1.391433 .050732 9.06 0.000 1.295469 1.494505
n_off_oth | 1.736333 .0634332 15.10 0.000 1.616353 1.865219
psy_com2 | 1.118317 .0550598 2.27 0.023 1.015445 1.231611
psy_com3 | 1.10008 .042403 2.47 0.013 1.020033 1.186408
dep2 | 1.036187 .0441172 0.83 0.404 .9532286 1.126366
rural2 | .898404 .0559613 -1.72 0.085 .7951528 1.015063
rural3 | .8602576 .0595512 -2.17 0.030 .7511112 .9852644
porc_pobr | 1.569604 .3928647 1.80 0.072 .9610325 2.56355
susini2 | 1.187866 .1082804 1.89 0.059 .993518 1.420231
susini3 | 1.27073 .0819019 3.72 0.000 1.11993 1.441834
susini4 | 1.180545 .0440176 4.45 0.000 1.097349 1.270048
susini5 | 1.421629 .1319786 3.79 0.000 1.185124 1.70533
ano_nac_corr | .8509346 .0080255 -17.12 0.000 .8353493 .8668106
cohab2 | .8799318 .0590943 -1.90 0.057 .7714082 1.003723
cohab3 | 1.074929 .0859531 0.90 0.366 .9190019 1.257313
cohab4 | .9639766 .0641742 -0.55 0.582 .8460577 1.09833
fis_com2 | 1.058817 .0364989 1.66 0.097 .9896431 1.132825
fis_com3 | .8191627 .070974 -2.30 0.021 .6912263 .9707783
rc_x1 | .8511924 .0101907 -13.46 0.000 .8314515 .8714021
rc_x2 | .8816832 .0351632 -3.16 0.002 .8153893 .953367
rc_x3 | 1.278283 .1359681 2.31 0.021 1.037736 1.57459
_rcs1 | 2.17348 .0725391 23.26 0.000 2.035856 2.320407
_rcs2 | 1.06174 .0278292 2.29 0.022 1.008573 1.11771
_rcs_mot_egr_early1 | .9014013 .03371 -2.78 0.006 .8376943 .9699532
_rcs_mot_egr_early2 | .9965217 .0283197 -0.12 0.902 .9425337 1.053602
_rcs_mot_egr_early3 | 1.023484 .0109264 2.17 0.030 1.002291 1.045125
_rcs_mot_egr_early4 | 1.009608 .0074052 1.30 0.192 .9951984 1.024227
_rcs_mot_egr_early5 | 1.009032 .0055442 1.64 0.102 .9982239 1.019957
_rcs_mot_egr_early6 | 1.00991 .0045006 2.21 0.027 1.001127 1.01877
_rcs_mot_egr_early7 | 1.00571 .0037252 1.54 0.124 .9984347 1.013038
_rcs_mot_egr_late1 | .929128 .0336345 -2.03 0.042 .8654898 .9974453
_rcs_mot_egr_late2 | 1.012502 .0283878 0.44 0.658 .9583644 1.069699
_rcs_mot_egr_late3 | 1.025221 .010082 2.53 0.011 1.00565 1.045173
_rcs_mot_egr_late4 | 1.019469 .0063666 3.09 0.002 1.007067 1.032024
_rcs_mot_egr_late5 | 1.00953 .0044462 2.15 0.031 1.000853 1.018282
_rcs_mot_egr_late6 | 1.00893 .0034909 2.57 0.010 1.002111 1.015796
_rcs_mot_egr_late7 | 1.004289 .0028814 1.49 0.136 .998657 1.009952
_cons | 2.0e+138 3.8e+139 16.77 0.000 1.4e+122 2.9e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16989.781
Iteration 1: log likelihood = -16983.996
Iteration 2: log likelihood = -16983.958
Iteration 3: log likelihood = -16983.958
Log likelihood = -16983.958 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.01049 .1267979 11.07 0.000 1.776717 2.275022
mot_egr_late | 1.695679 .0921215 9.72 0.000 1.524404 1.886197
tr_mod2 | 1.218362 .0518274 4.64 0.000 1.120901 1.324296
sex_dum2 | .6066797 .0294896 -10.28 0.000 .551549 .6673211
edad_ini_cons | .9714776 .0047125 -5.97 0.000 .962285 .980758
esc1 | 1.430725 .0886658 5.78 0.000 1.267083 1.615501
esc2 | 1.26445 .07326 4.05 0.000 1.128716 1.416507
sus_prin2 | 1.155305 .078112 2.14 0.033 1.011919 1.319009
sus_prin3 | 1.680176 .0915877 9.52 0.000 1.509924 1.869625
sus_prin4 | 1.169943 .0932759 1.97 0.049 1.000693 1.367818
sus_prin5 | 1.588178 .2387458 3.08 0.002 1.18288 2.132345
fr_cons_sus_prin2 | .9675922 .1088787 -0.29 0.770 .7760872 1.206352
fr_cons_sus_prin3 | .9785822 .0894334 -0.24 0.813 .818098 1.170548
fr_cons_sus_prin4 | 1.003286 .0951209 0.03 0.972 .8331496 1.208166
fr_cons_sus_prin5 | 1.030029 .093459 0.33 0.744 .8622158 1.230503
cond_ocu2 | 1.049756 .0746033 0.68 0.494 .9132625 1.206648
cond_ocu3 | 1.142787 .3083144 0.49 0.621 .6734703 1.939155
cond_ocu4 | 1.223066 .0892016 2.76 0.006 1.060155 1.411011
cond_ocu5 | 1.057766 .1641366 0.36 0.717 .78038 1.433749
cond_ocu6 | 1.188992 .0464864 4.43 0.000 1.101284 1.283686
policonsumo | .9911239 .0485884 -0.18 0.856 .9003245 1.091081
num_hij2 | 1.125481 .0447792 2.97 0.003 1.04105 1.216759
tenviv1 | 1.065296 .1348002 0.50 0.617 .8313061 1.365147
tenviv2 | 1.122757 .0967206 1.34 0.179 .9483279 1.32927
tenviv4 | 1.037305 .0509721 0.75 0.456 .9420613 1.142177
tenviv5 | 1.009857 .0382926 0.26 0.796 .9375256 1.087768
mzone2 | 1.449284 .0607999 8.85 0.000 1.334886 1.573485
mzone3 | 1.528088 .0964906 6.72 0.000 1.350204 1.729407
n_off_vio | 1.466853 .0554638 10.13 0.000 1.362077 1.57969
n_off_acq | 2.801564 .0973965 29.63 0.000 2.617028 2.999111
n_off_sud | 1.391776 .0507499 9.07 0.000 1.295779 1.494884
n_off_oth | 1.737022 .063473 15.11 0.000 1.616967 1.86599
psy_com2 | 1.11819 .0550447 2.27 0.023 1.015346 1.231452
psy_com3 | 1.100465 .0424184 2.48 0.013 1.02039 1.186825
dep2 | 1.036374 .0441237 0.84 0.401 .9534027 1.126565
rural2 | .8985623 .0559707 -1.72 0.086 .7952937 1.01524
rural3 | .860297 .0595413 -2.17 0.030 .7511672 .9852812
porc_pobr | 1.559038 .3902765 1.77 0.076 .9544957 2.546475
susini2 | 1.188744 .1083592 1.90 0.058 .9942548 1.421278
susini3 | 1.26882 .0817784 3.69 0.000 1.118248 1.439667
susini4 | 1.180893 .04403 4.46 0.000 1.097673 1.270421
susini5 | 1.420705 .1318834 3.78 0.000 1.18437 1.7042
ano_nac_corr | .8515786 .0080238 -17.05 0.000 .8359966 .8674511
cohab2 | .8802525 .0591169 -1.90 0.058 .7716874 1.004091
cohab3 | 1.075844 .0860287 0.91 0.361 .9197795 1.258388
cohab4 | .9643935 .0642054 -0.54 0.586 .8464176 1.098813
fis_com2 | 1.058976 .0365025 1.66 0.096 .9897954 1.132991
fis_com3 | .8195002 .0710019 -2.30 0.022 .6915133 .9711753
rc_x1 | .8518306 .0101925 -13.40 0.000 .832086 .8720436
rc_x2 | .8819475 .0351711 -3.15 0.002 .8156386 .953647
rc_x3 | 1.276994 .1358189 2.30 0.022 1.036708 1.572973
_rcs1 | 2.203342 .0695138 25.04 0.000 2.071224 2.343887
_rcs2 | 1.068928 .0082907 8.59 0.000 1.052801 1.085301
_rcs3 | 1.034416 .0058799 5.95 0.000 1.022955 1.046004
_rcs_mot_egr_early1 | .8917137 .031401 -3.25 0.001 .8322447 .9554322
_rcs_mot_egr_late1 | .9129009 .0309431 -2.69 0.007 .8542241 .9756081
_cons | 4.3e+137 8.2e+138 16.71 0.000 3.1e+121 6.1e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16990.961
Iteration 1: log likelihood = -16983.235
Iteration 2: log likelihood = -16983.165
Iteration 3: log likelihood = -16983.165
Log likelihood = -16983.165 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.008647 .1267788 11.05 0.000 1.77492 2.273153
mot_egr_late | 1.696956 .0922375 9.73 0.000 1.52547 1.887719
tr_mod2 | 1.217805 .0518072 4.63 0.000 1.120382 1.323698
sex_dum2 | .6067374 .0294925 -10.28 0.000 .5516013 .6673847
edad_ini_cons | .9714846 .0047124 -5.96 0.000 .9622922 .9807648
esc1 | 1.430726 .0886655 5.78 0.000 1.267084 1.615502
esc2 | 1.264387 .0732563 4.05 0.000 1.128659 1.416436
sus_prin2 | 1.155369 .0781161 2.14 0.033 1.011975 1.319081
sus_prin3 | 1.68019 .0915863 9.52 0.000 1.509941 1.869636
sus_prin4 | 1.169922 .0932734 1.97 0.049 1.000677 1.367792
sus_prin5 | 1.588657 .238823 3.08 0.002 1.183229 2.133003
fr_cons_sus_prin2 | .967543 .1088734 -0.29 0.769 .7760475 1.206291
fr_cons_sus_prin3 | .9785855 .0894334 -0.24 0.813 .8181011 1.170552
fr_cons_sus_prin4 | 1.003275 .0951194 0.03 0.972 .833141 1.208152
fr_cons_sus_prin5 | 1.030038 .0934587 0.33 0.744 .8622258 1.230511
cond_ocu2 | 1.049589 .0745917 0.68 0.496 .9131174 1.206458
cond_ocu3 | 1.142339 .3081939 0.49 0.622 .6732052 1.938395
cond_ocu4 | 1.223342 .0892186 2.76 0.006 1.0604 1.411322
cond_ocu5 | 1.058372 .1642333 0.37 0.715 .7808236 1.434577
cond_ocu6 | 1.189 .0464859 4.43 0.000 1.101293 1.283693
policonsumo | .9911884 .0485911 -0.18 0.857 .9003839 1.091151
num_hij2 | 1.125545 .0447816 2.97 0.003 1.04111 1.216828
tenviv1 | 1.065185 .1347864 0.50 0.618 .8312191 1.365005
tenviv2 | 1.122417 .0966936 1.34 0.180 .9480373 1.328873
tenviv4 | 1.037425 .0509782 0.75 0.455 .94217 1.14231
tenviv5 | 1.01001 .0382986 0.26 0.793 .937668 1.087934
mzone2 | 1.449413 .0608042 8.85 0.000 1.335007 1.573624
mzone3 | 1.528576 .0965202 6.72 0.000 1.350638 1.729957
n_off_vio | 1.466804 .0554627 10.13 0.000 1.362029 1.579638
n_off_acq | 2.80181 .0974061 29.63 0.000 2.617257 2.999377
n_off_sud | 1.391844 .0507521 9.07 0.000 1.295843 1.494957
n_off_oth | 1.737075 .0634749 15.11 0.000 1.617017 1.866047
psy_com2 | 1.118673 .0550715 2.28 0.023 1.015779 1.23199
psy_com3 | 1.100276 .0424113 2.48 0.013 1.020214 1.186621
dep2 | 1.036356 .0441237 0.84 0.402 .9533855 1.126548
rural2 | .898494 .0559663 -1.72 0.086 .7952335 1.015163
rural3 | .859973 .0595227 -2.18 0.029 .7508777 .9849188
porc_pobr | 1.558595 .390164 1.77 0.076 .9542265 2.545746
susini2 | 1.188415 .1083304 1.89 0.058 .9939775 1.420887
susini3 | 1.269148 .0818004 3.70 0.000 1.118535 1.44004
susini4 | 1.180798 .0440264 4.46 0.000 1.097585 1.270319
susini5 | 1.420765 .1318899 3.78 0.000 1.184418 1.704274
ano_nac_corr | .8516557 .0080263 -17.04 0.000 .8360687 .8675332
cohab2 | .8800395 .059104 -1.90 0.057 .7714982 1.003851
cohab3 | 1.075596 .0860103 0.91 0.362 .9195652 1.258101
cohab4 | .9642631 .0641968 -0.55 0.585 .8463031 1.098665
fis_com2 | 1.058948 .0365024 1.66 0.097 .9897681 1.132964
fis_com3 | .8193944 .070993 -2.30 0.022 .6914236 .9710505
rc_x1 | .8518962 .0101947 -13.39 0.000 .8321475 .8721135
rc_x2 | .8819511 .0351711 -3.15 0.002 .8156421 .9536509
rc_x3 | 1.277054 .1358256 2.30 0.021 1.036756 1.573047
_rcs1 | 2.187237 .0727359 23.53 0.000 2.049224 2.334545
_rcs2 | 1.052925 .025926 2.09 0.036 1.003318 1.104985
_rcs3 | 1.033221 .006051 5.58 0.000 1.021429 1.045149
_rcs_mot_egr_early1 | .8960553 .0332457 -2.96 0.003 .8332077 .9636434
_rcs_mot_egr_early2 | 1.006814 .0275032 0.25 0.804 .9543267 1.062189
_rcs_mot_egr_late1 | .9229568 .0332105 -2.23 0.026 .8601078 .9903983
_rcs_mot_egr_late2 | 1.024438 .0273835 0.90 0.366 .972149 1.079539
_cons | 3.6e+137 6.9e+138 16.69 0.000 2.5e+121 5.1e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16989.932
Iteration 1: log likelihood = -16983.017
Iteration 2: log likelihood = -16982.97
Iteration 3: log likelihood = -16982.97
Log likelihood = -16982.97 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.008909 .126821 11.05 0.000 1.775107 2.273506
mot_egr_late | 1.697157 .0922725 9.73 0.000 1.525608 1.887995
tr_mod2 | 1.217714 .0518045 4.63 0.000 1.120297 1.323602
sex_dum2 | .6067492 .029493 -10.28 0.000 .5516121 .6673977
edad_ini_cons | .9714858 .0047124 -5.96 0.000 .9622934 .980766
esc1 | 1.430778 .0886683 5.78 0.000 1.267131 1.61556
esc2 | 1.264431 .0732589 4.05 0.000 1.128699 1.416486
sus_prin2 | 1.15552 .078127 2.14 0.033 1.012106 1.319255
sus_prin3 | 1.680415 .0916004 9.52 0.000 1.510139 1.86989
sus_prin4 | 1.169963 .0932768 1.97 0.049 1.000712 1.36784
sus_prin5 | 1.589286 .2389195 3.08 0.002 1.183695 2.133852
fr_cons_sus_prin2 | .9675085 .1088695 -0.29 0.769 .7760198 1.206248
fr_cons_sus_prin3 | .9786337 .0894378 -0.24 0.813 .8181415 1.170609
fr_cons_sus_prin4 | 1.00331 .0951227 0.03 0.972 .8331697 1.208193
fr_cons_sus_prin5 | 1.030039 .0934585 0.33 0.744 .8622272 1.230512
cond_ocu2 | 1.049499 .0745854 0.68 0.497 .9130386 1.206354
cond_ocu3 | 1.142858 .3083353 0.49 0.621 .6735097 1.939281
cond_ocu4 | 1.223447 .0892253 2.77 0.006 1.060492 1.411441
cond_ocu5 | 1.058613 .1642723 0.37 0.714 .7809991 1.434908
cond_ocu6 | 1.189007 .0464864 4.43 0.000 1.101299 1.2837
policonsumo | .9912528 .0485946 -0.18 0.858 .9004418 1.091222
num_hij2 | 1.125623 .0447845 2.97 0.003 1.041182 1.216912
tenviv1 | 1.065182 .1347871 0.50 0.618 .8312157 1.365004
tenviv2 | 1.122399 .0966929 1.34 0.180 .9480198 1.328853
tenviv4 | 1.037465 .0509804 0.75 0.454 .9422065 1.142355
tenviv5 | 1.01009 .0383019 0.26 0.791 .9377415 1.08802
mzone2 | 1.449504 .060808 8.85 0.000 1.335091 1.573723
mzone3 | 1.528844 .0965388 6.72 0.000 1.350872 1.730263
n_off_vio | 1.466764 .0554615 10.13 0.000 1.361992 1.579596
n_off_acq | 2.801839 .097406 29.64 0.000 2.617286 2.999406
n_off_sud | 1.391864 .0507525 9.07 0.000 1.295863 1.494978
n_off_oth | 1.737092 .0634751 15.11 0.000 1.617034 1.866065
psy_com2 | 1.118904 .0550845 2.28 0.022 1.015985 1.232248
psy_com3 | 1.10019 .0424083 2.48 0.013 1.020134 1.18653
dep2 | 1.036365 .0441242 0.84 0.401 .9533929 1.126558
rural2 | .8985006 .0559667 -1.72 0.086 .7952395 1.01517
rural3 | .8598059 .0595123 -2.18 0.029 .7507299 .9847299
porc_pobr | 1.556942 .3897635 1.77 0.077 .9531984 2.543089
susini2 | 1.188247 .1083152 1.89 0.058 .9938369 1.420687
susini3 | 1.2693 .0818111 3.70 0.000 1.118668 1.440215
susini4 | 1.180766 .0440256 4.46 0.000 1.097555 1.270286
susini5 | 1.420617 .1318763 3.78 0.000 1.184294 1.704097
ano_nac_corr | .8516204 .0080263 -17.04 0.000 .8360336 .8674978
cohab2 | .8799546 .059099 -1.90 0.057 .7714226 1.003756
cohab3 | 1.075505 .0860038 0.91 0.363 .9194867 1.257998
cohab4 | .9642077 .0641933 -0.55 0.584 .8462541 1.098602
fis_com2 | 1.058841 .0364992 1.66 0.097 .9896666 1.132849
fis_com3 | .8192994 .0709852 -2.30 0.021 .6913428 .9709388
rc_x1 | .8518607 .0101942 -13.40 0.000 .8321129 .8720772
rc_x2 | .8819385 .0351702 -3.15 0.002 .8156312 .9536364
rc_x3 | 1.277102 .1358292 2.30 0.021 1.036798 1.573103
_rcs1 | 2.188267 .0737766 23.23 0.000 2.048341 2.337751
_rcs2 | 1.050676 .0261648 1.99 0.047 1.000625 1.10323
_rcs3 | 1.036343 .0187214 1.98 0.048 1.000291 1.073693
_rcs_mot_egr_early1 | .8943841 .033697 -2.96 0.003 .8307188 .9629286
_rcs_mot_egr_early2 | 1.009651 .0279149 0.35 0.728 .9563951 1.065873
_rcs_mot_egr_early3 | .9933821 .0201693 -0.33 0.744 .9546272 1.03371
_rcs_mot_egr_late1 | .9231606 .0337086 -2.19 0.029 .8594016 .9916498
_rcs_mot_egr_late2 | 1.026021 .0278141 0.95 0.343 .9729295 1.08201
_rcs_mot_egr_late3 | 1.000678 .0196174 0.03 0.972 .9629578 1.039876
_cons | 3.9e+137 7.4e+138 16.70 0.000 2.8e+121 5.6e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16995.684
Iteration 1: log likelihood = -16982.325
Iteration 2: log likelihood = -16982.175
Iteration 3: log likelihood = -16982.175
Log likelihood = -16982.175 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009531 .1268829 11.05 0.000 1.775617 2.27426
mot_egr_late | 1.696988 .0922788 9.73 0.000 1.525429 1.88784
tr_mod2 | 1.217562 .0517974 4.63 0.000 1.120158 1.323436
sex_dum2 | .6068761 .0294994 -10.27 0.000 .551727 .6675377
edad_ini_cons | .9714856 .0047124 -5.96 0.000 .9622932 .9807658
esc1 | 1.430634 .0886604 5.78 0.000 1.267002 1.6154
esc2 | 1.264322 .0732529 4.05 0.000 1.128601 1.416364
sus_prin2 | 1.155928 .0781559 2.14 0.032 1.012461 1.319724
sus_prin3 | 1.680672 .0916168 9.52 0.000 1.510366 1.870181
sus_prin4 | 1.170224 .0932987 1.97 0.049 1.000933 1.368147
sus_prin5 | 1.589637 .2389802 3.08 0.002 1.183944 2.134344
fr_cons_sus_prin2 | .9674115 .1088587 -0.29 0.768 .7759418 1.206128
fr_cons_sus_prin3 | .9786208 .0894366 -0.24 0.813 .8181309 1.170594
fr_cons_sus_prin4 | 1.003214 .0951134 0.03 0.973 .8330905 1.208077
fr_cons_sus_prin5 | 1.030056 .0934598 0.33 0.744 .862242 1.230532
cond_ocu2 | 1.049412 .0745787 0.68 0.497 .9129639 1.206253
cond_ocu3 | 1.143111 .3084044 0.50 0.620 .6736582 1.939713
cond_ocu4 | 1.223083 .0891991 2.76 0.006 1.060176 1.411022
cond_ocu5 | 1.058615 .1642717 0.37 0.714 .7810018 1.434908
cond_ocu6 | 1.189103 .0464896 4.43 0.000 1.101389 1.283803
policonsumo | .9914512 .0486045 -0.18 0.861 .9006218 1.091441
num_hij2 | 1.12564 .0447853 2.97 0.003 1.041197 1.216931
tenviv1 | 1.065609 .134839 0.50 0.616 .831552 1.365546
tenviv2 | 1.122763 .0967261 1.34 0.179 .9483242 1.329288
tenviv4 | 1.037629 .0509881 0.75 0.452 .942356 1.142535
tenviv5 | 1.010344 .0383124 0.27 0.786 .9379754 1.088296
mzone2 | 1.449787 .0608223 8.85 0.000 1.335347 1.574034
mzone3 | 1.529184 .0965657 6.73 0.000 1.351163 1.730661
n_off_vio | 1.466729 .0554575 10.13 0.000 1.361964 1.579553
n_off_acq | 2.801278 .0973833 29.63 0.000 2.616768 2.998799
n_off_sud | 1.391698 .0507457 9.06 0.000 1.295709 1.494798
n_off_oth | 1.736806 .0634614 15.11 0.000 1.616774 1.86575
psy_com2 | 1.118824 .0550813 2.28 0.023 1.015912 1.232162
psy_com3 | 1.100154 .0424066 2.48 0.013 1.020101 1.18649
dep2 | 1.036348 .0441237 0.84 0.402 .9533777 1.12654
rural2 | .8985187 .0559686 -1.72 0.086 .7952542 1.015192
rural3 | .8598383 .0595171 -2.18 0.029 .7507538 .9847729
porc_pobr | 1.561333 .3908506 1.78 0.075 .9559011 2.550222
susini2 | 1.188181 .1083097 1.89 0.059 .9937804 1.420609
susini3 | 1.269406 .0818176 3.70 0.000 1.118761 1.440334
susini4 | 1.18068 .0440227 4.45 0.000 1.097474 1.270193
susini5 | 1.420753 .1318924 3.78 0.000 1.184402 1.704269
ano_nac_corr | .8512941 .0080285 -17.07 0.000 .8357031 .8671759
cohab2 | .8799323 .0590967 -1.90 0.057 .7714045 1.003729
cohab3 | 1.075359 .0859911 0.91 0.364 .9193637 1.257824
cohab4 | .9641882 .0641912 -0.55 0.584 .8462385 1.098578
fis_com2 | 1.058691 .0364952 1.65 0.098 .989525 1.132692
fis_com3 | .8192604 .0709821 -2.30 0.021 .6913093 .9708934
rc_x1 | .8515427 .0101945 -13.42 0.000 .8317945 .8717597
rc_x2 | .8818586 .0351679 -3.15 0.002 .8155557 .9535519
rc_x3 | 1.277466 .1358706 2.30 0.021 1.037089 1.573557
_rcs1 | 2.181582 .0731504 23.26 0.000 2.042819 2.32977
_rcs2 | 1.053274 .02685 2.04 0.042 1.001942 1.107236
_rcs3 | 1.026695 .0179524 1.51 0.132 .9921055 1.062491
_rcs_mot_egr_early1 | .8972114 .0336563 -2.89 0.004 .8336129 .9656619
_rcs_mot_egr_early2 | 1.006851 .0284481 0.24 0.809 .9526098 1.064181
_rcs_mot_egr_early3 | 1.002613 .0195835 0.13 0.894 .9649553 1.04174
_rcs_mot_egr_early4 | 1.004115 .0079716 0.52 0.605 .9886123 1.019862
_rcs_mot_egr_late1 | .9258202 .0336404 -2.12 0.034 .8621793 .9941587
_rcs_mot_egr_late2 | 1.023223 .0284361 0.83 0.409 .9689797 1.080502
_rcs_mot_egr_late3 | 1.008367 .0190653 0.44 0.659 .9716834 1.046435
_rcs_mot_egr_late4 | 1.008113 .0067389 1.21 0.227 .9949915 1.021408
_cons | 8.5e+137 1.6e+139 16.73 0.000 5.8e+121 1.2e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.472
Iteration 1: log likelihood = -16978.982
Iteration 2: log likelihood = -16978.93
Iteration 3: log likelihood = -16978.93
Log likelihood = -16978.93 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.013112 .1271419 11.08 0.000 1.778724 2.278385
mot_egr_late | 1.698516 .0923944 9.74 0.000 1.526746 1.889612
tr_mod2 | 1.217647 .0517976 4.63 0.000 1.120243 1.323521
sex_dum2 | .6071316 .0295118 -10.27 0.000 .5519594 .6678187
edad_ini_cons | .9714653 .0047125 -5.97 0.000 .9622727 .9807458
esc1 | 1.430507 .0886528 5.78 0.000 1.266888 1.615256
esc2 | 1.264221 .073247 4.05 0.000 1.12851 1.416251
sus_prin2 | 1.156684 .0782088 2.15 0.031 1.01312 1.320592
sus_prin3 | 1.681264 .0916541 9.53 0.000 1.510889 1.87085
sus_prin4 | 1.170777 .0933448 1.98 0.048 1.001402 1.368798
sus_prin5 | 1.590408 .2391054 3.09 0.002 1.184505 2.135403
fr_cons_sus_prin2 | .9673919 .1088565 -0.29 0.768 .7759261 1.206103
fr_cons_sus_prin3 | .9787079 .0894443 -0.24 0.814 .8182039 1.170697
fr_cons_sus_prin4 | 1.003258 .0951174 0.03 0.973 .8331272 1.20813
fr_cons_sus_prin5 | 1.030149 .0934686 0.33 0.743 .862319 1.230643
cond_ocu2 | 1.049026 .0745512 0.67 0.501 .9126282 1.20581
cond_ocu3 | 1.144792 .3088564 0.50 0.616 .6746505 1.94256
cond_ocu4 | 1.22195 .089116 2.75 0.006 1.059195 1.409714
cond_ocu5 | 1.058751 .1642928 0.37 0.713 .7811017 1.435092
cond_ocu6 | 1.189303 .046498 4.43 0.000 1.101573 1.28402
policonsumo | .9916082 .0486125 -0.17 0.864 .9007636 1.091615
num_hij2 | 1.125682 .0447873 2.98 0.003 1.041236 1.216977
tenviv1 | 1.066579 .1349596 0.51 0.610 .8323125 1.366784
tenviv2 | 1.123698 .0968107 1.35 0.176 .9491073 1.330404
tenviv4 | 1.03807 .0510101 0.76 0.447 .9427559 1.143021
tenviv5 | 1.010658 .0383245 0.28 0.780 .9382673 1.088635
mzone2 | 1.450094 .0608369 8.86 0.000 1.335627 1.574372
mzone3 | 1.529234 .0965738 6.73 0.000 1.351198 1.730728
n_off_vio | 1.466606 .0554462 10.13 0.000 1.361862 1.579406
n_off_acq | 2.79986 .0973202 29.62 0.000 2.615469 2.997252
n_off_sud | 1.391202 .050724 9.06 0.000 1.295254 1.494258
n_off_oth | 1.736434 .0634378 15.10 0.000 1.616445 1.86533
psy_com2 | 1.11854 .0550683 2.28 0.023 1.015652 1.231851
psy_com3 | 1.100034 .0424018 2.47 0.013 1.01999 1.18636
dep2 | 1.036345 .044124 0.84 0.402 .9533735 1.126537
rural2 | .8984744 .0559663 -1.72 0.086 .795214 1.015143
rural3 | .860052 .059534 -2.18 0.029 .7509367 .9850224
porc_pobr | 1.566297 .392067 1.79 0.073 .9589717 2.558245
susini2 | 1.18802 .1082944 1.89 0.059 .993647 1.420415
susini3 | 1.270116 .0818632 3.71 0.000 1.119388 1.44114
susini4 | 1.180581 .0440197 4.45 0.000 1.097381 1.270089
susini5 | 1.421421 .1319595 3.79 0.000 1.184951 1.705082
ano_nac_corr | .8505862 .0080261 -17.15 0.000 .8349999 .8664635
cohab2 | .8799736 .059097 -1.90 0.057 .7714448 1.003771
cohab3 | 1.075072 .085966 0.91 0.365 .9191219 1.257484
cohab4 | .9640354 .0641787 -0.55 0.582 .8461084 1.098399
fis_com2 | 1.058356 .0364828 1.65 0.100 .9892129 1.132332
fis_com3 | .8191121 .0709696 -2.30 0.021 .6911836 .9707183
rc_x1 | .8508384 .0101891 -13.49 0.000 .8311006 .8710449
rc_x2 | .8818281 .0351675 -3.15 0.002 .8155261 .9535205
rc_x3 | 1.277547 .1358828 2.30 0.021 1.037149 1.573666
_rcs1 | 2.188283 .0738423 23.21 0.000 2.048237 2.337905
_rcs2 | 1.050314 .0261042 1.98 0.048 1.000377 1.102744
_rcs3 | 1.036791 .0186488 2.01 0.045 1.000877 1.073994
_rcs_mot_egr_early1 | .8949923 .0337709 -2.94 0.003 .831191 .963691
_rcs_mot_egr_early2 | 1.010453 .0279696 0.38 0.707 .957094 1.066787
_rcs_mot_egr_early3 | .9956379 .0189942 -0.23 0.819 .9590974 1.033571
_rcs_mot_egr_early4 | .9968597 .0097762 -0.32 0.748 .9778817 1.016206
_rcs_mot_egr_early5 | 1.00974 .0052599 1.86 0.063 .9994837 1.020102
_rcs_mot_egr_late1 | .9228936 .0337343 -2.20 0.028 .8590884 .9914377
_rcs_mot_egr_late2 | 1.026419 .0279439 0.96 0.338 .9730854 1.082675
_rcs_mot_egr_late3 | 1.000225 .0185045 0.01 0.990 .9646061 1.037158
_rcs_mot_egr_late4 | 1.002622 .0088821 0.30 0.768 .9853637 1.020183
_rcs_mot_egr_late5 | 1.008405 .0041343 2.04 0.041 1.000334 1.01654
_cons | 4.5e+138 8.6e+139 16.81 0.000 3.1e+122 6.7e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.494
Iteration 1: log likelihood = -16977.485
Iteration 2: log likelihood = -16977.393
Iteration 3: log likelihood = -16977.393
Log likelihood = -16977.393 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.013704 .1271856 11.08 0.000 1.779236 2.279069
mot_egr_late | 1.698661 .0924089 9.74 0.000 1.526864 1.889787
tr_mod2 | 1.217654 .0517959 4.63 0.000 1.120252 1.323524
sex_dum2 | .6073038 .0295201 -10.26 0.000 .5521162 .6680079
edad_ini_cons | .9714524 .0047126 -5.97 0.000 .9622597 .9807329
esc1 | 1.430417 .0886476 5.78 0.000 1.266808 1.615156
esc2 | 1.264169 .0732439 4.05 0.000 1.128464 1.416192
sus_prin2 | 1.156967 .0782286 2.16 0.031 1.013367 1.320916
sus_prin3 | 1.681386 .0916614 9.53 0.000 1.510998 1.870988
sus_prin4 | 1.170858 .0933518 1.98 0.048 1.001471 1.368894
sus_prin5 | 1.59044 .2391112 3.09 0.002 1.184528 2.13545
fr_cons_sus_prin2 | .9674405 .1088622 -0.29 0.769 .7759647 1.206165
fr_cons_sus_prin3 | .9787283 .0894461 -0.24 0.814 .8182212 1.170721
fr_cons_sus_prin4 | 1.003299 .0951212 0.03 0.972 .833162 1.20818
fr_cons_sus_prin5 | 1.030175 .0934712 0.33 0.743 .86234 1.230675
cond_ocu2 | 1.048748 .0745315 0.67 0.503 .9123866 1.20549
cond_ocu3 | 1.145659 .3090898 0.50 0.614 .6751621 1.94403
cond_ocu4 | 1.221556 .089085 2.74 0.006 1.058857 1.409254
cond_ocu5 | 1.058385 .1642362 0.37 0.715 .7808321 1.434597
cond_ocu6 | 1.1894 .0465013 4.44 0.000 1.101664 1.284124
policonsumo | .9915965 .048611 -0.17 0.863 .9007547 1.0916
num_hij2 | 1.125693 .0447881 2.98 0.003 1.041245 1.216989
tenviv1 | 1.066987 .1350092 0.51 0.608 .8326335 1.367301
tenviv2 | 1.124314 .0968655 1.36 0.174 .9496247 1.331138
tenviv4 | 1.038269 .05102 0.76 0.445 .9429358 1.14324
tenviv5 | 1.010875 .0383328 0.29 0.775 .9384677 1.088868
mzone2 | 1.450255 .0608448 8.86 0.000 1.335773 1.574549
mzone3 | 1.529435 .096589 6.73 0.000 1.351372 1.730961
n_off_vio | 1.466544 .0554404 10.13 0.000 1.361811 1.579332
n_off_acq | 2.799295 .0972937 29.62 0.000 2.614953 2.996632
n_off_sud | 1.391059 .050717 9.05 0.000 1.295124 1.4941
n_off_oth | 1.736272 .0634266 15.10 0.000 1.616304 1.865144
psy_com2 | 1.11851 .0550674 2.27 0.023 1.015624 1.231819
psy_com3 | 1.100031 .0424016 2.47 0.013 1.019987 1.186356
dep2 | 1.036342 .044124 0.84 0.402 .9533703 1.126534
rural2 | .898358 .0559587 -1.72 0.085 .7951117 1.015011
rural3 | .8600984 .0595388 -2.18 0.029 .7509745 .985079
porc_pobr | 1.569457 .3928531 1.80 0.072 .960913 2.563392
susini2 | 1.187875 .1082807 1.89 0.059 .9935267 1.420241
susini3 | 1.27077 .0819049 3.72 0.000 1.119965 1.441881
susini4 | 1.18057 .0440192 4.45 0.000 1.097371 1.270076
susini5 | 1.421628 .131979 3.79 0.000 1.185123 1.70533
ano_nac_corr | .850397 .008026 -17.17 0.000 .8348109 .866274
cohab2 | .880049 .0591015 -1.90 0.057 .7715119 1.003855
cohab3 | 1.07501 .0859601 0.90 0.366 .9190698 1.257408
cohab4 | .9640153 .0641769 -0.55 0.582 .8460915 1.098375
fis_com2 | 1.058349 .036482 1.65 0.100 .9892071 1.132323
fis_com3 | .8190394 .0709637 -2.30 0.021 .6911217 .9706331
rc_x1 | .850653 .0101881 -13.51 0.000 .8309172 .8708576
rc_x2 | .8817875 .0351662 -3.15 0.002 .8154879 .9534772
rc_x3 | 1.277704 .1359023 2.30 0.021 1.037272 1.573867
_rcs1 | 2.188307 .0738681 23.20 0.000 2.048214 2.337983
_rcs2 | 1.050707 .0261601 1.99 0.047 1.000665 1.103252
_rcs3 | 1.036626 .0187254 1.99 0.046 1.000567 1.073985
_rcs_mot_egr_early1 | .8949085 .0337776 -2.94 0.003 .831095 .9636218
_rcs_mot_egr_early2 | 1.009759 .0280118 0.35 0.726 .9563229 1.066181
_rcs_mot_egr_early3 | .9961497 .0184128 -0.21 0.835 .9607072 1.0329
_rcs_mot_egr_early4 | .9953759 .0108575 -0.42 0.671 .9743215 1.016885
_rcs_mot_egr_early5 | 1.005253 .0056641 0.93 0.352 .994213 1.016416
_rcs_mot_egr_early6 | 1.009673 .004319 2.25 0.024 1.001244 1.018174
_rcs_mot_egr_late1 | .9227025 .0337369 -2.20 0.028 .858893 .9912525
_rcs_mot_egr_late2 | 1.026093 .0280153 0.94 0.345 .9726277 1.082498
_rcs_mot_egr_late3 | .999867 .0178988 -0.01 0.994 .9653943 1.035571
_rcs_mot_egr_late4 | 1.002228 .0102251 0.22 0.827 .9823865 1.022471
_rcs_mot_egr_late5 | 1.005931 .0046936 1.27 0.205 .9967738 1.015173
_rcs_mot_egr_late6 | 1.007269 .0033753 2.16 0.031 1.000675 1.013906
_cons | 7.1e+138 1.3e+140 16.83 0.000 4.7e+122 1.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.256
Iteration 1: log likelihood = -16977.38
Iteration 2: log likelihood = -16977.287
Iteration 3: log likelihood = -16977.287
Log likelihood = -16977.287 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.013799 .1271891 11.08 0.000 1.779325 2.279171
mot_egr_late | 1.698672 .0924069 9.74 0.000 1.526879 1.889794
tr_mod2 | 1.217647 .0517956 4.63 0.000 1.120246 1.323516
sex_dum2 | .6073336 .0295216 -10.26 0.000 .5521431 .6680408
edad_ini_cons | .9714499 .0047126 -5.97 0.000 .9622571 .9807305
esc1 | 1.43049 .0886517 5.78 0.000 1.266873 1.615237
esc2 | 1.264209 .0732462 4.05 0.000 1.128501 1.416238
sus_prin2 | 1.157043 .078234 2.16 0.031 1.013433 1.321003
sus_prin3 | 1.681482 .0916677 9.53 0.000 1.511083 1.871097
sus_prin4 | 1.170927 .0933574 1.98 0.048 1.00153 1.368976
sus_prin5 | 1.590613 .2391374 3.09 0.002 1.184657 2.135682
fr_cons_sus_prin2 | .9674001 .1088576 -0.29 0.768 .7759323 1.206114
fr_cons_sus_prin3 | .978713 .0894446 -0.24 0.814 .8182086 1.170703
fr_cons_sus_prin4 | 1.003282 .0951195 0.03 0.972 .8331476 1.208159
fr_cons_sus_prin5 | 1.03013 .0934672 0.33 0.744 .8623023 1.230621
cond_ocu2 | 1.048717 .0745292 0.67 0.503 .9123593 1.205454
cond_ocu3 | 1.145943 .3091669 0.50 0.614 .6753285 1.944513
cond_ocu4 | 1.221439 .089076 2.74 0.006 1.058756 1.409118
cond_ocu5 | 1.058394 .1642375 0.37 0.715 .7808386 1.434609
cond_ocu6 | 1.189464 .046504 4.44 0.000 1.101723 1.284194
policonsumo | .9915918 .0486107 -0.17 0.863 .9007506 1.091594
num_hij2 | 1.125717 .0447893 2.98 0.003 1.041268 1.217016
tenviv1 | 1.066999 .1350105 0.51 0.608 .8326428 1.367316
tenviv2 | 1.124435 .0968769 1.36 0.173 .9497261 1.331284
tenviv4 | 1.038302 .0510216 0.76 0.444 .9429663 1.143277
tenviv5 | 1.010906 .0383341 0.29 0.775 .9384968 1.088902
mzone2 | 1.450307 .0608475 8.86 0.000 1.33582 1.574606
mzone3 | 1.529513 .0965949 6.73 0.000 1.351439 1.731051
n_off_vio | 1.466493 .0554377 10.13 0.000 1.361764 1.579275
n_off_acq | 2.799166 .0972876 29.62 0.000 2.614835 2.996491
n_off_sud | 1.391012 .0507148 9.05 0.000 1.295081 1.494049
n_off_oth | 1.736204 .0634229 15.10 0.000 1.616243 1.865069
psy_com2 | 1.118543 .0550696 2.28 0.023 1.015652 1.231857
psy_com3 | 1.100053 .0424024 2.47 0.013 1.020007 1.18638
dep2 | 1.036303 .0441224 0.84 0.402 .9533343 1.126492
rural2 | .8983637 .055959 -1.72 0.085 .7951168 1.015017
rural3 | .86009 .0595384 -2.18 0.029 .7509669 .9850698
porc_pobr | 1.569674 .392904 1.80 0.072 .9610498 2.563735
susini2 | 1.187877 .1082808 1.89 0.059 .9935287 1.420243
susini3 | 1.270773 .0819059 3.72 0.000 1.119966 1.441886
susini4 | 1.180564 .0440192 4.45 0.000 1.097365 1.270071
susini5 | 1.421691 .1319852 3.79 0.000 1.185175 1.705407
ano_nac_corr | .8503227 .008026 -17.18 0.000 .8347367 .8661998
cohab2 | .8800333 .0591004 -1.90 0.057 .7714983 1.003837
cohab3 | 1.075011 .0859599 0.90 0.366 .9190713 1.257409
cohab4 | .9639755 .0641741 -0.55 0.582 .8460568 1.098329
fis_com2 | 1.058311 .0364807 1.64 0.100 .989172 1.132283
fis_com3 | .8190025 .0709606 -2.30 0.021 .6910904 .9705896
rc_x1 | .8505821 .0101878 -13.51 0.000 .8308471 .870786
rc_x2 | .8817706 .0351656 -3.15 0.002 .8154722 .9534592
rc_x3 | 1.27776 .1359088 2.30 0.021 1.037317 1.573937
_rcs1 | 2.188506 .0738804 23.20 0.000 2.04839 2.338207
_rcs2 | 1.05051 .0260967 1.98 0.047 1.000586 1.102924
_rcs3 | 1.037354 .0187189 2.03 0.042 1.001307 1.074699
_rcs_mot_egr_early1 | .8949913 .0337875 -2.94 0.003 .8311597 .9637251
_rcs_mot_egr_early2 | 1.010305 .0279586 0.37 0.711 .9569666 1.066616
_rcs_mot_egr_early3 | .9973336 .0177424 -0.15 0.881 .9631584 1.032722
_rcs_mot_egr_early4 | .9924479 .0117152 -0.64 0.521 .9697501 1.015677
_rcs_mot_egr_early5 | 1.002888 .006331 0.46 0.648 .9905561 1.015374
_rcs_mot_egr_early6 | 1.00887 .0045172 1.97 0.049 1.000055 1.017763
_rcs_mot_egr_early7 | 1.005813 .0037244 1.57 0.117 .99854 1.013139
_rcs_mot_egr_late1 | .9225844 .0337321 -2.20 0.028 .858784 .9911247
_rcs_mot_egr_late2 | 1.026492 .0280002 0.96 0.338 .9730536 1.082865
_rcs_mot_egr_late3 | .9990581 .0172648 -0.05 0.957 .9657863 1.033476
_rcs_mot_egr_late4 | 1.002159 .0111754 0.19 0.847 .9804934 1.024304
_rcs_mot_egr_late5 | 1.003376 .0054135 0.62 0.532 .9928215 1.014042
_rcs_mot_egr_late6 | 1.007893 .003518 2.25 0.024 1.001021 1.014812
_rcs_mot_egr_late7 | 1.004392 .0028809 1.53 0.127 .9987612 1.010054
_cons | 8.5e+138 1.6e+140 16.83 0.000 5.6e+122 1.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16994.223
Iteration 1: log likelihood = -16981.784
Iteration 2: log likelihood = -16981.677
Iteration 3: log likelihood = -16981.677
Log likelihood = -16981.677 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.011147 .1268489 11.08 0.000 1.777281 2.275787
mot_egr_late | 1.69502 .0920946 9.71 0.000 1.523797 1.885484
tr_mod2 | 1.218334 .0518242 4.64 0.000 1.12088 1.324262
sex_dum2 | .6068833 .0294995 -10.27 0.000 .551734 .6675451
edad_ini_cons | .9714635 .0047126 -5.97 0.000 .9622708 .9807441
esc1 | 1.430584 .0886577 5.78 0.000 1.266956 1.615343
esc2 | 1.264297 .0732515 4.05 0.000 1.128579 1.416337
sus_prin2 | 1.156309 .0781841 2.15 0.032 1.012791 1.320165
sus_prin3 | 1.681103 .0916461 9.53 0.000 1.510743 1.870673
sus_prin4 | 1.170581 .0933299 1.98 0.048 1.001234 1.368571
sus_prin5 | 1.589823 .2390039 3.08 0.002 1.18409 2.134583
fr_cons_sus_prin2 | .9674246 .1088597 -0.29 0.769 .7759531 1.206143
fr_cons_sus_prin3 | .9785271 .0894284 -0.24 0.812 .8180519 1.170482
fr_cons_sus_prin4 | 1.003243 .0951171 0.03 0.973 .8331131 1.208115
fr_cons_sus_prin5 | 1.030037 .0934604 0.33 0.744 .8622218 1.230514
cond_ocu2 | 1.049461 .0745819 0.68 0.497 .9130065 1.206308
cond_ocu3 | 1.144014 .3086449 0.50 0.618 .6741939 1.941235
cond_ocu4 | 1.221876 .0891171 2.75 0.006 1.059119 1.409643
cond_ocu5 | 1.058158 .1641985 0.36 0.716 .7806676 1.434283
cond_ocu6 | 1.189181 .0464942 4.43 0.000 1.101458 1.28389
policonsumo | .9915293 .0486095 -0.17 0.862 .9006905 1.09153
num_hij2 | 1.125513 .0447807 2.97 0.003 1.041079 1.216794
tenviv1 | 1.066207 .1349126 0.51 0.612 .8320221 1.366308
tenviv2 | 1.123731 .0968079 1.35 0.176 .949145 1.330431
tenviv4 | 1.037512 .050982 0.75 0.454 .9422504 1.142405
tenviv5 | 1.010165 .038305 0.27 0.790 .9378109 1.088102
mzone2 | 1.449872 .0608279 8.85 0.000 1.335422 1.574132
mzone3 | 1.528216 .0965051 6.72 0.000 1.350306 1.729566
n_off_vio | 1.466816 .0554556 10.13 0.000 1.362054 1.579636
n_off_acq | 2.800245 .0973376 29.62 0.000 2.61582 2.997672
n_off_sud | 1.391244 .050728 9.06 0.000 1.295289 1.494308
n_off_oth | 1.736536 .0634464 15.11 0.000 1.616531 1.865449
psy_com2 | 1.118141 .0550422 2.27 0.023 1.015301 1.231398
psy_com3 | 1.100377 .0424146 2.48 0.013 1.020309 1.186729
dep2 | 1.036418 .0441258 0.84 0.401 .9534432 1.126614
rural2 | .8985345 .0559697 -1.72 0.086 .795268 1.01521
rural3 | .8603208 .0595459 -2.17 0.030 .7511828 .9853152
porc_pobr | 1.565278 .391814 1.79 0.073 .9583453 2.556588
susini2 | 1.188655 .1083519 1.90 0.058 .9941789 1.421174
susini3 | 1.269071 .0817949 3.70 0.000 1.118469 1.439952
susini4 | 1.180739 .0440253 4.46 0.000 1.097529 1.270258
susini5 | 1.421134 .1319288 3.79 0.000 1.184719 1.704728
ano_nac_corr | .8508473 .0080253 -17.12 0.000 .8352625 .8667228
cohab2 | .8802385 .059114 -1.90 0.058 .7716785 1.004071
cohab3 | 1.075541 .0860026 0.91 0.362 .9195235 1.258029
cohab4 | .9642611 .0641946 -0.55 0.585 .846305 1.098658
fis_com2 | 1.058453 .0364849 1.65 0.099 .9893055 1.132432
fis_com3 | .8193668 .0709909 -2.30 0.021 .6913998 .9710185
rc_x1 | .8511057 .01019 -13.47 0.000 .8313662 .8713139
rc_x2 | .881888 .0351691 -3.15 0.002 .815583 .9535836
rc_x3 | 1.277209 .1358429 2.30 0.021 1.036881 1.57324
_rcs1 | 2.201337 .0694587 25.01 0.000 2.069325 2.341771
_rcs2 | 1.067538 .0083695 8.34 0.000 1.05126 1.084069
_rcs3 | 1.033924 .0061081 5.65 0.000 1.022022 1.045965
_rcs4 | 1.01392 .0041661 3.36 0.001 1.005787 1.022119
_rcs_mot_egr_early1 | .8924728 .0314287 -3.23 0.001 .8329514 .9562475
_rcs_mot_egr_late1 | .9134262 .0309628 -2.67 0.008 .8547123 .9761734
_cons | 2.4e+138 4.6e+139 16.78 0.000 1.7e+122 3.6e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16995.365
Iteration 1: log likelihood = -16980.95
Iteration 2: log likelihood = -16980.806
Iteration 3: log likelihood = -16980.806
Log likelihood = -16980.806 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009407 .1268455 11.05 0.000 1.775559 2.274054
mot_egr_late | 1.696397 .0922233 9.72 0.000 1.524939 1.887132
tr_mod2 | 1.217746 .0518028 4.63 0.000 1.120332 1.32363
sex_dum2 | .6069492 .0295028 -10.27 0.000 .5517937 .6676177
edad_ini_cons | .9714708 .0047125 -5.97 0.000 .9622782 .9807512
esc1 | 1.430579 .0886571 5.78 0.000 1.266953 1.615337
esc2 | 1.264226 .0732474 4.05 0.000 1.128515 1.416257
sus_prin2 | 1.156389 .0781892 2.15 0.032 1.012862 1.320256
sus_prin3 | 1.681127 .0916453 9.53 0.000 1.510769 1.870696
sus_prin4 | 1.17057 .0933281 1.98 0.048 1.001226 1.368556
sus_prin5 | 1.590302 .2390814 3.09 0.002 1.184438 2.13524
fr_cons_sus_prin2 | .9673744 .1088542 -0.29 0.768 .7759126 1.206081
fr_cons_sus_prin3 | .9785291 .0894283 -0.24 0.812 .818054 1.170484
fr_cons_sus_prin4 | 1.003229 .0951153 0.03 0.973 .8331028 1.208097
fr_cons_sus_prin5 | 1.030047 .0934601 0.33 0.744 .8622325 1.230523
cond_ocu2 | 1.049288 .0745698 0.68 0.498 .9128564 1.206111
cond_ocu3 | 1.143542 .3085181 0.50 0.619 .673915 1.940436
cond_ocu4 | 1.222134 .0891326 2.75 0.006 1.059348 1.409933
cond_ocu5 | 1.058802 .164301 0.37 0.713 .7811389 1.435162
cond_ocu6 | 1.189193 .0464938 4.43 0.000 1.10147 1.283901
policonsumo | .9916017 .0486125 -0.17 0.863 .9007573 1.091608
num_hij2 | 1.125581 .0447833 2.97 0.003 1.041142 1.216867
tenviv1 | 1.066113 .1349008 0.51 0.613 .8319481 1.366187
tenviv2 | 1.123407 .0967822 1.35 0.177 .9488672 1.330052
tenviv4 | 1.037643 .0509886 0.75 0.452 .9423689 1.14255
tenviv5 | 1.010334 .0383116 0.27 0.786 .937967 1.088284
mzone2 | 1.450015 .0608329 8.86 0.000 1.335555 1.574284
mzone3 | 1.528734 .0965366 6.72 0.000 1.350766 1.73015
n_off_vio | 1.46676 .0554542 10.13 0.000 1.362001 1.579576
n_off_acq | 2.800464 .0973462 29.62 0.000 2.616024 2.997909
n_off_sud | 1.391306 .0507299 9.06 0.000 1.295347 1.494374
n_off_oth | 1.736574 .0634475 15.11 0.000 1.616567 1.865489
psy_com2 | 1.118627 .0550692 2.28 0.023 1.015737 1.23194
psy_com3 | 1.100181 .0424073 2.48 0.013 1.020127 1.186518
dep2 | 1.036399 .0441258 0.84 0.401 .9534245 1.126595
rural2 | .8984677 .0559654 -1.72 0.086 .795209 1.015135
rural3 | .8599924 .0595273 -2.18 0.029 .7508891 .9849482
porc_pobr | 1.56501 .3917434 1.79 0.074 .9581858 2.556139
susini2 | 1.188316 .1083223 1.89 0.058 .9938934 1.420771
susini3 | 1.269412 .0818177 3.70 0.000 1.118768 1.440341
susini4 | 1.180637 .0440215 4.45 0.000 1.097433 1.270148
susini5 | 1.421207 .1319368 3.79 0.000 1.184777 1.704818
ano_nac_corr | .8509166 .0080277 -17.11 0.000 .8353273 .866797
cohab2 | .8800183 .0591006 -1.90 0.057 .7714831 1.003823
cohab3 | 1.075272 .0859825 0.91 0.364 .9192913 1.257718
cohab4 | .9641233 .0641854 -0.55 0.583 .8461841 1.098501
fis_com2 | 1.058419 .0364846 1.65 0.100 .9892724 1.132398
fis_com3 | .8192586 .0709818 -2.30 0.021 .691308 .9708908
rc_x1 | .8511635 .010192 -13.46 0.000 .8314202 .8713757
rc_x2 | .8818878 .035169 -3.15 0.002 .8155828 .9535832
rc_x3 | 1.27729 .1358519 2.30 0.021 1.036946 1.573341
_rcs1 | 2.183521 .0725369 23.51 0.000 2.04588 2.330421
_rcs2 | 1.049903 .0257693 1.98 0.047 1.000592 1.101644
_rcs3 | 1.031836 .0065786 4.92 0.000 1.019022 1.04481
_rcs4 | 1.013962 .0041654 3.38 0.001 1.00583 1.022159
_rcs_mot_egr_early1 | .8974553 .0332649 -2.92 0.004 .8345693 .9650799
_rcs_mot_egr_early2 | 1.008345 .0275377 0.30 0.761 .955791 1.063788
_rcs_mot_egr_late1 | .924357 .0332277 -2.19 0.029 .861473 .9918311
_rcs_mot_egr_late2 | 1.026527 .027438 0.98 0.327 .9741338 1.081738
_cons | 2.1e+138 3.9e+139 16.77 0.000 1.4e+122 3.1e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16995.178
Iteration 1: log likelihood = -16980.737
Iteration 2: log likelihood = -16980.573
Iteration 3: log likelihood = -16980.573
Log likelihood = -16980.573 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010777 .1269656 11.06 0.000 1.776711 2.275679
mot_egr_late | 1.697494 .0923134 9.73 0.000 1.525872 1.888419
tr_mod2 | 1.217775 .0518046 4.63 0.000 1.120358 1.323663
sex_dum2 | .6069563 .0295031 -10.27 0.000 .5518004 .6676253
edad_ini_cons | .9714679 .0047125 -5.97 0.000 .9622754 .9807483
esc1 | 1.430654 .0886612 5.78 0.000 1.26702 1.615421
esc2 | 1.26429 .073251 4.05 0.000 1.128572 1.416328
sus_prin2 | 1.156617 .0782057 2.15 0.031 1.013059 1.320518
sus_prin3 | 1.681467 .0916667 9.53 0.000 1.511069 1.87108
sus_prin4 | 1.170658 .0933359 1.98 0.048 1.0013 1.368661
sus_prin5 | 1.591234 .2392227 3.09 0.002 1.185131 2.136495
fr_cons_sus_prin2 | .9673433 .1088506 -0.30 0.768 .7758877 1.206042
fr_cons_sus_prin3 | .9785776 .0894327 -0.24 0.813 .8180946 1.170542
fr_cons_sus_prin4 | 1.003283 .0951205 0.03 0.972 .8331467 1.208162
fr_cons_sus_prin5 | 1.030042 .0934597 0.33 0.744 .8622278 1.230517
cond_ocu2 | 1.049161 .074561 0.68 0.499 .912745 1.205965
cond_ocu3 | 1.144395 .3087491 0.50 0.617 .6744162 1.941885
cond_ocu4 | 1.22209 .0891294 2.75 0.006 1.059311 1.409883
cond_ocu5 | 1.059 .164334 0.37 0.712 .781282 1.435437
cond_ocu6 | 1.189203 .0464949 4.43 0.000 1.101479 1.283914
policonsumo | .9916754 .0486168 -0.17 0.865 .900823 1.091691
num_hij2 | 1.125648 .0447858 2.97 0.003 1.041205 1.21694
tenviv1 | 1.066168 .1349094 0.51 0.613 .8319889 1.366262
tenviv2 | 1.123501 .0967915 1.35 0.176 .9489452 1.330167
tenviv4 | 1.037663 .0509899 0.75 0.452 .9423868 1.142573
tenviv5 | 1.010377 .0383132 0.27 0.785 .9380067 1.08833
mzone2 | 1.450115 .060837 8.86 0.000 1.335647 1.574392
mzone3 | 1.528873 .0965467 6.72 0.000 1.350887 1.73031
n_off_vio | 1.466724 .0554526 10.13 0.000 1.361968 1.579537
n_off_acq | 2.800397 .0973412 29.63 0.000 2.615966 2.997832
n_off_sud | 1.391255 .0507275 9.06 0.000 1.295301 1.494318
n_off_oth | 1.736593 .0634471 15.11 0.000 1.616587 1.865508
psy_com2 | 1.118843 .0550808 2.28 0.023 1.015931 1.232179
psy_com3 | 1.100095 .0424044 2.47 0.013 1.020046 1.186426
dep2 | 1.036425 .0441271 0.84 0.401 .9534481 1.126624
rural2 | .8984783 .0559659 -1.72 0.086 .7952185 1.015146
rural3 | .8598586 .0595184 -2.18 0.029 .7507718 .9847958
porc_pobr | 1.562794 .3912078 1.78 0.074 .9568059 2.552581
susini2 | 1.188187 .1083104 1.89 0.059 .9937858 1.420617
susini3 | 1.269594 .0818307 3.70 0.000 1.118926 1.44055
susini4 | 1.180623 .0440214 4.45 0.000 1.09742 1.270135
susini5 | 1.421082 .1319254 3.79 0.000 1.184673 1.704669
ano_nac_corr | .8508498 .0080277 -17.12 0.000 .8352605 .8667301
cohab2 | .8799608 .0590971 -1.90 0.057 .7714319 1.003758
cohab3 | 1.075197 .0859773 0.91 0.365 .9192259 1.257632
cohab4 | .9640651 .0641817 -0.55 0.583 .8461327 1.098435
fis_com2 | 1.058251 .0364789 1.64 0.100 .989116 1.132219
fis_com3 | .8191569 .0709734 -2.30 0.021 .6912216 .9707712
rc_x1 | .8510965 .0101914 -13.46 0.000 .8313543 .8713076
rc_x2 | .8818925 .0351686 -3.15 0.002 .8155883 .953587
rc_x3 | 1.277233 .1358436 2.30 0.021 1.036903 1.573266
_rcs1 | 2.191727 .0741398 23.20 0.000 2.051128 2.341963
_rcs2 | 1.047435 .0258656 1.88 0.061 .9979463 1.099377
_rcs3 | 1.041326 .0178552 2.36 0.018 1.006912 1.076916
_rcs4 | 1.015735 .0055119 2.88 0.004 1.004989 1.026596
_rcs_mot_egr_early1 | .8927818 .0337205 -3.00 0.003 .8290779 .9613805
_rcs_mot_egr_early2 | 1.011075 .0277425 0.40 0.688 .9581369 1.066938
_rcs_mot_egr_early3 | .9872124 .0193105 -0.66 0.511 .9500808 1.025795
_rcs_mot_egr_late1 | .9211662 .0337268 -2.24 0.025 .8573789 .9896992
_rcs_mot_egr_late2 | 1.027989 .0276348 1.03 0.304 .9752281 1.083605
_rcs_mot_egr_late3 | .9935573 .0186932 -0.34 0.731 .9575865 1.030879
_cons | 2.4e+138 4.6e+139 16.77 0.000 1.6e+122 3.6e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16994.509
Iteration 1: log likelihood = -16980.071
Iteration 2: log likelihood = -16979.835
Iteration 3: log likelihood = -16979.835
Log likelihood = -16979.835 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.007664 .126728 11.04 0.000 1.774031 2.272064
mot_egr_late | 1.695209 .0921463 9.71 0.000 1.523893 1.885784
tr_mod2 | 1.217787 .0518063 4.63 0.000 1.120366 1.323678
sex_dum2 | .6069391 .029502 -10.27 0.000 .5517853 .6676059
edad_ini_cons | .9714651 .0047125 -5.97 0.000 .9622725 .9807454
esc1 | 1.430694 .0886635 5.78 0.000 1.267056 1.615466
esc2 | 1.264305 .0732519 4.05 0.000 1.128586 1.416345
sus_prin2 | 1.156684 .0782109 2.15 0.031 1.013117 1.320596
sus_prin3 | 1.681693 .091681 9.53 0.000 1.511269 1.871336
sus_prin4 | 1.17066 .0933358 1.98 0.048 1.001302 1.368662
sus_prin5 | 1.591279 .2392308 3.09 0.002 1.185163 2.136559
fr_cons_sus_prin2 | .9673627 .1088528 -0.29 0.768 .7759034 1.206066
fr_cons_sus_prin3 | .9785651 .0894316 -0.24 0.813 .8180841 1.170527
fr_cons_sus_prin4 | 1.003332 .0951256 0.04 0.972 .8331867 1.208222
fr_cons_sus_prin5 | 1.029945 .0934514 0.33 0.745 .8621458 1.230403
cond_ocu2 | 1.049213 .0745646 0.68 0.499 .9127906 1.206024
cond_ocu3 | 1.144494 .3087763 0.50 0.617 .6744747 1.942056
cond_ocu4 | 1.222021 .0891252 2.75 0.006 1.059249 1.409805
cond_ocu5 | 1.059299 .1643828 0.37 0.710 .7814992 1.435849
cond_ocu6 | 1.189209 .0464956 4.43 0.000 1.101483 1.283921
policonsumo | .9916497 .0486153 -0.17 0.864 .9008001 1.091662
num_hij2 | 1.125603 .0447837 2.97 0.003 1.041164 1.216891
tenviv1 | 1.066042 .1348953 0.51 0.613 .8318872 1.366104
tenviv2 | 1.123595 .0967994 1.35 0.176 .949025 1.330277
tenviv4 | 1.037586 .0509862 0.75 0.453 .9423161 1.142488
tenviv5 | 1.010346 .0383123 0.27 0.786 .9379784 1.088298
mzone2 | 1.45014 .0608382 8.86 0.000 1.33567 1.57442
mzone3 | 1.528911 .0965501 6.72 0.000 1.350919 1.730355
n_off_vio | 1.466703 .0554517 10.13 0.000 1.361949 1.579514
n_off_acq | 2.80037 .09734 29.62 0.000 2.615941 2.997802
n_off_sud | 1.391245 .0507265 9.06 0.000 1.295292 1.494306
n_off_oth | 1.736614 .0634477 15.11 0.000 1.616607 1.86553
psy_com2 | 1.118952 .0550865 2.28 0.022 1.016029 1.2323
psy_com3 | 1.10001 .0424014 2.47 0.013 1.019966 1.186335
dep2 | 1.036445 .0441279 0.84 0.400 .9534663 1.126645
rural2 | .8985695 .0559712 -1.72 0.086 .7952999 1.015249
rural3 | .8597636 .0595119 -2.18 0.029 .7506886 .9846873
porc_pobr | 1.561409 .3908746 1.78 0.075 .9559417 2.550362
susini2 | 1.188261 .1083173 1.89 0.058 .9938476 1.420706
susini3 | 1.269616 .0818327 3.70 0.000 1.118944 1.440577
susini4 | 1.18062 .0440213 4.45 0.000 1.097418 1.270131
susini5 | 1.421184 .1319352 3.79 0.000 1.184757 1.704791
ano_nac_corr | .8508998 .0080283 -17.11 0.000 .8353093 .8667813
cohab2 | .8799965 .0591004 -1.90 0.057 .7714618 1.003801
cohab3 | 1.075174 .0859766 0.91 0.365 .9192045 1.257608
cohab4 | .9641067 .0641854 -0.55 0.583 .8461676 1.098484
fis_com2 | 1.058109 .036474 1.64 0.101 .9889831 1.132068
fis_com3 | .8191176 .07097 -2.30 0.021 .6911884 .9707248
rc_x1 | .8511603 .0101921 -13.46 0.000 .8314168 .8713726
rc_x2 | .8818345 .0351655 -3.15 0.002 .8155361 .9535227
rc_x3 | 1.277416 .1358605 2.30 0.021 1.037056 1.573485
_rcs1 | 2.184576 .0733038 23.29 0.000 2.045526 2.333079
_rcs2 | 1.0511 .0275233 1.90 0.057 .998516 1.106453
_rcs3 | 1.024441 .0195129 1.27 0.205 .986901 1.063408
_rcs4 | 1.029629 .0133659 2.25 0.024 1.003762 1.056161
_rcs_mot_egr_early1 | .895965 .0336212 -2.93 0.003 .8324335 .9643453
_rcs_mot_egr_early2 | 1.007971 .0290513 0.28 0.783 .9526102 1.06655
_rcs_mot_egr_early3 | 1.006342 .0214273 0.30 0.767 .96521 1.049228
_rcs_mot_egr_early4 | .9807731 .0143818 -1.32 0.186 .9529864 1.00937
_rcs_mot_egr_late1 | .9244742 .0336122 -2.16 0.031 .8608881 .9927569
_rcs_mot_egr_late2 | 1.024284 .0290365 0.85 0.397 .9689261 1.082806
_rcs_mot_egr_late3 | 1.012189 .0209337 0.59 0.558 .9719802 1.054061
_rcs_mot_egr_late4 | .9845829 .0138419 -1.11 0.269 .9578236 1.01209
_cons | 2.2e+138 4.1e+139 16.77 0.000 1.5e+122 3.2e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.571
Iteration 1: log likelihood = -16978.798
Iteration 2: log likelihood = -16978.739
Iteration 3: log likelihood = -16978.739
Log likelihood = -16978.739 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010521 .1269798 11.06 0.000 1.776433 2.275456
mot_egr_late | 1.696416 .0922814 9.72 0.000 1.524856 1.887279
tr_mod2 | 1.217701 .0518002 4.63 0.000 1.120291 1.32358
sex_dum2 | .6071203 .0295112 -10.27 0.000 .5519492 .6678061
edad_ini_cons | .9714624 .0047125 -5.97 0.000 .9622698 .9807429
esc1 | 1.430543 .0886548 5.78 0.000 1.266921 1.615296
esc2 | 1.264214 .0732467 4.05 0.000 1.128505 1.416244
sus_prin2 | 1.156825 .0782195 2.15 0.031 1.013242 1.320755
sus_prin3 | 1.681521 .0916707 9.53 0.000 1.511115 1.871142
sus_prin4 | 1.17085 .0933512 1.98 0.048 1.001465 1.368886
sus_prin5 | 1.590782 .2391629 3.09 0.002 1.184782 2.135909
fr_cons_sus_prin2 | .9673749 .1088544 -0.29 0.768 .7759127 1.206082
fr_cons_sus_prin3 | .9786795 .0894419 -0.24 0.814 .81818 1.170664
fr_cons_sus_prin4 | 1.003285 .0951205 0.03 0.972 .8331494 1.208164
fr_cons_sus_prin5 | 1.030102 .0934649 0.33 0.744 .8622787 1.230589
cond_ocu2 | 1.049027 .0745514 0.67 0.501 .9126291 1.205811
cond_ocu3 | 1.145026 .3089195 0.50 0.616 .6747886 1.942957
cond_ocu4 | 1.221719 .0891008 2.75 0.006 1.058992 1.409451
cond_ocu5 | 1.059084 .1643463 0.37 0.711 .7813449 1.435549
cond_ocu6 | 1.189322 .0464993 4.43 0.000 1.101589 1.284041
policonsumo | .9916459 .0486148 -0.17 0.864 .9007973 1.091657
num_hij2 | 1.125672 .0447869 2.98 0.003 1.041227 1.216966
tenviv1 | 1.066616 .1349649 0.51 0.610 .8323396 1.366833
tenviv2 | 1.123792 .0968188 1.35 0.176 .9491866 1.330515
tenviv4 | 1.038012 .0510073 0.76 0.448 .9427031 1.142957
tenviv5 | 1.010617 .0383229 0.28 0.781 .9382285 1.08859
mzone2 | 1.450162 .0608399 8.86 0.000 1.335689 1.574446
mzone3 | 1.529104 .0965661 6.72 0.000 1.351083 1.730582
n_off_vio | 1.466621 .055446 10.13 0.000 1.361877 1.57942
n_off_acq | 2.799747 .0973143 29.62 0.000 2.615366 2.997126
n_off_sud | 1.391119 .0507206 9.05 0.000 1.295178 1.494168
n_off_oth | 1.736427 .0634367 15.11 0.000 1.61644 1.86532
psy_com2 | 1.118579 .0550698 2.28 0.023 1.015688 1.231892
psy_com3 | 1.099988 .0424002 2.47 0.013 1.019947 1.186311
dep2 | 1.036368 .044125 0.84 0.401 .953395 1.126563
rural2 | .8985142 .0559688 -1.72 0.086 .7952494 1.015188
rural3 | .8600252 .059532 -2.18 0.029 .7509136 .9849913
porc_pobr | 1.565694 .3919194 1.79 0.073 .9585991 2.557272
susini2 | 1.188082 .1083004 1.89 0.059 .993698 1.42049
susini3 | 1.270063 .0818606 3.71 0.000 1.11934 1.441081
susini4 | 1.180566 .0440193 4.45 0.000 1.097367 1.270073
susini5 | 1.421499 .1319674 3.79 0.000 1.185015 1.705177
ano_nac_corr | .8505518 .0080266 -17.15 0.000 .8349646 .86643
cohab2 | .8799783 .0590974 -1.90 0.057 .7714489 1.003776
cohab3 | 1.075021 .085962 0.90 0.366 .9190774 1.257423
cohab4 | .9640182 .0641775 -0.55 0.582 .8460932 1.098379
fis_com2 | 1.058206 .0364778 1.64 0.101 .9890724 1.132171
fis_com3 | .8190924 .0709679 -2.30 0.021 .6911671 .970695
rc_x1 | .8508078 .0101892 -13.49 0.000 .8310698 .8710146
rc_x2 | .8818158 .0351664 -3.15 0.002 .8155157 .953506
rc_x3 | 1.277559 .1358822 2.30 0.021 1.037162 1.573677
_rcs1 | 2.184931 .0736997 23.17 0.000 2.045153 2.334261
_rcs2 | 1.050174 .0266807 1.93 0.054 .9991619 1.103791
_rcs3 | 1.032541 .0194057 1.70 0.088 .9951985 1.071285
_rcs4 | 1.015115 .0115987 1.31 0.189 .9926346 1.038105
_rcs_mot_egr_early1 | .8965653 .0338238 -2.89 0.004 .8326636 .9653712
_rcs_mot_egr_early2 | 1.009056 .02839 0.32 0.749 .9549191 1.066262
_rcs_mot_egr_early3 | 1.001457 .0209847 0.07 0.945 .9611607 1.043442
_rcs_mot_egr_early4 | .9918303 .0129243 -0.63 0.529 .9668199 1.017488
_rcs_mot_egr_early5 | 1.0055 .0068142 0.81 0.418 .9922328 1.018945
_rcs_mot_egr_late1 | .9244656 .0337899 -2.15 0.032 .8605551 .9931225
_rcs_mot_egr_late2 | 1.024928 .0283774 0.89 0.374 .9707913 1.082083
_rcs_mot_egr_late3 | 1.006139 .0206405 0.30 0.765 .9664866 1.047418
_rcs_mot_egr_late4 | .9974524 .0124166 -0.20 0.838 .9734108 1.022088
_rcs_mot_egr_late5 | 1.004249 .0059208 0.72 0.472 .9927112 1.015921
_cons | 4.9e+138 9.4e+139 16.81 0.000 3.3e+122 7.3e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.65
Iteration 1: log likelihood = -16976.016
Iteration 2: log likelihood = -16975.84
Iteration 3: log likelihood = -16975.84
Log likelihood = -16975.84 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.01056 .1269523 11.06 0.000 1.776519 2.275434
mot_egr_late | 1.695982 .0922301 9.71 0.000 1.524514 1.886735
tr_mod2 | 1.217736 .0517997 4.63 0.000 1.120328 1.323614
sex_dum2 | .6073093 .02952 -10.26 0.000 .5521217 .6680132
edad_ini_cons | .971441 .0047126 -5.97 0.000 .9622482 .9807217
esc1 | 1.430461 .0886499 5.78 0.000 1.266848 1.615204
esc2 | 1.264125 .0732415 4.05 0.000 1.128425 1.416144
sus_prin2 | 1.157421 .0782619 2.16 0.031 1.01376 1.32144
sus_prin3 | 1.68208 .0917051 9.54 0.000 1.511612 1.871774
sus_prin4 | 1.171092 .0933716 1.98 0.048 1.00167 1.369171
sus_prin5 | 1.591308 .2392442 3.09 0.002 1.18517 2.136621
fr_cons_sus_prin2 | .9674339 .1088611 -0.29 0.769 .7759599 1.206156
fr_cons_sus_prin3 | .9786754 .0894414 -0.24 0.814 .8181767 1.170658
fr_cons_sus_prin4 | 1.00339 .0951306 0.04 0.972 .8332356 1.20829
fr_cons_sus_prin5 | 1.030083 .0934641 0.33 0.744 .862261 1.230568
cond_ocu2 | 1.048681 .0745268 0.67 0.504 .9123278 1.205413
cond_ocu3 | 1.146276 .3092556 0.51 0.613 .6755262 1.945074
cond_ocu4 | 1.220878 .0890386 2.74 0.006 1.058264 1.408479
cond_ocu5 | 1.059177 .164363 0.37 0.711 .7814102 1.435681
cond_ocu6 | 1.189459 .0465049 4.44 0.000 1.101716 1.28419
policonsumo | .9917029 .0486168 -0.17 0.865 .9008504 1.091718
num_hij2 | 1.125671 .0447871 2.98 0.003 1.041225 1.216965
tenviv1 | 1.0672 .1350375 0.51 0.607 .8327974 1.367577
tenviv2 | 1.124804 .0969081 1.37 0.172 .9500382 1.331719
tenviv4 | 1.038169 .0510153 0.76 0.446 .942845 1.143131
tenviv5 | 1.010826 .0383309 0.28 0.776 .9384225 1.088815
mzone2 | 1.450468 .0608542 8.86 0.000 1.335968 1.574781
mzone3 | 1.529232 .0965771 6.73 0.000 1.351191 1.730734
n_off_vio | 1.466552 .055438 10.13 0.000 1.361823 1.579335
n_off_acq | 2.798812 .0972706 29.61 0.000 2.614513 2.996102
n_off_sud | 1.390792 .0507058 9.05 0.000 1.294878 1.493811
n_off_oth | 1.736182 .0634198 15.10 0.000 1.616226 1.86504
psy_com2 | 1.118582 .05507 2.28 0.023 1.01569 1.231896
psy_com3 | 1.099911 .0423972 2.47 0.013 1.019876 1.186228
dep2 | 1.036425 .0441276 0.84 0.401 .9534471 1.126625
rural2 | .8984156 .0559617 -1.72 0.085 .7951636 1.015075
rural3 | .860048 .0595353 -2.18 0.029 .7509306 .9850213
porc_pobr | 1.568624 .3926484 1.80 0.072 .9603979 2.562043
susini2 | 1.18798 .1082909 1.89 0.059 .9936129 1.420367
susini3 | 1.270819 .0819092 3.72 0.000 1.120006 1.441939
susini4 | 1.18051 .0440174 4.45 0.000 1.097314 1.270013
susini5 | 1.421945 .1320103 3.79 0.000 1.185384 1.705716
ano_nac_corr | .8502811 .0080263 -17.18 0.000 .8346946 .8661587
cohab2 | .8800681 .0591028 -1.90 0.057 .7715287 1.003877
cohab3 | 1.074816 .0859453 0.90 0.367 .9189033 1.257184
cohab4 | .9639467 .0641722 -0.55 0.581 .8460315 1.098296
fis_com2 | 1.057946 .0364675 1.63 0.102 .9888317 1.131891
fis_com3 | .8189717 .0709579 -2.30 0.021 .6910644 .9705531
rc_x1 | .8505454 .0101875 -13.51 0.000 .8308108 .8707487
rc_x2 | .8817549 .0351636 -3.16 0.002 .8154601 .9534393
rc_x3 | 1.277747 .1359026 2.30 0.021 1.037314 1.573909
_rcs1 | 2.184636 .0733809 23.26 0.000 2.045444 2.3333
_rcs2 | 1.05147 .0276182 1.91 0.056 .9987091 1.107018
_rcs3 | 1.023488 .0193679 1.23 0.220 .9862228 1.062161
_rcs4 | 1.02977 .0132038 2.29 0.022 1.004214 1.055977
_rcs_mot_egr_early1 | .8964141 .0336888 -2.91 0.004 .8327584 .9649356
_rcs_mot_egr_early2 | 1.006744 .0292044 0.23 0.817 .9511008 1.065642
_rcs_mot_egr_early3 | 1.013285 .0210555 0.64 0.525 .9728461 1.055405
_rcs_mot_egr_early4 | .9840165 .0124649 -1.27 0.203 .9598866 1.008753
_rcs_mot_egr_early5 | .990949 .0094697 -0.95 0.341 .9725614 1.009684
_rcs_mot_egr_early6 | 1.007058 .0044454 1.59 0.111 .9983824 1.015808
_rcs_mot_egr_late1 | .9243533 .0336467 -2.16 0.031 .8607045 .9927089
_rcs_mot_egr_late2 | 1.023014 .0292527 0.80 0.426 .9672568 1.081985
_rcs_mot_egr_late3 | 1.017237 .0207257 0.84 0.402 .9774158 1.058681
_rcs_mot_egr_late4 | .9907364 .0119707 -0.77 0.441 .9675498 1.014479
_rcs_mot_egr_late5 | .9915409 .0089769 -0.94 0.348 .9741017 1.009292
_rcs_mot_egr_late6 | 1.004676 .0035394 1.32 0.185 .9977628 1.011637
_cons | 9.3e+138 1.8e+140 16.84 0.000 6.2e+122 1.4e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.45
Iteration 1: log likelihood = -16976.397
Iteration 2: log likelihood = -16976.236
Iteration 3: log likelihood = -16976.236
Log likelihood = -16976.236 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010259 .1269299 11.06 0.000 1.776259 2.275086
mot_egr_late | 1.695757 .092215 9.71 0.000 1.524317 1.886478
tr_mod2 | 1.21773 .0517993 4.63 0.000 1.120322 1.323607
sex_dum2 | .6073526 .0295222 -10.26 0.000 .5521609 .6680611
edad_ini_cons | .9714395 .0047127 -5.97 0.000 .9622466 .9807202
esc1 | 1.430532 .0886539 5.78 0.000 1.266911 1.615283
esc2 | 1.26418 .0732446 4.05 0.000 1.128474 1.416205
sus_prin2 | 1.157444 .0782635 2.16 0.031 1.01378 1.321466
sus_prin3 | 1.68208 .0917055 9.54 0.000 1.511611 1.871774
sus_prin4 | 1.17113 .0933746 1.98 0.048 1.001702 1.369215
sus_prin5 | 1.59138 .2392551 3.09 0.002 1.185225 2.136719
fr_cons_sus_prin2 | .9673827 .1088554 -0.29 0.768 .7759188 1.206092
fr_cons_sus_prin3 | .9786555 .0894395 -0.24 0.813 .8181602 1.170634
fr_cons_sus_prin4 | 1.003354 .0951271 0.04 0.972 .833206 1.208247
fr_cons_sus_prin5 | 1.030036 .0934598 0.33 0.744 .8622215 1.230511
cond_ocu2 | 1.048641 .0745238 0.67 0.504 .9122928 1.205366
cond_ocu3 | 1.146549 .3093298 0.51 0.612 .6756865 1.945539
cond_ocu4 | 1.220833 .0890345 2.74 0.006 1.058226 1.408425
cond_ocu5 | 1.059034 .1643401 0.37 0.712 .7813055 1.435485
cond_ocu6 | 1.189523 .0465074 4.44 0.000 1.101775 1.284259
policonsumo | .9916812 .0486155 -0.17 0.865 .900831 1.091694
num_hij2 | 1.125694 .0447883 2.98 0.003 1.041247 1.216991
tenviv1 | 1.067173 .1350336 0.51 0.607 .8327771 1.367542
tenviv2 | 1.12488 .0969157 1.37 0.172 .9501007 1.331811
tenviv4 | 1.038235 .0510185 0.76 0.445 .9429049 1.143203
tenviv5 | 1.010882 .0383331 0.29 0.775 .9384746 1.088876
mzone2 | 1.450501 .0608562 8.86 0.000 1.335998 1.574819
mzone3 | 1.52934 .0965851 6.73 0.000 1.351284 1.730858
n_off_vio | 1.466497 .0554354 10.13 0.000 1.361773 1.579275
n_off_acq | 2.798736 .0972669 29.61 0.000 2.614445 2.996019
n_off_sud | 1.390786 .0507051 9.05 0.000 1.294873 1.493803
n_off_oth | 1.736122 .0634166 15.10 0.000 1.616173 1.864974
psy_com2 | 1.118608 .0550721 2.28 0.023 1.015713 1.231927
psy_com3 | 1.099947 .0423986 2.47 0.013 1.019908 1.186266
dep2 | 1.036367 .0441251 0.84 0.401 .9533933 1.126561
rural2 | .8984232 .0559623 -1.72 0.086 .7951702 1.015084
rural3 | .8600417 .0595351 -2.18 0.029 .7509245 .9850146
porc_pobr | 1.569272 .3928062 1.80 0.072 .9608003 2.563087
susini2 | 1.187959 .1082887 1.89 0.059 .9935961 1.420342
susini3 | 1.270855 .0819123 3.72 0.000 1.120036 1.441981
susini4 | 1.180514 .0440177 4.45 0.000 1.097318 1.270018
susini5 | 1.421973 .132013 3.79 0.000 1.185407 1.705749
ano_nac_corr | .8502135 .0080264 -17.19 0.000 .8346266 .8660915
cohab2 | .8800611 .0591021 -1.90 0.057 .771523 1.003868
cohab3 | 1.074861 .0859483 0.90 0.367 .9189423 1.257234
cohab4 | .9639238 .0641705 -0.55 0.581 .8460116 1.09827
fis_com2 | 1.057966 .0364684 1.63 0.102 .9888504 1.131913
fis_com3 | .8189402 .0709553 -2.31 0.021 .6910376 .970516
rc_x1 | .8504814 .0101874 -13.52 0.000 .830747 .8706845
rc_x2 | .8817354 .0351631 -3.16 0.002 .8154415 .9534188
rc_x3 | 1.277824 .135912 2.30 0.021 1.037375 1.574007
_rcs1 | 2.183738 .0733661 23.25 0.000 2.044575 2.332373
_rcs2 | 1.051246 .0274163 1.92 0.055 .9988616 1.106378
_rcs3 | 1.025565 .019549 1.32 0.185 .9879564 1.064605
_rcs4 | 1.026527 .0132436 2.03 0.042 1.000896 1.052815
_rcs_mot_egr_early1 | .8971111 .0337222 -2.89 0.004 .8333928 .9657011
_rcs_mot_egr_early2 | 1.006981 .0290642 0.24 0.810 .9515977 1.065588
_rcs_mot_egr_early3 | 1.012844 .020748 0.62 0.533 .9729845 1.054337
_rcs_mot_egr_early4 | .9872073 .0120455 -1.06 0.291 .9638785 1.011101
_rcs_mot_egr_early5 | .9900745 .010504 -0.94 0.347 .9696996 1.010877
_rcs_mot_egr_early6 | 1.002733 .0057038 0.48 0.631 .9916155 1.013974
_rcs_mot_egr_early7 | 1.005128 .003728 1.38 0.168 .9978483 1.012462
_rcs_mot_egr_late1 | .9246828 .0336638 -2.15 0.031 .8610019 .9930736
_rcs_mot_egr_late2 | 1.023109 .0291505 0.80 0.423 .9675411 1.081868
_rcs_mot_egr_late3 | 1.014574 .0203846 0.72 0.471 .9753971 1.055324
_rcs_mot_egr_late4 | .9968263 .0115515 -0.27 0.784 .974441 1.019726
_rcs_mot_egr_late5 | .9905437 .0100056 -0.94 0.347 .9711259 1.01035
_rcs_mot_egr_late6 | 1.001755 .0049616 0.35 0.723 .9920777 1.011527
_rcs_mot_egr_late7 | 1.003702 .0028895 1.28 0.199 .9980546 1.009381
_cons | 1.1e+139 2.1e+140 16.84 0.000 7.3e+122 1.7e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16985.96
Iteration 1: log likelihood = -16979.149
Iteration 2: log likelihood = -16979.103
Iteration 3: log likelihood = -16979.103
Log likelihood = -16979.103 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012129 .1269215 11.08 0.000 1.77813 2.276921
mot_egr_late | 1.694649 .0920831 9.71 0.000 1.523448 1.88509
tr_mod2 | 1.21838 .0518237 4.64 0.000 1.120926 1.324306
sex_dum2 | .6071271 .0295114 -10.27 0.000 .5519557 .6678133
edad_ini_cons | .9714466 .0047127 -5.97 0.000 .9622538 .9807274
esc1 | 1.430463 .0886504 5.78 0.000 1.266849 1.615207
esc2 | 1.264177 .0732444 4.05 0.000 1.128471 1.416201
sus_prin2 | 1.156999 .0782323 2.16 0.031 1.013392 1.320956
sus_prin3 | 1.681691 .0916832 9.53 0.000 1.511263 1.871339
sus_prin4 | 1.171035 .0933677 1.98 0.048 1.001619 1.369105
sus_prin5 | 1.590511 .2391156 3.09 0.002 1.18459 2.135528
fr_cons_sus_prin2 | .9674122 .1088583 -0.29 0.768 .7759432 1.206127
fr_cons_sus_prin3 | .9785862 .0894337 -0.24 0.813 .8181015 1.170553
fr_cons_sus_prin4 | 1.003252 .0951179 0.03 0.973 .8331209 1.208126
fr_cons_sus_prin5 | 1.030059 .0934628 0.33 0.744 .8622398 1.230541
cond_ocu2 | 1.049109 .0745566 0.67 0.500 .9127015 1.205904
cond_ocu3 | 1.145624 .3090777 0.50 0.614 .6751445 1.943962
cond_ocu4 | 1.22086 .0890421 2.74 0.006 1.058241 1.408469
cond_ocu5 | 1.058374 .1642321 0.37 0.715 .7808265 1.434575
cond_ocu6 | 1.18939 .0465024 4.44 0.000 1.101652 1.284116
policonsumo | .9916223 .0486138 -0.17 0.864 .9007754 1.091631
num_hij2 | 1.125552 .0447825 2.97 0.003 1.041115 1.216837
tenviv1 | 1.066955 .1350055 0.51 0.609 .8326082 1.367262
tenviv2 | 1.124562 .0968833 1.36 0.173 .9498407 1.331424
tenviv4 | 1.037883 .0510004 0.76 0.449 .9425866 1.142814
tenviv5 | 1.010486 .0383174 0.28 0.783 .9381086 1.088449
mzone2 | 1.45019 .0608432 8.86 0.000 1.335711 1.574481
mzone3 | 1.528339 .0965193 6.72 0.000 1.350404 1.729719
n_off_vio | 1.466697 .0554449 10.13 0.000 1.361955 1.579494
n_off_acq | 2.798992 .0972821 29.61 0.000 2.614672 2.996306
n_off_sud | 1.390827 .0507092 9.05 0.000 1.294906 1.493852
n_off_oth | 1.736197 .0634248 15.10 0.000 1.616233 1.865066
psy_com2 | 1.117981 .0550349 2.27 0.023 1.015154 1.231222
psy_com3 | 1.100229 .0424087 2.48 0.013 1.020171 1.186569
dep2 | 1.036411 .0441261 0.84 0.401 .953436 1.126608
rural2 | .8985623 .0559718 -1.72 0.086 .7952918 1.015243
rural3 | .8605226 .0595623 -2.17 0.030 .751355 .9855517
porc_pobr | 1.568951 .3927089 1.80 0.072 .9606235 2.562508
susini2 | 1.188579 .1083449 1.90 0.058 .9941153 1.421083
susini3 | 1.269722 .0818376 3.70 0.000 1.119041 1.440693
susini4 | 1.180627 .0440216 4.45 0.000 1.097424 1.270139
susini5 | 1.421697 .1319853 3.79 0.000 1.18518 1.705413
ano_nac_corr | .8503175 .0080237 -17.18 0.000 .8347359 .8661899
cohab2 | .8802375 .0591122 -1.90 0.057 .7716805 1.004066
cohab3 | 1.075229 .0859759 0.91 0.364 .9192599 1.25766
cohab4 | .9641082 .0641826 -0.55 0.583 .8461739 1.098479
fis_com2 | 1.058096 .0364718 1.64 0.101 .9889734 1.132049
fis_com3 | .8192466 .0709809 -2.30 0.021 .6912978 .9708768
rc_x1 | .8505878 .0101863 -13.51 0.000 .8308556 .8707887
rc_x2 | .8817925 .0351655 -3.15 0.002 .8154942 .9534808
rc_x3 | 1.277561 .1358823 2.30 0.021 1.037164 1.573679
_rcs1 | 2.201568 .0694739 25.01 0.000 2.069527 2.342033
_rcs2 | 1.066428 .0083328 8.23 0.000 1.050221 1.082886
_rcs3 | 1.034867 .0062318 5.69 0.000 1.022724 1.047153
_rcs4 | 1.015479 .0043482 3.59 0.000 1.006992 1.024037
_rcs5 | 1.010226 .0030941 3.32 0.001 1.00418 1.016309
_rcs_mot_egr_early1 | .892624 .0314331 -3.23 0.001 .8330942 .9564075
_rcs_mot_egr_late1 | .9135289 .0309673 -2.67 0.008 .8548065 .9762854
_cons | 8.6e+138 1.6e+140 16.84 0.000 5.8e+122 1.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.138
Iteration 1: log likelihood = -16978.313
Iteration 2: log likelihood = -16978.23
Iteration 3: log likelihood = -16978.23
Log likelihood = -16978.23 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010479 .1269256 11.06 0.000 1.776485 2.275295
mot_egr_late | 1.69603 .0922143 9.72 0.000 1.52459 1.886748
tr_mod2 | 1.217794 .0518024 4.63 0.000 1.12038 1.323677
sex_dum2 | .6071923 .0295147 -10.26 0.000 .5520148 .6678852
edad_ini_cons | .9714539 .0047126 -5.97 0.000 .9622612 .9807345
esc1 | 1.430457 .0886497 5.78 0.000 1.266844 1.6152
esc2 | 1.264106 .0732404 4.05 0.000 1.128408 1.416122
sus_prin2 | 1.157073 .078237 2.16 0.031 1.013458 1.32104
sus_prin3 | 1.681709 .0916819 9.53 0.000 1.511283 1.871354
sus_prin4 | 1.171021 .0933657 1.98 0.048 1.001609 1.369087
sus_prin5 | 1.590965 .2391896 3.09 0.002 1.18492 2.136153
fr_cons_sus_prin2 | .9673647 .1088531 -0.29 0.768 .7759049 1.206069
fr_cons_sus_prin3 | .9785886 .0894336 -0.24 0.813 .818104 1.170555
fr_cons_sus_prin4 | 1.003239 .0951162 0.03 0.973 .8331108 1.208109
fr_cons_sus_prin5 | 1.03007 .0934625 0.33 0.744 .862251 1.230552
cond_ocu2 | 1.048941 .0745449 0.67 0.501 .9125547 1.205711
cond_ocu3 | 1.145139 .3089474 0.50 0.615 .6748581 1.943141
cond_ocu4 | 1.221119 .0890578 2.74 0.006 1.05847 1.40876
cond_ocu5 | 1.059004 .1643324 0.37 0.712 .781288 1.435436
cond_ocu6 | 1.189401 .0465021 4.44 0.000 1.101663 1.284126
policonsumo | .9916923 .0486167 -0.17 0.865 .9008401 1.091707
num_hij2 | 1.125619 .044785 2.97 0.003 1.041177 1.21691
tenviv1 | 1.066856 .1349932 0.51 0.609 .8325311 1.367136
tenviv2 | 1.124241 .0968579 1.36 0.174 .949566 1.331049
tenviv4 | 1.038012 .0510068 0.76 0.448 .9427032 1.142956
tenviv5 | 1.010653 .038324 0.28 0.780 .9382633 1.088629
mzone2 | 1.450329 .0608479 8.86 0.000 1.335841 1.574629
mzone3 | 1.528854 .0965505 6.72 0.000 1.350861 1.730299
n_off_vio | 1.46664 .0554435 10.13 0.000 1.361901 1.579434
n_off_acq | 2.79921 .0972908 29.62 0.000 2.614873 2.996541
n_off_sud | 1.39089 .0507111 9.05 0.000 1.294966 1.493919
n_off_oth | 1.736233 .0634259 15.10 0.000 1.616267 1.865104
psy_com2 | 1.118458 .0550614 2.27 0.023 1.015582 1.231754
psy_com3 | 1.100036 .0424015 2.47 0.013 1.019993 1.186362
dep2 | 1.036392 .044126 0.84 0.401 .9534168 1.126589
rural2 | .898497 .0559676 -1.72 0.086 .7952343 1.015169
rural3 | .8601988 .0595439 -2.18 0.030 .7510653 .9851899
porc_pobr | 1.568696 .3926413 1.80 0.072 .9604722 2.56208
susini2 | 1.188245 .1083157 1.89 0.058 .9938339 1.420686
susini3 | 1.270059 .08186 3.71 0.000 1.119336 1.441076
susini4 | 1.180525 .0440178 4.45 0.000 1.097329 1.270029
susini5 | 1.421768 .1319931 3.79 0.000 1.185237 1.705501
ano_nac_corr | .8503888 .0080261 -17.17 0.000 .8348025 .8662661
cohab2 | .88002 .059099 -1.90 0.057 .7714874 1.003821
cohab3 | 1.074963 .085956 0.90 0.366 .9190304 1.257353
cohab4 | .9639723 .0641735 -0.55 0.582 .8460546 1.098325
fis_com2 | 1.058066 .0364716 1.64 0.102 .9889441 1.132019
fis_com3 | .8191417 .070972 -2.30 0.021 .6912088 .9707532
rc_x1 | .8506477 .0101883 -13.51 0.000 .8309116 .8708527
rc_x2 | .881792 .0351655 -3.15 0.002 .8154937 .9534801
rc_x3 | 1.277644 .1358916 2.30 0.021 1.037231 1.573782
_rcs1 | 2.183578 .0725092 23.52 0.000 2.045989 2.33042
_rcs2 | 1.048694 .0255923 1.95 0.051 .9997145 1.100073
_rcs3 | 1.032218 .0069804 4.69 0.000 1.018627 1.045991
_rcs4 | 1.015307 .0043544 3.54 0.000 1.006808 1.023877
_rcs5 | 1.01024 .0030935 3.33 0.001 1.004195 1.016321
_rcs_mot_egr_early1 | .897668 .0332567 -2.91 0.004 .8347962 .9652748
_rcs_mot_egr_early2 | 1.008632 .0274681 0.32 0.752 .9562073 1.063931
_rcs_mot_egr_late1 | .9245321 .0332186 -2.18 0.029 .8616644 .9919867
_rcs_mot_egr_late2 | 1.02668 .0273762 0.99 0.323 .9744012 1.081763
_cons | 7.2e+138 1.4e+140 16.83 0.000 4.8e+122 1.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.253
Iteration 1: log likelihood = -16978.212
Iteration 2: log likelihood = -16978.147
Iteration 3: log likelihood = -16978.146
Log likelihood = -16978.146 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010558 .1269474 11.06 0.000 1.776525 2.275421
mot_egr_late | 1.695968 .0922288 9.71 0.000 1.524503 1.886718
tr_mod2 | 1.217751 .0518014 4.63 0.000 1.120339 1.323632
sex_dum2 | .6071941 .0295147 -10.26 0.000 .5520165 .6678871
edad_ini_cons | .9714549 .0047126 -5.97 0.000 .9622622 .9807354
esc1 | 1.430507 .0886525 5.78 0.000 1.266889 1.615256
esc2 | 1.264152 .073243 4.05 0.000 1.128449 1.416173
sus_prin2 | 1.157192 .0782457 2.16 0.031 1.013561 1.321177
sus_prin3 | 1.6819 .091694 9.54 0.000 1.511452 1.87157
sus_prin4 | 1.171048 .093368 1.98 0.048 1.001631 1.369119
sus_prin5 | 1.591529 .2392754 3.09 0.002 1.185338 2.136913
fr_cons_sus_prin2 | .9673349 .1088497 -0.30 0.768 .7758809 1.206031
fr_cons_sus_prin3 | .9786338 .0894377 -0.24 0.813 .8181418 1.170609
fr_cons_sus_prin4 | 1.003272 .0951194 0.03 0.973 .8331381 1.208149
fr_cons_sus_prin5 | 1.030071 .0934624 0.33 0.744 .862252 1.230552
cond_ocu2 | 1.048868 .0745398 0.67 0.502 .912491 1.205627
cond_ocu3 | 1.145638 .3090831 0.50 0.614 .6751502 1.94399
cond_ocu4 | 1.22123 .0890653 2.74 0.006 1.058567 1.408887
cond_ocu5 | 1.059178 .1643608 0.37 0.711 .7814145 1.435676
cond_ocu6 | 1.189404 .0465024 4.44 0.000 1.101665 1.28413
policonsumo | .9917428 .0486195 -0.17 0.866 .9008852 1.091764
num_hij2 | 1.125685 .0447876 2.98 0.003 1.041238 1.216981
tenviv1 | 1.06684 .1349921 0.51 0.609 .8325164 1.367117
tenviv2 | 1.124203 .0968553 1.36 0.174 .9495318 1.331005
tenviv4 | 1.038036 .0510082 0.76 0.447 .9427254 1.142983
tenviv5 | 1.010709 .0383262 0.28 0.779 .9383147 1.088689
mzone2 | 1.450398 .0608507 8.86 0.000 1.335905 1.574704
mzone3 | 1.529053 .0965646 6.72 0.000 1.351035 1.730528
n_off_vio | 1.466614 .0554428 10.13 0.000 1.361876 1.579407
n_off_acq | 2.799264 .0972918 29.62 0.000 2.614925 2.996597
n_off_sud | 1.390915 .0507119 9.05 0.000 1.294989 1.493945
n_off_oth | 1.736264 .0634268 15.10 0.000 1.616296 1.865137
psy_com2 | 1.118655 .0550724 2.28 0.023 1.015759 1.231974
psy_com3 | 1.099969 .0423991 2.47 0.013 1.01993 1.186289
dep2 | 1.036401 .0441266 0.84 0.401 .9534248 1.126599
rural2 | .898501 .0559678 -1.72 0.086 .7952378 1.015173
rural3 | .8600561 .0595348 -2.18 0.029 .7509395 .9850281
porc_pobr | 1.56708 .3922515 1.79 0.073 .9594656 2.559488
susini2 | 1.188106 .1083031 1.89 0.059 .993718 1.420521
susini3 | 1.270174 .0818683 3.71 0.000 1.119437 1.441209
susini4 | 1.180504 .0440173 4.45 0.000 1.097309 1.270007
susini5 | 1.421613 .1319787 3.79 0.000 1.185109 1.705315
ano_nac_corr | .8503613 .0080263 -17.17 0.000 .8347747 .8662389
cohab2 | .8799522 .0590951 -1.90 0.057 .771427 1.003745
cohab3 | 1.074908 .0859523 0.90 0.366 .9189825 1.25729
cohab4 | .9639318 .0641711 -0.55 0.581 .8460187 1.098279
fis_com2 | 1.057978 .036469 1.64 0.102 .9888611 1.131926
fis_com3 | .8190616 .0709654 -2.30 0.021 .6911406 .9706589
rc_x1 | .8506202 .0101881 -13.51 0.000 .8308844 .8708248
rc_x2 | .8817843 .0351648 -3.15 0.002 .8154873 .9534711
rc_x3 | 1.277667 .1358926 2.30 0.021 1.037251 1.573807
_rcs1 | 2.186591 .073672 23.22 0.000 2.046861 2.33586
_rcs2 | 1.049144 .0264646 1.90 0.057 .998536 1.102317
_rcs3 | 1.034729 .0170878 2.07 0.039 1.001774 1.068768
_rcs4 | 1.015964 .0076611 2.10 0.036 1.001058 1.031091
_rcs5 | 1.010221 .0031151 3.30 0.001 1.004134 1.016344
_rcs_mot_egr_early1 | .8953045 .0337036 -2.94 0.003 .8316247 .9638604
_rcs_mot_egr_early2 | 1.008559 .0280749 0.31 0.759 .955007 1.065113
_rcs_mot_egr_early3 | .9946837 .019652 -0.27 0.787 .9569027 1.033956
_rcs_mot_egr_late1 | .9237452 .0336996 -2.17 0.030 .8600012 .9922139
_rcs_mot_egr_late2 | 1.025114 .0279959 0.91 0.364 .9716861 1.081481
_rcs_mot_egr_late3 | 1.001214 .0191036 0.06 0.949 .9644627 1.039365
_cons | 7.7e+138 1.5e+140 16.83 0.000 5.2e+122 1.2e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.264
Iteration 1: log likelihood = -16976.156
Iteration 2: log likelihood = -16976.048
Iteration 3: log likelihood = -16976.048
Log likelihood = -16976.048 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.01022 .1269077 11.06 0.000 1.776259 2.274998
mot_egr_late | 1.695913 .0921964 9.72 0.000 1.524505 1.886593
tr_mod2 | 1.217861 .0518064 4.63 0.000 1.12044 1.323753
sex_dum2 | .6072457 .0295166 -10.26 0.000 .5520644 .6679425
edad_ini_cons | .9714394 .0047126 -5.97 0.000 .9622466 .9807199
esc1 | 1.430554 .088655 5.78 0.000 1.266932 1.615308
esc2 | 1.264167 .0732438 4.05 0.000 1.128462 1.41619
sus_prin2 | 1.157552 .0782716 2.16 0.030 1.013874 1.321592
sus_prin3 | 1.682476 .0917299 9.54 0.000 1.511962 1.872221
sus_prin4 | 1.171202 .0933808 1.98 0.047 1.001763 1.3693
sus_prin5 | 1.592092 .2393616 3.09 0.002 1.185755 2.137673
fr_cons_sus_prin2 | .9673817 .1088548 -0.29 0.768 .7759188 1.206089
fr_cons_sus_prin3 | .9786186 .0894363 -0.24 0.813 .8181292 1.170591
fr_cons_sus_prin4 | 1.003391 .0951312 0.04 0.972 .833236 1.208293
fr_cons_sus_prin5 | 1.029943 .0934519 0.33 0.745 .8621429 1.230402
cond_ocu2 | 1.048737 .0745304 0.67 0.503 .9123777 1.205477
cond_ocu3 | 1.146467 .3093063 0.51 0.612 .6756395 1.945395
cond_ocu4 | 1.220693 .0890273 2.73 0.006 1.0581 1.408271
cond_ocu5 | 1.059485 .1644126 0.37 0.710 .7816354 1.436104
cond_ocu6 | 1.189457 .0465055 4.44 0.000 1.101713 1.284189
policonsumo | .9917222 .0486181 -0.17 0.865 .9008672 1.09174
num_hij2 | 1.125626 .0447849 2.97 0.003 1.041184 1.216916
tenviv1 | 1.066906 .1350026 0.51 0.609 .8325649 1.367207
tenviv2 | 1.124814 .0969087 1.37 0.172 .9500467 1.33173
tenviv4 | 1.037997 .0510066 0.76 0.448 .9426889 1.14294
tenviv5 | 1.010736 .0383272 0.28 0.778 .9383399 1.088718
mzone2 | 1.450543 .0608574 8.87 0.000 1.336037 1.574862
mzone3 | 1.529064 .0965666 6.72 0.000 1.351042 1.730543
n_off_vio | 1.46654 .0554374 10.13 0.000 1.361812 1.579321
n_off_acq | 2.798752 .0972681 29.61 0.000 2.614458 2.996037
n_off_sud | 1.390715 .0507024 9.05 0.000 1.294808 1.493727
n_off_oth | 1.736193 .0634203 15.10 0.000 1.616237 1.865052
psy_com2 | 1.118818 .0550804 2.28 0.023 1.015907 1.232154
psy_com3 | 1.099846 .0423948 2.47 0.014 1.019814 1.186157
dep2 | 1.036462 .0441294 0.84 0.400 .9534809 1.126666
rural2 | .8985919 .0559725 -1.72 0.086 .79532 1.015274
rural3 | .8599969 .0595309 -2.18 0.029 .7508875 .9849607
porc_pobr | 1.565599 .391896 1.79 0.073 .9585404 2.557118
susini2 | 1.188194 .108311 1.89 0.059 .9937913 1.420625
susini3 | 1.270534 .0818926 3.71 0.000 1.119752 1.441619
susini4 | 1.18049 .0440169 4.45 0.000 1.097295 1.269992
susini5 | 1.421931 .1320092 3.79 0.000 1.185372 1.705699
ano_nac_corr | .8502989 .0080264 -17.18 0.000 .8347121 .8661767
cohab2 | .8800189 .0591 -1.90 0.057 .7714846 1.003822
cohab3 | 1.074788 .0859439 0.90 0.367 .9188771 1.257152
cohab4 | .9639164 .0641707 -0.55 0.581 .8460041 1.098263
fis_com2 | 1.057635 .0364562 1.63 0.104 .988542 1.131556
fis_com3 | .8189607 .0709569 -2.31 0.021 .6910551 .9705399
rc_x1 | .8505742 .0101877 -13.51 0.000 .8308392 .8707779
rc_x2 | .8817182 .0351608 -3.16 0.002 .8154286 .9533968
rc_x3 | 1.277832 .1359063 2.31 0.021 1.037391 1.574001
_rcs1 | 2.186853 .0733183 23.34 0.000 2.047771 2.33538
_rcs2 | 1.050262 .0278456 1.85 0.064 .9970798 1.106282
_rcs3 | 1.018454 .0189618 0.98 0.326 .9819598 1.056305
_rcs4 | 1.034235 .0116034 3.00 0.003 1.011741 1.057229
_rcs5 | 1.018587 .0052416 3.58 0.000 1.008366 1.028913
_rcs_mot_egr_early1 | .8949857 .0335605 -2.96 0.003 .8315673 .9632405
_rcs_mot_egr_early2 | 1.008479 .0292559 0.29 0.771 .952738 1.067481
_rcs_mot_egr_early3 | 1.0096 .0210737 0.46 0.647 .9691293 1.05176
_rcs_mot_egr_early4 | .9745616 .0128397 -1.96 0.050 .9497183 1.000055
_rcs_mot_egr_late1 | .9234761 .033551 -2.19 0.028 .8600041 .9916326
_rcs_mot_egr_late2 | 1.02453 .0292225 0.85 0.396 .9688259 1.083436
_rcs_mot_egr_late3 | 1.015669 .0204884 0.77 0.441 .976296 1.05663
_rcs_mot_egr_late4 | .9780452 .0122073 -1.78 0.075 .9544096 1.002266
_cons | 9.0e+138 1.7e+140 16.84 0.000 6.0e+122 1.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.225
Iteration 1: log likelihood = -16977.155
Iteration 2: log likelihood = -16977.055
Iteration 3: log likelihood = -16977.055
Log likelihood = -16977.055 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009279 .1268542 11.05 0.000 1.775417 2.273946
mot_egr_late | 1.695335 .092177 9.71 0.000 1.523964 1.885976
tr_mod2 | 1.217797 .051804 4.63 0.000 1.120381 1.323684
sex_dum2 | .6071903 .0295142 -10.26 0.000 .5520136 .6678822
edad_ini_cons | .9714482 .0047126 -5.97 0.000 .9622554 .9807287
esc1 | 1.430583 .0886568 5.78 0.000 1.266957 1.615341
esc2 | 1.264186 .073245 4.05 0.000 1.12848 1.416212
sus_prin2 | 1.157303 .0782541 2.16 0.031 1.013656 1.321305
sus_prin3 | 1.68219 .0917126 9.54 0.000 1.511707 1.871899
sus_prin4 | 1.171098 .0933721 1.98 0.048 1.001674 1.369178
sus_prin5 | 1.591662 .2392979 3.09 0.002 1.185433 2.137098
fr_cons_sus_prin2 | .9673584 .1088523 -0.29 0.768 .7758999 1.206061
fr_cons_sus_prin3 | .9786282 .0894372 -0.24 0.813 .8181371 1.170602
fr_cons_sus_prin4 | 1.003368 .0951289 0.04 0.972 .8332174 1.208266
fr_cons_sus_prin5 | 1.030004 .0934571 0.33 0.745 .8621945 1.230474
cond_ocu2 | 1.048867 .0745398 0.67 0.502 .9124898 1.205626
cond_ocu3 | 1.145936 .3091641 0.50 0.614 .6753255 1.944498
cond_ocu4 | 1.220962 .0890478 2.74 0.006 1.058332 1.408583
cond_ocu5 | 1.059592 .1644282 0.37 0.709 .7817154 1.436246
cond_ocu6 | 1.189402 .0465034 4.44 0.000 1.101662 1.28413
policonsumo | .9917144 .0486181 -0.17 0.865 .9008594 1.091732
num_hij2 | 1.12565 .044786 2.97 0.003 1.041207 1.216943
tenviv1 | 1.066867 .1349974 0.51 0.609 .8325346 1.367156
tenviv2 | 1.124401 .0968722 1.36 0.174 .9496995 1.331239
tenviv4 | 1.037989 .0510062 0.76 0.448 .9426816 1.142932
tenviv5 | 1.010654 .0383242 0.28 0.780 .9382638 1.08863
mzone2 | 1.450399 .0608507 8.86 0.000 1.335906 1.574705
mzone3 | 1.528985 .0965603 6.72 0.000 1.350974 1.730451
n_off_vio | 1.466586 .0554411 10.13 0.000 1.361852 1.579376
n_off_acq | 2.799117 .0972846 29.62 0.000 2.614792 2.996436
n_off_sud | 1.390845 .0507085 9.05 0.000 1.294926 1.493869
n_off_oth | 1.736297 .063427 15.10 0.000 1.616328 1.86517
psy_com2 | 1.118678 .0550742 2.28 0.023 1.015779 1.232001
psy_com3 | 1.09989 .0423966 2.47 0.014 1.019856 1.186206
dep2 | 1.036427 .0441277 0.84 0.401 .9534488 1.126627
rural2 | .8985729 .0559719 -1.72 0.086 .7953022 1.015253
rural3 | .8600136 .0595316 -2.18 0.029 .7509027 .9849789
porc_pobr | 1.565595 .3918945 1.79 0.073 .958538 2.557109
susini2 | 1.188174 .1083092 1.89 0.059 .9937744 1.420601
susini3 | 1.270292 .0818764 3.71 0.000 1.11954 1.441344
susini4 | 1.180524 .0440181 4.45 0.000 1.097327 1.270029
susini5 | 1.421834 .1320004 3.79 0.000 1.185291 1.705583
ano_nac_corr | .8503774 .0080267 -17.17 0.000 .83479 .8662559
cohab2 | .8799994 .0590984 -1.90 0.057 .771468 1.003799
cohab3 | 1.074824 .0859465 0.90 0.367 .9189088 1.257194
cohab4 | .9639379 .0641718 -0.55 0.581 .8460235 1.098287
fis_com2 | 1.057822 .0364638 1.63 0.103 .9887155 1.13176
fis_com3 | .8190081 .0709608 -2.30 0.021 .6910955 .9705956
rc_x1 | .8506421 .0101883 -13.51 0.000 .8309059 .870847
rc_x2 | .8817738 .0351637 -3.16 0.002 .8154789 .9534583
rc_x3 | 1.277653 .1358888 2.30 0.021 1.037243 1.573784
_rcs1 | 2.184569 .0733303 23.28 0.000 2.04547 2.333127
_rcs2 | 1.051106 .0279348 1.88 0.061 .9977558 1.107308
_rcs3 | 1.019642 .0203766 0.97 0.330 .9804763 1.060372
_rcs4 | 1.032213 .0143207 2.29 0.022 1.004524 1.060666
_rcs5 | 1.013292 .0100013 1.34 0.181 .9938784 1.033085
_rcs_mot_egr_early1 | .8966058 .0336728 -2.91 0.004 .8329788 .9650928
_rcs_mot_egr_early2 | 1.007237 .0293562 0.25 0.805 .9513121 1.066449
_rcs_mot_egr_early3 | 1.013131 .0224394 0.59 0.556 .9700918 1.05808
_rcs_mot_egr_early4 | .9787665 .0152519 -1.38 0.168 .9493251 1.009121
_rcs_mot_egr_early5 | .9974015 .0110541 -0.23 0.814 .9759694 1.019304
_rcs_mot_egr_late1 | .9244998 .0336312 -2.16 0.031 .8608788 .9928226
_rcs_mot_egr_late2 | 1.023159 .0293748 0.80 0.425 .9671755 1.082383
_rcs_mot_egr_late3 | 1.017858 .0220201 0.82 0.413 .9756018 1.061945
_rcs_mot_egr_late4 | .9843624 .0147856 -1.05 0.294 .9558056 1.013772
_rcs_mot_egr_late5 | .9960668 .0105932 -0.37 0.711 .9755194 1.017047
_cons | 7.4e+138 1.4e+140 16.83 0.000 5.0e+122 1.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.529
Iteration 1: log likelihood = -16975.542
Iteration 2: log likelihood = -16975.345
Iteration 3: log likelihood = -16975.345
Log likelihood = -16975.345 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010059 .1269143 11.06 0.000 1.776087 2.274853
mot_egr_late | 1.695637 .0922044 9.71 0.000 1.524217 1.886336
tr_mod2 | 1.217773 .0518013 4.63 0.000 1.120362 1.323655
sex_dum2 | .6073245 .0295206 -10.26 0.000 .5521358 .6680297
edad_ini_cons | .9714373 .0047126 -5.97 0.000 .9622444 .9807179
esc1 | 1.430477 .0886507 5.78 0.000 1.266863 1.615222
esc2 | 1.264117 .073241 4.05 0.000 1.128418 1.416135
sus_prin2 | 1.157539 .0782704 2.16 0.030 1.013862 1.321576
sus_prin3 | 1.682284 .0917178 9.54 0.000 1.511791 1.872003
sus_prin4 | 1.171159 .0933772 1.98 0.048 1.001726 1.36925
sus_prin5 | 1.591596 .2392883 3.09 0.002 1.185384 2.13701
fr_cons_sus_prin2 | .9674195 .1088594 -0.29 0.768 .7759485 1.206137
fr_cons_sus_prin3 | .9786592 .0894399 -0.24 0.813 .8181632 1.170639
fr_cons_sus_prin4 | 1.003418 .0951335 0.04 0.971 .8332593 1.208326
fr_cons_sus_prin5 | 1.030052 .0934616 0.33 0.744 .8622347 1.230532
cond_ocu2 | 1.048639 .0745238 0.67 0.504 .9122919 1.205365
cond_ocu3 | 1.146547 .3093286 0.51 0.612 .6756863 1.945534
cond_ocu4 | 1.220656 .0890232 2.73 0.006 1.05807 1.408225
cond_ocu5 | 1.059343 .1643899 0.37 0.710 .7815309 1.435909
cond_ocu6 | 1.189475 .0465058 4.44 0.000 1.10173 1.284208
policonsumo | .9917124 .0486173 -0.17 0.865 .900859 1.091729
num_hij2 | 1.125662 .0447867 2.98 0.003 1.041217 1.216956
tenviv1 | 1.067244 .1350435 0.51 0.607 .8328316 1.367636
tenviv2 | 1.12492 .096918 1.37 0.172 .9501362 1.331856
tenviv4 | 1.038146 .0510142 0.76 0.446 .9428241 1.143106
tenviv5 | 1.010827 .0383309 0.28 0.776 .9384236 1.088816
mzone2 | 1.450522 .0608566 8.86 0.000 1.336018 1.57484
mzone3 | 1.529151 .0965727 6.72 0.000 1.351118 1.730643
n_off_vio | 1.466546 .0554369 10.13 0.000 1.361819 1.579327
n_off_acq | 2.79865 .0972628 29.61 0.000 2.614366 2.995924
n_off_sud | 1.390726 .0507027 9.05 0.000 1.294818 1.493738
n_off_oth | 1.736168 .063418 15.10 0.000 1.616216 1.865022
psy_com2 | 1.118621 .0550718 2.28 0.023 1.015726 1.231939
psy_com3 | 1.099885 .0423962 2.47 0.014 1.019851 1.186199
dep2 | 1.036445 .0441286 0.84 0.400 .953465 1.126647
rural2 | .8984605 .0559645 -1.72 0.086 .7952033 1.015126
rural3 | .8600644 .0595364 -2.18 0.029 .7509449 .9850401
porc_pobr | 1.568169 .3925368 1.80 0.072 .9601165 2.561307
susini2 | 1.188039 .1082965 1.89 0.059 .9936621 1.420439
susini3 | 1.270848 .0819116 3.72 0.000 1.120031 1.441973
susini4 | 1.180503 .0440172 4.45 0.000 1.097307 1.270005
susini5 | 1.422026 .1320183 3.79 0.000 1.185451 1.705814
ano_nac_corr | .8502419 .0080264 -17.19 0.000 .834655 .8661199
cohab2 | .8800568 .0591019 -1.90 0.057 .771519 1.003864
cohab3 | 1.074746 .0859397 0.90 0.367 .9188428 1.257101
cohab4 | .9639137 .0641698 -0.55 0.581 .8460028 1.098258
fis_com2 | 1.057821 .0364631 1.63 0.103 .9887153 1.131757
fis_com3 | .818957 .0709567 -2.31 0.021 .6910519 .9705356
rc_x1 | .8505072 .0101874 -13.52 0.000 .8307728 .8707103
rc_x2 | .8817489 .035163 -3.16 0.002 .8154553 .9534319
rc_x3 | 1.277755 .135902 2.30 0.021 1.037323 1.573916
_rcs1 | 2.184683 .0733339 23.28 0.000 2.045577 2.333248
_rcs2 | 1.051599 .0280821 1.88 0.060 .9979751 1.108105
_rcs3 | 1.01798 .0200883 0.90 0.366 .9793598 1.058124
_rcs4 | 1.03352 .0135821 2.51 0.012 1.00724 1.060486
_rcs5 | 1.013139 .0083824 1.58 0.115 .996842 1.029702
_rcs_mot_egr_early1 | .8964222 .033669 -2.91 0.004 .8328027 .9649018
_rcs_mot_egr_early2 | 1.006059 .0295042 0.21 0.837 .9498628 1.065581
_rcs_mot_egr_early3 | 1.016533 .0222646 0.75 0.454 .9738189 1.061121
_rcs_mot_egr_early4 | .9814612 .0140665 -1.31 0.192 .954275 1.009422
_rcs_mot_egr_early5 | .9895371 .0098668 -1.05 0.291 .9703864 1.009066
_rcs_mot_egr_early6 | 1.003927 .0061417 0.64 0.522 .9919609 1.016037
_rcs_mot_egr_late1 | .9242874 .0336233 -2.16 0.030 .8606814 .992594
_rcs_mot_egr_late2 | 1.022313 .0295682 0.76 0.445 .9659721 1.081939
_rcs_mot_egr_late3 | 1.020468 .0219647 0.94 0.347 .9783135 1.064439
_rcs_mot_egr_late4 | .9881715 .0137163 -0.86 0.391 .9616505 1.015424
_rcs_mot_egr_late5 | .9901165 .0094206 -1.04 0.297 .9718234 1.008754
_rcs_mot_egr_late6 | 1.001534 .005513 0.28 0.781 .9907869 1.012398
_cons | 1.0e+139 2.0e+140 16.84 0.000 6.8e+122 1.5e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.404
Iteration 1: log likelihood = -16975.667
Iteration 2: log likelihood = -16975.507
Iteration 3: log likelihood = -16975.507
Log likelihood = -16975.507 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010298 .1269403 11.06 0.000 1.776279 2.275148
mot_egr_late | 1.695739 .0922193 9.71 0.000 1.524292 1.886469
tr_mod2 | 1.217741 .0517999 4.63 0.000 1.120332 1.32362
sex_dum2 | .6073523 .0295221 -10.26 0.000 .5521608 .6680606
edad_ini_cons | .9714367 .0047127 -5.97 0.000 .9622438 .9807174
esc1 | 1.430533 .088654 5.78 0.000 1.266913 1.615285
esc2 | 1.264169 .0732439 4.05 0.000 1.128464 1.416192
sus_prin2 | 1.157544 .0782707 2.16 0.030 1.013868 1.321582
sus_prin3 | 1.682249 .0917159 9.54 0.000 1.51176 1.871964
sus_prin4 | 1.171181 .0933789 1.98 0.047 1.001746 1.369276
sus_prin5 | 1.59159 .2392877 3.09 0.002 1.185379 2.137003
fr_cons_sus_prin2 | .9673759 .1088545 -0.29 0.768 .7759135 1.206083
fr_cons_sus_prin3 | .978651 .0894391 -0.24 0.813 .8181565 1.170629
fr_cons_sus_prin4 | 1.003386 .0951302 0.04 0.972 .8332323 1.208286
fr_cons_sus_prin5 | 1.030028 .0934593 0.33 0.744 .8622147 1.230502
cond_ocu2 | 1.048619 .0745223 0.67 0.504 .9122741 1.205342
cond_ocu3 | 1.146724 .3093769 0.51 0.612 .6757895 1.945836
cond_ocu4 | 1.220681 .0890246 2.73 0.006 1.058093 1.408252
cond_ocu5 | 1.059209 .1643685 0.37 0.711 .7814336 1.435726
cond_ocu6 | 1.18952 .0465075 4.44 0.000 1.101772 1.284256
policonsumo | .9917096 .0486169 -0.17 0.865 .9008567 1.091725
num_hij2 | 1.125697 .0447882 2.98 0.003 1.041249 1.216993
tenviv1 | 1.067245 .1350432 0.51 0.607 .8328325 1.367635
tenviv2 | 1.124983 .0969244 1.37 0.172 .9501881 1.331933
tenviv4 | 1.038203 .051017 0.76 0.445 .9428754 1.143168
tenviv5 | 1.010872 .0383327 0.29 0.776 .9384656 1.088866
mzone2 | 1.450541 .0608579 8.87 0.000 1.336035 1.574862
mzone3 | 1.52928 .0965815 6.73 0.000 1.351231 1.730791
n_off_vio | 1.466495 .0554348 10.13 0.000 1.361772 1.579271
n_off_acq | 2.798599 .0972609 29.61 0.000 2.614319 2.995869
n_off_sud | 1.390726 .0507025 9.05 0.000 1.294818 1.493737
n_off_oth | 1.736104 .0634152 15.10 0.000 1.616158 1.864953
psy_com2 | 1.118617 .0550723 2.28 0.023 1.015721 1.231936
psy_com3 | 1.099917 .0423975 2.47 0.013 1.019881 1.186234
dep2 | 1.036399 .0441265 0.84 0.401 .9534231 1.126597
rural2 | .8984475 .0559637 -1.72 0.086 .7951919 1.015111
rural3 | .8600557 .0595361 -2.18 0.029 .7509369 .9850306
porc_pobr | 1.568755 .3926789 1.80 0.072 .9604807 2.56225
susini2 | 1.188006 .1082933 1.89 0.059 .9936354 1.420399
susini3 | 1.270861 .081913 3.72 0.000 1.120042 1.441989
susini4 | 1.180503 .0440174 4.45 0.000 1.097308 1.270006
susini5 | 1.422033 .1320192 3.79 0.000 1.185456 1.705822
ano_nac_corr | .8501886 .0080265 -17.19 0.000 .8346017 .8660667
cohab2 | .8800429 .0591009 -1.90 0.057 .771507 1.003848
cohab3 | 1.074772 .0859415 0.90 0.367 .9188658 1.257131
cohab4 | .9638847 .0641678 -0.55 0.581 .8459776 1.098225
fis_com2 | 1.057865 .0364649 1.63 0.103 .9887554 1.131804
fis_com3 | .8189344 .0709548 -2.31 0.021 .6910328 .9705092
rc_x1 | .8504554 .0101872 -13.52 0.000 .8307215 .8706581
rc_x2 | .8817407 .035163 -3.16 0.002 .815447 .9534239
rc_x3 | 1.277792 .1359074 2.30 0.021 1.03735 1.573964
_rcs1 | 2.184411 .0733545 23.27 0.000 2.045268 2.33302
_rcs2 | 1.051866 .0280911 1.89 0.058 .9982245 1.10839
_rcs3 | 1.01827 .0201404 0.92 0.360 .9795513 1.05852
_rcs4 | 1.03405 .0140136 2.47 0.013 1.006946 1.061885
_rcs5 | 1.010127 .0095359 1.07 0.286 .9916089 1.028991
_rcs_mot_egr_early1 | .896739 .0336986 -2.90 0.004 .8330646 .9652803
_rcs_mot_egr_early2 | 1.005594 .0295452 0.19 0.849 .9493219 1.065201
_rcs_mot_egr_early3 | 1.018994 .0221105 0.87 0.386 .9765666 1.063264
_rcs_mot_egr_early4 | .9823664 .0139905 -1.25 0.212 .9553246 1.010174
_rcs_mot_egr_early5 | .9881548 .0098991 -1.19 0.234 .9689421 1.007748
_rcs_mot_egr_early6 | 1.001539 .0087101 0.18 0.860 .9846125 1.018757
_rcs_mot_egr_early7 | 1.00365 .004202 0.87 0.384 .9954481 1.01192
_rcs_mot_egr_late1 | .9243835 .0336372 -2.16 0.031 .8607519 .9927191
_rcs_mot_egr_late2 | 1.021579 .0296611 0.74 0.462 .9650679 1.0814
_rcs_mot_egr_late3 | 1.020928 .021858 0.97 0.333 .9789737 1.06468
_rcs_mot_egr_late4 | .9919616 .0135678 -0.59 0.555 .9657224 1.018914
_rcs_mot_egr_late5 | .9885763 .0093892 -1.21 0.226 .9703441 1.007151
_rcs_mot_egr_late6 | 1.000514 .0082508 0.06 0.950 .9844728 1.016817
_rcs_mot_egr_late7 | 1.002247 .0034775 0.65 0.518 .9954543 1.009086
_cons | 1.2e+139 2.2e+140 16.85 0.000 7.7e+122 1.8e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.752
Iteration 1: log likelihood = -16977.502
Iteration 2: log likelihood = -16977.437
Iteration 3: log likelihood = -16977.437
Log likelihood = -16977.437 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012536 .12695 11.09 0.000 1.778485 2.277389
mot_egr_late | 1.694464 .0920751 9.71 0.000 1.523278 1.884888
tr_mod2 | 1.218438 .0518245 4.65 0.000 1.120982 1.324366
sex_dum2 | .607298 .0295195 -10.26 0.000 .5521115 .6680008
edad_ini_cons | .9714319 .0047127 -5.97 0.000 .9622389 .9807126
esc1 | 1.430409 .0886472 5.78 0.000 1.266802 1.615147
esc2 | 1.264154 .073243 4.05 0.000 1.128451 1.416176
sus_prin2 | 1.157338 .0782559 2.16 0.031 1.013688 1.321344
sus_prin3 | 1.681938 .0916982 9.54 0.000 1.511482 1.871617
sus_prin4 | 1.17118 .0933799 1.98 0.048 1.001743 1.369277
sus_prin5 | 1.590815 .2391632 3.09 0.002 1.184813 2.135941
fr_cons_sus_prin2 | .967409 .1088579 -0.29 0.768 .7759406 1.206124
fr_cons_sus_prin3 | .9785847 .0894334 -0.24 0.813 .8181005 1.170551
fr_cons_sus_prin4 | 1.003281 .0951204 0.03 0.972 .8331449 1.208159
fr_cons_sus_prin5 | 1.030036 .0934609 0.33 0.744 .8622201 1.230514
cond_ocu2 | 1.048814 .0745353 0.67 0.502 .9124447 1.205563
cond_ocu3 | 1.146648 .3093534 0.51 0.612 .6757487 1.945697
cond_ocu4 | 1.220389 .0890058 2.73 0.006 1.057835 1.407921
cond_ocu5 | 1.057984 .1641719 0.36 0.716 .7805393 1.434048
cond_ocu6 | 1.189485 .0465057 4.44 0.000 1.101741 1.284218
policonsumo | .9915966 .0486117 -0.17 0.863 .9007536 1.091601
num_hij2 | 1.125554 .0447828 2.97 0.003 1.041117 1.21684
tenviv1 | 1.067279 .1350448 0.51 0.607 .8328636 1.367672
tenviv2 | 1.125202 .0969405 1.37 0.171 .9503774 1.332185
tenviv4 | 1.038047 .0510085 0.76 0.447 .9427354 1.142994
tenviv5 | 1.010717 .0383263 0.28 0.779 .9383225 1.088697
mzone2 | 1.450399 .0608534 8.86 0.000 1.335901 1.574711
mzone3 | 1.528535 .0965347 6.72 0.000 1.350572 1.729948
n_off_vio | 1.466613 .0554377 10.13 0.000 1.361884 1.579395
n_off_acq | 2.798335 .0972513 29.61 0.000 2.614073 2.995586
n_off_sud | 1.390646 .0507004 9.05 0.000 1.294743 1.493654
n_off_oth | 1.736015 .0634121 15.10 0.000 1.616074 1.864858
psy_com2 | 1.118023 .0550376 2.27 0.023 1.015191 1.23127
psy_com3 | 1.100216 .0424081 2.48 0.013 1.02016 1.186555
dep2 | 1.036419 .0441269 0.84 0.401 .9534424 1.126617
rural2 | .898513 .0559683 -1.72 0.086 .795249 1.015186
rural3 | .8606054 .0595695 -2.17 0.030 .7514247 .9856499
porc_pobr | 1.571197 .3932657 1.81 0.071 .962005 2.56616
susini2 | 1.188536 .1083406 1.89 0.058 .9940805 1.421031
susini3 | 1.270308 .0818754 3.71 0.000 1.119558 1.441358
susini4 | 1.18061 .0440211 4.45 0.000 1.097408 1.270121
susini5 | 1.421915 .1320064 3.79 0.000 1.185361 1.705677
ano_nac_corr | .8500967 .0080232 -17.21 0.000 .8345161 .8659682
cohab2 | .8802602 .0591132 -1.90 0.058 .7717015 1.00409
cohab3 | 1.075106 .0859654 0.91 0.365 .9191564 1.257516
cohab4 | .964041 .0641775 -0.55 0.582 .846116 1.098401
fis_com2 | 1.057973 .0364668 1.63 0.102 .9888599 1.131916
fis_com3 | .8191694 .0709746 -2.30 0.021 .6912319 .9707863
rc_x1 | .8503726 .0101848 -13.53 0.000 .8306432 .8705706
rc_x2 | .8817405 .0351634 -3.16 0.002 .815446 .9534245
rc_x3 | 1.277763 .1359053 2.30 0.021 1.037326 1.573931
_rcs1 | 2.200956 .0694371 25.01 0.000 2.068984 2.341346
_rcs2 | 1.065717 .0083576 8.12 0.000 1.049462 1.082225
_rcs3 | 1.033663 .006363 5.38 0.000 1.021266 1.04621
_rcs4 | 1.017806 .0044294 4.06 0.000 1.009161 1.026524
_rcs5 | 1.010267 .0032115 3.21 0.001 1.003993 1.016581
_rcs6 | 1.008379 .0025225 3.34 0.001 1.003447 1.013335
_rcs_mot_egr_early1 | .8926688 .0314254 -3.23 0.001 .8331531 .9564359
_rcs_mot_egr_late1 | .9136598 .0309637 -2.66 0.008 .8549437 .9764086
_cons | 1.4e+139 2.7e+140 16.86 0.000 9.7e+122 2.2e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.908
Iteration 1: log likelihood = -16976.68
Iteration 2: log likelihood = -16976.577
Iteration 3: log likelihood = -16976.577
Log likelihood = -16976.577 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.01082 .1269471 11.06 0.000 1.776786 2.275681
mot_egr_late | 1.695791 .0922009 9.71 0.000 1.524376 1.886482
tr_mod2 | 1.217859 .0518034 4.63 0.000 1.120443 1.323744
sex_dum2 | .6073616 .0295226 -10.26 0.000 .5521691 .6680708
edad_ini_cons | .971439 .0047126 -5.97 0.000 .9622462 .9807196
esc1 | 1.430405 .0886466 5.78 0.000 1.266798 1.615142
esc2 | 1.264084 .073239 4.04 0.000 1.128389 1.416098
sus_prin2 | 1.15742 .0782611 2.16 0.031 1.013761 1.321438
sus_prin3 | 1.681966 .0916976 9.54 0.000 1.511511 1.871643
sus_prin4 | 1.17117 .0933782 1.98 0.048 1.001735 1.369263
sus_prin5 | 1.591304 .2392422 3.09 0.002 1.18517 2.136613
fr_cons_sus_prin2 | .9673587 .1088524 -0.29 0.768 .7759 1.206061
fr_cons_sus_prin3 | .9785867 .0894333 -0.24 0.813 .8181026 1.170552
fr_cons_sus_prin4 | 1.003268 .0951187 0.03 0.973 .8331352 1.208143
fr_cons_sus_prin5 | 1.030046 .0934606 0.33 0.744 .8622309 1.230524
cond_ocu2 | 1.048642 .0745233 0.67 0.504 .9122948 1.205366
cond_ocu3 | 1.146183 .3092284 0.51 0.613 .6754738 1.94491
cond_ocu4 | 1.220646 .0890213 2.73 0.006 1.058063 1.40821
cond_ocu5 | 1.058617 .1642726 0.37 0.714 .7810023 1.434913
cond_ocu6 | 1.189496 .0465054 4.44 0.000 1.101752 1.284228
policonsumo | .99167 .0486147 -0.17 0.865 .9008213 1.091681
num_hij2 | 1.125622 .0447854 2.97 0.003 1.04118 1.216913
tenviv1 | 1.06718 .1350324 0.51 0.607 .8327861 1.367546
tenviv2 | 1.124878 .0969148 1.37 0.172 .9501005 1.331808
tenviv4 | 1.038173 .0510149 0.76 0.446 .9428497 1.143134
tenviv5 | 1.010882 .0383328 0.29 0.775 .9384752 1.088875
mzone2 | 1.450542 .0608584 8.87 0.000 1.336035 1.574864
mzone3 | 1.529044 .0965656 6.72 0.000 1.351024 1.730521
n_off_vio | 1.466559 .0554364 10.13 0.000 1.361833 1.579338
n_off_acq | 2.798557 .09726 29.61 0.000 2.614278 2.995825
n_off_sud | 1.390707 .0507022 9.05 0.000 1.294799 1.493718
n_off_oth | 1.736054 .0634132 15.10 0.000 1.616111 1.864898
psy_com2 | 1.118505 .0550643 2.27 0.023 1.015624 1.231808
psy_com3 | 1.100024 .0424008 2.47 0.013 1.019981 1.186348
dep2 | 1.036401 .0441268 0.84 0.401 .9534247 1.1266
rural2 | .8984449 .0559639 -1.72 0.086 .7951889 1.015109
rural3 | .8602784 .0595509 -2.17 0.030 .7511323 .9852844
porc_pobr | 1.570913 .3931912 1.80 0.071 .9618352 2.565685
susini2 | 1.1882 .1083112 1.89 0.059 .9937973 1.420632
susini3 | 1.270643 .0818979 3.72 0.000 1.119851 1.44174
susini4 | 1.180509 .0440173 4.45 0.000 1.097313 1.270012
susini5 | 1.421981 .1320138 3.79 0.000 1.185414 1.705759
ano_nac_corr | .8501657 .0080257 -17.20 0.000 .8345804 .8660422
cohab2 | .8800415 .0590999 -1.90 0.057 .7715073 1.003844
cohab3 | 1.074844 .0859458 0.90 0.367 .9189296 1.257212
cohab4 | .9639042 .0641684 -0.55 0.581 .8459959 1.098246
fis_com2 | 1.057938 .0364664 1.63 0.102 .9888263 1.131881
fis_com3 | .8190631 .0709657 -2.30 0.021 .6911418 .9706611
rc_x1 | .85043 .0101869 -13.53 0.000 .8306967 .8706321
rc_x2 | .8817414 .0351634 -3.16 0.002 .815447 .9534255
rc_x3 | 1.277838 .1359136 2.31 0.021 1.037386 1.574025
_rcs1 | 2.183644 .0725582 23.50 0.000 2.045965 2.330589
_rcs2 | 1.048754 .0256088 1.95 0.051 .9997439 1.100167
_rcs3 | 1.030835 .0072779 4.30 0.000 1.016669 1.045199
_rcs4 | 1.017417 .0044593 3.94 0.000 1.008715 1.026195
_rcs5 | 1.01026 .0032107 3.21 0.001 1.003987 1.016573
_rcs6 | 1.008376 .0025223 3.33 0.001 1.003444 1.013332
_rcs_mot_egr_early1 | .8973802 .0332651 -2.92 0.003 .8344938 .9650055
_rcs_mot_egr_early2 | 1.007799 .0275077 0.28 0.776 .9553015 1.063181
_rcs_mot_egr_late1 | .9243864 .0332333 -2.19 0.029 .8614923 .9918722
_rcs_mot_egr_late2 | 1.025964 .0274166 0.96 0.337 .9736116 1.081132
_cons | 1.2e+139 2.3e+140 16.85 0.000 8.2e+122 1.9e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.921
Iteration 1: log likelihood = -16976.535
Iteration 2: log likelihood = -16976.449
Iteration 3: log likelihood = -16976.449
Log likelihood = -16976.449 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.011198 .1269925 11.07 0.000 1.777083 2.276156
mot_egr_late | 1.695958 .0922319 9.71 0.000 1.524487 1.886715
tr_mod2 | 1.217819 .0518025 4.63 0.000 1.120405 1.323703
sex_dum2 | .6073645 .0295227 -10.26 0.000 .5521719 .6680739
edad_ini_cons | .9714396 .0047126 -5.97 0.000 .9622468 .9807201
esc1 | 1.43046 .0886496 5.78 0.000 1.266847 1.615202
esc2 | 1.264134 .0732418 4.05 0.000 1.128433 1.416153
sus_prin2 | 1.157551 .0782706 2.16 0.030 1.013874 1.321588
sus_prin3 | 1.682176 .0917108 9.54 0.000 1.511697 1.871881
sus_prin4 | 1.171202 .093381 1.98 0.047 1.001763 1.369301
sus_prin5 | 1.59191 .2393343 3.09 0.002 1.185619 2.137428
fr_cons_sus_prin2 | .9673308 .1088493 -0.30 0.768 .7758776 1.206026
fr_cons_sus_prin3 | .9786337 .0894375 -0.24 0.813 .818142 1.170608
fr_cons_sus_prin4 | 1.003305 .0951223 0.03 0.972 .8331662 1.208188
fr_cons_sus_prin5 | 1.030047 .0934604 0.33 0.744 .8622313 1.230524
cond_ocu2 | 1.048562 .0745178 0.67 0.505 .9122249 1.205275
cond_ocu3 | 1.146723 .3093751 0.51 0.612 .6757905 1.945829
cond_ocu4 | 1.220739 .0890275 2.73 0.006 1.058146 1.408317
cond_ocu5 | 1.058797 .164302 0.37 0.713 .7811328 1.43516
cond_ocu6 | 1.189499 .0465058 4.44 0.000 1.101754 1.284232
policonsumo | .9917225 .0486177 -0.17 0.865 .9008683 1.09174
num_hij2 | 1.125689 .0447879 2.98 0.003 1.041242 1.216985
tenviv1 | 1.06717 .1350323 0.51 0.607 .8327765 1.367536
tenviv2 | 1.124862 .0969143 1.37 0.172 .9500857 1.331791
tenviv4 | 1.038197 .0510163 0.76 0.446 .9428715 1.143161
tenviv5 | 1.010938 .038335 0.29 0.774 .9385267 1.088936
mzone2 | 1.450614 .0608613 8.87 0.000 1.336101 1.574942
mzone3 | 1.529246 .0965797 6.73 0.000 1.351199 1.730753
n_off_vio | 1.466527 .0554355 10.13 0.000 1.361803 1.579305
n_off_acq | 2.798593 .0972602 29.61 0.000 2.614313 2.995861
n_off_sud | 1.390723 .0507026 9.05 0.000 1.294816 1.493735
n_off_oth | 1.736081 .0634139 15.10 0.000 1.616137 1.864928
psy_com2 | 1.118707 .0550754 2.28 0.023 1.015806 1.232033
psy_com3 | 1.099954 .0423984 2.47 0.013 1.019916 1.186273
dep2 | 1.036413 .0441275 0.84 0.401 .9534346 1.126612
rural2 | .8984504 .0559642 -1.72 0.086 .7951938 1.015115
rural3 | .8601343 .0595416 -2.18 0.030 .7510053 .9851208
porc_pobr | 1.569173 .392771 1.80 0.072 .9607523 2.562893
susini2 | 1.188062 .1082985 1.89 0.059 .9936816 1.420466
susini3 | 1.270772 .081907 3.72 0.000 1.119963 1.441887
susini4 | 1.180488 .0440168 4.45 0.000 1.097294 1.26999
susini5 | 1.421829 .1319995 3.79 0.000 1.185287 1.705576
ano_nac_corr | .8501357 .0080257 -17.20 0.000 .8345502 .8660123
cohab2 | .879974 .0590959 -1.90 0.057 .7714472 1.003768
cohab3 | 1.074782 .0859416 0.90 0.367 .918876 1.257141
cohab4 | .9638603 .0641657 -0.55 0.580 .845957 1.098196
fis_com2 | 1.05784 .0364634 1.63 0.103 .9887336 1.131776
fis_com3 | .8189792 .0709587 -2.30 0.021 .6910704 .9705623
rc_x1 | .8503999 .0101866 -13.53 0.000 .8306671 .8706014
rc_x2 | .8817351 .0351628 -3.16 0.002 .8154419 .9534177
rc_x3 | 1.277853 .1359136 2.31 0.021 1.0374 1.574039
_rcs1 | 2.186828 .0736829 23.22 0.000 2.047078 2.336119
_rcs2 | 1.047741 .0263908 1.85 0.064 .9972717 1.100764
_rcs3 | 1.034307 .0164059 2.13 0.033 1.002647 1.066968
_rcs4 | 1.018944 .008971 2.13 0.033 1.001512 1.036679
_rcs5 | 1.010494 .0036386 2.90 0.004 1.003388 1.017651
_rcs6 | 1.008389 .0025232 3.34 0.001 1.003456 1.013347
_rcs_mot_egr_early1 | .8948884 .0336899 -2.95 0.003 .8312347 .9634166
_rcs_mot_egr_early2 | 1.00914 .0280513 0.33 0.743 .9556313 1.065645
_rcs_mot_egr_early3 | .9931437 .0196848 -0.35 0.729 .9553019 1.032484
_rcs_mot_egr_late1 | .9235347 .0336919 -2.18 0.029 .8598054 .9919877
_rcs_mot_egr_late2 | 1.025808 .02797 0.93 0.350 .9724266 1.082119
_rcs_mot_egr_late3 | .9997944 .0191274 -0.01 0.991 .9629996 1.037995
_cons | 1.3e+139 2.5e+140 16.85 0.000 8.7e+122 2.0e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.242
Iteration 1: log likelihood = -16975.674
Iteration 2: log likelihood = -16975.555
Iteration 3: log likelihood = -16975.555
Log likelihood = -16975.555 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009591 .126868 11.06 0.000 1.775703 2.274286
mot_egr_late | 1.694951 .0921472 9.71 0.000 1.523635 1.88553
tr_mod2 | 1.217913 .0518072 4.63 0.000 1.12049 1.323806
sex_dum2 | .6073687 .0295225 -10.26 0.000 .5521764 .6680778
edad_ini_cons | .9714319 .0047126 -5.97 0.000 .9622391 .9807124
esc1 | 1.430528 .0886534 5.78 0.000 1.266909 1.615279
esc2 | 1.264167 .0732437 4.05 0.000 1.128463 1.416191
sus_prin2 | 1.157737 .0782842 2.17 0.030 1.014035 1.321803
sus_prin3 | 1.682551 .0917343 9.54 0.000 1.512028 1.872304
sus_prin4 | 1.17128 .0933875 1.98 0.047 1.001829 1.369393
sus_prin5 | 1.592286 .2393919 3.09 0.002 1.185898 2.137936
fr_cons_sus_prin2 | .9673456 .1088508 -0.30 0.768 .7758897 1.206044
fr_cons_sus_prin3 | .9786115 .0894355 -0.24 0.813 .8181234 1.170582
fr_cons_sus_prin4 | 1.003367 .0951286 0.04 0.972 .8332167 1.208264
fr_cons_sus_prin5 | 1.02993 .0934507 0.33 0.745 .8621324 1.230386
cond_ocu2 | 1.048543 .0745163 0.67 0.505 .9122085 1.205252
cond_ocu3 | 1.147187 .3095004 0.51 0.611 .6760645 1.946617
cond_ocu4 | 1.22046 .0890084 2.73 0.006 1.057901 1.407997
cond_ocu5 | 1.059071 .1643475 0.37 0.712 .7813305 1.43554
cond_ocu6 | 1.189524 .0465076 4.44 0.000 1.101775 1.28426
policonsumo | .9917094 .048617 -0.17 0.865 .9008564 1.091725
num_hij2 | 1.12564 .0447857 2.97 0.003 1.041197 1.216932
tenviv1 | 1.067112 .1350271 0.51 0.608 .8327278 1.367467
tenviv2 | 1.125138 .0969383 1.37 0.171 .9503184 1.332118
tenviv4 | 1.038132 .0510132 0.76 0.446 .942812 1.143089
tenviv5 | 1.010909 .0383339 0.29 0.775 .9385001 1.088905
mzone2 | 1.450682 .0608644 8.87 0.000 1.336163 1.575016
mzone3 | 1.529223 .096579 6.73 0.000 1.351178 1.730728
n_off_vio | 1.466487 .0554329 10.13 0.000 1.361768 1.579259
n_off_acq | 2.798392 .0972506 29.61 0.000 2.614131 2.995642
n_off_sud | 1.390624 .0506978 9.05 0.000 1.294725 1.493625
n_off_oth | 1.736074 .0634122 15.10 0.000 1.616133 1.864916
psy_com2 | 1.118833 .0550816 2.28 0.023 1.01592 1.232171
psy_com3 | 1.099861 .0423952 2.47 0.014 1.019829 1.186174
dep2 | 1.036439 .0441287 0.84 0.401 .9534586 1.126641
rural2 | .8985518 .05597 -1.72 0.086 .7952844 1.015228
rural3 | .8600607 .0595363 -2.18 0.029 .7509415 .985036
porc_pobr | 1.567556 .3923804 1.80 0.073 .9597453 2.560297
susini2 | 1.188146 .1083061 1.89 0.059 .9937523 1.420567
susini3 | 1.270883 .0819152 3.72 0.000 1.120059 1.442016
susini4 | 1.180494 .0440171 4.45 0.000 1.097299 1.269996
susini5 | 1.421993 .1320155 3.79 0.000 1.185423 1.705774
ano_nac_corr | .850127 .0080261 -17.20 0.000 .8345408 .8660043
cohab2 | .880028 .0591001 -1.90 0.057 .7714936 1.003831
cohab3 | 1.074749 .0859398 0.90 0.367 .9188454 1.257104
cohab4 | .9638821 .0641678 -0.55 0.581 .845975 1.098223
fis_com2 | 1.057619 .0364553 1.63 0.104 .9885276 1.131539
fis_com3 | .8189147 .0709533 -2.31 0.021 .6910158 .9704862
rc_x1 | .8504051 .0101867 -13.53 0.000 .8306722 .8706069
rc_x2 | .8816864 .0351598 -3.16 0.002 .8153986 .953363
rc_x3 | 1.277971 .135923 2.31 0.021 1.037501 1.574176
_rcs1 | 2.184731 .073336 23.28 0.000 2.045621 2.3333
_rcs2 | 1.049247 .0274734 1.84 0.066 .9967588 1.1045
_rcs3 | 1.021322 .019249 1.12 0.263 .9842831 1.059755
_rcs4 | 1.027742 .0108158 2.60 0.009 1.006761 1.049161
_rcs5 | 1.020019 .008187 2.47 0.014 1.004099 1.036192
_rcs6 | 1.009784 .0027454 3.58 0.000 1.004418 1.01518
_rcs_mot_egr_early1 | .8958809 .0336275 -2.93 0.003 .8323382 .9642747
_rcs_mot_egr_early2 | 1.008816 .0289302 0.31 0.760 .9536775 1.067141
_rcs_mot_egr_early3 | 1.005251 .02141 0.25 0.806 .9641521 1.048102
_rcs_mot_egr_early4 | .9808191 .0139058 -1.37 0.172 .9539395 1.008456
_rcs_mot_egr_late1 | .9244294 .0336192 -2.16 0.031 .8608306 .992727
_rcs_mot_egr_late2 | 1.02498 .0288921 0.88 0.381 .969888 1.083201
_rcs_mot_egr_late3 | 1.011144 .0208341 0.54 0.591 .9711234 1.052814
_rcs_mot_egr_late4 | .9843492 .0133315 -1.16 0.244 .9585637 1.010828
_cons | 1.3e+139 2.6e+140 16.85 0.000 8.9e+122 2.0e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.322
Iteration 1: log likelihood = -16975.529
Iteration 2: log likelihood = -16975.398
Iteration 3: log likelihood = -16975.398
Log likelihood = -16975.398 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009323 .1268542 11.05 0.000 1.775461 2.27399
mot_egr_late | 1.694963 .0921542 9.71 0.000 1.523635 1.885557
tr_mod2 | 1.217867 .0518051 4.63 0.000 1.120449 1.323756
sex_dum2 | .6073649 .0295224 -10.26 0.000 .5521729 .6680736
edad_ini_cons | .9714322 .0047126 -5.97 0.000 .9622395 .9807128
esc1 | 1.430533 .0886538 5.78 0.000 1.266913 1.615284
esc2 | 1.264163 .0732435 4.05 0.000 1.128459 1.416186
sus_prin2 | 1.157671 .0782797 2.17 0.030 1.013978 1.321727
sus_prin3 | 1.682473 .0917297 9.54 0.000 1.511958 1.872217
sus_prin4 | 1.171269 .0933864 1.98 0.047 1.001819 1.369379
sus_prin5 | 1.592032 .2393546 3.09 0.002 1.185707 2.137598
fr_cons_sus_prin2 | .9673453 .1088508 -0.30 0.768 .7758894 1.206044
fr_cons_sus_prin3 | .9786201 .0894363 -0.24 0.813 .8181306 1.170592
fr_cons_sus_prin4 | 1.003399 .0951316 0.04 0.971 .8332428 1.208302
fr_cons_sus_prin5 | 1.029971 .0934544 0.33 0.745 .8621667 1.230435
cond_ocu2 | 1.048551 .0745171 0.67 0.505 .9122157 1.205263
cond_ocu3 | 1.146954 .3094382 0.51 0.611 .6759256 1.946223
cond_ocu4 | 1.22046 .0890093 2.73 0.006 1.0579 1.407999
cond_ocu5 | 1.059224 .1643713 0.37 0.711 .781444 1.435748
cond_ocu6 | 1.189507 .0465071 4.44 0.000 1.101759 1.284242
policonsumo | .9916888 .0486161 -0.17 0.865 .9008377 1.091703
num_hij2 | 1.125641 .0447858 2.97 0.003 1.041198 1.216933
tenviv1 | 1.067174 .1350344 0.51 0.607 .8327772 1.367545
tenviv2 | 1.125049 .0969304 1.37 0.171 .9502437 1.332012
tenviv4 | 1.038155 .0510144 0.76 0.446 .9428329 1.143115
tenviv5 | 1.010888 .0383331 0.29 0.775 .9384806 1.088882
mzone2 | 1.450619 .0608613 8.87 0.000 1.336106 1.574947
mzone3 | 1.529175 .0965754 6.73 0.000 1.351137 1.730673
n_off_vio | 1.466507 .0554339 10.13 0.000 1.361786 1.579281
n_off_acq | 2.79846 .0972531 29.61 0.000 2.614194 2.995714
n_off_sud | 1.390661 .0506993 9.05 0.000 1.294759 1.493666
n_off_oth | 1.736131 .0634145 15.10 0.000 1.616186 1.864978
psy_com2 | 1.118741 .0550778 2.28 0.023 1.015835 1.232071
psy_com3 | 1.099886 .0423962 2.47 0.014 1.019852 1.1862
dep2 | 1.036433 .0441284 0.84 0.401 .9534532 1.126634
rural2 | .898527 .0559687 -1.72 0.086 .7952622 1.015201
rural3 | .8600892 .0595383 -2.18 0.029 .7509663 .9850688
porc_pobr | 1.567867 .3924558 1.80 0.072 .9599381 2.560797
susini2 | 1.188115 .1083033 1.89 0.059 .9937262 1.420529
susini3 | 1.270904 .081916 3.72 0.000 1.120079 1.442039
susini4 | 1.180507 .0440176 4.45 0.000 1.097311 1.27001
susini5 | 1.422084 .1320242 3.79 0.000 1.185498 1.705884
ano_nac_corr | .8501474 .0080263 -17.20 0.000 .8345609 .8660251
cohab2 | .8800258 .0590995 -1.90 0.057 .7714923 1.003828
cohab3 | 1.074722 .0859376 0.90 0.367 .9188231 1.257073
cohab4 | .9638769 .0641671 -0.55 0.580 .8459711 1.098216
fis_com2 | 1.057677 .0364577 1.63 0.104 .9885812 1.131602
fis_com3 | .8189181 .0709535 -2.31 0.021 .6910188 .97049
rc_x1 | .8504171 .0101868 -13.53 0.000 .8306838 .8706191
rc_x2 | .8817251 .0351617 -3.16 0.002 .8154339 .9534055
rc_x3 | 1.277839 .1359099 2.31 0.021 1.037392 1.574016
_rcs1 | 2.184163 .0733191 23.27 0.000 2.045086 2.332699
_rcs2 | 1.050736 .0279415 1.86 0.063 .9973739 1.106952
_rcs3 | 1.018078 .0205193 0.89 0.374 .9786449 1.0591
_rcs4 | 1.030773 .0128476 2.43 0.015 1.005897 1.056264
_rcs5 | 1.017638 .0086444 2.06 0.040 1.000835 1.034722
_rcs6 | 1.009821 .004845 2.04 0.042 1.00037 1.019362
_rcs_mot_egr_early1 | .8965643 .0336691 -2.91 0.004 .8329442 .9650438
_rcs_mot_egr_early2 | 1.007006 .0293147 0.24 0.810 .9511588 1.066133
_rcs_mot_egr_early3 | 1.011748 .0225751 0.52 0.601 .9684551 1.056976
_rcs_mot_egr_early4 | .9797263 .0143695 -1.40 0.163 .9519635 1.008299
_rcs_mot_egr_early5 | .9968348 .0094233 -0.34 0.737 .9785356 1.015476
_rcs_mot_egr_late1 | .9245172 .0336302 -2.16 0.031 .860898 .9928378
_rcs_mot_egr_late2 | 1.02335 .0293333 0.81 0.421 .9674427 1.082487
_rcs_mot_egr_late3 | 1.016054 .0220269 0.73 0.463 .9737863 1.060156
_rcs_mot_egr_late4 | .9857731 .0138102 -1.02 0.306 .9590738 1.013216
_rcs_mot_egr_late5 | .9951726 .0088124 -0.55 0.585 .9780496 1.012595
_cons | 1.3e+139 2.4e+140 16.85 0.000 8.5e+122 1.9e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.168
Iteration 1: log likelihood = -16975.154
Iteration 2: log likelihood = -16974.99
Iteration 3: log likelihood = -16974.989
Log likelihood = -16974.989 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009503 .1268694 11.05 0.000 1.775612 2.274202
mot_egr_late | 1.695218 .0921724 9.71 0.000 1.523857 1.88585
tr_mod2 | 1.217829 .0518034 4.63 0.000 1.120413 1.323714
sex_dum2 | .6073795 .0295232 -10.26 0.000 .552186 .6680898
edad_ini_cons | .9714314 .0047126 -5.97 0.000 .9622386 .980712
esc1 | 1.430517 .0886528 5.78 0.000 1.266898 1.615266
esc2 | 1.264139 .0732422 4.05 0.000 1.128438 1.41616
sus_prin2 | 1.157662 .0782791 2.17 0.030 1.01397 1.321718
sus_prin3 | 1.68248 .0917299 9.54 0.000 1.511965 1.872224
sus_prin4 | 1.171241 .0933841 1.98 0.047 1.001796 1.369346
sus_prin5 | 1.591958 .2393443 3.09 0.002 1.185651 2.1375
fr_cons_sus_prin2 | .9673744 .1088542 -0.29 0.768 .7759127 1.206081
fr_cons_sus_prin3 | .978633 .0894375 -0.24 0.813 .8181414 1.170608
fr_cons_sus_prin4 | 1.003438 .0951353 0.04 0.971 .8332756 1.208349
fr_cons_sus_prin5 | 1.029998 .0934569 0.33 0.745 .8621895 1.230468
cond_ocu2 | 1.048541 .0745165 0.67 0.505 .9122063 1.205251
cond_ocu3 | 1.147015 .3094544 0.51 0.611 .675962 1.946326
cond_ocu4 | 1.220409 .0890055 2.73 0.006 1.057856 1.40794
cond_ocu5 | 1.059319 .1643866 0.37 0.710 .7815133 1.435878
cond_ocu6 | 1.189504 .046507 4.44 0.000 1.101757 1.284239
policonsumo | .9916933 .0486161 -0.17 0.865 .900842 1.091707
num_hij2 | 1.125644 .0447859 2.97 0.003 1.0412 1.216936
tenviv1 | 1.067285 .1350486 0.51 0.607 .8328633 1.367688
tenviv2 | 1.125053 .0969301 1.37 0.171 .9502481 1.332015
tenviv4 | 1.038154 .0510145 0.76 0.446 .9428316 1.143114
tenviv5 | 1.01088 .0383328 0.29 0.775 .9384731 1.088873
mzone2 | 1.450593 .06086 8.87 0.000 1.336082 1.574918
mzone3 | 1.529116 .0965712 6.72 0.000 1.351085 1.730605
n_off_vio | 1.466513 .055434 10.13 0.000 1.361792 1.579288
n_off_acq | 2.798434 .097252 29.61 0.000 2.614171 2.995686
n_off_sud | 1.390656 .050699 9.05 0.000 1.294755 1.493661
n_off_oth | 1.73614 .0634148 15.10 0.000 1.616194 1.864988
psy_com2 | 1.11869 .0550753 2.28 0.023 1.015789 1.232015
psy_com3 | 1.099876 .0423959 2.47 0.014 1.019843 1.18619
dep2 | 1.036443 .0441289 0.84 0.401 .9534628 1.126646
rural2 | .8985255 .0559686 -1.72 0.086 .7952608 1.015199
rural3 | .8601045 .0595394 -2.18 0.029 .7509796 .9850863
porc_pobr | 1.567867 .3924605 1.80 0.072 .9599329 2.560812
susini2 | 1.188111 .1083031 1.89 0.059 .9937226 1.420525
susini3 | 1.270969 .08192 3.72 0.000 1.120137 1.442112
susini4 | 1.180517 .0440179 4.45 0.000 1.09732 1.270021
susini5 | 1.422101 .132026 3.79 0.000 1.185512 1.705905
ano_nac_corr | .850163 .0080264 -17.19 0.000 .8345761 .8660409
cohab2 | .8800382 .0591004 -1.90 0.057 .7715031 1.003842
cohab3 | 1.074674 .0859338 0.90 0.368 .9187816 1.257016
cohab4 | .9638746 .0641669 -0.55 0.580 .8459691 1.098213
fis_com2 | 1.05771 .0364591 1.63 0.104 .988612 1.131638
fis_com3 | .81893 .0709544 -2.31 0.021 .6910289 .9705039
rc_x1 | .8504292 .0101869 -13.53 0.000 .8306958 .8706315
rc_x2 | .8817413 .0351623 -3.16 0.002 .8154489 .953423
rc_x3 | 1.277779 .1359036 2.30 0.021 1.037344 1.573943
_rcs1 | 2.18466 .0733157 23.29 0.000 2.045588 2.333187
_rcs2 | 1.052507 .0286149 1.88 0.060 .997891 1.110112
_rcs3 | 1.013361 .0213503 0.63 0.529 .9723675 1.056083
_rcs4 | 1.034776 .0147458 2.40 0.016 1.006275 1.064085
_rcs5 | 1.016962 .010247 1.67 0.095 .997075 1.037245
_rcs6 | 1.010184 .0078309 1.31 0.191 .9949513 1.025649
_rcs_mot_egr_early1 | .8964851 .0336617 -2.91 0.004 .8328786 .9649491
_rcs_mot_egr_early2 | 1.004944 .0298538 0.17 0.868 .9481027 1.065194
_rcs_mot_egr_early3 | 1.019055 .0236271 0.81 0.416 .9737836 1.066432
_rcs_mot_egr_early4 | .9779905 .0155617 -1.40 0.162 .9479607 1.008972
_rcs_mot_egr_early5 | .9922177 .0112645 -0.69 0.491 .9703834 1.014543
_rcs_mot_egr_early6 | .9995785 .0088073 -0.05 0.962 .9824646 1.01699
_rcs_mot_egr_late1 | .9242682 .0336153 -2.17 0.030 .8606769 .9925579
_rcs_mot_egr_late2 | 1.021236 .029923 0.72 0.473 .9642399 1.081601
_rcs_mot_egr_late3 | 1.022877 .0232454 1.00 0.320 .9783169 1.069467
_rcs_mot_egr_late4 | .9847136 .0152024 -1.00 0.318 .9553638 1.014965
_rcs_mot_egr_late5 | .9928534 .0108441 -0.66 0.511 .9718252 1.014337
_rcs_mot_egr_late6 | .9971661 .0083948 -0.34 0.736 .9808476 1.013756
_cons | 1.2e+139 2.4e+140 16.85 0.000 8.2e+122 1.9e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.873
Iteration 1: log likelihood = -16974.946
Iteration 2: log likelihood = -16974.764
Iteration 3: log likelihood = -16974.764
Log likelihood = -16974.764 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010009 .1269149 11.06 0.000 1.776036 2.274804
mot_egr_late | 1.695283 .092187 9.71 0.000 1.523896 1.885947
tr_mod2 | 1.217766 .051801 4.63 0.000 1.120355 1.323646
sex_dum2 | .6073492 .029522 -10.26 0.000 .552158 .6680571
edad_ini_cons | .9714345 .0047127 -5.97 0.000 .9622416 .9807152
esc1 | 1.430522 .0886532 5.78 0.000 1.266902 1.615272
esc2 | 1.264153 .073243 4.05 0.000 1.12845 1.416175
sus_prin2 | 1.157584 .0782736 2.16 0.030 1.013902 1.321628
sus_prin3 | 1.682369 .0917231 9.54 0.000 1.511867 1.872099
sus_prin4 | 1.171209 .0933813 1.98 0.047 1.001768 1.369308
sus_prin5 | 1.591818 .2393224 3.09 0.002 1.185548 2.13731
fr_cons_sus_prin2 | .9673558 .1088521 -0.29 0.768 .7758977 1.206058
fr_cons_sus_prin3 | .9786617 .0894401 -0.24 0.813 .8181654 1.170642
fr_cons_sus_prin4 | 1.003422 .0951336 0.04 0.971 .8332626 1.208329
fr_cons_sus_prin5 | 1.030042 .0934604 0.33 0.744 .8622267 1.230519
cond_ocu2 | 1.048591 .0745202 0.67 0.504 .9122503 1.20531
cond_ocu3 | 1.146902 .3094251 0.51 0.611 .6758946 1.946139
cond_ocu4 | 1.22059 .0890188 2.73 0.006 1.058013 1.40815
cond_ocu5 | 1.059316 .1643856 0.37 0.710 .7815118 1.435873
cond_ocu6 | 1.189496 .0465067 4.44 0.000 1.101749 1.28423
policonsumo | .9917137 .0486172 -0.17 0.865 .9008604 1.09173
num_hij2 | 1.125699 .0447882 2.98 0.003 1.041252 1.216996
tenviv1 | 1.067294 .1350498 0.51 0.607 .8328707 1.3677
tenviv2 | 1.12495 .0969213 1.37 0.172 .9501607 1.331894
tenviv4 | 1.03815 .0510146 0.76 0.446 .9428275 1.14311
tenviv5 | 1.010865 .0383324 0.28 0.776 .9384588 1.088857
mzone2 | 1.450543 .060858 8.87 0.000 1.336036 1.574864
mzone3 | 1.529157 .0965739 6.72 0.000 1.351122 1.730652
n_off_vio | 1.466502 .0554346 10.13 0.000 1.36178 1.579278
n_off_acq | 2.798505 .0972564 29.61 0.000 2.614233 2.995766
n_off_sud | 1.390705 .0507014 9.05 0.000 1.294799 1.493714
n_off_oth | 1.736126 .0634155 15.10 0.000 1.616178 1.864975
psy_com2 | 1.118624 .0550723 2.28 0.023 1.015728 1.231943
psy_com3 | 1.099905 .042397 2.47 0.013 1.01987 1.186221
dep2 | 1.036439 .0441285 0.84 0.401 .953459 1.126641
rural2 | .8984892 .0559665 -1.72 0.086 .7952284 1.015158
rural3 | .8601166 .0595401 -2.18 0.029 .7509904 .9850999
porc_pobr | 1.567609 .3923965 1.80 0.073 .9597741 2.560392
susini2 | 1.188095 .1083017 1.89 0.059 .9937093 1.420506
susini3 | 1.270838 .0819119 3.72 0.000 1.12002 1.441964
susini4 | 1.1805 .0440174 4.45 0.000 1.097305 1.270003
susini5 | 1.422003 .1320165 3.79 0.000 1.185431 1.705787
ano_nac_corr | .8501646 .0080265 -17.19 0.000 .8345775 .8660428
cohab2 | .8799895 .0590973 -1.90 0.057 .7714601 1.003787
cohab3 | 1.074666 .0859333 0.90 0.368 .9187751 1.257008
cohab4 | .96383 .0641639 -0.55 0.580 .84593 1.098162
fis_com2 | 1.057777 .0364617 1.63 0.103 .9886737 1.13171
fis_com3 | .8189534 .0709564 -2.31 0.021 .6910488 .9705316
rc_x1 | .8504262 .010187 -13.53 0.000 .8306927 .8706286
rc_x2 | .8817608 .0351634 -3.16 0.002 .8154663 .9534449
rc_x3 | 1.277722 .1358985 2.30 0.021 1.037295 1.573874
_rcs1 | 2.184993 .0733813 23.27 0.000 2.045799 2.333656
_rcs2 | 1.052845 .0285096 1.90 0.057 .9984246 1.110233
_rcs3 | 1.014052 .021032 0.67 0.501 .9736565 1.056123
_rcs4 | 1.035875 .0139297 2.62 0.009 1.00893 1.06354
_rcs5 | 1.013503 .0095514 1.42 0.155 .9949545 1.032398
_rcs6 | 1.0093 .0066314 1.41 0.159 .9963856 1.022381
_rcs_mot_egr_early1 | .8963643 .0336887 -2.91 0.004 .832709 .9648856
_rcs_mot_egr_early2 | 1.004458 .0298018 0.15 0.881 .9477131 1.0646
_rcs_mot_egr_early3 | 1.021557 .0232223 0.94 0.348 .977041 1.068101
_rcs_mot_egr_early4 | .9775086 .0147131 -1.51 0.131 .9490926 1.006775
_rcs_mot_egr_early5 | .9927105 .010437 -0.70 0.487 .9724637 1.013379
_rcs_mot_egr_early6 | .9993138 .0084118 -0.08 0.935 .9829622 1.015937
_rcs_mot_egr_early7 | .9998814 .0055881 -0.02 0.983 .9889887 1.010894
_rcs_mot_egr_late1 | .9242958 .0336303 -2.16 0.030 .8606771 .992617
_rcs_mot_egr_late2 | 1.020283 .029925 0.68 0.494 .9632847 1.080653
_rcs_mot_egr_late3 | 1.02389 .0230223 1.05 0.294 .979747 1.070022
_rcs_mot_egr_late4 | .9870975 .0143234 -0.89 0.371 .9594195 1.015574
_rcs_mot_egr_late5 | .9930022 .0098847 -0.71 0.481 .9738162 1.012566
_rcs_mot_egr_late6 | .9982425 .0079418 -0.22 0.825 .9827976 1.01393
_rcs_mot_egr_late7 | .9985358 .0050358 -0.29 0.771 .9887145 1.008455
_cons | 1.2e+139 2.3e+140 16.85 0.000 8.1e+122 1.9e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.665
Iteration 1: log likelihood = -16977.25
Iteration 2: log likelihood = -16977.18
Iteration 3: log likelihood = -16977.18
Log likelihood = -16977.18 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.01206 .126916 11.08 0.000 1.778071 2.276841
mot_egr_late | 1.694047 .0920493 9.70 0.000 1.522909 1.884418
tr_mod2 | 1.218464 .0518252 4.65 0.000 1.121007 1.324394
sex_dum2 | .6073583 .0295224 -10.26 0.000 .5521663 .6680671
edad_ini_cons | .9714275 .0047127 -5.98 0.000 .9622345 .9807083
esc1 | 1.430457 .0886499 5.78 0.000 1.266844 1.6152
esc2 | 1.264176 .0732442 4.05 0.000 1.128471 1.4162
sus_prin2 | 1.157465 .0782647 2.16 0.031 1.013799 1.32149
sus_prin3 | 1.682068 .0917063 9.54 0.000 1.511597 1.871763
sus_prin4 | 1.171277 .0933878 1.98 0.047 1.001825 1.36939
sus_prin5 | 1.590973 .2391882 3.09 0.002 1.184929 2.136157
fr_cons_sus_prin2 | .9673763 .1088542 -0.29 0.768 .7759144 1.206083
fr_cons_sus_prin3 | .9785618 .0894312 -0.24 0.813 .8180814 1.170523
fr_cons_sus_prin4 | 1.003262 .0951186 0.03 0.973 .8331298 1.208138
fr_cons_sus_prin5 | 1.029978 .0934558 0.33 0.745 .862171 1.230445
cond_ocu2 | 1.048731 .0745294 0.67 0.503 .9123731 1.205469
cond_ocu3 | 1.146998 .3094479 0.51 0.611 .6759544 1.946291
cond_ocu4 | 1.220196 .0889911 2.73 0.006 1.05767 1.407698
cond_ocu5 | 1.05798 .1641713 0.36 0.716 .7805359 1.434043
cond_ocu6 | 1.189551 .0465084 4.44 0.000 1.101801 1.28429
policonsumo | .9915765 .0486105 -0.17 0.863 .9007358 1.091579
num_hij2 | 1.12555 .0447828 2.97 0.003 1.041112 1.216836
tenviv1 | 1.067321 .1350498 0.51 0.607 .8328963 1.367725
tenviv2 | 1.125371 .0969562 1.37 0.170 .9505186 1.332389
tenviv4 | 1.038104 .0510112 0.76 0.447 .9427873 1.143057
tenviv5 | 1.010778 .0383286 0.28 0.777 .9383785 1.088762
mzone2 | 1.450465 .0608568 8.86 0.000 1.33596 1.574784
mzone3 | 1.528623 .0965415 6.72 0.000 1.350647 1.73005
n_off_vio | 1.466562 .0554344 10.13 0.000 1.36184 1.579337
n_off_acq | 2.798141 .0972416 29.61 0.000 2.613897 2.995372
n_off_sud | 1.390564 .0506965 9.04 0.000 1.294667 1.493563
n_off_oth | 1.735955 .0634077 15.10 0.000 1.616022 1.864788
psy_com2 | 1.118058 .0550398 2.27 0.023 1.015223 1.23131
psy_com3 | 1.100213 .0424079 2.48 0.013 1.020157 1.186551
dep2 | 1.036385 .0441256 0.84 0.401 .9534106 1.12658
rural2 | .8985411 .0559699 -1.72 0.086 .795274 1.015218
rural3 | .8606346 .0595718 -2.17 0.030 .7514497 .985684
porc_pobr | 1.57144 .3933217 1.81 0.071 .96216 2.566541
susini2 | 1.188527 .1083394 1.89 0.058 .9940731 1.421019
susini3 | 1.270438 .0818845 3.71 0.000 1.119671 1.441506
susini4 | 1.180615 .0440214 4.45 0.000 1.097412 1.270126
susini5 | 1.42205 .1320197 3.79 0.000 1.185472 1.70584
ano_nac_corr | .8500049 .0080232 -17.22 0.000 .8344244 .8658764
cohab2 | .8802579 .0591128 -1.90 0.058 .7716999 1.004087
cohab3 | 1.075076 .0859625 0.91 0.365 .919131 1.257479
cohab4 | .9640136 .0641754 -0.55 0.582 .8460924 1.09837
fis_com2 | 1.057913 .0364645 1.63 0.102 .9888049 1.131852
fis_com3 | .8191187 .0709704 -2.30 0.021 .6911888 .9707266
rc_x1 | .850285 .0101844 -13.54 0.000 .8305565 .8704821
rc_x2 | .8817159 .0351624 -3.16 0.002 .8154233 .953398
rc_x3 | 1.277851 .1359149 2.31 0.021 1.037396 1.57404
_rcs1 | 2.199935 .0693901 25.00 0.000 2.068052 2.340229
_rcs2 | 1.064907 .0083548 8.02 0.000 1.048657 1.081409
_rcs3 | 1.033826 .0064494 5.33 0.000 1.021262 1.046544
_rcs4 | 1.018905 .0045604 4.18 0.000 1.010006 1.027882
_rcs5 | 1.010463 .0032879 3.20 0.001 1.00404 1.016928
_rcs6 | 1.009965 .0026258 3.81 0.000 1.004832 1.015125
_rcs7 | 1.005206 .0021656 2.41 0.016 1.000971 1.00946
_rcs_mot_egr_early1 | .893235 .0314392 -3.21 0.001 .8336927 .9570299
_rcs_mot_egr_late1 | .914117 .0309738 -2.65 0.008 .8553813 .9768858
_cons | 1.8e+139 3.4e+140 16.87 0.000 1.2e+123 2.7e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.821
Iteration 1: log likelihood = -16976.437
Iteration 2: log likelihood = -16976.324
Iteration 3: log likelihood = -16976.324
Log likelihood = -16976.324 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010359 .1269139 11.06 0.000 1.776386 2.275149
mot_egr_late | 1.695372 .0921749 9.71 0.000 1.524005 1.886008
tr_mod2 | 1.217887 .0518042 4.63 0.000 1.12047 1.323774
sex_dum2 | .6074213 .0295255 -10.26 0.000 .5522234 .6681365
edad_ini_cons | .9714347 .0047126 -5.97 0.000 .9622419 .9807153
esc1 | 1.430452 .0886493 5.78 0.000 1.266841 1.615194
esc2 | 1.264106 .0732402 4.05 0.000 1.128408 1.416122
sus_prin2 | 1.157546 .0782699 2.16 0.030 1.013871 1.321582
sus_prin3 | 1.682096 .0917057 9.54 0.000 1.511626 1.87179
sus_prin4 | 1.171266 .0933861 1.98 0.047 1.001817 1.369376
sus_prin5 | 1.591461 .239267 3.09 0.002 1.185285 2.136826
fr_cons_sus_prin2 | .9673263 .1088487 -0.30 0.768 .7758741 1.206021
fr_cons_sus_prin3 | .978564 .0894311 -0.24 0.813 .8180838 1.170525
fr_cons_sus_prin4 | 1.00325 .095117 0.03 0.973 .8331203 1.208122
fr_cons_sus_prin5 | 1.029989 .0934555 0.33 0.745 .8621821 1.230455
cond_ocu2 | 1.048561 .0745175 0.67 0.505 .9122242 1.205273
cond_ocu3 | 1.146533 .309323 0.51 0.612 .6756797 1.945504
cond_ocu4 | 1.220453 .0890067 2.73 0.006 1.057898 1.407986
cond_ocu5 | 1.058611 .1642718 0.37 0.714 .780998 1.434905
cond_ocu6 | 1.189562 .0465081 4.44 0.000 1.101813 1.284299
policonsumo | .9916499 .0486136 -0.17 0.864 .9008034 1.091658
num_hij2 | 1.125618 .0447853 2.97 0.003 1.041175 1.216908
tenviv1 | 1.067222 .1350375 0.51 0.607 .8328191 1.367599
tenviv2 | 1.125047 .0969305 1.37 0.171 .9502413 1.33201
tenviv4 | 1.038229 .0510175 0.76 0.445 .942901 1.143195
tenviv5 | 1.010941 .038335 0.29 0.774 .9385302 1.088939
mzone2 | 1.450607 .0608617 8.87 0.000 1.336094 1.574936
mzone3 | 1.529128 .0965722 6.72 0.000 1.351096 1.73062
n_off_vio | 1.466508 .0554331 10.13 0.000 1.361789 1.579281
n_off_acq | 2.798363 .0972502 29.61 0.000 2.614103 2.995612
n_off_sud | 1.390624 .0506983 9.04 0.000 1.294724 1.493627
n_off_oth | 1.735994 .0634089 15.10 0.000 1.616059 1.86483
psy_com2 | 1.118539 .0550664 2.28 0.023 1.015654 1.231846
psy_com3 | 1.100021 .0424007 2.47 0.013 1.019978 1.186344
dep2 | 1.036367 .0441255 0.84 0.401 .9533929 1.126563
rural2 | .8984731 .0559656 -1.72 0.086 .795214 1.01514
rural3 | .8603087 .0595533 -2.17 0.030 .7511583 .9853197
porc_pobr | 1.571153 .3932464 1.81 0.071 .9619882 2.566061
susini2 | 1.188192 .1083101 1.89 0.059 .9937911 1.420621
susini3 | 1.270771 .0819067 3.72 0.000 1.119962 1.441886
susini4 | 1.180514 .0440176 4.45 0.000 1.097318 1.270017
susini5 | 1.422115 .132027 3.79 0.000 1.185524 1.705921
ano_nac_corr | .8500739 .0080256 -17.20 0.000 .8344886 .8659503
cohab2 | .8800399 .0590995 -1.90 0.057 .7715063 1.003842
cohab3 | 1.074814 .085943 0.90 0.367 .9189051 1.257176
cohab4 | .9638774 .0641663 -0.55 0.580 .8459729 1.098214
fis_com2 | 1.057879 .0364641 1.63 0.103 .9887715 1.131817
fis_com3 | .819013 .0709615 -2.30 0.021 .6910993 .970602
rc_x1 | .8503424 .0101864 -13.53 0.000 .83061 .8705437
rc_x2 | .8817171 .0351625 -3.16 0.002 .8154245 .9533992
rc_x3 | 1.277925 .1359231 2.31 0.021 1.037456 1.574132
_rcs1 | 2.182729 .0725025 23.50 0.000 2.045153 2.329559
_rcs2 | 1.048107 .0254911 1.93 0.053 .9993179 1.099279
_rcs3 | 1.030656 .0076083 4.09 0.000 1.015851 1.045676
_rcs4 | 1.018305 .0046261 3.99 0.000 1.009278 1.027412
_rcs5 | 1.010415 .0032886 3.18 0.001 1.00399 1.016881
_rcs6 | 1.009966 .0026256 3.81 0.000 1.004833 1.015125
_rcs7 | 1.005196 .0021656 2.41 0.016 1.00096 1.009449
_rcs_mot_egr_early1 | .8979083 .0332747 -2.91 0.004 .8350033 .9655523
_rcs_mot_egr_early2 | 1.00774 .0274908 0.28 0.777 .9552736 1.063087
_rcs_mot_egr_late1 | .9248023 .0332386 -2.18 0.030 .8618975 .9922982
_rcs_mot_egr_late2 | 1.025862 .0273969 0.96 0.339 .9735466 1.08099
_cons | 1.5e+139 2.9e+140 16.86 0.000 1.0e+123 2.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.787
Iteration 1: log likelihood = -16976.303
Iteration 2: log likelihood = -16976.213
Iteration 3: log likelihood = -16976.213
Log likelihood = -16976.213 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010579 .1269476 11.06 0.000 1.776546 2.275442
mot_egr_late | 1.69539 .0921968 9.71 0.000 1.523984 1.886074
tr_mod2 | 1.21784 .051803 4.63 0.000 1.120425 1.323725
sex_dum2 | .607425 .0295257 -10.26 0.000 .5522268 .6681406
edad_ini_cons | .9714356 .0047126 -5.97 0.000 .9622428 .9807162
esc1 | 1.430502 .088652 5.78 0.000 1.266886 1.61525
esc2 | 1.264152 .0732428 4.05 0.000 1.12845 1.416173
sus_prin2 | 1.157665 .0782786 2.17 0.030 1.013974 1.321719
sus_prin3 | 1.682287 .0917178 9.54 0.000 1.511794 1.872006
sus_prin4 | 1.171293 .0933885 1.98 0.047 1.00184 1.369408
sus_prin5 | 1.592015 .2393515 3.09 0.002 1.185696 2.137573
fr_cons_sus_prin2 | .9672988 .1088457 -0.30 0.768 .775852 1.205986
fr_cons_sus_prin3 | .9786105 .0894354 -0.24 0.813 .8181227 1.170581
fr_cons_sus_prin4 | 1.003284 .0951203 0.03 0.972 .8331488 1.208163
fr_cons_sus_prin5 | 1.029989 .0934554 0.33 0.745 .8621832 1.230456
cond_ocu2 | 1.048486 .0745124 0.67 0.505 .9121595 1.205188
cond_ocu3 | 1.147033 .309459 0.51 0.611 .6759728 1.946356
cond_ocu4 | 1.220559 .0890138 2.73 0.006 1.057991 1.408107
cond_ocu5 | 1.058786 .1643003 0.37 0.713 .7811249 1.435145
cond_ocu6 | 1.189565 .0465085 4.44 0.000 1.101815 1.284303
policonsumo | .9916987 .0486163 -0.17 0.865 .9008471 1.091713
num_hij2 | 1.125684 .0447879 2.98 0.003 1.041237 1.21698
tenviv1 | 1.067206 .1350365 0.51 0.607 .8328053 1.367582
tenviv2 | 1.125018 .0969288 1.37 0.172 .9502151 1.331977
tenviv4 | 1.038257 .051019 0.76 0.445 .9429255 1.143226
tenviv5 | 1.011 .0383374 0.29 0.773 .9385842 1.089003
mzone2 | 1.450676 .0608644 8.87 0.000 1.336157 1.57501
mzone3 | 1.529335 .0965866 6.73 0.000 1.351276 1.730856
n_off_vio | 1.466478 .0554323 10.13 0.000 1.36176 1.579249
n_off_acq | 2.798407 .0972509 29.61 0.000 2.614145 2.995657
n_off_sud | 1.390648 .050699 9.05 0.000 1.294747 1.493653
n_off_oth | 1.736021 .0634097 15.10 0.000 1.616085 1.864858
psy_com2 | 1.118735 .0550773 2.28 0.023 1.01583 1.232064
psy_com3 | 1.099952 .0423983 2.47 0.013 1.019915 1.186271
dep2 | 1.036375 .044126 0.84 0.401 .9534003 1.126572
rural2 | .8984783 .0559659 -1.72 0.086 .7952187 1.015146
rural3 | .8601667 .0595442 -2.18 0.030 .7510331 .9851586
porc_pobr | 1.56953 .3928544 1.80 0.072 .9609781 2.563457
susini2 | 1.188054 .1082975 1.89 0.059 .9936752 1.420456
susini3 | 1.270893 .0819155 3.72 0.000 1.120069 1.442026
susini4 | 1.180492 .0440171 4.45 0.000 1.097297 1.269994
susini5 | 1.421963 .1320128 3.79 0.000 1.185398 1.705739
ano_nac_corr | .8500459 .0080257 -17.21 0.000 .8344604 .8659225
cohab2 | .8799721 .0590955 -1.90 0.057 .771446 1.003766
cohab3 | 1.074755 .0859389 0.90 0.367 .9188533 1.257108
cohab4 | .9638356 .0641637 -0.55 0.580 .8459358 1.098167
fis_com2 | 1.057791 .0364615 1.63 0.103 .9886878 1.131723
fis_com3 | .8189322 .0709548 -2.31 0.021 .6910305 .970507
rc_x1 | .8503145 .0101862 -13.54 0.000 .8305825 .8705153
rc_x2 | .8817085 .0351617 -3.16 0.002 .8154172 .9533891
rc_x3 | 1.277952 .1359246 2.31 0.021 1.03748 1.574162
_rcs1 | 2.185334 .0735891 23.22 0.000 2.045759 2.334432
_rcs2 | 1.047627 .0264698 1.84 0.066 .9970107 1.100813
_rcs3 | 1.033158 .01569 2.15 0.032 1.00286 1.064372
_rcs4 | 1.019564 .0098606 2.00 0.045 1.00042 1.039075
_rcs5 | 1.010723 .0044028 2.45 0.014 1.002131 1.01939
_rcs6 | 1.009996 .0026693 3.76 0.000 1.004777 1.015241
_rcs7 | 1.005207 .0021659 2.41 0.016 1.000971 1.009461
_rcs_mot_egr_early1 | .8956917 .0337012 -2.93 0.003 .8320154 .9642413
_rcs_mot_egr_early2 | 1.008548 .0280905 0.31 0.760 .9549674 1.065135
_rcs_mot_egr_early3 | .9942549 .0196616 -0.29 0.771 .9564561 1.033547
_rcs_mot_egr_late1 | .9242139 .0337015 -2.16 0.031 .8604653 .9926853
_rcs_mot_egr_late2 | 1.025149 .0280072 0.91 0.363 .9716998 1.081539
_rcs_mot_egr_late3 | 1.000865 .019122 0.05 0.964 .9640795 1.039054
_cons | 1.6e+139 3.1e+140 16.86 0.000 1.1e+123 2.5e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.056
Iteration 1: log likelihood = -16975.239
Iteration 2: log likelihood = -16975.099
Iteration 3: log likelihood = -16975.099
Log likelihood = -16975.099 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009634 .1268668 11.06 0.000 1.775748 2.274326
mot_egr_late | 1.694924 .0921431 9.71 0.000 1.523615 1.885493
tr_mod2 | 1.217934 .0518079 4.63 0.000 1.12051 1.323828
sex_dum2 | .6074159 .0295249 -10.26 0.000 .5522192 .6681297
edad_ini_cons | .9714277 .0047126 -5.98 0.000 .9622349 .9807083
esc1 | 1.430575 .0886561 5.78 0.000 1.266951 1.615332
esc2 | 1.264182 .0732445 4.05 0.000 1.128476 1.416207
sus_prin2 | 1.157858 .0782927 2.17 0.030 1.01414 1.321942
sus_prin3 | 1.682698 .0917435 9.54 0.000 1.512158 1.872471
sus_prin4 | 1.171375 .0933953 1.98 0.047 1.00191 1.369505
sus_prin5 | 1.592421 .2394136 3.09 0.002 1.185996 2.138121
fr_cons_sus_prin2 | .9673309 .1088491 -0.30 0.768 .7758781 1.206026
fr_cons_sus_prin3 | .9785966 .0894341 -0.24 0.813 .818111 1.170564
fr_cons_sus_prin4 | 1.003364 .0951284 0.04 0.972 .8332137 1.20826
fr_cons_sus_prin5 | 1.029878 .0934462 0.32 0.746 .8620885 1.230325
cond_ocu2 | 1.048481 .0745119 0.67 0.505 .9121547 1.205182
cond_ocu3 | 1.147488 .3095818 0.51 0.610 .6762415 1.947128
cond_ocu4 | 1.220259 .0889934 2.73 0.006 1.057728 1.407765
cond_ocu5 | 1.059142 .1643589 0.37 0.711 .7813823 1.435637
cond_ocu6 | 1.189583 .0465102 4.44 0.000 1.10183 1.284325
policonsumo | .9916848 .0486156 -0.17 0.865 .9008345 1.091698
num_hij2 | 1.125635 .0447856 2.97 0.003 1.041192 1.216927
tenviv1 | 1.067155 .1350326 0.51 0.607 .8327615 1.367522
tenviv2 | 1.125307 .0969537 1.37 0.171 .950459 1.33232
tenviv4 | 1.038169 .051015 0.76 0.446 .9428458 1.14313
tenviv5 | 1.010948 .0383354 0.29 0.774 .938536 1.088946
mzone2 | 1.450738 .0608673 8.87 0.000 1.336214 1.575078
mzone3 | 1.529291 .0965842 6.73 0.000 1.351237 1.730808
n_off_vio | 1.466437 .0554298 10.13 0.000 1.361724 1.579203
n_off_acq | 2.798198 .0972413 29.61 0.000 2.613955 2.995428
n_off_sud | 1.390533 .0506937 9.04 0.000 1.294642 1.493527
n_off_oth | 1.736018 .0634083 15.10 0.000 1.616084 1.864852
psy_com2 | 1.118868 .0550837 2.28 0.023 1.015951 1.23221
psy_com3 | 1.09985 .0423948 2.47 0.014 1.019819 1.186162
dep2 | 1.036412 .0441276 0.84 0.401 .9534338 1.126612
rural2 | .8985782 .0559715 -1.72 0.086 .7953081 1.015258
rural3 | .8600878 .0595383 -2.18 0.029 .7509649 .9850675
porc_pobr | 1.567474 .3923562 1.80 0.073 .9596991 2.560151
susini2 | 1.188157 .1083069 1.89 0.059 .9937614 1.420579
susini3 | 1.270984 .0819224 3.72 0.000 1.120148 1.442132
susini4 | 1.180496 .0440174 4.45 0.000 1.097301 1.27
susini5 | 1.422137 .1320297 3.79 0.000 1.185542 1.705949
ano_nac_corr | .8500512 .0080262 -17.21 0.000 .8344649 .8659288
cohab2 | .8800244 .0590997 -1.90 0.057 .7714907 1.003827
cohab3 | 1.074702 .0859359 0.90 0.368 .918806 1.257049
cohab4 | .9638529 .0641657 -0.55 0.580 .8459497 1.098189
fis_com2 | 1.057545 .0364525 1.62 0.105 .9884596 1.13146
fis_com3 | .8188671 .0709492 -2.31 0.021 .6909754 .97043
rc_x1 | .8503338 .0101864 -13.53 0.000 .8306013 .8705351
rc_x2 | .8816625 .0351588 -3.16 0.002 .8153767 .953337
rc_x3 | 1.278051 .1359314 2.31 0.021 1.037566 1.574275
_rcs1 | 2.184756 .0733036 23.29 0.000 2.045705 2.333257
_rcs2 | 1.049181 .0276633 1.82 0.069 .9963395 1.104826
_rcs3 | 1.018956 .0187385 1.02 0.307 .9828829 1.056352
_rcs4 | 1.026227 .0102727 2.59 0.010 1.006289 1.04656
_rcs5 | 1.022645 .0092509 2.48 0.013 1.004673 1.040938
_rcs6 | 1.014889 .0043128 3.48 0.001 1.006471 1.023377
_rcs7 | 1.005571 .0021802 2.56 0.010 1.001307 1.009853
_rcs_mot_egr_early1 | .8959164 .0336158 -2.93 0.003 .8323949 .9642853
_rcs_mot_egr_early2 | 1.008599 .0290281 0.30 0.766 .9532796 1.067128
_rcs_mot_egr_early3 | 1.006627 .0213731 0.31 0.756 .9655958 1.049401
_rcs_mot_egr_early4 | .9787846 .0140137 -1.50 0.134 .9517 1.00664
_rcs_mot_egr_late1 | .9244427 .0336077 -2.16 0.031 .8608648 .9927161
_rcs_mot_egr_late2 | 1.024703 .0289863 0.86 0.388 .9694374 1.08312
_rcs_mot_egr_late3 | 1.012584 .0208451 0.61 0.544 .9725421 1.054275
_rcs_mot_egr_late4 | .9823102 .0134851 -1.30 0.194 .9562324 1.009099
_cons | 1.6e+139 3.1e+140 16.86 0.000 1.1e+123 2.4e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16987.078
Iteration 1: log likelihood = -16975.336
Iteration 2: log likelihood = -16975.201
Iteration 3: log likelihood = -16975.201
Log likelihood = -16975.201 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009082 .1268229 11.05 0.000 1.775276 2.273681
mot_egr_late | 1.694788 .0921284 9.70 0.000 1.523506 1.885326
tr_mod2 | 1.217929 .0518073 4.63 0.000 1.120506 1.323822
sex_dum2 | .6074368 .0295258 -10.26 0.000 .5522384 .6681525
edad_ini_cons | .9714266 .0047126 -5.98 0.000 .9622338 .9807072
esc1 | 1.430612 .0886583 5.78 0.000 1.266984 1.615373
esc2 | 1.264186 .0732448 4.05 0.000 1.12848 1.416212
sus_prin2 | 1.157839 .0782916 2.17 0.030 1.014124 1.321921
sus_prin3 | 1.682698 .0917439 9.54 0.000 1.512157 1.872471
sus_prin4 | 1.171375 .0933952 1.98 0.047 1.00191 1.369504
sus_prin5 | 1.592342 .239402 3.09 0.002 1.185937 2.138016
fr_cons_sus_prin2 | .9673101 .1088468 -0.30 0.768 .7758613 1.206
fr_cons_sus_prin3 | .9785781 .0894325 -0.24 0.813 .8180954 1.170542
fr_cons_sus_prin4 | 1.003381 .0951301 0.04 0.972 .8332283 1.208281
fr_cons_sus_prin5 | 1.029876 .0934462 0.32 0.746 .8620864 1.230323
cond_ocu2 | 1.048453 .07451 0.67 0.506 .9121308 1.20515
cond_ocu3 | 1.147464 .3095757 0.51 0.610 .6762268 1.947089
cond_ocu4 | 1.220139 .088985 2.73 0.006 1.057623 1.407627
cond_ocu5 | 1.059304 .1643842 0.37 0.710 .7815016 1.435857
cond_ocu6 | 1.189587 .0465104 4.44 0.000 1.101834 1.28433
policonsumo | .9916495 .0486138 -0.17 0.864 .9008025 1.091659
num_hij2 | 1.125637 .0447859 2.97 0.003 1.041194 1.216929
tenviv1 | 1.067191 .1350364 0.51 0.607 .8327906 1.367566
tenviv2 | 1.125292 .0969522 1.37 0.171 .9504475 1.332302
tenviv4 | 1.038179 .0510153 0.76 0.446 .9428545 1.14314
tenviv5 | 1.01094 .0383351 0.29 0.774 .938529 1.088938
mzone2 | 1.450712 .0608659 8.87 0.000 1.33619 1.575049
mzone3 | 1.529225 .0965803 6.73 0.000 1.351178 1.730733
n_off_vio | 1.466445 .0554296 10.13 0.000 1.361731 1.57921
n_off_acq | 2.798207 .09724 29.61 0.000 2.613966 2.995435
n_off_sud | 1.390556 .0506941 9.04 0.000 1.294664 1.49355
n_off_oth | 1.736062 .0634092 15.10 0.000 1.616127 1.864898
psy_com2 | 1.118828 .0550825 2.28 0.023 1.015913 1.232168
psy_com3 | 1.099883 .042396 2.47 0.014 1.01985 1.186197
dep2 | 1.036394 .0441271 0.84 0.401 .9534167 1.126592
rural2 | .8985738 .0559714 -1.72 0.086 .7953039 1.015253
rural3 | .8601171 .0595405 -2.18 0.029 .7509902 .9851013
porc_pobr | 1.56768 .3924058 1.80 0.072 .9598275 2.560481
susini2 | 1.18815 .1083063 1.89 0.059 .9937557 1.420571
susini3 | 1.271031 .0819253 3.72 0.000 1.120189 1.442185
susini4 | 1.180516 .0440181 4.45 0.000 1.097319 1.270021
susini5 | 1.422221 .1320375 3.79 0.000 1.185612 1.70605
ano_nac_corr | .8500467 .0080263 -17.21 0.000 .8344602 .8659244
cohab2 | .8800249 .0590992 -1.90 0.057 .771492 1.003826
cohab3 | 1.074674 .0859331 0.90 0.368 .9187829 1.257015
cohab4 | .9638415 .0641645 -0.55 0.580 .8459404 1.098175
fis_com2 | 1.057562 .0364533 1.62 0.104 .9884748 1.131478
fis_com3 | .8188552 .0709482 -2.31 0.021 .6909654 .9704158
rc_x1 | .8503234 .0101864 -13.53 0.000 .830591 .8705247
rc_x2 | .8816838 .0351597 -3.16 0.002 .8153963 .9533603
rc_x3 | 1.277983 .1359245 2.31 0.021 1.037511 1.574192
_rcs1 | 2.184185 .0732931 23.28 0.000 2.045155 2.332666
_rcs2 | 1.049395 .0277274 1.82 0.068 .9964333 1.105171
_rcs3 | 1.019859 .0201999 0.99 0.321 .9810271 1.060229
_rcs4 | 1.025382 .0123217 2.09 0.037 1.001514 1.049819
_rcs5 | 1.020292 .0085678 2.39 0.017 1.003637 1.037224
_rcs6 | 1.016002 .0075939 2.12 0.034 1.001227 1.030996
_rcs7 | 1.006738 .0028155 2.40 0.016 1.001235 1.012272
_rcs_mot_egr_early1 | .8966293 .0336566 -2.91 0.004 .8330317 .9650822
_rcs_mot_egr_early2 | 1.007843 .0291214 0.27 0.787 .9523521 1.066567
_rcs_mot_egr_early3 | 1.009573 .0222899 0.43 0.666 .9668173 1.054219
_rcs_mot_egr_early4 | .9828406 .014601 -1.17 0.244 .9546357 1.011879
_rcs_mot_egr_early5 | .992868 .0104051 -0.68 0.495 .9726823 1.013473
_rcs_mot_egr_late1 | .9244782 .0336137 -2.16 0.031 .8608893 .9927641
_rcs_mot_egr_late2 | 1.024169 .0291277 0.84 0.401 .9686416 1.082879
_rcs_mot_egr_late3 | 1.013804 .0217342 0.64 0.522 .9720887 1.05731
_rcs_mot_egr_late4 | .9889301 .0141126 -0.78 0.435 .9616532 1.016981
_rcs_mot_egr_late5 | .991313 .0099307 -0.87 0.384 .972039 1.010969
_cons | 1.6e+139 3.1e+140 16.86 0.000 1.1e+123 2.5e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.483
Iteration 1: log likelihood = -16974.801
Iteration 2: log likelihood = -16974.654
Iteration 3: log likelihood = -16974.654
Log likelihood = -16974.654 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009549 .1268638 11.06 0.000 1.775668 2.274235
mot_egr_late | 1.695254 .0921643 9.71 0.000 1.523907 1.885868
tr_mod2 | 1.217872 .0518049 4.63 0.000 1.120453 1.32376
sex_dum2 | .6074263 .0295254 -10.26 0.000 .5522286 .6681413
edad_ini_cons | .9714266 .0047126 -5.98 0.000 .9622338 .9807072
esc1 | 1.430563 .0886555 5.78 0.000 1.26694 1.615318
esc2 | 1.264135 .073242 4.05 0.000 1.128434 1.416155
sus_prin2 | 1.157819 .0782903 2.17 0.030 1.014106 1.321897
sus_prin3 | 1.682673 .0917423 9.54 0.000 1.512136 1.872444
sus_prin4 | 1.171336 .0933919 1.98 0.047 1.001876 1.369458
sus_prin5 | 1.592148 .2393726 3.09 0.002 1.185793 2.137755
fr_cons_sus_prin2 | .9673711 .1088537 -0.29 0.768 .77591 1.206076
fr_cons_sus_prin3 | .9786051 .089435 -0.24 0.813 .818118 1.170574
fr_cons_sus_prin4 | 1.003428 .0951346 0.04 0.971 .8332666 1.208337
fr_cons_sus_prin5 | 1.029928 .0934509 0.32 0.745 .8621297 1.230385
cond_ocu2 | 1.04847 .0745115 0.67 0.505 .9121447 1.20517
cond_ocu3 | 1.147282 .3095269 0.51 0.611 .6761188 1.94678
cond_ocu4 | 1.220148 .0889863 2.73 0.006 1.05763 1.407639
cond_ocu5 | 1.05947 .1644107 0.37 0.710 .7816233 1.436084
cond_ocu6 | 1.189575 .04651 4.44 0.000 1.101822 1.284317
policonsumo | .9916657 .0486146 -0.17 0.864 .9008172 1.091676
num_hij2 | 1.125639 .0447859 2.97 0.003 1.041196 1.216931
tenviv1 | 1.067292 .1350494 0.51 0.607 .832869 1.367696
tenviv2 | 1.125295 .0969517 1.37 0.171 .9504505 1.332303
tenviv4 | 1.038164 .0510148 0.76 0.446 .9428408 1.143124
tenviv5 | 1.010906 .0383339 0.29 0.775 .9384974 1.088902
mzone2 | 1.45067 .0608638 8.87 0.000 1.336152 1.575003
mzone3 | 1.529162 .0965756 6.72 0.000 1.351123 1.73066
n_off_vio | 1.466469 .0554309 10.13 0.000 1.361754 1.579238
n_off_acq | 2.798228 .0972415 29.61 0.000 2.613984 2.995459
n_off_sud | 1.390562 .0506946 9.04 0.000 1.294669 1.493557
n_off_oth | 1.736087 .0634106 15.10 0.000 1.616149 1.864926
psy_com2 | 1.118757 .0550789 2.28 0.023 1.015849 1.232089
psy_com3 | 1.099871 .0423956 2.47 0.014 1.019839 1.186185
dep2 | 1.036421 .0441281 0.84 0.401 .9534424 1.126622
rural2 | .8985434 .0559696 -1.72 0.086 .7952769 1.015219
rural3 | .8601089 .0595399 -2.18 0.029 .7509831 .9850919
porc_pobr | 1.56773 .3924229 1.80 0.072 .9598532 2.560578
susini2 | 1.188105 .1083024 1.89 0.059 .9937181 1.420518
susini3 | 1.271074 .0819276 3.72 0.000 1.120228 1.442233
susini4 | 1.180507 .0440177 4.45 0.000 1.097311 1.27001
susini5 | 1.422255 .1320401 3.79 0.000 1.18564 1.706089
ano_nac_corr | .8500846 .0080267 -17.20 0.000 .8344973 .8659631
cohab2 | .8800479 .0591009 -1.90 0.057 .7715118 1.003853
cohab3 | 1.074649 .0859315 0.90 0.368 .9187611 1.256987
cohab4 | .9638525 .0641653 -0.55 0.580 .8459498 1.098188
fis_com2 | 1.057601 .0364549 1.62 0.104 .9885106 1.13152
fis_com3 | .8188709 .0709495 -2.31 0.021 .6909788 .9704343
rc_x1 | .8503571 .0101868 -13.53 0.000 .8306239 .8705592
rc_x2 | .8817052 .0351606 -3.16 0.002 .815416 .9533834
rc_x3 | 1.2779 .1359157 2.31 0.021 1.037443 1.57409
_rcs1 | 2.184909 .0733099 23.29 0.000 2.045847 2.333423
_rcs2 | 1.050018 .0281304 1.82 0.068 .9963062 1.106626
_rcs3 | 1.016805 .0208238 0.81 0.416 .9767992 1.058449
_rcs4 | 1.0261 .0133766 1.98 0.048 1.000215 1.052656
_rcs5 | 1.023481 .0093411 2.54 0.011 1.005336 1.041954
_rcs6 | 1.015856 .0073062 2.19 0.029 1.001636 1.030277
_rcs7 | 1.005891 .0044833 1.32 0.188 .9971422 1.014717
_rcs_mot_egr_early1 | .8963491 .0336527 -2.91 0.004 .8327593 .9647947
_rcs_mot_egr_early2 | 1.006982 .0294703 0.24 0.812 .9508461 1.066431
_rcs_mot_egr_early3 | 1.014573 .0229385 0.64 0.522 .970596 1.060543
_rcs_mot_egr_early4 | .9840205 .0148124 -1.07 0.285 .9554128 1.013485
_rcs_mot_egr_early5 | .9863102 .0104801 -1.30 0.195 .9659819 1.007066
_rcs_mot_egr_early6 | .9992931 .0075657 -0.09 0.926 .9845741 1.014232
_rcs_mot_egr_late1 | .9241139 .0335992 -2.17 0.030 .8605523 .9923702
_rcs_mot_egr_late2 | 1.023377 .0295077 0.80 0.423 .967147 1.082877
_rcs_mot_egr_late3 | 1.018271 .0223904 0.82 0.410 .9753185 1.063114
_rcs_mot_egr_late4 | .9909063 .0143973 -0.63 0.530 .963086 1.01953
_rcs_mot_egr_late5 | .9869682 .0100706 -1.29 0.199 .9674262 1.006905
_rcs_mot_egr_late6 | .996874 .0071144 -0.44 0.661 .9830271 1.010916
_cons | 1.5e+139 2.8e+140 16.86 0.000 9.8e+122 2.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.966
Iteration 1: log likelihood = -16974.762
Iteration 2: log likelihood = -16974.579
Iteration 3: log likelihood = -16974.579
Log likelihood = -16974.579 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009339 .1268506 11.05 0.000 1.775483 2.273998
mot_egr_late | 1.695064 .0921563 9.71 0.000 1.523731 1.885662
tr_mod2 | 1.217856 .0518044 4.63 0.000 1.120438 1.323743
sex_dum2 | .6074321 .0295257 -10.26 0.000 .5522338 .6681476
edad_ini_cons | .9714262 .0047126 -5.98 0.000 .9622334 .9807068
esc1 | 1.430612 .0886583 5.78 0.000 1.266984 1.615373
esc2 | 1.264178 .0732444 4.05 0.000 1.128472 1.416202
sus_prin2 | 1.157786 .0782878 2.17 0.030 1.014078 1.32186
sus_prin3 | 1.682685 .0917424 9.54 0.000 1.512147 1.872455
sus_prin4 | 1.171347 .0933927 1.98 0.047 1.001886 1.369471
sus_prin5 | 1.592252 .2393891 3.09 0.002 1.18587 2.137897
fr_cons_sus_prin2 | .9673114 .1088469 -0.30 0.768 .7758623 1.206002
fr_cons_sus_prin3 | .9785902 .0894336 -0.24 0.813 .8181056 1.170556
fr_cons_sus_prin4 | 1.003416 .0951333 0.04 0.971 .8332571 1.208323
fr_cons_sus_prin5 | 1.02991 .0934492 0.32 0.745 .8621155 1.230363
cond_ocu2 | 1.048462 .0745107 0.67 0.505 .9121384 1.20516
cond_ocu3 | 1.147444 .3095703 0.51 0.610 .6762149 1.947055
cond_ocu4 | 1.220132 .0889849 2.73 0.006 1.057616 1.40762
cond_ocu5 | 1.059432 .1644042 0.37 0.710 .7815957 1.436031
cond_ocu6 | 1.189572 .04651 4.44 0.000 1.10182 1.284314
policonsumo | .9916568 .0486141 -0.17 0.864 .9008093 1.091666
num_hij2 | 1.125663 .0447869 2.98 0.003 1.041218 1.216957
tenviv1 | 1.067249 .1350441 0.51 0.607 .832835 1.367641
tenviv2 | 1.125199 .0969434 1.37 0.171 .95037 1.33219
tenviv4 | 1.038161 .0510148 0.76 0.446 .9428383 1.143122
tenviv5 | 1.010916 .0383341 0.29 0.775 .9385063 1.088912
mzone2 | 1.450661 .0608635 8.87 0.000 1.336144 1.574993
mzone3 | 1.529114 .0965726 6.72 0.000 1.351081 1.730607
n_off_vio | 1.466447 .0554301 10.13 0.000 1.361733 1.579214
n_off_acq | 2.79821 .0972407 29.61 0.000 2.613967 2.995439
n_off_sud | 1.390577 .050695 9.04 0.000 1.294683 1.493573
n_off_oth | 1.736075 .0634102 15.10 0.000 1.616137 1.864913
psy_com2 | 1.11875 .0550788 2.28 0.023 1.015843 1.232083
psy_com3 | 1.099885 .0423962 2.47 0.014 1.019851 1.186199
dep2 | 1.03641 .0441278 0.84 0.401 .9534316 1.12661
rural2 | .8985986 .0559733 -1.72 0.086 .7953253 1.015282
rural3 | .8601498 .0595426 -2.18 0.030 .751019 .9851384
porc_pobr | 1.567075 .392262 1.79 0.073 .9594485 2.559518
susini2 | 1.188163 .1083078 1.89 0.059 .9937663 1.420587
susini3 | 1.270986 .0819226 3.72 0.000 1.120149 1.442135
susini4 | 1.180505 .0440178 4.45 0.000 1.097309 1.270009
susini5 | 1.422188 .1320345 3.79 0.000 1.185584 1.706011
ano_nac_corr | .8500655 .0080264 -17.20 0.000 .8344786 .8659435
cohab2 | .8799965 .0590974 -1.90 0.057 .771467 1.003794
cohab3 | 1.074613 .0859285 0.90 0.368 .9187307 1.256945
cohab4 | .9637965 .0641613 -0.55 0.580 .8459013 1.098123
fis_com2 | 1.057596 .0364548 1.62 0.104 .9885058 1.131515
fis_com3 | .8188932 .0709514 -2.31 0.021 .6909976 .9704606
rc_x1 | .8503344 .0101865 -13.53 0.000 .8306018 .8705358
rc_x2 | .8817185 .0351612 -3.16 0.002 .8154282 .9533978
rc_x3 | 1.277867 .1359121 2.31 0.021 1.037417 1.574049
_rcs1 | 2.185196 .0733593 23.29 0.000 2.046043 2.333814
_rcs2 | 1.0534 .0291368 1.88 0.060 .997813 1.112083
_rcs3 | 1.011068 .0222099 0.50 0.616 .9684614 1.05555
_rcs4 | 1.033514 .0147873 2.30 0.021 1.004934 1.062906
_rcs5 | 1.017055 .0102208 1.68 0.092 .9972188 1.037286
_rcs6 | 1.01562 .0084446 1.86 0.062 .9992034 1.032307
_rcs7 | 1.008342 .0069101 1.21 0.225 .9948891 1.021977
_rcs_mot_egr_early1 | .896458 .0336723 -2.91 0.004 .8328324 .9649445
_rcs_mot_egr_early2 | 1.00356 .0302304 0.12 0.906 .9460249 1.064595
_rcs_mot_egr_early3 | 1.022708 .0245393 0.94 0.349 .9757254 1.071953
_rcs_mot_egr_early4 | .9789184 .0157047 -1.33 0.184 .9486166 1.010188
_rcs_mot_egr_early5 | .9924039 .0113384 -0.67 0.505 .9704281 1.014877
_rcs_mot_egr_early6 | .994464 .009327 -0.59 0.554 .9763505 1.012914
_rcs_mot_egr_early7 | .9974319 .0077542 -0.33 0.741 .9823492 1.012746
_rcs_mot_egr_late1 | .9239919 .0336138 -2.17 0.030 .8604039 .9922793
_rcs_mot_egr_late2 | 1.01968 .0303495 0.65 0.513 .9618977 1.080933
_rcs_mot_egr_late3 | 1.024456 .0242135 1.02 0.307 .978081 1.07303
_rcs_mot_egr_late4 | .9884718 .0153971 -0.74 0.457 .9587501 1.019115
_rcs_mot_egr_late5 | .9928735 .0108739 -0.65 0.514 .9717881 1.014416
_rcs_mot_egr_late6 | .993469 .0089128 -0.73 0.465 .9761529 1.011092
_rcs_mot_egr_late7 | .9960156 .0073895 -0.54 0.590 .9816372 1.010605
_cons | 1.6e+139 3.0e+140 16.86 0.000 1.0e+123 2.4e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16985.603
Iteration 1: log likelihood = -16976.605
Iteration 2: log likelihood = -16976.536
Iteration 3: log likelihood = -16976.536
Log likelihood = -16976.536 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012549 .12695 11.09 0.000 1.778497 2.277401
mot_egr_late | 1.694236 .0920617 9.70 0.000 1.523074 1.884632
tr_mod2 | 1.218477 .0518255 4.65 0.000 1.121019 1.324407
sex_dum2 | .6074126 .0295251 -10.26 0.000 .5522156 .6681268
edad_ini_cons | .9714235 .0047127 -5.98 0.000 .9622305 .9807043
esc1 | 1.430479 .0886513 5.78 0.000 1.266864 1.615225
esc2 | 1.264184 .0732447 4.05 0.000 1.128478 1.416209
sus_prin2 | 1.157552 .0782709 2.16 0.030 1.013875 1.32159
sus_prin3 | 1.682148 .0917114 9.54 0.000 1.511668 1.871854
sus_prin4 | 1.171296 .0933895 1.98 0.047 1.001841 1.369413
sus_prin5 | 1.591061 .2392023 3.09 0.002 1.184994 2.136277
fr_cons_sus_prin2 | .9673802 .1088547 -0.29 0.768 .7759175 1.206087
fr_cons_sus_prin3 | .9785621 .0894312 -0.24 0.813 .8180818 1.170523
fr_cons_sus_prin4 | 1.003276 .0951198 0.03 0.972 .8331411 1.208153
fr_cons_sus_prin5 | 1.029964 .0934546 0.33 0.745 .8621592 1.230429
cond_ocu2 | 1.04865 .0745237 0.67 0.504 .9123027 1.205376
cond_ocu3 | 1.147449 .3095699 0.51 0.610 .6762203 1.947058
cond_ocu4 | 1.220052 .0889797 2.73 0.006 1.057546 1.407529
cond_ocu5 | 1.057859 .1641524 0.36 0.717 .7804464 1.433878
cond_ocu6 | 1.189587 .0465098 4.44 0.000 1.101834 1.284328
policonsumo | .9915397 .0486083 -0.17 0.862 .900703 1.091537
num_hij2 | 1.125571 .0447838 2.97 0.003 1.041132 1.216859
tenviv1 | 1.06746 .135067 0.52 0.606 .833006 1.367903
tenviv2 | 1.125589 .0969757 1.37 0.170 .9507019 1.332649
tenviv4 | 1.03812 .051012 0.76 0.446 .9428018 1.143074
tenviv5 | 1.010834 .0383308 0.28 0.776 .9384313 1.088824
mzone2 | 1.450524 .06086 8.86 0.000 1.336013 1.574849
mzone3 | 1.528739 .0965497 6.72 0.000 1.350749 1.730184
n_off_vio | 1.466513 .0554314 10.13 0.000 1.361796 1.579282
n_off_acq | 2.797941 .0972321 29.61 0.000 2.613715 2.995152
n_off_sud | 1.390486 .0506931 9.04 0.000 1.294596 1.493479
n_off_oth | 1.735876 .0634031 15.10 0.000 1.615952 1.8647
psy_com2 | 1.118095 .055042 2.27 0.023 1.015255 1.231351
psy_com3 | 1.10022 .0424082 2.48 0.013 1.020163 1.186559
dep2 | 1.036379 .0441254 0.84 0.401 .953405 1.126574
rural2 | .8985025 .0559672 -1.72 0.086 .7952404 1.015173
rural3 | .860676 .0595751 -2.17 0.030 .7514852 .9857322
porc_pobr | 1.571477 .3933278 1.81 0.071 .9621864 2.566591
susini2 | 1.188557 .108342 1.90 0.058 .9940982 1.421054
susini3 | 1.270597 .0818948 3.72 0.000 1.119811 1.441687
susini4 | 1.180631 .0440221 4.45 0.000 1.097427 1.270144
susini5 | 1.422074 .1320221 3.79 0.000 1.185491 1.705869
ano_nac_corr | .8499488 .0080232 -17.22 0.000 .8343682 .8658202
cohab2 | .8802459 .059112 -1.90 0.058 .7716892 1.004074
cohab3 | 1.074986 .0859553 0.90 0.366 .9190545 1.257374
cohab4 | .9639736 .0641727 -0.55 0.582 .8460574 1.098324
fis_com2 | 1.057897 .0364637 1.63 0.102 .9887897 1.131833
fis_com3 | .8190884 .0709679 -2.30 0.021 .691163 .9706911
rc_x1 | .850232 .0101841 -13.55 0.000 .8305039 .8704287
rc_x2 | .8816912 .0351615 -3.16 0.002 .8154005 .9533712
rc_x3 | 1.277946 .1359253 2.31 0.021 1.037472 1.574157
_rcs1 | 2.200358 .0694095 25.00 0.000 2.068438 2.340692
_rcs2 | 1.064443 .0083388 7.97 0.000 1.048225 1.080913
_rcs3 | 1.033701 .0064592 5.30 0.000 1.021119 1.046439
_rcs4 | 1.018926 .0046466 4.11 0.000 1.009859 1.028074
_rcs5 | 1.011762 .0032904 3.60 0.000 1.005333 1.018231
_rcs6 | 1.008531 .0026488 3.23 0.001 1.003353 1.013736
_rcs7 | 1.008814 .0023261 3.81 0.000 1.004265 1.013383
_rcs8 | 1.003518 .0019569 1.80 0.072 .9996898 1.007361
_rcs_mot_egr_early1 | .8929325 .0314321 -3.22 0.001 .8334038 .9567132
_rcs_mot_egr_late1 | .9139667 .0309701 -2.65 0.008 .8552382 .976728
_cons | 2.1e+139 3.9e+140 16.88 0.000 1.4e+123 3.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.855
Iteration 1: log likelihood = -16975.788
Iteration 2: log likelihood = -16975.675
Iteration 3: log likelihood = -16975.675
Log likelihood = -16975.675 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010836 .1269465 11.07 0.000 1.776803 2.275695
mot_egr_late | 1.695552 .0921864 9.71 0.000 1.524164 1.886213
tr_mod2 | 1.217899 .0518044 4.63 0.000 1.120482 1.323787
sex_dum2 | .6074752 .0295282 -10.25 0.000 .5522723 .6681959
edad_ini_cons | .9714307 .0047127 -5.97 0.000 .9622378 .9807114
esc1 | 1.430474 .0886507 5.78 0.000 1.26686 1.61522
esc2 | 1.264114 .0732407 4.05 0.000 1.128416 1.416131
sus_prin2 | 1.157636 .0782763 2.16 0.030 1.013949 1.321685
sus_prin3 | 1.682179 .0917109 9.54 0.000 1.511699 1.871884
sus_prin4 | 1.171286 .0933878 1.98 0.047 1.001834 1.369399
sus_prin5 | 1.591559 .2392827 3.09 0.002 1.185357 2.136961
fr_cons_sus_prin2 | .9673298 .1088491 -0.30 0.768 .7758769 1.206025
fr_cons_sus_prin3 | .978565 .0894312 -0.24 0.813 .8180847 1.170526
fr_cons_sus_prin4 | 1.003264 .0951182 0.03 0.973 .8331321 1.208138
fr_cons_sus_prin5 | 1.029976 .0934545 0.33 0.745 .8621712 1.23044
cond_ocu2 | 1.048478 .0745117 0.67 0.505 .9121527 1.205179
cond_ocu3 | 1.146989 .3094463 0.51 0.611 .6759483 1.946278
cond_ocu4 | 1.220309 .0889953 2.73 0.006 1.057774 1.407819
cond_ocu5 | 1.058493 .1642533 0.37 0.714 .7809105 1.434744
cond_ocu6 | 1.189597 .0465095 4.44 0.000 1.101845 1.284338
policonsumo | .9916146 .0486114 -0.17 0.864 .900772 1.091619
num_hij2 | 1.12564 .0447864 2.97 0.003 1.041195 1.216933
tenviv1 | 1.067362 .1350547 0.52 0.606 .8329289 1.367777
tenviv2 | 1.125264 .0969499 1.37 0.171 .9504233 1.332268
tenviv4 | 1.038245 .0510183 0.76 0.445 .9429151 1.143212
tenviv5 | 1.010998 .0383372 0.29 0.773 .9385832 1.089001
mzone2 | 1.450667 .060865 8.87 0.000 1.336147 1.575003
mzone3 | 1.529245 .0965804 6.73 0.000 1.351198 1.730753
n_off_vio | 1.46646 .0554302 10.13 0.000 1.361746 1.579227
n_off_acq | 2.798164 .0972408 29.61 0.000 2.613921 2.995393
n_off_sud | 1.390546 .0506949 9.04 0.000 1.294653 1.493542
n_off_oth | 1.735915 .0634043 15.10 0.000 1.615989 1.864742
psy_com2 | 1.118578 .0550687 2.28 0.023 1.015689 1.231889
psy_com3 | 1.100027 .042401 2.47 0.013 1.019984 1.186351
dep2 | 1.036362 .0441254 0.84 0.402 .9533879 1.126557
rural2 | .898433 .0559628 -1.72 0.086 .7951791 1.015094
rural3 | .8603482 .0595564 -2.17 0.030 .7511921 .9853657
porc_pobr | 1.571182 .3932506 1.81 0.071 .9620099 2.566099
susini2 | 1.18822 .1083126 1.89 0.059 .9938148 1.420655
susini3 | 1.270931 .0819171 3.72 0.000 1.120103 1.442067
susini4 | 1.180529 .0440183 4.45 0.000 1.097332 1.270034
susini5 | 1.422137 .1320292 3.79 0.000 1.185542 1.705948
ano_nac_corr | .8500172 .0080257 -17.21 0.000 .8344318 .8658937
cohab2 | .8800265 .0590987 -1.90 0.057 .7714945 1.003826
cohab3 | 1.074723 .0859356 0.90 0.367 .9188274 1.257069
cohab4 | .9638364 .0641635 -0.55 0.580 .8459369 1.098168
fis_com2 | 1.057861 .0364633 1.63 0.103 .9887551 1.131797
fis_com3 | .8189822 .070959 -2.30 0.021 .691073 .9705659
rc_x1 | .8502888 .0101862 -13.54 0.000 .8305568 .8704896
rc_x2 | .8816928 .0351615 -3.16 0.002 .8154021 .9533729
rc_x3 | 1.278017 .1359333 2.31 0.021 1.03753 1.574247
_rcs1 | 2.183258 .0725412 23.50 0.000 2.04561 2.330168
_rcs2 | 1.047808 .0254565 1.92 0.055 .9990831 1.098908
_rcs3 | 1.030403 .0077246 4.00 0.000 1.015373 1.045654
_rcs4 | 1.018135 .0047599 3.84 0.000 1.008848 1.027507
_rcs5 | 1.01166 .0032941 3.56 0.000 1.005225 1.018137
_rcs6 | 1.008516 .0026487 3.23 0.001 1.003338 1.013721
_rcs7 | 1.008816 .0023261 3.81 0.000 1.004267 1.013386
_rcs8 | 1.003504 .0019571 1.79 0.073 .9996755 1.007347
_rcs_mot_egr_early1 | .8975271 .033271 -2.92 0.004 .8346298 .9651643
_rcs_mot_egr_early2 | 1.007554 .0274965 0.28 0.783 .9550782 1.062914
_rcs_mot_egr_late1 | .9246241 .0332412 -2.18 0.029 .8617151 .9921258
_rcs_mot_egr_late2 | 1.025787 .0274121 0.95 0.341 .9734427 1.080945
_cons | 1.7e+139 3.3e+140 16.87 0.000 1.2e+123 2.7e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16985.78
Iteration 1: log likelihood = -16975.628
Iteration 2: log likelihood = -16975.537
Iteration 3: log likelihood = -16975.537
Log likelihood = -16975.537 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.011208 .126991 11.07 0.000 1.777096 2.276163
mot_egr_late | 1.695686 .0922148 9.71 0.000 1.524247 1.886407
tr_mod2 | 1.217855 .0518034 4.63 0.000 1.120439 1.32374
sex_dum2 | .6074787 .0295283 -10.25 0.000 .5522756 .6681997
edad_ini_cons | .9714314 .0047126 -5.97 0.000 .9622386 .980712
esc1 | 1.43053 .0886537 5.78 0.000 1.26691 1.615281
esc2 | 1.264164 .0732435 4.05 0.000 1.12846 1.416187
sus_prin2 | 1.157768 .0782858 2.17 0.030 1.014063 1.321837
sus_prin3 | 1.682391 .0917243 9.54 0.000 1.511887 1.872124
sus_prin4 | 1.171317 .0933906 1.98 0.047 1.00186 1.369437
sus_prin5 | 1.592172 .2393757 3.09 0.002 1.185811 2.137785
fr_cons_sus_prin2 | .9673017 .108846 -0.30 0.768 .7758543 1.20599
fr_cons_sus_prin3 | .9786144 .0894357 -0.24 0.813 .818126 1.170585
fr_cons_sus_prin4 | 1.003302 .0951219 0.03 0.972 .8331639 1.208184
fr_cons_sus_prin5 | 1.029977 .0934544 0.33 0.745 .8621723 1.230441
cond_ocu2 | 1.048397 .074506 0.67 0.506 .9120817 1.205085
cond_ocu3 | 1.147542 .3095967 0.51 0.610 .6762731 1.947221
cond_ocu4 | 1.220409 .0890019 2.73 0.006 1.057862 1.407932
cond_ocu5 | 1.058679 .1642837 0.37 0.713 .7810457 1.435
cond_ocu6 | 1.1896 .0465099 4.44 0.000 1.101847 1.284341
policonsumo | .991668 .0486145 -0.17 0.864 .9008198 1.091678
num_hij2 | 1.12571 .0447891 2.98 0.003 1.041261 1.217009
tenviv1 | 1.067352 .1350547 0.52 0.606 .8329198 1.367768
tenviv2 | 1.125246 .0969491 1.37 0.171 .9504066 1.332249
tenviv4 | 1.038271 .0510198 0.76 0.445 .9429387 1.143242
tenviv5 | 1.011058 .0383396 0.29 0.772 .9386379 1.089065
mzone2 | 1.450741 .0608679 8.87 0.000 1.336215 1.575082
mzone3 | 1.529457 .0965951 6.73 0.000 1.351382 1.730996
n_off_vio | 1.466427 .0554292 10.13 0.000 1.361715 1.579192
n_off_acq | 2.798201 .0972411 29.61 0.000 2.613958 2.995431
n_off_sud | 1.390566 .0506954 9.04 0.000 1.294671 1.493563
n_off_oth | 1.735943 .063405 15.10 0.000 1.616015 1.86477
psy_com2 | 1.118785 .0550801 2.28 0.023 1.015875 1.23212
psy_com3 | 1.099954 .0423984 2.47 0.013 1.019916 1.186273
dep2 | 1.036373 .044126 0.84 0.401 .9533975 1.126569
rural2 | .8984379 .055963 -1.72 0.086 .7951835 1.0151
rural3 | .8601994 .0595467 -2.18 0.030 .7510611 .9851968
porc_pobr | 1.569409 .3928213 1.80 0.072 .9609066 2.563249
susini2 | 1.188077 .1082995 1.89 0.059 .9936951 1.420483
susini3 | 1.271063 .0819265 3.72 0.000 1.120218 1.442219
susini4 | 1.180507 .0440178 4.45 0.000 1.097311 1.270011
susini5 | 1.421977 .1320143 3.79 0.000 1.185409 1.705756
ano_nac_corr | .8499864 .0080258 -17.21 0.000 .8344009 .865863
cohab2 | .8799561 .0590945 -1.90 0.057 .7714318 1.003747
cohab3 | 1.074658 .0859311 0.90 0.368 .9187704 1.256995
cohab4 | .9637906 .0641607 -0.55 0.580 .8458964 1.098116
fis_com2 | 1.057762 .0364602 1.63 0.103 .9886618 1.131692
fis_com3 | .818896 .0709519 -2.31 0.021 .6909997 .9704646
rc_x1 | .8502579 .0101859 -13.54 0.000 .8305265 .8704582
rc_x2 | .8816853 .0351608 -3.16 0.002 .8153959 .9533639
rc_x3 | 1.278037 .1359339 2.31 0.021 1.037549 1.574268
_rcs1 | 2.186316 .0736395 23.22 0.000 2.046647 2.335518
_rcs2 | 1.046684 .0263642 1.81 0.070 .9962662 1.099654
_rcs3 | 1.033484 .0151363 2.25 0.025 1.004239 1.063581
_rcs4 | 1.019964 .0103049 1.96 0.050 .9999659 1.040363
_rcs5 | 1.012354 .005129 2.42 0.015 1.002351 1.022457
_rcs6 | 1.008647 .0028757 3.02 0.003 1.003027 1.014299
_rcs7 | 1.008854 .0023334 3.81 0.000 1.004291 1.013438
_rcs8 | 1.003506 .0019576 1.79 0.073 .9996763 1.00735
_rcs_mot_egr_early1 | .8950405 .0336873 -2.95 0.003 .8313911 .9635628
_rcs_mot_egr_early2 | 1.00893 .0280301 0.32 0.749 .9554612 1.065391
_rcs_mot_egr_early3 | .9931202 .0196219 -0.35 0.727 .9553971 1.032333
_rcs_mot_egr_late1 | .9238629 .0336932 -2.17 0.030 .8601303 .9923179
_rcs_mot_egr_late2 | 1.025598 .0279506 0.93 0.354 .9722532 1.08187
_rcs_mot_egr_late3 | 1.000003 .0190724 0.00 1.000 .9633117 1.038091
_cons | 1.9e+139 3.6e+140 16.87 0.000 1.2e+123 2.9e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.014
Iteration 1: log likelihood = -16974.786
Iteration 2: log likelihood = -16974.651
Iteration 3: log likelihood = -16974.651
Log likelihood = -16974.651 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009773 .1268758 11.06 0.000 1.77587 2.274484
mot_egr_late | 1.694747 .0921329 9.70 0.000 1.523457 1.885296
tr_mod2 | 1.217928 .0518073 4.63 0.000 1.120505 1.323821
sex_dum2 | .6074671 .0295274 -10.25 0.000 .5522657 .6681863
edad_ini_cons | .9714251 .0047126 -5.98 0.000 .9622323 .9807058
esc1 | 1.430587 .088657 5.78 0.000 1.266961 1.615345
esc2 | 1.264187 .0732448 4.05 0.000 1.12848 1.416212
sus_prin2 | 1.157921 .0782971 2.17 0.030 1.014195 1.322014
sus_prin3 | 1.682732 .0917457 9.55 0.000 1.512188 1.872509
sus_prin4 | 1.171376 .0933955 1.98 0.047 1.001911 1.369506
sus_prin5 | 1.592484 .2394237 3.09 0.002 1.186043 2.138208
fr_cons_sus_prin2 | .9673273 .1088487 -0.30 0.768 .7758751 1.206022
fr_cons_sus_prin3 | .978601 .0894345 -0.24 0.813 .8181147 1.170569
fr_cons_sus_prin4 | 1.00337 .0951288 0.04 0.972 .8332191 1.208267
fr_cons_sus_prin5 | 1.029879 .0934463 0.32 0.746 .8620897 1.230326
cond_ocu2 | 1.048404 .0745065 0.67 0.506 .9120876 1.205093
cond_ocu3 | 1.147885 .3096891 0.51 0.609 .6764753 1.947803
cond_ocu4 | 1.220172 .0889861 2.73 0.006 1.057655 1.407663
cond_ocu5 | 1.059009 .1643381 0.37 0.712 .7812849 1.435456
cond_ocu6 | 1.189613 .0465112 4.44 0.000 1.101858 1.284357
policonsumo | .9916595 .048614 -0.17 0.864 .9008121 1.091669
num_hij2 | 1.125667 .0447871 2.98 0.003 1.041221 1.216961
tenviv1 | 1.067301 .1350503 0.51 0.607 .8328761 1.367707
tenviv2 | 1.125474 .0969688 1.37 0.170 .950599 1.332519
tenviv4 | 1.038191 .0510161 0.76 0.446 .9428659 1.143154
tenviv5 | 1.011011 .0383378 0.29 0.773 .9385943 1.089014
mzone2 | 1.450788 .0608701 8.87 0.000 1.336258 1.575133
mzone3 | 1.529411 .0965926 6.73 0.000 1.351341 1.730945
n_off_vio | 1.466398 .0554274 10.13 0.000 1.361689 1.579159
n_off_acq | 2.798042 .0972338 29.61 0.000 2.613812 2.995256
n_off_sud | 1.390481 .0506914 9.04 0.000 1.294594 1.49347
n_off_oth | 1.735944 .0634041 15.10 0.000 1.616018 1.864769
psy_com2 | 1.118888 .055085 2.28 0.023 1.015968 1.232233
psy_com3 | 1.099864 .0423953 2.47 0.014 1.019832 1.186176
dep2 | 1.036408 .0441275 0.84 0.401 .9534297 1.126607
rural2 | .8985271 .0559681 -1.72 0.086 .7952633 1.0152
rural3 | .8601262 .0595414 -2.18 0.030 .7509976 .9851123
porc_pobr | 1.567711 .3924109 1.80 0.072 .9598497 2.560523
susini2 | 1.188167 .1083077 1.89 0.059 .9937699 1.420591
susini3 | 1.27113 .0819317 3.72 0.000 1.120276 1.442297
susini4 | 1.180506 .0440178 4.45 0.000 1.09731 1.27001
susini5 | 1.422124 .1320285 3.79 0.000 1.18553 1.705933
ano_nac_corr | .8499974 .0080262 -17.21 0.000 .834411 .865875
cohab2 | .880003 .0590982 -1.90 0.057 .7714719 1.003802
cohab3 | 1.074613 .0859286 0.90 0.368 .9187304 1.256945
cohab4 | .9638097 .0641627 -0.55 0.580 .8459118 1.098139
fis_com2 | 1.057557 .0364528 1.62 0.104 .9884706 1.131472
fis_com3 | .8188456 .0709475 -2.31 0.021 .690957 .9704049
rc_x1 | .8502813 .0101862 -13.54 0.000 .8305492 .8704821
rc_x2 | .8816433 .0351582 -3.16 0.002 .8153587 .9533165
rc_x3 | 1.278134 .135941 2.31 0.021 1.037632 1.574379
_rcs1 | 2.184258 .0732832 23.29 0.000 2.045247 2.332718
_rcs2 | 1.048899 .0276201 1.81 0.070 .9961382 1.104455
_rcs3 | 1.019573 .0183647 1.08 0.282 .984207 1.05621
_rcs4 | 1.022757 .0100911 2.28 0.023 1.003169 1.042728
_rcs5 | 1.022213 .0091235 2.46 0.014 1.004487 1.040253
_rcs6 | 1.015371 .005949 2.60 0.009 1.003778 1.027098
_rcs7 | 1.010743 .0027566 3.92 0.000 1.005355 1.01616
_rcs8 | 1.003485 .0019575 1.78 0.074 .999656 1.007329
_rcs_mot_egr_early1 | .8959609 .0336173 -2.93 0.003 .8324366 .9643327
_rcs_mot_egr_early2 | 1.008447 .0289787 0.29 0.770 .9532196 1.066874
_rcs_mot_egr_early3 | 1.005687 .0213505 0.27 0.789 .9646996 1.048416
_rcs_mot_egr_early4 | .9808762 .0139769 -1.36 0.175 .9538609 1.008657
_rcs_mot_egr_late1 | .9247958 .0336171 -2.15 0.031 .8611999 .9930879
_rcs_mot_egr_late2 | 1.024567 .0289379 0.86 0.390 .9693911 1.082883
_rcs_mot_egr_late3 | 1.012074 .0208479 0.58 0.560 .9720266 1.053771
_rcs_mot_egr_late4 | .9841927 .0134568 -1.17 0.244 .9581681 1.010924
_cons | 1.8e+139 3.5e+140 16.87 0.000 1.2e+123 2.8e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16986.085
Iteration 1: log likelihood = -16974.489
Iteration 2: log likelihood = -16974.339
Iteration 3: log likelihood = -16974.339
Log likelihood = -16974.339 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010165 .1269175 11.06 0.000 1.776187 2.274965
mot_egr_late | 1.695203 .0921754 9.71 0.000 1.523836 1.885841
tr_mod2 | 1.217886 .0518052 4.63 0.000 1.120467 1.323775
sex_dum2 | .6074791 .029528 -10.25 0.000 .5522765 .6681994
edad_ini_cons | .9714248 .0047127 -5.98 0.000 .9622319 .9807054
esc1 | 1.430598 .0886577 5.78 0.000 1.266971 1.615357
esc2 | 1.264194 .0732453 4.05 0.000 1.128487 1.416221
sus_prin2 | 1.157875 .0782938 2.17 0.030 1.014155 1.321961
sus_prin3 | 1.682658 .0917412 9.54 0.000 1.512122 1.872426
sus_prin4 | 1.171373 .0933951 1.98 0.047 1.001908 1.369502
sus_prin5 | 1.592246 .2393895 3.09 0.002 1.185863 2.137892
fr_cons_sus_prin2 | .967327 .1088487 -0.30 0.768 .7758747 1.206021
fr_cons_sus_prin3 | .9786134 .0894356 -0.24 0.813 .8181251 1.170584
fr_cons_sus_prin4 | 1.003395 .095131 0.04 0.971 .8332402 1.208297
fr_cons_sus_prin5 | 1.029921 .09345 0.32 0.745 .8621243 1.230375
cond_ocu2 | 1.048386 .0745054 0.66 0.506 .9120715 1.205073
cond_ocu3 | 1.147807 .3096687 0.51 0.609 .6764287 1.947673
cond_ocu4 | 1.220155 .0889851 2.73 0.006 1.057639 1.407643
cond_ocu5 | 1.059061 .164346 0.37 0.712 .7813231 1.435526
cond_ocu6 | 1.1896 .0465109 4.44 0.000 1.101846 1.284344
policonsumo | .991637 .0486129 -0.17 0.864 .9007917 1.091644
num_hij2 | 1.125676 .0447875 2.98 0.003 1.041229 1.216971
tenviv1 | 1.067401 .1350624 0.52 0.606 .8329551 1.367834
tenviv2 | 1.125434 .0969653 1.37 0.170 .9505656 1.332472
tenviv4 | 1.038245 .0510188 0.76 0.445 .942914 1.143213
tenviv5 | 1.011011 .0383378 0.29 0.773 .938595 1.089015
mzone2 | 1.450747 .0608681 8.87 0.000 1.336221 1.575089
mzone3 | 1.529414 .096592 6.73 0.000 1.351345 1.730947
n_off_vio | 1.466403 .0554276 10.13 0.000 1.361694 1.579165
n_off_acq | 2.798063 .0972342 29.61 0.000 2.613833 2.995279
n_off_sud | 1.390494 .0506919 9.04 0.000 1.294607 1.493484
n_off_oth | 1.735975 .0634051 15.10 0.000 1.616047 1.864803
psy_com2 | 1.118802 .0550816 2.28 0.023 1.015889 1.23214
psy_com3 | 1.099884 .0423962 2.47 0.014 1.019851 1.186199
dep2 | 1.036397 .0441271 0.84 0.401 .9534202 1.126596
rural2 | .898492 .0559659 -1.72 0.086 .7952321 1.01516
rural3 | .8601596 .0595438 -2.18 0.030 .7510266 .9851508
porc_pobr | 1.56816 .3925212 1.80 0.072 .9601275 2.56125
susini2 | 1.188126 .1083038 1.89 0.059 .9937362 1.420542
susini3 | 1.271203 .0819358 3.72 0.000 1.120341 1.442379
susini4 | 1.180529 .0440187 4.45 0.000 1.097331 1.270035
susini5 | 1.422219 .132038 3.79 0.000 1.185609 1.706049
ano_nac_corr | .8499988 .0080262 -17.21 0.000 .8344125 .8658763
cohab2 | .8800044 .0590979 -1.90 0.057 .7714738 1.003803
cohab3 | 1.074576 .0859256 0.90 0.368 .9186992 1.256902
cohab4 | .9637991 .0641615 -0.55 0.580 .8459033 1.098126
fis_com2 | 1.057627 .0364556 1.63 0.104 .9885358 1.131548
fis_com3 | .8188409 .0709471 -2.31 0.021 .6909531 .9703992
rc_x1 | .8502744 .0101861 -13.54 0.000 .8305426 .8704749
rc_x2 | .8816823 .0351601 -3.16 0.002 .815394 .9533594
rc_x3 | 1.277999 .135928 2.31 0.021 1.037521 1.574216
_rcs1 | 2.184991 .0733633 23.28 0.000 2.04583 2.333617
_rcs2 | 1.050719 .0282605 1.84 0.066 .9967645 1.107595
_rcs3 | 1.01497 .0199407 0.76 0.449 .9766302 1.054816
_rcs4 | 1.027759 .0120226 2.34 0.019 1.004463 1.051595
_rcs5 | 1.023053 .0090778 2.57 0.010 1.005414 1.041001
_rcs6 | 1.0122 .0078847 1.56 0.120 .9968634 1.027772
_rcs7 | 1.009671 .0052364 1.86 0.063 .9994593 1.019986
_rcs8 | 1.003649 .0020214 1.81 0.071 .9996947 1.007618
_rcs_mot_egr_early1 | .8959534 .0336589 -2.92 0.003 .8323534 .964413
_rcs_mot_egr_early2 | 1.006535 .0294535 0.22 0.824 .9504318 1.065951
_rcs_mot_egr_early3 | 1.012197 .0224376 0.55 0.584 .9691621 1.057143
_rcs_mot_egr_early4 | .977412 .0148872 -1.50 0.134 .9486648 1.00703
_rcs_mot_egr_early5 | .9990174 .010878 -0.09 0.928 .9779228 1.020567
_rcs_mot_egr_late1 | .924332 .0336292 -2.16 0.031 .8607152 .9926508
_rcs_mot_egr_late2 | 1.022801 .0294586 0.78 0.434 .9666629 1.0822
_rcs_mot_egr_late3 | 1.017293 .021992 0.79 0.428 .9750902 1.061323
_rcs_mot_egr_late4 | .9832208 .0144371 -1.15 0.249 .9553278 1.011928
_rcs_mot_egr_late5 | .9973744 .0103907 -0.25 0.801 .9772155 1.017949
_cons | 1.8e+139 3.5e+140 16.87 0.000 1.2e+123 2.8e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16985.925
Iteration 1: log likelihood = -16974.242
Iteration 2: log likelihood = -16974.096
Iteration 3: log likelihood = -16974.096
Log likelihood = -16974.096 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010143 .1269025 11.06 0.000 1.776191 2.27491
mot_egr_late | 1.695614 .092186 9.71 0.000 1.524226 1.886273
tr_mod2 | 1.217888 .0518053 4.63 0.000 1.120468 1.323777
sex_dum2 | .607502 .0295291 -10.25 0.000 .5522974 .6682245
edad_ini_cons | .9714215 .0047126 -5.98 0.000 .9622287 .9807022
esc1 | 1.430624 .0886591 5.78 0.000 1.266994 1.615387
esc2 | 1.26417 .0732439 4.05 0.000 1.128465 1.416193
sus_prin2 | 1.157921 .0782973 2.17 0.030 1.014196 1.322015
sus_prin3 | 1.682818 .0917508 9.55 0.000 1.512265 1.872606
sus_prin4 | 1.171393 .0933966 1.98 0.047 1.001925 1.369525
sus_prin5 | 1.592412 .2394144 3.09 0.002 1.185987 2.138115
fr_cons_sus_prin2 | .9673423 .1088504 -0.30 0.768 .7758871 1.20604
fr_cons_sus_prin3 | .9785925 .0894337 -0.24 0.813 .8181076 1.170559
fr_cons_sus_prin4 | 1.003446 .0951361 0.04 0.971 .8332818 1.208358
fr_cons_sus_prin5 | 1.02989 .0934475 0.32 0.745 .862098 1.230339
cond_ocu2 | 1.048353 .0745031 0.66 0.506 .9120433 1.205035
cond_ocu3 | 1.147925 .3097002 0.51 0.609 .6764981 1.947871
cond_ocu4 | 1.219924 .0889685 2.73 0.006 1.057438 1.407377
cond_ocu5 | 1.05932 .1643872 0.37 0.710 .7815127 1.43588
cond_ocu6 | 1.189619 .0465119 4.44 0.000 1.101863 1.284365
policonsumo | .9916056 .0486112 -0.17 0.863 .9007635 1.091609
num_hij2 | 1.125653 .0447866 2.97 0.003 1.041208 1.216946
tenviv1 | 1.067423 .1350656 0.52 0.606 .8329717 1.367863
tenviv2 | 1.125473 .0969679 1.37 0.170 .9505997 1.332516
tenviv4 | 1.038186 .0510159 0.76 0.446 .9428612 1.143149
tenviv5 | 1.010985 .0383368 0.29 0.773 .9385708 1.088987
mzone2 | 1.450748 .0608679 8.87 0.000 1.336223 1.57509
mzone3 | 1.529272 .0965833 6.73 0.000 1.351219 1.730786
n_off_vio | 1.466402 .0554268 10.13 0.000 1.361694 1.579162
n_off_acq | 2.797985 .0972295 29.61 0.000 2.613764 2.995191
n_off_sud | 1.390458 .0506899 9.04 0.000 1.294574 1.493444
n_off_oth | 1.736005 .0634052 15.10 0.000 1.616077 1.864833
psy_com2 | 1.118843 .0550837 2.28 0.023 1.015926 1.232186
psy_com3 | 1.09988 .042396 2.47 0.014 1.019847 1.186194
dep2 | 1.036405 .0441277 0.84 0.401 .9534265 1.126605
rural2 | .8985521 .0559698 -1.72 0.086 .7952852 1.015228
rural3 | .8601815 .0595452 -2.18 0.030 .7510459 .9851757
porc_pobr | 1.567244 .3922973 1.80 0.073 .9595603 2.559771
susini2 | 1.188189 .10831 1.89 0.059 .9937882 1.420618
susini3 | 1.271242 .0819389 3.72 0.000 1.120375 1.442424
susini4 | 1.180538 .0440191 4.45 0.000 1.097339 1.270044
susini5 | 1.422283 .1320438 3.79 0.000 1.185662 1.706125
ano_nac_corr | .85001 .0080266 -17.21 0.000 .8344229 .8658882
cohab2 | .8800045 .059098 -1.90 0.057 .7714739 1.003803
cohab3 | 1.07451 .0859202 0.90 0.369 .9186425 1.256824
cohab4 | .9637867 .0641606 -0.55 0.580 .8458926 1.098112
fis_com2 | 1.057552 .0364528 1.62 0.105 .9884653 1.131466
fis_com3 | .8188347 .0709465 -2.31 0.021 .6909479 .9703918
rc_x1 | .8502845 .0101864 -13.54 0.000 .8305522 .8704857
rc_x2 | .8816833 .0351597 -3.16 0.002 .8153959 .9533596
rc_x3 | 1.277991 .1359254 2.31 0.021 1.037517 1.574202
_rcs1 | 2.185872 .0733577 23.30 0.000 2.04672 2.334485
_rcs2 | 1.050885 .0284866 1.83 0.067 .9965096 1.108227
_rcs3 | 1.013364 .0208286 0.65 0.518 .9733519 1.055021
_rcs4 | 1.027705 .0131373 2.14 0.033 1.002276 1.053779
_rcs5 | 1.021655 .0088855 2.46 0.014 1.004387 1.03922
_rcs6 | 1.014847 .0076552 1.95 0.051 .9999534 1.029962
_rcs7 | 1.013597 .006576 2.08 0.037 1.00079 1.026568
_rcs8 | 1.004754 .0027611 1.73 0.084 .9993571 1.01018
_rcs_mot_egr_early1 | .8957636 .0336346 -2.93 0.003 .8322084 .9641726
_rcs_mot_egr_early2 | 1.006029 .0296767 0.20 0.839 .9495129 1.065908
_rcs_mot_egr_early3 | 1.016327 .0231363 0.71 0.477 .9719769 1.0627
_rcs_mot_egr_early4 | .9803503 .0149521 -1.30 0.193 .9514784 1.010098
_rcs_mot_egr_early5 | .9909573 .0108362 -0.83 0.406 .9699447 1.012425
_rcs_mot_egr_early6 | .996281 .0080996 -0.46 0.647 .9805319 1.012283
_rcs_mot_egr_late1 | .9237582 .0335964 -2.18 0.029 .8602026 .9920095
_rcs_mot_egr_late2 | 1.022453 .029705 0.76 0.445 .9658588 1.082363
_rcs_mot_egr_late3 | 1.020581 .0226863 0.92 0.359 .9770713 1.066028
_rcs_mot_egr_late4 | .9870517 .0145531 -0.88 0.377 .9589364 1.015991
_rcs_mot_egr_late5 | .9915898 .010399 -0.81 0.421 .9714162 1.012182
_rcs_mot_egr_late6 | .9939184 .0076297 -0.79 0.427 .9790763 1.008985
_cons | 1.8e+139 3.4e+140 16.86 0.000 1.2e+123 2.7e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16984.216
Iteration 1: log likelihood = -16973.024
Iteration 2: log likelihood = -16972.887
Iteration 3: log likelihood = -16972.887
Log likelihood = -16972.887 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.011767 .1270205 11.07 0.000 1.777599 2.276782
mot_egr_late | 1.696599 .0922505 9.72 0.000 1.525093 1.887393
tr_mod2 | 1.21784 .0518037 4.63 0.000 1.120424 1.323726
sex_dum2 | .6075207 .0295299 -10.25 0.000 .5523145 .6682449
edad_ini_cons | .9714188 .0047127 -5.98 0.000 .9622259 .9806994
esc1 | 1.430729 .0886654 5.78 0.000 1.267087 1.615505
esc2 | 1.26422 .0732469 4.05 0.000 1.12851 1.41625
sus_prin2 | 1.157943 .0782983 2.17 0.030 1.014215 1.322038
sus_prin3 | 1.68291 .0917559 9.55 0.000 1.512347 1.872709
sus_prin4 | 1.171478 .0934031 1.98 0.047 1.001998 1.369623
sus_prin5 | 1.592404 .239413 3.09 0.002 1.185981 2.138104
fr_cons_sus_prin2 | .9672631 .1088413 -0.30 0.767 .7758239 1.205941
fr_cons_sus_prin3 | .9785688 .0894314 -0.24 0.813 .818088 1.17053
fr_cons_sus_prin4 | 1.003408 .0951321 0.04 0.971 .8332514 1.208312
fr_cons_sus_prin5 | 1.029819 .0934406 0.32 0.746 .8620399 1.230254
cond_ocu2 | 1.048316 .0745003 0.66 0.507 .9120111 1.204992
cond_ocu3 | 1.147922 .3096997 0.51 0.609 .6764964 1.947868
cond_ocu4 | 1.219902 .0889663 2.73 0.006 1.05742 1.407351
cond_ocu5 | 1.059432 .1644046 0.37 0.710 .7815954 1.436032
cond_ocu6 | 1.189658 .0465136 4.44 0.000 1.101899 1.284407
policonsumo | .9915513 .0486081 -0.17 0.863 .9007149 1.091548
num_hij2 | 1.125691 .0447882 2.98 0.003 1.041243 1.216988
tenviv1 | 1.067325 .1350537 0.51 0.607 .8328942 1.367739
tenviv2 | 1.125481 .0969685 1.37 0.170 .9506066 1.332525
tenviv4 | 1.038145 .0510138 0.76 0.446 .9428237 1.143103
tenviv5 | 1.010991 .0383369 0.29 0.773 .9385766 1.088993
mzone2 | 1.450748 .0608683 8.87 0.000 1.336222 1.575091
mzone3 | 1.529377 .0965904 6.73 0.000 1.351311 1.730906
n_off_vio | 1.466308 .0554228 10.13 0.000 1.361607 1.579059
n_off_acq | 2.79789 .0972241 29.61 0.000 2.613678 2.995084
n_off_sud | 1.390419 .0506878 9.04 0.000 1.294539 1.493401
n_off_oth | 1.735973 .0634027 15.10 0.000 1.616049 1.864796
psy_com2 | 1.118877 .0550862 2.28 0.023 1.015956 1.232225
psy_com3 | 1.099924 .0423976 2.47 0.013 1.019887 1.186241
dep2 | 1.036385 .044127 0.84 0.401 .9534085 1.126584
rural2 | .8986428 .0559754 -1.72 0.086 .7953656 1.01533
rural3 | .8602764 .0595516 -2.17 0.030 .7511292 .9852839
porc_pobr | 1.564999 .3917344 1.79 0.074 .9581869 2.556102
susini2 | 1.188248 .108315 1.89 0.058 .993838 1.420687
susini3 | 1.271156 .0819343 3.72 0.000 1.120297 1.442329
susini4 | 1.180538 .0440195 4.45 0.000 1.097339 1.270046
susini5 | 1.422382 .132053 3.80 0.000 1.185745 1.706245
ano_nac_corr | .8499524 .0080259 -17.22 0.000 .8343665 .8658294
cohab2 | .8798853 .0590902 -1.91 0.057 .771369 1.003668
cohab3 | 1.074379 .08591 0.90 0.370 .91853 1.256671
cohab4 | .9636614 .0641519 -0.56 0.578 .8457834 1.097968
fis_com2 | 1.057493 .0364507 1.62 0.105 .9884111 1.131404
fis_com3 | .8188068 .0709442 -2.31 0.021 .6909243 .9703589
rc_x1 | .8502196 .0101855 -13.54 0.000 .8304888 .870419
rc_x2 | .8817093 .0351606 -3.16 0.002 .81542 .9533875
rc_x3 | 1.277919 .1359173 2.31 0.021 1.037459 1.574112
_rcs1 | 2.188338 .0735344 23.31 0.000 2.048857 2.337315
_rcs2 | 1.05157 .0289051 1.83 0.067 .996416 1.109777
_rcs3 | 1.011888 .0217525 0.55 0.583 .9701391 1.055433
_rcs4 | 1.030826 .0143444 2.18 0.029 1.003091 1.059328
_rcs5 | 1.021277 .0091945 2.34 0.019 1.003415 1.039458
_rcs6 | 1.010748 .0075863 1.42 0.154 .9959876 1.025726
_rcs7 | 1.014623 .0065448 2.25 0.024 1.001876 1.027532
_rcs8 | 1.008779 .0040831 2.16 0.031 1.000808 1.016813
_rcs_mot_egr_early1 | .8945434 .0336375 -2.96 0.003 .830986 .9629618
_rcs_mot_egr_early2 | 1.005324 .0300856 0.18 0.859 .9480535 1.066054
_rcs_mot_egr_early3 | 1.020261 .0239685 0.85 0.393 .9743483 1.068336
_rcs_mot_egr_early4 | .9784048 .015614 -1.37 0.171 .9482757 1.009491
_rcs_mot_egr_early5 | .9918752 .0106064 -0.76 0.446 .9713034 1.012883
_rcs_mot_egr_early6 | .9974511 .0086616 -0.29 0.769 .9806182 1.014573
_rcs_mot_egr_early7 | .9928559 .0064504 -1.10 0.270 .9802935 1.005579
_rcs_mot_egr_late1 | .9228189 .0335928 -2.21 0.027 .8592721 .9910653
_rcs_mot_egr_late2 | 1.02135 .0301497 0.72 0.474 .9639348 1.082185
_rcs_mot_egr_late3 | 1.022803 .0235847 0.98 0.328 .9776069 1.070089
_rcs_mot_egr_late4 | .9883778 .0152514 -0.76 0.449 .9589331 1.018727
_rcs_mot_egr_late5 | .9921793 .0101148 -0.77 0.441 .9725515 1.012203
_rcs_mot_egr_late6 | .9963899 .0082112 -0.44 0.661 .9804255 1.012614
_rcs_mot_egr_late7 | .9915253 .0059989 -1.41 0.160 .9798372 1.003353
_cons | 2.0e+139 3.9e+140 16.87 0.000 1.3e+123 3.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.345
Iteration 1: log likelihood = -16975.816
Iteration 2: log likelihood = -16975.759
Iteration 3: log likelihood = -16975.759
Log likelihood = -16975.759 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.01118 .1268504 11.08 0.000 1.777311 2.275823
mot_egr_late | 1.69292 .0919797 9.69 0.000 1.52191 1.883145
tr_mod2 | 1.218473 .0518252 4.65 0.000 1.121016 1.324402
sex_dum2 | .6074549 .029527 -10.26 0.000 .5522542 .6681732
edad_ini_cons | .9714193 .0047128 -5.98 0.000 .9622262 .9807002
esc1 | 1.430587 .0886577 5.78 0.000 1.26696 1.615347
esc2 | 1.264238 .0732477 4.05 0.000 1.128526 1.41627
sus_prin2 | 1.157675 .0782794 2.17 0.030 1.013983 1.321731
sus_prin3 | 1.682305 .0917214 9.54 0.000 1.511806 1.872032
sus_prin4 | 1.171436 .0934009 1.98 0.047 1.00196 1.369577
sus_prin5 | 1.591135 .2392131 3.09 0.002 1.185049 2.136376
fr_cons_sus_prin2 | .9672987 .1088454 -0.30 0.768 .7758523 1.205986
fr_cons_sus_prin3 | .9785207 .0894273 -0.24 0.812 .8180474 1.170473
fr_cons_sus_prin4 | 1.003238 .095116 0.03 0.973 .8331099 1.208107
fr_cons_sus_prin5 | 1.029851 .0934444 0.32 0.746 .8620648 1.230294
cond_ocu2 | 1.048593 .0745197 0.67 0.504 .9122529 1.20531
cond_ocu3 | 1.147569 .3096023 0.51 0.610 .6762906 1.947261
cond_ocu4 | 1.21998 .0889735 2.73 0.006 1.057485 1.407444
cond_ocu5 | 1.057994 .1641739 0.36 0.716 .7805456 1.434063
cond_ocu6 | 1.189698 .0465145 4.44 0.000 1.101936 1.284448
policonsumo | .9915032 .0486062 -0.17 0.862 .9006704 1.091496
num_hij2 | 1.125557 .0447834 2.97 0.003 1.041119 1.216844
tenviv1 | 1.067434 .135064 0.52 0.606 .8329855 1.36787
tenviv2 | 1.125745 .0969895 1.37 0.169 .9508327 1.332834
tenviv4 | 1.038111 .0510113 0.76 0.447 .9427948 1.143065
tenviv5 | 1.010857 .0383316 0.28 0.776 .938452 1.088847
mzone2 | 1.450538 .0608613 8.86 0.000 1.336025 1.574866
mzone3 | 1.528956 .0965651 6.72 0.000 1.350938 1.730433
n_off_vio | 1.466427 .0554269 10.13 0.000 1.361719 1.579187
n_off_acq | 2.797829 .0972242 29.61 0.000 2.613618 2.995024
n_off_sud | 1.390407 .0506893 9.04 0.000 1.294524 1.493391
n_off_oth | 1.735831 .0633989 15.10 0.000 1.615915 1.864646
psy_com2 | 1.118172 .0550468 2.27 0.023 1.015324 1.231438
psy_com3 | 1.100283 .0424107 2.48 0.013 1.020222 1.186627
dep2 | 1.036333 .0441234 0.84 0.402 .9533626 1.126524
rural2 | .8985349 .0559691 -1.72 0.086 .7952692 1.01521
rural3 | .860734 .059579 -2.17 0.030 .7515361 .9857984
porc_pobr | 1.570392 .3930504 1.80 0.071 .9615288 2.564801
susini2 | 1.18856 .1083418 1.90 0.058 .9941022 1.421057
susini3 | 1.270666 .0818999 3.72 0.000 1.11987 1.441766
susini4 | 1.180657 .0440235 4.45 0.000 1.09745 1.270173
susini5 | 1.422299 .1320432 3.79 0.000 1.185679 1.70614
ano_nac_corr | .8498721 .0080227 -17.23 0.000 .8342924 .8657426
cohab2 | .8802075 .0591096 -1.90 0.057 .7716552 1.00403
cohab3 | 1.074953 .0859525 0.90 0.366 .9190268 1.257335
cohab4 | .963934 .0641701 -0.55 0.581 .8460225 1.098279
fis_com2 | 1.057833 .0364614 1.63 0.103 .9887309 1.131766
fis_com3 | .8189974 .0709602 -2.30 0.021 .6910859 .9705837
rc_x1 | .8501578 .0101835 -13.55 0.000 .8304311 .8703532
rc_x2 | .8816808 .035161 -3.16 0.002 .8153909 .9533599
rc_x3 | 1.277973 .1359283 2.31 0.021 1.037495 1.574192
_rcs1 | 2.197565 .0692823 24.97 0.000 2.065884 2.337639
_rcs2 | 1.064088 .0083261 7.94 0.000 1.047894 1.080533
_rcs3 | 1.033742 .0064627 5.31 0.000 1.021152 1.046486
_rcs4 | 1.018956 .0047 4.07 0.000 1.009785 1.028209
_rcs5 | 1.012889 .0033094 3.92 0.000 1.006424 1.019396
_rcs6 | 1.007948 .0027003 2.95 0.003 1.002669 1.013254
_rcs7 | 1.00934 .0022907 4.10 0.000 1.00486 1.013839
_rcs8 | 1.005614 .0020825 2.70 0.007 1.001541 1.009704
_rcs9 | 1.003698 .0018116 2.04 0.041 1.000153 1.007255
_rcs_mot_egr_early1 | .8941976 .0314589 -3.18 0.001 .834617 .9580315
_rcs_mot_egr_late1 | .9154308 .0310088 -2.61 0.009 .8566283 .9782698
_cons | 2.5e+139 4.7e+140 16.89 0.000 1.6e+123 3.7e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16984.517
Iteration 1: log likelihood = -16974.986
Iteration 2: log likelihood = -16974.888
Iteration 3: log likelihood = -16974.888
Log likelihood = -16974.888 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009477 .1268478 11.06 0.000 1.775625 2.274129
mot_egr_late | 1.694224 .0921043 9.70 0.000 1.522987 1.884713
tr_mod2 | 1.217892 .051804 4.63 0.000 1.120475 1.323778
sex_dum2 | .6075182 .0295302 -10.25 0.000 .5523116 .6682429
edad_ini_cons | .9714265 .0047127 -5.98 0.000 .9622336 .9807073
esc1 | 1.430581 .088657 5.78 0.000 1.266955 1.61534
esc2 | 1.264168 .0732437 4.05 0.000 1.128464 1.416191
sus_prin2 | 1.157755 .0782845 2.17 0.030 1.014052 1.321821
sus_prin3 | 1.682329 .0917206 9.54 0.000 1.511832 1.872055
sus_prin4 | 1.171424 .0933991 1.98 0.047 1.001952 1.369561
sus_prin5 | 1.59161 .2392899 3.09 0.002 1.185395 2.137028
fr_cons_sus_prin2 | .9672499 .1088401 -0.30 0.767 .7758129 1.205925
fr_cons_sus_prin3 | .9785239 .0894273 -0.24 0.812 .8180506 1.170477
fr_cons_sus_prin4 | 1.003226 .0951144 0.03 0.973 .8331009 1.208092
fr_cons_sus_prin5 | 1.029863 .0934442 0.32 0.746 .8620768 1.230305
cond_ocu2 | 1.048424 .0745079 0.67 0.506 .9121055 1.205116
cond_ocu3 | 1.147094 .3094748 0.51 0.611 .6760101 1.946457
cond_ocu4 | 1.220239 .0889892 2.73 0.006 1.057715 1.407735
cond_ocu5 | 1.058627 .1642747 0.37 0.714 .7810093 1.434928
cond_ocu6 | 1.189708 .0465141 4.44 0.000 1.101947 1.284458
policonsumo | .9915761 .0486092 -0.17 0.863 .9007377 1.091576
num_hij2 | 1.125626 .044786 2.97 0.003 1.041182 1.216918
tenviv1 | 1.067335 .1350515 0.52 0.607 .8329077 1.367743
tenviv2 | 1.12542 .0969638 1.37 0.170 .9505547 1.332455
tenviv4 | 1.038237 .0510176 0.76 0.445 .9429091 1.143204
tenviv5 | 1.011021 .038338 0.29 0.773 .9386045 1.089025
mzone2 | 1.450678 .0608661 8.87 0.000 1.336156 1.575016
mzone3 | 1.529464 .0965959 6.73 0.000 1.351389 1.731005
n_off_vio | 1.466372 .0554256 10.13 0.000 1.361667 1.57913
n_off_acq | 2.798049 .0972329 29.61 0.000 2.613821 2.995261
n_off_sud | 1.390468 .0506912 9.04 0.000 1.294582 1.493457
n_off_oth | 1.735868 .0634001 15.10 0.000 1.615949 1.864685
psy_com2 | 1.11865 .0550733 2.28 0.023 1.015753 1.231972
psy_com3 | 1.100091 .0424034 2.47 0.013 1.020044 1.18642
dep2 | 1.036314 .0441233 0.84 0.402 .9533443 1.126505
rural2 | .8984669 .0559648 -1.72 0.086 .7952092 1.015132
rural3 | .8604085 .0595605 -2.17 0.030 .751245 .9854346
porc_pobr | 1.570118 .3929781 1.80 0.071 .9613655 2.564341
susini2 | 1.188225 .1083125 1.89 0.058 .9938199 1.420659
susini3 | 1.270998 .0819222 3.72 0.000 1.120162 1.442146
susini4 | 1.180555 .0440196 4.45 0.000 1.097355 1.270063
susini5 | 1.422366 .1320506 3.79 0.000 1.185733 1.706223
ano_nac_corr | .8499404 .0080252 -17.22 0.000 .8343559 .8658159
cohab2 | .8799887 .0590963 -1.90 0.057 .771461 1.003784
cohab3 | 1.074689 .0859328 0.90 0.368 .9187985 1.257029
cohab4 | .963797 .064161 -0.55 0.580 .8459023 1.098123
fis_com2 | 1.057801 .0364611 1.63 0.103 .9886988 1.131732
fis_com3 | .8188925 .0709514 -2.31 0.021 .6909969 .97046
rc_x1 | .8502146 .0101855 -13.54 0.000 .8304839 .870414
rc_x2 | .8816816 .035161 -3.16 0.002 .8153918 .9533607
rc_x3 | 1.278049 .1359367 2.31 0.021 1.037556 1.574286
_rcs1 | 2.179981 .0723895 23.47 0.000 2.042619 2.326581
_rcs2 | 1.047053 .0253442 1.90 0.057 .9985394 1.097924
_rcs3 | 1.030234 .0078188 3.92 0.000 1.015022 1.045673
_rcs4 | 1.017978 .0048677 3.73 0.000 1.008482 1.027563
_rcs5 | 1.012706 .003317 3.85 0.000 1.006225 1.019228
_rcs6 | 1.007912 .0027004 2.94 0.003 1.002633 1.013219
_rcs7 | 1.009348 .0022905 4.10 0.000 1.004869 1.013847
_rcs8 | 1.005603 .0020828 2.70 0.007 1.001529 1.009693
_rcs9 | 1.003691 .0018121 2.04 0.041 1.000146 1.007249
_rcs_mot_egr_early1 | .8990326 .0333093 -2.87 0.004 .8360616 .9667464
_rcs_mot_egr_early2 | 1.008078 .0274692 0.30 0.768 .9556516 1.06338
_rcs_mot_egr_late1 | .9263347 .0332933 -2.13 0.033 .8633264 .9939416
_rcs_mot_egr_late2 | 1.026256 .0273845 0.97 0.331 .9739631 1.081357
_cons | 2.1e+139 4.0e+140 16.88 0.000 1.4e+123 3.2e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.547
Iteration 1: log likelihood = -16974.811
Iteration 2: log likelihood = -16974.736
Iteration 3: log likelihood = -16974.736
Log likelihood = -16974.736 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009846 .1268925 11.06 0.000 1.775913 2.274593
mot_egr_late | 1.694329 .0921316 9.70 0.000 1.523044 1.884877
tr_mod2 | 1.217844 .0518029 4.63 0.000 1.120429 1.323728
sex_dum2 | .6075224 .0295303 -10.25 0.000 .5523155 .6682475
edad_ini_cons | .9714273 .0047127 -5.98 0.000 .9622344 .980708
esc1 | 1.430641 .0886603 5.78 0.000 1.267009 1.615406
esc2 | 1.264221 .0732467 4.05 0.000 1.128511 1.416251
sus_prin2 | 1.157895 .0782946 2.17 0.030 1.014174 1.321983
sus_prin3 | 1.682554 .0917347 9.54 0.000 1.512031 1.872309
sus_prin4 | 1.171458 .0934021 1.98 0.047 1.00198 1.369602
sus_prin5 | 1.592256 .2393879 3.09 0.002 1.185875 2.137898
fr_cons_sus_prin2 | .9672192 .1088366 -0.30 0.767 .7757883 1.205887
fr_cons_sus_prin3 | .9785763 .089432 -0.24 0.813 .8180944 1.170539
fr_cons_sus_prin4 | 1.003266 .0951183 0.03 0.973 .8331344 1.208141
fr_cons_sus_prin5 | 1.029863 .093444 0.32 0.746 .8620774 1.230305
cond_ocu2 | 1.048338 .0745019 0.66 0.507 .91203 1.205017
cond_ocu3 | 1.14768 .3096339 0.51 0.610 .676354 1.947455
cond_ocu4 | 1.220347 .0889963 2.73 0.006 1.05781 1.407858
cond_ocu5 | 1.058827 .1643072 0.37 0.713 .7811545 1.435203
cond_ocu6 | 1.189712 .0465146 4.44 0.000 1.10195 1.284463
policonsumo | .9916324 .0486124 -0.17 0.864 .9007881 1.091638
num_hij2 | 1.125701 .0447889 2.98 0.003 1.041252 1.216999
tenviv1 | 1.067324 .1350514 0.51 0.607 .8328975 1.367732
tenviv2 | 1.125401 .0969629 1.37 0.170 .9505366 1.332433
tenviv4 | 1.038266 .0510192 0.76 0.445 .9429345 1.143235
tenviv5 | 1.011085 .0383406 0.29 0.771 .9386634 1.089094
mzone2 | 1.450755 .0608692 8.87 0.000 1.336227 1.575099
mzone3 | 1.529692 .0966117 6.73 0.000 1.351588 1.731267
n_off_vio | 1.466336 .0554245 10.13 0.000 1.361633 1.579091
n_off_acq | 2.798088 .0972331 29.61 0.000 2.61386 2.995301
n_off_sud | 1.39049 .0506917 9.04 0.000 1.294602 1.493479
n_off_oth | 1.735897 .0634008 15.10 0.000 1.615977 1.864716
psy_com2 | 1.118871 .0550853 2.28 0.023 1.015951 1.232217
psy_com3 | 1.100015 .0424008 2.47 0.013 1.019972 1.186338
dep2 | 1.036325 .044124 0.84 0.402 .9533539 1.126517
rural2 | .8984723 .0559651 -1.72 0.086 .795214 1.015138
rural3 | .8602508 .0595502 -2.17 0.030 .7511061 .9852554
porc_pobr | 1.568231 .3925206 1.80 0.072 .9601932 2.561307
susini2 | 1.188072 .1082985 1.89 0.059 .9936922 1.420476
susini3 | 1.271139 .0819321 3.72 0.000 1.120285 1.442308
susini4 | 1.180532 .0440191 4.45 0.000 1.097333 1.270038
susini5 | 1.422197 .1320348 3.79 0.000 1.185592 1.70602
ano_nac_corr | .8499066 .0080253 -17.22 0.000 .834322 .8657823
cohab2 | .879913 .0590918 -1.90 0.057 .7713936 1.003699
cohab3 | 1.074619 .085928 0.90 0.368 .9187376 1.25695
cohab4 | .9637478 .0641579 -0.55 0.579 .8458586 1.098067
fis_com2 | 1.057696 .0364578 1.63 0.104 .9886002 1.131621
fis_com3 | .8188002 .0709438 -2.31 0.021 .6909185 .9703514
rc_x1 | .8501808 .0101852 -13.55 0.000 .8304507 .8703796
rc_x2 | .8816735 .0351602 -3.16 0.002 .8153851 .953351
rc_x3 | 1.278072 .1359374 2.31 0.021 1.037577 1.57431
_rcs1 | 2.183213 .0735272 23.18 0.000 2.043756 2.332187
_rcs2 | 1.046012 .0262738 1.79 0.073 .9957637 1.098797
_rcs3 | 1.033282 .0146342 2.31 0.021 1.004994 1.062367
_rcs4 | 1.019926 .0105675 1.90 0.057 .9994227 1.04085
_rcs5 | 1.0136 .0057717 2.37 0.018 1.00235 1.024976
_rcs6 | 1.008161 .0032471 2.52 0.012 1.001817 1.014545
_rcs7 | 1.009418 .0023345 4.05 0.000 1.004853 1.014004
_rcs8 | 1.005615 .0020839 2.70 0.007 1.001539 1.009708
_rcs9 | 1.003705 .0018127 2.05 0.041 1.000159 1.007265
_rcs_mot_egr_early1 | .8963864 .0337336 -2.91 0.004 .8326492 .9650025
_rcs_mot_egr_early2 | 1.009377 .0279902 0.34 0.736 .955981 1.065754
_rcs_mot_egr_early3 | .9928408 .019593 -0.36 0.716 .9551724 1.031995
_rcs_mot_egr_late1 | .9255381 .033762 -2.12 0.034 .861676 .9941333
_rcs_mot_egr_late2 | 1.025868 .0278983 0.94 0.348 .9726202 1.082031
_rcs_mot_egr_late3 | 1.000114 .0190517 0.01 0.995 .9634619 1.03816
_cons | 2.3e+139 4.3e+140 16.88 0.000 1.5e+123 3.5e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.767
Iteration 1: log likelihood = -16973.83
Iteration 2: log likelihood = -16973.713
Iteration 3: log likelihood = -16973.713
Log likelihood = -16973.713 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.008423 .126775 11.05 0.000 1.774704 2.272921
mot_egr_late | 1.693435 .0920492 9.69 0.000 1.522299 1.883809
tr_mod2 | 1.217916 .0518068 4.63 0.000 1.120494 1.323808
sex_dum2 | .6075096 .0295294 -10.25 0.000 .5523045 .6682327
edad_ini_cons | .9714208 .0047127 -5.98 0.000 .9622279 .9807016
esc1 | 1.430698 .0886635 5.78 0.000 1.26706 1.61547
esc2 | 1.264242 .073248 4.05 0.000 1.12853 1.416275
sus_prin2 | 1.15805 .0783061 2.17 0.030 1.014308 1.322162
sus_prin3 | 1.682906 .0917568 9.55 0.000 1.512342 1.872707
sus_prin4 | 1.171517 .0934069 1.99 0.047 1.00203 1.369671
sus_prin5 | 1.592551 .2394333 3.10 0.002 1.186093 2.138296
fr_cons_sus_prin2 | .9672496 .1088399 -0.30 0.767 .7758129 1.205924
fr_cons_sus_prin3 | .9785629 .0894308 -0.24 0.813 .8180832 1.170523
fr_cons_sus_prin4 | 1.003339 .0951256 0.04 0.972 .8331934 1.208229
fr_cons_sus_prin5 | 1.029762 .0934357 0.32 0.747 .8619916 1.230186
cond_ocu2 | 1.04835 .0745027 0.66 0.506 .9120406 1.205031
cond_ocu3 | 1.148018 .3097249 0.51 0.609 .6765532 1.948028
cond_ocu4 | 1.2201 .0889798 2.73 0.006 1.057593 1.407576
cond_ocu5 | 1.05918 .1643652 0.37 0.711 .78141 1.435689
cond_ocu6 | 1.189724 .0465159 4.44 0.000 1.10196 1.284478
policonsumo | .9916218 .0486118 -0.17 0.864 .9007785 1.091627
num_hij2 | 1.125656 .0447868 2.97 0.003 1.041211 1.21695
tenviv1 | 1.067266 .1350462 0.51 0.607 .8328485 1.367663
tenviv2 | 1.125641 .0969836 1.37 0.170 .9507398 1.332718
tenviv4 | 1.038183 .0510154 0.76 0.446 .9428588 1.143145
tenviv5 | 1.011035 .0383387 0.29 0.772 .9386168 1.08904
mzone2 | 1.450802 .0608712 8.87 0.000 1.33627 1.57515
mzone3 | 1.529644 .0966091 6.73 0.000 1.351545 1.731213
n_off_vio | 1.466304 .0554226 10.13 0.000 1.361604 1.579055
n_off_acq | 2.797916 .0972255 29.61 0.000 2.613702 2.995113
n_off_sud | 1.390403 .0506875 9.04 0.000 1.294523 1.493384
n_off_oth | 1.735897 .0633999 15.10 0.000 1.615979 1.864714
psy_com2 | 1.118977 .0550905 2.28 0.022 1.016047 1.232333
psy_com3 | 1.099918 .0423974 2.47 0.013 1.019882 1.186235
dep2 | 1.036361 .0441255 0.84 0.402 .9533867 1.126556
rural2 | .8985666 .0559704 -1.72 0.086 .7952985 1.015244
rural3 | .8601752 .0595447 -2.18 0.030 .7510406 .9851683
porc_pobr | 1.566485 .3920983 1.79 0.073 .9591064 2.558503
susini2 | 1.188168 .1083072 1.89 0.059 .9937715 1.42059
susini3 | 1.271206 .0819373 3.72 0.000 1.120341 1.442385
susini4 | 1.18053 .0440191 4.45 0.000 1.097331 1.270037
susini5 | 1.422358 .1320504 3.79 0.000 1.185726 1.706215
ano_nac_corr | .8499219 .0080258 -17.22 0.000 .8343364 .8657986
cohab2 | .8799617 .0590957 -1.90 0.057 .7714352 1.003756
cohab3 | 1.07457 .0859252 0.90 0.368 .9186936 1.256895
cohab4 | .9637657 .0641599 -0.55 0.579 .8458731 1.09809
fis_com2 | 1.057481 .03645 1.62 0.105 .9884004 1.13139
fis_com3 | .8187487 .0709393 -2.31 0.021 .6908749 .9702906
rc_x1 | .8502091 .0101855 -13.55 0.000 .8304783 .8704086
rc_x2 | .8816286 .0351574 -3.16 0.002 .8153454 .9533004
rc_x3 | 1.278177 .1359453 2.31 0.021 1.037668 1.574431
_rcs1 | 2.180923 .0731202 23.26 0.000 2.042218 2.32905
_rcs2 | 1.048562 .0276658 1.80 0.072 .9957164 1.104213
_rcs3 | 1.01813 .0178658 1.02 0.306 .9837091 1.053755
_rcs4 | 1.020587 .0101549 2.05 0.041 1.000877 1.040686
_rcs5 | 1.022841 .0087081 2.65 0.008 1.005915 1.040051
_rcs6 | 1.016674 .0069612 2.42 0.016 1.003122 1.03041
_rcs7 | 1.013414 .0037164 3.63 0.000 1.006156 1.020724
_rcs8 | 1.006432 .0021651 2.98 0.003 1.002197 1.010684
_rcs9 | 1.003644 .0018125 2.01 0.044 1.000098 1.007203
_rcs_mot_egr_early1 | .8974339 .0336504 -2.89 0.004 .8338456 .9658715
_rcs_mot_egr_early2 | 1.008898 .02902 0.31 0.758 .9535932 1.06741
_rcs_mot_egr_early3 | 1.006661 .0213198 0.31 0.754 .9657307 1.049327
_rcs_mot_egr_early4 | .9796079 .0139571 -1.45 0.148 .9526308 1.007349
_rcs_mot_egr_late1 | .926565 .033672 -2.10 0.036 .8628645 .9949681
_rcs_mot_egr_late2 | 1.024782 .0289686 0.87 0.386 .9695492 1.083162
_rcs_mot_egr_late3 | 1.01337 .0208176 0.65 0.518 .9733786 1.055004
_rcs_mot_egr_late4 | .9830909 .0134256 -1.25 0.212 .9571263 1.00976
_cons | 2.2e+139 4.2e+140 16.88 0.000 1.4e+123 3.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.791
Iteration 1: log likelihood = -16973.765
Iteration 2: log likelihood = -16973.644
Iteration 3: log likelihood = -16973.644
Log likelihood = -16973.644 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.00823 .1267709 11.05 0.000 1.77452 2.272721
mot_egr_late | 1.693466 .0920614 9.69 0.000 1.522309 1.883866
tr_mod2 | 1.217873 .0518047 4.63 0.000 1.120455 1.323761
sex_dum2 | .6075224 .02953 -10.25 0.000 .5523161 .6682468
edad_ini_cons | .9714208 .0047127 -5.98 0.000 .9622279 .9807016
esc1 | 1.43071 .0886642 5.78 0.000 1.26707 1.615483
esc2 | 1.264248 .0732483 4.05 0.000 1.128535 1.41628
sus_prin2 | 1.157994 .0783021 2.17 0.030 1.014259 1.322097
sus_prin3 | 1.682822 .0917517 9.55 0.000 1.512267 1.872612
sus_prin4 | 1.171505 .0934058 1.99 0.047 1.00202 1.369656
sus_prin5 | 1.592321 .2393998 3.09 0.002 1.18592 2.13799
fr_cons_sus_prin2 | .9672408 .108839 -0.30 0.767 .7758057 1.205914
fr_cons_sus_prin3 | .9785697 .0894315 -0.24 0.813 .8180888 1.170531
fr_cons_sus_prin4 | 1.003362 .0951277 0.04 0.972 .8332129 1.208256
fr_cons_sus_prin5 | 1.0298 .093439 0.32 0.746 .8620231 1.230231
cond_ocu2 | 1.048336 .0745018 0.66 0.507 .9120281 1.205015
cond_ocu3 | 1.147902 .3096944 0.51 0.609 .6764846 1.947834
cond_ocu4 | 1.22009 .0889793 2.73 0.006 1.057585 1.407566
cond_ocu5 | 1.059253 .1643763 0.37 0.711 .7814638 1.435787
cond_ocu6 | 1.189713 .0465156 4.44 0.000 1.10195 1.284466
policonsumo | .991598 .0486106 -0.17 0.863 .9007569 1.0916
num_hij2 | 1.125664 .0447872 2.98 0.003 1.041218 1.216958
tenviv1 | 1.067353 .1350567 0.52 0.606 .8329179 1.367774
tenviv2 | 1.125577 .0969779 1.37 0.170 .9506863 1.332642
tenviv4 | 1.038225 .0510175 0.76 0.445 .9428966 1.143191
tenviv5 | 1.011036 .0383387 0.29 0.772 .9386175 1.089041
mzone2 | 1.450753 .0608689 8.87 0.000 1.336226 1.575097
mzone3 | 1.529633 .0966078 6.73 0.000 1.351535 1.731199
n_off_vio | 1.466314 .055423 10.13 0.000 1.361613 1.579066
n_off_acq | 2.797958 .0972265 29.61 0.000 2.613742 2.995157
n_off_sud | 1.390432 .0506887 9.04 0.000 1.29455 1.493416
n_off_oth | 1.735934 .0634011 15.10 0.000 1.616014 1.864754
psy_com2 | 1.118889 .0550869 2.28 0.023 1.015966 1.232238
psy_com3 | 1.099944 .0423984 2.47 0.013 1.019906 1.186263
dep2 | 1.036348 .044125 0.84 0.402 .9533751 1.126543
rural2 | .8985351 .0559686 -1.72 0.086 .7952704 1.015209
rural3 | .8602074 .0595471 -2.18 0.030 .7510684 .9852055
porc_pobr | 1.566978 .3922186 1.79 0.073 .9594114 2.559298
susini2 | 1.188129 .1083036 1.89 0.059 .9937398 1.420544
susini3 | 1.271266 .0819406 3.72 0.000 1.120395 1.442452
susini4 | 1.180549 .0440198 4.45 0.000 1.097349 1.270057
susini5 | 1.422435 .132058 3.80 0.000 1.185789 1.706308
ano_nac_corr | .8499235 .0080258 -17.22 0.000 .8343379 .8658002
cohab2 | .8799588 .0590951 -1.90 0.057 .7714334 1.003751
cohab3 | 1.074536 .0859221 0.90 0.369 .9186646 1.256854
cohab4 | .963756 .0641588 -0.55 0.579 .8458653 1.098077
fis_com2 | 1.057555 .036453 1.62 0.104 .9884686 1.13147
fis_com3 | .8187471 .0709392 -2.31 0.021 .6908737 .9702886
rc_x1 | .8502025 .0101855 -13.55 0.000 .830472 .8704019
rc_x2 | .8816637 .0351592 -3.16 0.002 .8153772 .9533391
rc_x3 | 1.278062 .1359343 2.31 0.021 1.037572 1.574293
_rcs1 | 2.180458 .0731277 23.24 0.000 2.04174 2.328602
_rcs2 | 1.050242 .0282079 1.83 0.068 .9963859 1.10701
_rcs3 | 1.014863 .0196348 0.76 0.446 .9771 1.054085
_rcs4 | 1.023312 .0113788 2.07 0.038 1.001251 1.045858
_rcs5 | 1.02347 .0094822 2.50 0.012 1.005053 1.042225
_rcs6 | 1.014096 .0072549 1.96 0.050 .9999763 1.028416
_rcs7 | 1.01189 .0066355 1.80 0.071 .9989678 1.024979
_rcs8 | 1.006469 .0032539 1.99 0.046 1.000111 1.012866
_rcs9 | 1.003775 .0018185 2.08 0.038 1.000217 1.007345
_rcs_mot_egr_early1 | .8980566 .0336974 -2.87 0.004 .8343811 .9665915
_rcs_mot_egr_early2 | 1.007015 .0294102 0.24 0.811 .9509905 1.066339
_rcs_mot_egr_early3 | 1.01252 .0224263 0.56 0.574 .9695059 1.057443
_rcs_mot_egr_early4 | .9790399 .0148556 -1.40 0.163 .9503521 1.008594
_rcs_mot_egr_early5 | .9974992 .010755 -0.23 0.816 .976641 1.018803
_rcs_mot_egr_late1 | .9266174 .0336897 -2.10 0.036 .8628846 .9950576
_rcs_mot_egr_late2 | 1.023006 .0294065 0.79 0.429 .9669639 1.082296
_rcs_mot_egr_late3 | 1.017775 .0219891 0.82 0.415 .9755765 1.061798
_rcs_mot_egr_late4 | .9851163 .0144221 -1.02 0.306 .9572512 1.013793
_rcs_mot_egr_late5 | .9958474 .0102966 -0.40 0.687 .9758696 1.016234
_cons | 2.2e+139 4.2e+140 16.88 0.000 1.4e+123 3.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.648
Iteration 1: log likelihood = -16973.321
Iteration 2: log likelihood = -16973.194
Iteration 3: log likelihood = -16973.194
Log likelihood = -16973.194 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.008517 .1267957 11.05 0.000 1.774762 2.273061
mot_egr_late | 1.693844 .0920885 9.69 0.000 1.522638 1.884301
tr_mod2 | 1.217842 .0518032 4.63 0.000 1.120427 1.323727
sex_dum2 | .6075337 .0295306 -10.25 0.000 .5523263 .6682593
edad_ini_cons | .9714196 .0047127 -5.98 0.000 .9622266 .9807004
esc1 | 1.430686 .0886629 5.78 0.000 1.267049 1.615456
esc2 | 1.264217 .0732466 4.05 0.000 1.128507 1.416246
sus_prin2 | 1.158009 .0783033 2.17 0.030 1.014272 1.322115
sus_prin3 | 1.682831 .0917522 9.55 0.000 1.512275 1.872622
sus_prin4 | 1.171492 .0934047 1.99 0.047 1.002009 1.369641
sus_prin5 | 1.592205 .2393825 3.09 0.002 1.185834 2.137835
fr_cons_sus_prin2 | .9672812 .1088436 -0.30 0.768 .775838 1.205964
fr_cons_sus_prin3 | .9785812 .0894325 -0.24 0.813 .8180985 1.170545
fr_cons_sus_prin4 | 1.00339 .0951305 0.04 0.972 .833236 1.20829
fr_cons_sus_prin5 | 1.029822 .0934412 0.32 0.746 .8620417 1.230258
cond_ocu2 | 1.048322 .0745011 0.66 0.507 .9120162 1.205
cond_ocu3 | 1.147896 .3096929 0.51 0.609 .676481 1.947824
cond_ocu4 | 1.220045 .0889761 2.73 0.006 1.057546 1.407515
cond_ocu5 | 1.059342 .1643906 0.37 0.710 .7815292 1.43591
cond_ocu6 | 1.189722 .046516 4.44 0.000 1.101958 1.284476
policonsumo | .9916061 .0486109 -0.17 0.863 .9007644 1.091609
num_hij2 | 1.125661 .0447871 2.98 0.003 1.041215 1.216956
tenviv1 | 1.06744 .1350677 0.52 0.606 .8329852 1.367885
tenviv2 | 1.125625 .0969817 1.37 0.170 .9507272 1.332698
tenviv4 | 1.038236 .0510181 0.76 0.445 .9429069 1.143204
tenviv5 | 1.011025 .0383383 0.29 0.772 .9386075 1.089029
mzone2 | 1.450742 .0608682 8.87 0.000 1.336216 1.575084
mzone3 | 1.529607 .0966059 6.73 0.000 1.351513 1.731169
n_off_vio | 1.466324 .0554232 10.13 0.000 1.361622 1.579076
n_off_acq | 2.797942 .0972257 29.61 0.000 2.613727 2.99514
n_off_sud | 1.390422 .0506883 9.04 0.000 1.294541 1.493404
n_off_oth | 1.735946 .0634013 15.10 0.000 1.616025 1.864766
psy_com2 | 1.118852 .0550851 2.28 0.023 1.015933 1.232198
psy_com3 | 1.09994 .0423983 2.47 0.013 1.019902 1.186259
dep2 | 1.036354 .0441251 0.84 0.402 .9533802 1.126548
rural2 | .8985133 .0559672 -1.72 0.086 .7952511 1.015184
rural3 | .8601995 .0595465 -2.18 0.030 .7510615 .9851965
porc_pobr | 1.567237 .3922877 1.80 0.073 .959565 2.559735
susini2 | 1.188082 .1082993 1.89 0.059 .9936998 1.420487
susini3 | 1.271349 .0819458 3.72 0.000 1.120469 1.442546
susini4 | 1.180554 .0440199 4.45 0.000 1.097354 1.270062
susini5 | 1.422486 .1320626 3.80 0.000 1.185832 1.706369
ano_nac_corr | .849938 .0080259 -17.22 0.000 .8343521 .865815
cohab2 | .8799926 .0590974 -1.90 0.057 .771463 1.00379
cohab3 | 1.074537 .0859223 0.90 0.369 .9186653 1.256855
cohab4 | .963774 .06416 -0.55 0.579 .8458811 1.098098
fis_com2 | 1.057586 .0364542 1.62 0.104 .9884972 1.131504
fis_com3 | .8187435 .0709388 -2.31 0.021 .6908706 .9702842
rc_x1 | .850215 .0101856 -13.54 0.000 .8304842 .8704146
rc_x2 | .8816759 .0351597 -3.16 0.002 .8153883 .9533523
rc_x3 | 1.278011 .1359291 2.31 0.021 1.03753 1.57423
_rcs1 | 2.181264 .073162 23.25 0.000 2.042481 2.329477
_rcs2 | 1.050934 .0286025 1.83 0.068 .9963434 1.108516
_rcs3 | 1.012312 .0206168 0.60 0.548 .9726998 1.053538
_rcs4 | 1.024317 .0125996 1.95 0.051 .999918 1.049312
_rcs5 | 1.024767 .0091989 2.73 0.006 1.006895 1.042956
_rcs6 | 1.015784 .0081804 1.94 0.052 .9998769 1.031945
_rcs7 | 1.011755 .0065724 1.80 0.072 .9989553 1.024719
_rcs8 | 1.005427 .0052411 1.04 0.299 .9952067 1.015752
_rcs9 | 1.003719 .0019911 1.87 0.061 .9998238 1.007629
_rcs_mot_egr_early1 | .8977831 .0336949 -2.87 0.004 .8341127 .9663136
_rcs_mot_egr_early2 | 1.006083 .0297384 0.21 0.837 .9494531 1.066091
_rcs_mot_egr_early3 | 1.01715 .0231069 0.75 0.454 .9728547 1.063462
_rcs_mot_egr_early4 | .9794607 .0152931 -1.33 0.184 .9499407 1.009898
_rcs_mot_egr_early5 | .9899501 .0110433 -0.91 0.365 .9685407 1.011833
_rcs_mot_egr_early6 | 1.001714 .0085695 0.20 0.841 .9850579 1.018651
_rcs_mot_egr_late1 | .9261751 .0336789 -2.11 0.035 .8624629 .9945938
_rcs_mot_egr_late2 | 1.022165 .0297574 0.75 0.451 .9654743 1.082185
_rcs_mot_egr_late3 | 1.021675 .0227026 0.97 0.335 .9781337 1.067155
_rcs_mot_egr_late4 | .9865235 .0149141 -0.90 0.369 .9577213 1.016192
_rcs_mot_egr_late5 | .9906349 .0106119 -0.88 0.380 .9700527 1.011654
_rcs_mot_egr_late6 | .9992914 .0081267 -0.09 0.931 .9834896 1.015347
_cons | 2.1e+139 4.0e+140 16.87 0.000 1.4e+123 3.2e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.815
Iteration 1: log likelihood = -16973.279
Iteration 2: log likelihood = -16973.143
Iteration 3: log likelihood = -16973.143
Log likelihood = -16973.143 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.008914 .1268158 11.05 0.000 1.775121 2.273499
mot_egr_late | 1.694119 .0920976 9.70 0.000 1.522895 1.884594
tr_mod2 | 1.217839 .0518034 4.63 0.000 1.120423 1.323724
sex_dum2 | .6075356 .0295307 -10.25 0.000 .5523281 .6682613
edad_ini_cons | .9714183 .0047127 -5.98 0.000 .9622253 .9806991
esc1 | 1.430759 .0886671 5.78 0.000 1.267114 1.615538
esc2 | 1.264245 .0732483 4.05 0.000 1.128533 1.416278
sus_prin2 | 1.15801 .0783034 2.17 0.030 1.014273 1.322116
sus_prin3 | 1.682943 .0917588 9.55 0.000 1.512375 1.872748
sus_prin4 | 1.17152 .0934069 1.99 0.047 1.002034 1.369674
sus_prin5 | 1.592439 .2394175 3.09 0.002 1.186009 2.138149
fr_cons_sus_prin2 | .9672342 .1088382 -0.30 0.767 .7758005 1.205905
fr_cons_sus_prin3 | .9785558 .0894302 -0.24 0.813 .8180772 1.170515
fr_cons_sus_prin4 | 1.003393 .0951307 0.04 0.972 .8332385 1.208294
fr_cons_sus_prin5 | 1.029783 .0934376 0.32 0.746 .8620085 1.230211
cond_ocu2 | 1.048309 .0744999 0.66 0.507 .9120049 1.204984
cond_ocu3 | 1.148061 .3097372 0.51 0.609 .6765785 1.948104
cond_ocu4 | 1.21991 .0889664 2.73 0.006 1.057428 1.407359
cond_ocu5 | 1.059476 .1644115 0.37 0.710 .7816282 1.436092
cond_ocu6 | 1.189732 .0465166 4.44 0.000 1.101967 1.284487
policonsumo | .991572 .0486092 -0.17 0.863 .9007336 1.091571
num_hij2 | 1.125683 .044788 2.98 0.003 1.041236 1.21698
tenviv1 | 1.067376 .1350598 0.52 0.606 .832935 1.367804
tenviv2 | 1.125582 .0969776 1.37 0.170 .9506911 1.332646
tenviv4 | 1.038175 .0510152 0.76 0.446 .9428516 1.143137
tenviv5 | 1.011006 .0383375 0.29 0.773 .9385904 1.089009
mzone2 | 1.450745 .0608685 8.87 0.000 1.336219 1.575088
mzone3 | 1.529514 .0966003 6.73 0.000 1.35143 1.731064
n_off_vio | 1.466297 .0554219 10.13 0.000 1.361598 1.579047
n_off_acq | 2.797884 .0972225 29.61 0.000 2.613676 2.995075
n_off_sud | 1.390408 .0506873 9.04 0.000 1.294529 1.493389
n_off_oth | 1.735935 .0634005 15.10 0.000 1.616015 1.864753
psy_com2 | 1.118901 .0550877 2.28 0.022 1.015977 1.232252
psy_com3 | 1.099964 .0423992 2.47 0.013 1.019924 1.186284
dep2 | 1.036355 .0441255 0.84 0.402 .9533807 1.12655
rural2 | .8985791 .0559716 -1.72 0.086 .7953089 1.015259
rural3 | .860243 .0595493 -2.17 0.030 .7510999 .9852458
porc_pobr | 1.565699 .3919069 1.79 0.073 .958618 2.557236
susini2 | 1.188183 .1083089 1.89 0.059 .9937844 1.42061
susini3 | 1.271232 .0819392 3.72 0.000 1.120365 1.442416
susini4 | 1.180543 .0440198 4.45 0.000 1.097343 1.270051
susini5 | 1.422433 .1320574 3.80 0.000 1.185787 1.706304
ano_nac_corr | .8499214 .0080259 -17.22 0.000 .8343355 .8657984
cohab2 | .8799297 .0590932 -1.90 0.057 .7714078 1.003718
cohab3 | 1.074463 .0859164 0.90 0.369 .9186027 1.256769
cohab4 | .9637042 .0641552 -0.56 0.579 .8458201 1.098018
fis_com2 | 1.057511 .0364515 1.62 0.105 .988427 1.131423
fis_com3 | .8187546 .0709398 -2.31 0.021 .6908801 .9702974
rc_x1 | .8501962 .0101855 -13.55 0.000 .8304655 .8703956
rc_x2 | .8816774 .0351595 -3.16 0.002 .8153903 .9533533
rc_x3 | 1.278016 .1359283 2.31 0.021 1.037537 1.574234
_rcs1 | 2.182859 .0732864 23.25 0.000 2.043844 2.331329
_rcs2 | 1.052051 .0289971 1.84 0.066 .9967258 1.110448
_rcs3 | 1.010657 .021634 0.50 0.620 .9691325 1.053961
_rcs4 | 1.028065 .0135437 2.10 0.036 1.001859 1.054955
_rcs5 | 1.021197 .0092342 2.32 0.020 1.003258 1.039457
_rcs6 | 1.013023 .0077443 1.69 0.091 .9979582 1.028316
_rcs7 | 1.014318 .0070348 2.05 0.040 1.000623 1.0282
_rcs8 | 1.008937 .0059407 1.51 0.131 .9973599 1.020648
_rcs9 | 1.004804 .0028987 1.66 0.097 .9991386 1.010501
_rcs_mot_egr_early1 | .8970906 .0337077 -2.89 0.004 .8333987 .9656501
_rcs_mot_egr_early2 | 1.004804 .0300969 0.16 0.873 .9475134 1.065559
_rcs_mot_egr_early3 | 1.020597 .0239784 0.87 0.386 .9746659 1.068693
_rcs_mot_egr_early4 | .9792368 .015389 -1.34 0.182 .9495347 1.009868
_rcs_mot_egr_early5 | .9928461 .0109745 -0.65 0.516 .9715678 1.01459
_rcs_mot_egr_early6 | .994578 .009021 -0.60 0.549 .9770534 1.012417
_rcs_mot_egr_early7 | .9974741 .0070802 -0.36 0.722 .9836932 1.011448
_rcs_mot_egr_late1 | .9254742 .0336757 -2.13 0.033 .8617697 .993888
_rcs_mot_egr_late2 | 1.02072 .0301342 0.69 0.487 .9633341 1.081524
_rcs_mot_egr_late3 | 1.023248 .0236291 1.00 0.320 .9779683 1.070624
_rcs_mot_egr_late4 | .98928 .0149776 -0.71 0.477 .9603558 1.019075
_rcs_mot_egr_late5 | .9932123 .0104442 -0.65 0.517 .9729516 1.013895
_rcs_mot_egr_late6 | .9935748 .0086124 -0.74 0.457 .9768374 1.010599
_rcs_mot_egr_late7 | .9960854 .0066276 -0.59 0.556 .9831798 1.00916
_cons | 2.2e+139 4.2e+140 16.88 0.000 1.4e+123 3.4e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.26
Iteration 1: log likelihood = -16975.423
Iteration 2: log likelihood = -16975.364
Iteration 3: log likelihood = -16975.364
Log likelihood = -16975.364 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012389 .1269415 11.09 0.000 1.778353 2.277223
mot_egr_late | 1.694227 .0920607 9.70 0.000 1.523068 1.884622
tr_mod2 | 1.218439 .051824 4.65 0.000 1.120984 1.324366
sex_dum2 | .6074703 .0295278 -10.25 0.000 .5522683 .6681902
edad_ini_cons | .9714166 .0047128 -5.98 0.000 .9622233 .9806976
esc1 | 1.430606 .0886591 5.78 0.000 1.266976 1.615369
esc2 | 1.264235 .0732477 4.05 0.000 1.128524 1.416267
sus_prin2 | 1.15772 .0782825 2.17 0.030 1.014022 1.321782
sus_prin3 | 1.682362 .0917248 9.54 0.000 1.511857 1.872096
sus_prin4 | 1.171499 .0934058 1.99 0.047 1.002014 1.36965
sus_prin5 | 1.591176 .2392203 3.09 0.002 1.185078 2.136433
fr_cons_sus_prin2 | .9673322 .1088492 -0.30 0.768 .7758792 1.206028
fr_cons_sus_prin3 | .9785269 .0894278 -0.24 0.812 .8180526 1.170481
fr_cons_sus_prin4 | 1.003285 .0951203 0.03 0.972 .8331489 1.208163
fr_cons_sus_prin5 | 1.029863 .0934454 0.32 0.746 .8620751 1.230308
cond_ocu2 | 1.04857 .0745182 0.67 0.505 .9122329 1.205284
cond_ocu3 | 1.147614 .309615 0.51 0.610 .6763162 1.947339
cond_ocu4 | 1.219864 .0889647 2.73 0.006 1.057385 1.407309
cond_ocu5 | 1.057927 .1641634 0.36 0.717 .7804958 1.433971
cond_ocu6 | 1.189734 .0465162 4.44 0.000 1.10197 1.284489
policonsumo | .9914406 .048603 -0.18 0.861 .9006137 1.091427
num_hij2 | 1.125543 .0447829 2.97 0.003 1.041105 1.216829
tenviv1 | 1.067525 .1350752 0.52 0.606 .8330566 1.367986
tenviv2 | 1.1258 .0969948 1.38 0.169 .9508779 1.3329
tenviv4 | 1.038098 .0510107 0.76 0.447 .9427829 1.143051
tenviv5 | 1.010869 .0383321 0.29 0.776 .9384636 1.088861
mzone2 | 1.450566 .0608627 8.86 0.000 1.336051 1.574897
mzone3 | 1.529081 .0965735 6.72 0.000 1.351047 1.730575
n_off_vio | 1.46639 .055425 10.13 0.000 1.361686 1.579146
n_off_acq | 2.797718 .0972189 29.61 0.000 2.613516 2.994902
n_off_sud | 1.390285 .0506849 9.04 0.000 1.294411 1.493261
n_off_oth | 1.735781 .0633962 15.10 0.000 1.615869 1.86459
psy_com2 | 1.118254 .0550512 2.27 0.023 1.015398 1.23153
psy_com3 | 1.100291 .0424111 2.48 0.013 1.020229 1.186635
dep2 | 1.036323 .0441231 0.84 0.402 .9533533 1.126513
rural2 | .8985018 .055967 -1.72 0.086 .7952401 1.015172
rural3 | .8607768 .0595819 -2.17 0.030 .7515733 .9858474
porc_pobr | 1.569652 .3928623 1.80 0.072 .96108 2.563584
susini2 | 1.188579 .1083436 1.90 0.058 .9941174 1.421079
susini3 | 1.270719 .0819033 3.72 0.000 1.119917 1.441826
susini4 | 1.180653 .0440235 4.45 0.000 1.097446 1.270168
susini5 | 1.42234 .1320472 3.79 0.000 1.185713 1.706189
ano_nac_corr | .8498574 .0080229 -17.23 0.000 .8342774 .8657283
cohab2 | .8801728 .0591075 -1.90 0.057 .7716244 1.003991
cohab3 | 1.074876 .0859465 0.90 0.367 .9189605 1.257246
cohab4 | .9639091 .0641687 -0.55 0.581 .8460001 1.098251
fis_com2 | 1.057798 .0364602 1.63 0.103 .9886973 1.131727
fis_com3 | .8189632 .0709574 -2.31 0.021 .6910569 .9705435
rc_x1 | .8501474 .0101836 -13.55 0.000 .8304204 .870343
rc_x2 | .8816521 .0351598 -3.16 0.002 .8153644 .9533288
rc_x3 | 1.278081 .1359396 2.31 0.021 1.037582 1.574324
_rcs1 | 2.199876 .0693936 24.99 0.000 2.067986 2.340177
_rcs2 | 1.063703 .0083202 7.90 0.000 1.047521 1.080136
_rcs3 | 1.033531 .0064783 5.26 0.000 1.020912 1.046307
_rcs4 | 1.018988 .0047202 4.06 0.000 1.009779 1.028282
_rcs5 | 1.013521 .0033545 4.06 0.000 1.006968 1.020117
_rcs6 | 1.008225 .0026853 3.08 0.002 1.002976 1.013502
_rcs7 | 1.008094 .0023204 3.50 0.000 1.003556 1.012652
_rcs8 | 1.00816 .0020775 3.94 0.000 1.004096 1.01224
_rcs9 | 1.00425 .0019479 2.19 0.029 1.000439 1.008075
_rcs10 | 1.003326 .0017 1.96 0.050 1 1.006664
_rcs_mot_egr_early1 | .8932857 .0314481 -3.21 0.001 .8337271 .9570991
_rcs_mot_egr_late1 | .9141583 .0309771 -2.65 0.008 .8554165 .9769339
_cons | 2.6e+139 4.9e+140 16.89 0.000 1.7e+123 3.9e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16984.344
Iteration 1: log likelihood = -16974.602
Iteration 2: log likelihood = -16974.5
Iteration 3: log likelihood = -16974.5
Log likelihood = -16974.5 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010682 .1269374 11.06 0.000 1.776666 2.275523
mot_egr_late | 1.695533 .0921849 9.71 0.000 1.524148 1.886191
tr_mod2 | 1.217861 .051803 4.63 0.000 1.120446 1.323746
sex_dum2 | .6075332 .0295309 -10.25 0.000 .5523252 .6682595
edad_ini_cons | .9714237 .0047127 -5.98 0.000 .9622307 .9807046
esc1 | 1.430601 .0886585 5.78 0.000 1.266972 1.615363
esc2 | 1.264165 .0732437 4.05 0.000 1.128461 1.416189
sus_prin2 | 1.157803 .0782878 2.17 0.030 1.014095 1.321876
sus_prin3 | 1.682391 .0917243 9.54 0.000 1.511887 1.872124
sus_prin4 | 1.171488 .0934041 1.99 0.047 1.002006 1.369636
sus_prin5 | 1.591667 .2392996 3.09 0.002 1.185436 2.137107
fr_cons_sus_prin2 | .9672829 .1088438 -0.30 0.768 .7758394 1.205966
fr_cons_sus_prin3 | .9785301 .0894278 -0.24 0.812 .8180557 1.170484
fr_cons_sus_prin4 | 1.003273 .0951187 0.03 0.973 .8331402 1.208148
fr_cons_sus_prin5 | 1.029875 .0934453 0.32 0.746 .8620872 1.23032
cond_ocu2 | 1.048399 .0745063 0.67 0.506 .9120836 1.205088
cond_ocu3 | 1.147151 .3094908 0.51 0.611 .676043 1.946556
cond_ocu4 | 1.22012 .0889802 2.73 0.006 1.057612 1.407597
cond_ocu5 | 1.05856 .1642642 0.37 0.714 .7809592 1.434836
cond_ocu6 | 1.189745 .0465158 4.44 0.000 1.101981 1.284498
policonsumo | .9915146 .0486061 -0.17 0.862 .9006819 1.091508
num_hij2 | 1.125611 .0447855 2.97 0.003 1.041169 1.216902
tenviv1 | 1.067426 .1350628 0.52 0.606 .832979 1.367859
tenviv2 | 1.125475 .0969691 1.37 0.170 .9505999 1.332521
tenviv4 | 1.038224 .051017 0.76 0.445 .9428964 1.143189
tenviv5 | 1.011033 .0383385 0.29 0.772 .9386157 1.089038
mzone2 | 1.450709 .0608676 8.87 0.000 1.336184 1.57505
mzone3 | 1.529586 .0966042 6.73 0.000 1.351495 1.731145
n_off_vio | 1.466337 .0554237 10.13 0.000 1.361635 1.57909
n_off_acq | 2.797939 .0972275 29.61 0.000 2.613721 2.995141
n_off_sud | 1.390345 .0506866 9.04 0.000 1.294468 1.493325
n_off_oth | 1.735819 .0633974 15.10 0.000 1.615905 1.864631
psy_com2 | 1.118735 .0550778 2.28 0.023 1.015829 1.232066
psy_com3 | 1.100098 .0424038 2.47 0.013 1.02005 1.186428
dep2 | 1.036305 .044123 0.84 0.402 .9533358 1.126496
rural2 | .898433 .0559626 -1.72 0.086 .7951794 1.015094
rural3 | .8604502 .0595633 -2.17 0.030 .7512814 .9854823
porc_pobr | 1.569367 .3927875 1.80 0.072 .9609096 2.563107
susini2 | 1.188243 .1083142 1.89 0.058 .9938345 1.420681
susini3 | 1.271052 .0819256 3.72 0.000 1.120209 1.442207
susini4 | 1.180551 .0440197 4.45 0.000 1.097351 1.270059
susini5 | 1.422405 .1320545 3.80 0.000 1.185765 1.70627
ano_nac_corr | .8499256 .0080254 -17.22 0.000 .8343408 .8658015
cohab2 | .8799537 .0590942 -1.90 0.057 .77143 1.003744
cohab3 | 1.074613 .0859268 0.90 0.368 .9187328 1.25694
cohab4 | .9637717 .0641596 -0.55 0.579 .8458796 1.098095
fis_com2 | 1.057763 .0364598 1.63 0.103 .9886633 1.131692
fis_com3 | .8188576 .0709485 -2.31 0.021 .6909673 .9704189
rc_x1 | .850204 .0101857 -13.55 0.000 .830473 .8704037
rc_x2 | .8816534 .0351599 -3.16 0.002 .8153657 .9533302
rc_x3 | 1.278154 .1359478 2.31 0.021 1.037641 1.574415
_rcs1 | 2.182639 .0725531 23.48 0.000 2.044971 2.329575
_rcs2 | 1.047039 .0253629 1.90 0.058 .9984899 1.097948
_rcs3 | 1.029962 .0079257 3.84 0.000 1.014545 1.045614
_rcs4 | 1.017903 .0049403 3.66 0.000 1.008266 1.027632
_rcs5 | 1.013254 .0033686 3.96 0.000 1.006673 1.019878
_rcs6 | 1.008173 .0026871 3.05 0.002 1.00292 1.013453
_rcs7 | 1.008089 .0023204 3.50 0.000 1.003552 1.012647
_rcs8 | 1.008162 .0020775 3.94 0.000 1.004098 1.012242
_rcs9 | 1.004239 .0019483 2.18 0.029 1.000428 1.008065
_rcs10 | 1.003322 .0017004 1.96 0.050 .9999951 1.006661
_rcs_mot_egr_early1 | .8979403 .03331 -2.90 0.004 .8349708 .9656587
_rcs_mot_egr_early2 | 1.00769 .0275192 0.28 0.779 .9551713 1.063096
_rcs_mot_egr_late1 | .9248845 .0332661 -2.17 0.030 .8619293 .992438
_rcs_mot_egr_late2 | 1.025906 .0274266 0.96 0.339 .9735347 1.081094
_cons | 2.2e+139 4.1e+140 16.88 0.000 1.4e+123 3.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.418
Iteration 1: log likelihood = -16974.417
Iteration 2: log likelihood = -16974.34
Iteration 3: log likelihood = -16974.34
Log likelihood = -16974.34 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.011174 .1269888 11.07 0.000 1.777066 2.276124
mot_egr_late | 1.695762 .0922177 9.71 0.000 1.524318 1.886489
tr_mod2 | 1.217825 .0518022 4.63 0.000 1.120411 1.323707
sex_dum2 | .6075368 .029531 -10.25 0.000 .5523287 .6682634
edad_ini_cons | .971424 .0047127 -5.98 0.000 .962231 .9807048
esc1 | 1.430663 .0886619 5.78 0.000 1.267028 1.615432
esc2 | 1.26422 .0732468 4.05 0.000 1.12851 1.41625
sus_prin2 | 1.157953 .0782987 2.17 0.030 1.014225 1.322049
sus_prin3 | 1.68263 .0917393 9.54 0.000 1.512098 1.872395
sus_prin4 | 1.171528 .0934076 1.99 0.047 1.00204 1.369683
sus_prin5 | 1.592349 .239403 3.09 0.002 1.185943 2.138025
fr_cons_sus_prin2 | .9672527 .1088403 -0.30 0.767 .7758152 1.205929
fr_cons_sus_prin3 | .9785811 .0894325 -0.24 0.813 .8180984 1.170545
fr_cons_sus_prin4 | 1.003316 .0951228 0.03 0.972 .8331754 1.208199
fr_cons_sus_prin5 | 1.029875 .093445 0.32 0.746 .8620869 1.230319
cond_ocu2 | 1.048309 .0745 0.66 0.507 .9120047 1.204984
cond_ocu3 | 1.147767 .3096581 0.51 0.609 .6764048 1.947605
cond_ocu4 | 1.220208 .0889859 2.73 0.006 1.05769 1.407697
cond_ocu5 | 1.058755 .1642961 0.37 0.713 .7811008 1.435105
cond_ocu6 | 1.189749 .0465164 4.44 0.000 1.101984 1.284504
policonsumo | .9915723 .0486094 -0.17 0.863 .9007335 1.091572
num_hij2 | 1.125685 .0447883 2.98 0.003 1.041237 1.216982
tenviv1 | 1.067423 .1350637 0.52 0.606 .8329747 1.367858
tenviv2 | 1.125471 .0969696 1.37 0.170 .9505951 1.332518
tenviv4 | 1.038248 .0510185 0.76 0.445 .9429183 1.143216
tenviv5 | 1.011092 .0383408 0.29 0.771 .9386704 1.089102
mzone2 | 1.450789 .0608708 8.87 0.000 1.336258 1.575136
mzone3 | 1.529802 .0966191 6.73 0.000 1.351683 1.731391
n_off_vio | 1.466301 .0554226 10.13 0.000 1.361601 1.579052
n_off_acq | 2.797967 .0972272 29.61 0.000 2.613749 2.995168
n_off_sud | 1.390357 .0506867 9.04 0.000 1.294479 1.493336
n_off_oth | 1.735847 .063398 15.10 0.000 1.615932 1.86466
psy_com2 | 1.118956 .0550898 2.28 0.022 1.016028 1.232311
psy_com3 | 1.100022 .0424012 2.47 0.013 1.019979 1.186347
dep2 | 1.036318 .0441238 0.84 0.402 .9533474 1.12651
rural2 | .898438 .0559628 -1.72 0.086 .795184 1.0151
rural3 | .8602959 .0595532 -2.17 0.030 .7511456 .9853069
porc_pobr | 1.567431 .3923181 1.80 0.073 .9597055 2.559993
susini2 | 1.188096 .1083007 1.89 0.059 .9937115 1.420505
susini3 | 1.271195 .0819358 3.72 0.000 1.120334 1.442371
susini4 | 1.18053 .0440193 4.45 0.000 1.097331 1.270037
susini5 | 1.42224 .1320391 3.79 0.000 1.185628 1.706072
ano_nac_corr | .8498895 .0080254 -17.22 0.000 .8343046 .8657656
cohab2 | .879881 .0590899 -1.91 0.057 .7713652 1.003663
cohab3 | 1.074543 .0859221 0.90 0.369 .9186722 1.256861
cohab4 | .9637219 .0641565 -0.56 0.579 .8458355 1.098038
fis_com2 | 1.057651 .0364562 1.63 0.104 .9885581 1.131573
fis_com3 | .8187646 .0709408 -2.31 0.021 .6908883 .9703095
rc_x1 | .8501678 .0101853 -13.55 0.000 .8304376 .8703669
rc_x2 | .8816474 .0351591 -3.16 0.002 .815361 .9533227
rc_x3 | 1.278164 .1359471 2.31 0.021 1.037652 1.574423
_rcs1 | 2.186408 .0736955 23.21 0.000 2.046636 2.335727
_rcs2 | 1.045494 .0262406 1.77 0.076 .9953083 1.098211
_rcs3 | 1.033492 .0142384 2.39 0.017 1.005959 1.061779
_rcs4 | 1.020383 .010683 1.93 0.054 .9996586 1.041538
_rcs5 | 1.014558 .0062832 2.33 0.020 1.002318 1.026948
_rcs6 | 1.008683 .0036103 2.42 0.016 1.001632 1.015784
_rcs7 | 1.008244 .002474 3.35 0.001 1.003407 1.013105
_rcs8 | 1.008217 .0020898 3.95 0.000 1.004129 1.012321
_rcs9 | 1.004246 .001949 2.18 0.029 1.000433 1.008073
_rcs10 | 1.003342 .0017015 1.97 0.049 1.000012 1.006682
_rcs_mot_egr_early1 | .8950822 .0337173 -2.94 0.003 .8313781 .9636677
_rcs_mot_egr_early2 | 1.009352 .027985 0.34 0.737 .955966 1.065719
_rcs_mot_egr_early3 | .9919793 .0195583 -0.41 0.683 .954377 1.031063
_rcs_mot_egr_late1 | .9238228 .0337158 -2.17 0.030 .8600492 .9923253
_rcs_mot_egr_late2 | 1.02595 .0278899 0.94 0.346 .9727176 1.082096
_rcs_mot_egr_late3 | .9990909 .0190154 -0.05 0.962 .962508 1.037064
_cons | 2.4e+139 4.5e+140 16.88 0.000 1.6e+123 3.6e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.698
Iteration 1: log likelihood = -16973.486
Iteration 2: log likelihood = -16973.368
Iteration 3: log likelihood = -16973.368
Log likelihood = -16973.368 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009653 .1268653 11.06 0.000 1.775768 2.274341
mot_egr_late | 1.694782 .0921307 9.70 0.000 1.523496 1.885325
tr_mod2 | 1.217895 .0518061 4.63 0.000 1.120475 1.323786
sex_dum2 | .6075245 .0295301 -10.25 0.000 .5523181 .6682492
edad_ini_cons | .9714177 .0047127 -5.98 0.000 .9622246 .9806985
esc1 | 1.43072 .0886651 5.78 0.000 1.267079 1.615495
esc2 | 1.26424 .073248 4.05 0.000 1.128528 1.416272
sus_prin2 | 1.158106 .07831 2.17 0.030 1.014357 1.322226
sus_prin3 | 1.682976 .091761 9.55 0.000 1.512404 1.872785
sus_prin4 | 1.171586 .0934124 1.99 0.047 1.00209 1.369752
sus_prin5 | 1.592642 .2394479 3.10 0.002 1.186159 2.138421
fr_cons_sus_prin2 | .9672808 .1088433 -0.30 0.768 .775838 1.205963
fr_cons_sus_prin3 | .9785663 .0894312 -0.24 0.813 .818086 1.170527
fr_cons_sus_prin4 | 1.003385 .0951299 0.04 0.972 .8332319 1.208284
fr_cons_sus_prin5 | 1.029775 .0934368 0.32 0.746 .8620024 1.230202
cond_ocu2 | 1.048319 .0745006 0.66 0.507 .9120134 1.204996
cond_ocu3 | 1.148097 .309747 0.51 0.609 .6765994 1.948165
cond_ocu4 | 1.219965 .0889696 2.73 0.006 1.057477 1.40742
cond_ocu5 | 1.059104 .1643534 0.37 0.711 .7813538 1.435586
cond_ocu6 | 1.189762 .0465177 4.44 0.000 1.101995 1.284519
policonsumo | .991563 .0486089 -0.17 0.863 .9007251 1.091562
num_hij2 | 1.125641 .0447862 2.97 0.003 1.041197 1.216934
tenviv1 | 1.067364 .1350585 0.52 0.606 .8329257 1.367789
tenviv2 | 1.125706 .0969897 1.37 0.169 .9507934 1.332796
tenviv4 | 1.038168 .0510147 0.76 0.446 .9428452 1.143129
tenviv5 | 1.011044 .038339 0.29 0.772 .9386254 1.08905
mzone2 | 1.450836 .060873 8.87 0.000 1.336301 1.575188
mzone3 | 1.529751 .0966164 6.73 0.000 1.351638 1.731335
n_off_vio | 1.466272 .0554208 10.13 0.000 1.361575 1.579019
n_off_acq | 2.797802 .0972198 29.61 0.000 2.613599 2.994988
n_off_sud | 1.390273 .0506827 9.04 0.000 1.294402 1.493244
n_off_oth | 1.735849 .0633971 15.10 0.000 1.615936 1.86466
psy_com2 | 1.119059 .0550948 2.28 0.022 1.016122 1.232425
psy_com3 | 1.099928 .042398 2.47 0.013 1.019891 1.186246
dep2 | 1.036353 .0441253 0.84 0.402 .9533794 1.126548
rural2 | .8985299 .055968 -1.72 0.086 .7952662 1.015202
rural3 | .8602205 .0595478 -2.18 0.030 .7510802 .98522
porc_pobr | 1.565761 .3919144 1.79 0.073 .9586656 2.557311
susini2 | 1.188188 .1083092 1.89 0.059 .9937883 1.420615
susini3 | 1.27126 .0819408 3.72 0.000 1.120389 1.442446
susini4 | 1.180528 .0440192 4.45 0.000 1.097329 1.270035
susini5 | 1.422397 .1320543 3.80 0.000 1.185758 1.706262
ano_nac_corr | .8499048 .008026 -17.22 0.000 .8343189 .8657818
cohab2 | .8799285 .0590937 -1.90 0.057 .7714059 1.003718
cohab3 | 1.074498 .0859195 0.90 0.369 .9186313 1.25681
cohab4 | .9637398 .0641584 -0.55 0.579 .8458499 1.098061
fis_com2 | 1.057441 .0364486 1.62 0.105 .988363 1.131348
fis_com3 | .8187145 .0709365 -2.31 0.021 .6908459 .9702502
rc_x1 | .8501956 .0101857 -13.55 0.000 .8304647 .8703954
rc_x2 | .8816043 .0351565 -3.16 0.002 .8153229 .9532741
rc_x3 | 1.278264 .1359546 2.31 0.021 1.037738 1.574539
_rcs1 | 2.18387 .0732968 23.27 0.000 2.044834 2.332359
_rcs2 | 1.048038 .0276127 1.78 0.075 .9952913 1.103579
_rcs3 | 1.018855 .0175271 1.09 0.278 .9850747 1.053793
_rcs4 | 1.019437 .0102863 1.91 0.056 .9994747 1.039799
_rcs5 | 1.022168 .008263 2.71 0.007 1.0061 1.038492
_rcs6 | 1.017258 .0072829 2.39 0.017 1.003083 1.031633
_rcs7 | 1.0139 .0048466 2.89 0.004 1.004445 1.023444
_rcs8 | 1.010373 .0026249 3.97 0.000 1.005241 1.01553
_rcs9 | 1.004581 .0019626 2.34 0.019 1.000742 1.008435
_rcs10 | 1.003279 .0017007 1.93 0.053 .9999507 1.006617
_rcs_mot_egr_early1 | .8962446 .0336475 -2.92 0.004 .8326645 .9646794
_rcs_mot_egr_early2 | 1.009004 .0289912 0.31 0.755 .9537527 1.067456
_rcs_mot_egr_early3 | 1.00543 .0212879 0.26 0.798 .9645606 1.048032
_rcs_mot_egr_early4 | .9799264 .0139286 -1.43 0.154 .9530036 1.00761
_rcs_mot_egr_late1 | .9249634 .0336369 -2.14 0.032 .8613309 .9932968
_rcs_mot_egr_late2 | 1.025009 .0289341 0.88 0.382 .9698394 1.083317
_rcs_mot_egr_late3 | 1.012061 .0207804 0.58 0.559 .9721408 1.05362
_rcs_mot_egr_late4 | .9832558 .0134058 -1.24 0.216 .9573288 1.009885
_cons | 2.3e+139 4.3e+140 16.88 0.000 1.5e+123 3.5e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.659
Iteration 1: log likelihood = -16973.282
Iteration 2: log likelihood = -16973.155
Iteration 3: log likelihood = -16973.155
Log likelihood = -16973.155 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009666 .1268782 11.06 0.000 1.77576 2.274383
mot_egr_late | 1.69499 .0921566 9.71 0.000 1.523658 1.885589
tr_mod2 | 1.217849 .0518038 4.63 0.000 1.120432 1.323735
sex_dum2 | .6075359 .0295307 -10.25 0.000 .5523284 .6682617
edad_ini_cons | .9714175 .0047128 -5.98 0.000 .9622245 .9806984
esc1 | 1.430729 .0886657 5.78 0.000 1.267087 1.615506
esc2 | 1.264247 .0732484 4.05 0.000 1.128534 1.41628
sus_prin2 | 1.158051 .078306 2.17 0.030 1.014309 1.322163
sus_prin3 | 1.682889 .0917557 9.55 0.000 1.512326 1.872687
sus_prin4 | 1.171575 .0934113 1.99 0.047 1.002081 1.369739
sus_prin5 | 1.592395 .2394122 3.09 0.002 1.185973 2.138093
fr_cons_sus_prin2 | .9672768 .108843 -0.30 0.767 .7758347 1.205959
fr_cons_sus_prin3 | .9785768 .0894321 -0.24 0.813 .8180948 1.17054
fr_cons_sus_prin4 | 1.003412 .0951323 0.04 0.971 .8332547 1.208316
fr_cons_sus_prin5 | 1.029817 .0934405 0.32 0.746 .8620374 1.230251
cond_ocu2 | 1.048306 .0744999 0.66 0.507 .9120021 1.204981
cond_ocu3 | 1.147984 .3097171 0.51 0.609 .6765321 1.947975
cond_ocu4 | 1.219961 .0889696 2.73 0.006 1.057473 1.407416
cond_ocu5 | 1.05917 .1643636 0.37 0.711 .7814029 1.435676
cond_ocu6 | 1.189749 .0465173 4.44 0.000 1.101982 1.284505
policonsumo | .9915412 .0486078 -0.17 0.862 .9007054 1.091538
num_hij2 | 1.12565 .0447867 2.97 0.003 1.041205 1.216944
tenviv1 | 1.067461 .1350701 0.52 0.606 .8330022 1.367912
tenviv2 | 1.12565 .0969849 1.37 0.170 .9507469 1.33273
tenviv4 | 1.038214 .0510171 0.76 0.445 .9428869 1.14318
tenviv5 | 1.011045 .0383391 0.29 0.772 .9386265 1.089051
mzone2 | 1.450787 .0608706 8.87 0.000 1.336257 1.575134
mzone3 | 1.52975 .0966156 6.73 0.000 1.351638 1.731332
n_off_vio | 1.466278 .0554211 10.13 0.000 1.36158 1.579026
n_off_acq | 2.797834 .0972206 29.61 0.000 2.613629 2.995022
n_off_sud | 1.390295 .0506836 9.04 0.000 1.294423 1.493268
n_off_oth | 1.735883 .0633983 15.10 0.000 1.615968 1.864697
psy_com2 | 1.118968 .0550911 2.28 0.022 1.016037 1.232326
psy_com3 | 1.099949 .0423988 2.47 0.013 1.01991 1.186269
dep2 | 1.036342 .0441248 0.84 0.402 .953369 1.126536
rural2 | .8984969 .0559661 -1.72 0.086 .7952368 1.015165
rural3 | .8602548 .0595503 -2.17 0.030 .7511099 .9852596
porc_pobr | 1.566237 .3920307 1.79 0.073 .9589607 2.55808
susini2 | 1.188146 .1083053 1.89 0.059 .9937537 1.420565
susini3 | 1.271327 .0819445 3.72 0.000 1.120449 1.442521
susini4 | 1.180547 .0440199 4.45 0.000 1.097347 1.270055
susini5 | 1.422483 .1320628 3.80 0.000 1.185828 1.706366
ano_nac_corr | .849909 .008026 -17.22 0.000 .8343231 .8657861
cohab2 | .8799255 .0590931 -1.90 0.057 .7714039 1.003714
cohab3 | 1.074457 .0859161 0.90 0.369 .9185972 1.256763
cohab4 | .9637272 .0641571 -0.55 0.579 .8458396 1.098045
fis_com2 | 1.057516 .0364516 1.62 0.105 .9884316 1.131428
fis_com3 | .8187115 .0709362 -2.31 0.021 .6908435 .9702465
rc_x1 | .8501917 .0101856 -13.55 0.000 .8304609 .8703913
rc_x2 | .8816407 .0351583 -3.16 0.002 .8153559 .9533142
rc_x3 | 1.278143 .1359429 2.31 0.021 1.037638 1.574392
_rcs1 | 2.183661 .0733101 23.26 0.000 2.044601 2.332178
_rcs2 | 1.049992 .0282869 1.81 0.070 .9959888 1.106923
_rcs3 | 1.014132 .0193255 0.74 0.461 .9769534 1.052725
_rcs4 | 1.02228 .0111285 2.02 0.043 1.0007 1.044326
_rcs5 | 1.024628 .0097158 2.57 0.010 1.005761 1.043848
_rcs6 | 1.015744 .006898 2.30 0.021 1.002313 1.029354
_rcs7 | 1.011436 .0069552 1.65 0.098 .9978954 1.02516
_rcs8 | 1.009469 .0050018 1.90 0.057 .9997135 1.019321
_rcs9 | 1.004613 .0023745 1.95 0.051 .9999701 1.009278
_rcs10 | 1.003349 .0017012 1.97 0.049 1.00002 1.006688
_rcs_mot_egr_early1 | .8967489 .0336901 -2.90 0.004 .83309 .9652722
_rcs_mot_egr_early2 | 1.007082 .0294602 0.24 0.809 .9509649 1.06651
_rcs_mot_egr_early3 | 1.012297 .0224307 0.55 0.581 .9692746 1.057229
_rcs_mot_egr_early4 | .9775434 .0149066 -1.49 0.136 .9487594 1.007201
_rcs_mot_egr_early5 | .9982521 .0108939 -0.16 0.873 .9771272 1.019834
_rcs_mot_egr_late1 | .9248961 .0336506 -2.15 0.032 .8612388 .9932587
_rcs_mot_egr_late2 | 1.02314 .0294534 0.79 0.427 .9670104 1.082527
_rcs_mot_egr_late3 | 1.017572 .0219796 0.81 0.420 .9753922 1.061576
_rcs_mot_egr_late4 | .9834434 .0144626 -1.14 0.256 .9555018 1.012202
_rcs_mot_egr_late5 | .9965917 .0104347 -0.33 0.744 .9763485 1.017255
_cons | 2.3e+139 4.3e+140 16.88 0.000 1.5e+123 3.5e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.657
Iteration 1: log likelihood = -16973.068
Iteration 2: log likelihood = -16972.932
Iteration 3: log likelihood = -16972.932
Log likelihood = -16972.932 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009509 .126866 11.05 0.000 1.775625 2.2742
mot_egr_late | 1.695061 .0921598 9.71 0.000 1.523723 1.885667
tr_mod2 | 1.217828 .0518028 4.63 0.000 1.120414 1.323712
sex_dum2 | .60755 .0295314 -10.25 0.000 .5523411 .6682772
edad_ini_cons | .9714162 .0047128 -5.98 0.000 .9622231 .9806971
esc1 | 1.43072 .0886652 5.78 0.000 1.267079 1.615495
esc2 | 1.264227 .0732473 4.05 0.000 1.128516 1.416258
sus_prin2 | 1.158061 .0783069 2.17 0.030 1.014318 1.322175
sus_prin3 | 1.682922 .0917576 9.55 0.000 1.512357 1.872725
sus_prin4 | 1.171568 .0934108 1.99 0.047 1.002075 1.369731
sus_prin5 | 1.592383 .2394106 3.09 0.002 1.185964 2.138077
fr_cons_sus_prin2 | .9672945 .108845 -0.30 0.768 .7758488 1.205981
fr_cons_sus_prin3 | .9785796 .0894324 -0.24 0.813 .8180971 1.170543
fr_cons_sus_prin4 | 1.003442 .0951352 0.04 0.971 .8332794 1.208352
fr_cons_sus_prin5 | 1.029825 .0934413 0.32 0.746 .8620439 1.230261
cond_ocu2 | 1.04829 .0744989 0.66 0.507 .9119881 1.204964
cond_ocu3 | 1.148024 .3097279 0.51 0.609 .6765558 1.948043
cond_ocu4 | 1.219896 .088965 2.73 0.006 1.057417 1.407342
cond_ocu5 | 1.059267 .164379 0.37 0.711 .781474 1.435809
cond_ocu6 | 1.189757 .0465177 4.44 0.000 1.101989 1.284514
policonsumo | .9915433 .0486078 -0.17 0.862 .9007074 1.09154
num_hij2 | 1.125641 .0447863 2.97 0.003 1.041197 1.216934
tenviv1 | 1.067528 .1350787 0.52 0.606 .8330538 1.367997
tenviv2 | 1.125658 .0969851 1.37 0.169 .9507543 1.332739
tenviv4 | 1.038207 .0510168 0.76 0.445 .9428804 1.143172
tenviv5 | 1.011036 .0383387 0.29 0.772 .9386181 1.089041
mzone2 | 1.45077 .0608697 8.87 0.000 1.336241 1.575115
mzone3 | 1.529692 .0966118 6.73 0.000 1.351587 1.731267
n_off_vio | 1.466285 .0554211 10.13 0.000 1.361587 1.579033
n_off_acq | 2.797816 .0972194 29.61 0.000 2.613613 2.995001
n_off_sud | 1.39029 .0506833 9.04 0.000 1.294419 1.493263
n_off_oth | 1.7359 .0633987 15.10 0.000 1.615984 1.864714
psy_com2 | 1.118945 .05509 2.28 0.022 1.016017 1.232301
psy_com3 | 1.099947 .0423988 2.47 0.013 1.019909 1.186267
dep2 | 1.036348 .0441251 0.84 0.402 .9533742 1.126542
rural2 | .8985065 .0559668 -1.72 0.086 .7952451 1.015176
rural3 | .860262 .0595507 -2.17 0.030 .7511163 .9852678
porc_pobr | 1.566163 .3920161 1.79 0.073 .9589107 2.557971
susini2 | 1.188138 .1083046 1.89 0.059 .9937465 1.420555
susini3 | 1.271386 .0819484 3.73 0.000 1.120501 1.442588
susini4 | 1.180554 .0440202 4.45 0.000 1.097353 1.270063
susini5 | 1.422513 .1320655 3.80 0.000 1.185853 1.706402
ano_nac_corr | .8499179 .0080261 -17.22 0.000 .8343317 .8657953
cohab2 | .8799424 .0590942 -1.90 0.057 .7714187 1.003733
cohab3 | 1.074439 .0859146 0.90 0.369 .9185812 1.256741
cohab4 | .9637354 .0641577 -0.55 0.579 .8458468 1.098054
fis_com2 | 1.057526 .0364521 1.62 0.105 .9884408 1.131439
fis_com3 | .8187133 .0709363 -2.31 0.021 .6908451 .9702487
rc_x1 | .8501986 .0101857 -13.55 0.000 .8304676 .8703984
rc_x2 | .8816513 .0351587 -3.16 0.002 .8153658 .9533255
rc_x3 | 1.278101 .1359383 2.31 0.021 1.037604 1.57434
_rcs1 | 2.1838 .0733118 23.27 0.000 2.044737 2.332321
_rcs2 | 1.050901 .028699 1.82 0.069 .9961311 1.108683
_rcs3 | 1.01146 .0205604 0.56 0.575 .9719543 1.052571
_rcs4 | 1.023736 .0122486 1.96 0.050 1.000009 1.048027
_rcs5 | 1.024986 .0095285 2.65 0.008 1.006479 1.043832
_rcs6 | 1.015511 .0078973 1.98 0.048 1.000149 1.031108
_rcs7 | 1.012172 .0065178 1.88 0.060 .9994779 1.025028
_rcs8 | 1.009953 .0062035 1.61 0.107 .9978673 1.022185
_rcs9 | 1.004805 .0037406 1.29 0.198 .9975001 1.012163
_rcs10 | 1.003418 .0017165 1.99 0.046 1.00006 1.006788
_rcs_mot_egr_early1 | .8968543 .0336923 -2.90 0.004 .8331911 .9653819
_rcs_mot_egr_early2 | 1.006 .0297767 0.20 0.840 .9492989 1.066087
_rcs_mot_egr_early3 | 1.017052 .0232985 0.74 0.460 .9723978 1.063757
_rcs_mot_egr_early4 | .9781722 .015477 -1.39 0.163 .9483034 1.008982
_rcs_mot_egr_early5 | .9919725 .0111502 -0.72 0.473 .9703575 1.014069
_rcs_mot_egr_early6 | .9998657 .008539 -0.02 0.987 .983269 1.016743
_rcs_mot_egr_late1 | .9247491 .0336479 -2.15 0.032 .8610971 .9931063
_rcs_mot_egr_late2 | 1.022109 .0297955 0.75 0.453 .9653481 1.082208
_rcs_mot_egr_late3 | 1.021537 .0228638 0.95 0.341 .9776939 1.067347
_rcs_mot_egr_late4 | .9849768 .0151146 -0.99 0.324 .9557937 1.015051
_rcs_mot_egr_late5 | .9926394 .0107493 -0.68 0.495 .9717933 1.013933
_rcs_mot_egr_late6 | .9976216 .0081479 -0.29 0.771 .9817792 1.01372
_cons | 2.2e+139 4.2e+140 16.88 0.000 1.4e+123 3.4e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -16983.672
Iteration 1: log likelihood = -16972.981
Iteration 2: log likelihood = -16972.842
Iteration 3: log likelihood = -16972.842
Log likelihood = -16972.842 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.010092 .1269088 11.06 0.000 1.77613 2.274874
mot_egr_late | 1.695401 .0921806 9.71 0.000 1.524023 1.886049
tr_mod2 | 1.217821 .0518028 4.63 0.000 1.120407 1.323705
sex_dum2 | .607546 .0295312 -10.25 0.000 .5523375 .6682729
edad_ini_cons | .9714157 .0047128 -5.98 0.000 .9622227 .9806966
esc1 | 1.430778 .0886686 5.78 0.000 1.267131 1.615561
esc2 | 1.264254 .0732489 4.05 0.000 1.12854 1.416288
sus_prin2 | 1.158048 .0783059 2.17 0.030 1.014307 1.32216
sus_prin3 | 1.682968 .0917602 9.55 0.000 1.512397 1.872776
sus_prin4 | 1.171575 .0934111 1.99 0.047 1.002081 1.369738
sus_prin5 | 1.59247 .2394229 3.09 0.002 1.18603 2.138192
fr_cons_sus_prin2 | .9672597 .108841 -0.30 0.767 .775821 1.205937
fr_cons_sus_prin3 | .9785592 .0894305 -0.24 0.813 .8180801 1.170519
fr_cons_sus_prin4 | 1.003428 .0951339 0.04 0.971 .8332684 1.208336
fr_cons_sus_prin5 | 1.029794 .0934385 0.32 0.746 .8620185 1.230224
cond_ocu2 | 1.048285 .0744984 0.66 0.507 .9119835 1.204957
cond_ocu3 | 1.148067 .3097393 0.51 0.609 .676581 1.948115
cond_ocu4 | 1.219832 .0889605 2.72 0.006 1.05736 1.407268
cond_ocu5 | 1.059341 .1643906 0.37 0.710 .7815287 1.435909
cond_ocu6 | 1.18976 .0465179 4.44 0.000 1.101992 1.284517
policonsumo | .9915202 .0486067 -0.17 0.862 .9006866 1.091514
num_hij2 | 1.125674 .0447877 2.98 0.003 1.041227 1.216969
tenviv1 | 1.06746 .1350704 0.52 0.606 .8330009 1.367912
tenviv2 | 1.12564 .0969834 1.37 0.170 .950739 1.332716
tenviv4 | 1.038168 .0510149 0.76 0.446 .9428444 1.143129
tenviv5 | 1.011018 .0383379 0.29 0.773 .9386012 1.089022
mzone2 | 1.450774 .06087 8.87 0.000 1.336244 1.575119
mzone3 | 1.529643 .0966089 6.73 0.000 1.351544 1.731211
n_off_vio | 1.466258 .05542 10.13 0.000 1.361562 1.579004
n_off_acq | 2.797787 .0972178 29.61 0.000 2.613587 2.994969
n_off_sud | 1.390286 .0506829 9.04 0.000 1.294415 1.493258
n_off_oth | 1.735888 .063398 15.10 0.000 1.615973 1.864701
psy_com2 | 1.118965 .0550912 2.28 0.022 1.016034 1.232323
psy_com3 | 1.099968 .0423995 2.47 0.013 1.019928 1.186289
dep2 | 1.03635 .0441253 0.84 0.402 .9533765 1.126545
rural2 | .8985515 .0559697 -1.72 0.086 .7952847 1.015227
rural3 | .8602878 .0595524 -2.17 0.030 .7511391 .985297
porc_pobr | 1.565019 .391735 1.79 0.074 .9582043 2.55612
susini2 | 1.188182 .1083088 1.89 0.059 .9937831 1.420608
susini3 | 1.271287 .0819427 3.72 0.000 1.120413 1.442478
susini4 | 1.180537 .0440197 4.45 0.000 1.097337 1.270045
susini5 | 1.422481 .1320622 3.80 0.000 1.185827 1.706363
ano_nac_corr | .8498994 .008026 -17.22 0.000 .8343134 .8657765
cohab2 | .8798959 .0590912 -1.91 0.057 .7713779 1.00368
cohab3 | 1.074392 .0859109 0.90 0.370 .918541 1.256686
cohab4 | .9636755 .0641535 -0.56 0.578 .8457945 1.097986
fis_com2 | 1.057484 .0364506 1.62 0.105 .9884016 1.131394
fis_com3 | .8187184 .0709367 -2.31 0.021 .6908494 .9702547
rc_x1 | .8501771 .0101855 -13.55 0.000 .8304465 .8703764
rc_x2 | .8816582 .0351588 -3.16 0.002 .8153724 .9533327
rc_x3 | 1.278085 .1359359 2.31 0.021 1.037592 1.574319
_rcs1 | 2.185131 .0734203 23.26 0.000 2.045866 2.333876
_rcs2 | 1.050713 .0287764 1.81 0.071 .995799 1.108655
_rcs3 | 1.012298 .0214585 0.58 0.564 .9711018 1.055242
_rcs4 | 1.024805 .0131417 1.91 0.056 .9993686 1.050889
_rcs5 | 1.023882 .009236 2.62 0.009 1.005939 1.042145
_rcs6 | 1.014642 .0078681 1.87 0.061 .999338 1.030181
_rcs7 | 1.011258 .0071806 1.58 0.115 .9972817 1.02543
_rcs8 | 1.011218 .0059248 1.90 0.057 .9996716 1.022897
_rcs9 | 1.006919 .0054126 1.28 0.200 .9963661 1.017584
_rcs10 | 1.00391 .0020212 1.94 0.053 .9999561 1.007879
_rcs_mot_egr_early1 | .8962349 .033706 -2.91 0.004 .8325483 .9647932
_rcs_mot_egr_early2 | 1.005908 .0299303 0.20 0.843 .9489235 1.066315
_rcs_mot_egr_early3 | 1.018522 .0238487 0.78 0.433 .9728362 1.066354
_rcs_mot_egr_early4 | .9799517 .0156328 -1.27 0.204 .9497861 1.011075
_rcs_mot_egr_early5 | .9907072 .0110768 -0.84 0.404 .9692332 1.012657
_rcs_mot_egr_early6 | .9972299 .009101 -0.30 0.761 .9795509 1.015228
_rcs_mot_egr_early7 | .9971462 .0074582 -0.38 0.702 .9826351 1.011872
_rcs_mot_egr_late1 | .9241724 .0336461 -2.17 0.030 .8605251 .9925273
_rcs_mot_egr_late2 | 1.021826 .0299657 0.74 0.462 .9647498 1.082278
_rcs_mot_egr_late3 | 1.021095 .0234741 0.91 0.364 .976108 1.068156
_rcs_mot_egr_late4 | .9898061 .0152712 -0.66 0.507 .960323 1.020194
_rcs_mot_egr_late5 | .9911147 .0106036 -0.83 0.404 .9705484 1.012117
_rcs_mot_egr_late6 | .9962055 .00867 -0.44 0.662 .9793567 1.013344
_rcs_mot_egr_late7 | .9959376 .0070926 -0.57 0.568 .982133 1.009936
_cons | 2.3e+139 4.4e+140 16.88 0.000 1.5e+123 3.5e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
.
We obtained a summary of distributions by AICs and BICs.
. *file:///G:/Mi%20unidad/Alvacast/SISTRAT%202019%20(github)/_supp_mstates/stata/1806.01615.pdf
. *rcs - restricted cubic splines on log hazard scale
. *rp - Royston-Parmar model (restricted cubic spline on log cumulative hazard scale)
. qui count if _d == 1
. // we count the amount of cases with the event in the strata
. //we call the estimates stored, and the results...
. estimates stat m_nostag_rp*, n(`r(N)')
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
m_nostag_r~1 | 5,144 . -17041.83 55 34193.65 34553.66
m_nostag_r~2 | 5,144 . -16998.98 57 34111.96 34485.06
m_nostag_r~3 | 5,144 . -16987.43 59 34092.86 34479.05
m_nostag_r~4 | 5,144 . -16985.87 61 34093.74 34493.02
m_nostag_r~5 | 5,144 . -16983.43 63 34092.85 34505.22
m_nostag_r~6 | 5,144 . -16981.89 65 34093.78 34519.24
m_nostag_r~7 | 5,144 . -16981.86 67 34097.72 34536.27
m_nostag_r~1 | 5,144 . -16997.38 56 34106.76 34473.31
m_nostag_r~2 | 5,144 . -16996.3 58 34108.6 34488.25
m_nostag_r~3 | 5,144 . -16984.74 60 34089.48 34482.21
m_nostag_r~4 | 5,144 . -16983.05 62 34090.11 34495.94
m_nostag_r~5 | 5,144 . -16980.55 64 34089.11 34508.03
m_nostag_r~6 | 5,144 . -16979.07 66 34090.14 34522.15
m_nostag_r~7 | 5,144 . -16979.01 68 34094.03 34539.13
m_nostag_r~1 | 5,144 . -16983.96 57 34081.92 34455.01
m_nostag_r~2 | 5,144 . -16983.17 59 34084.33 34470.52
m_nostag_r~3 | 5,144 . -16982.97 61 34087.94 34487.22
m_nostag_r~4 | 5,144 . -16982.17 63 34090.35 34502.72
m_nostag_r~5 | 5,144 . -16978.93 65 34087.86 34513.32
m_nostag_r~6 | 5,144 . -16977.39 67 34088.79 34527.34
m_nostag_r~7 | 5,144 . -16977.29 69 34092.57 34544.22
m_nostag_r~1 | 5,144 . -16981.68 58 34079.35 34459
m_nostag_r~2 | 5,144 . -16980.81 60 34081.61 34474.35
m_nostag_r~3 | 5,144 . -16980.57 62 34085.15 34490.97
m_nostag_r~4 | 5,144 . -16979.83 64 34087.67 34506.59
m_nostag_r~5 | 5,144 . -16978.74 66 34089.48 34521.49
m_nostag_r~6 | 5,144 . -16975.84 68 34087.68 34532.78
m_nostag_r~7 | 5,144 . -16976.24 70 34092.47 34550.66
m_nostag_r~1 | 5,144 . -16979.1 59 34076.21 34462.4
m_nostag_r~2 | 5,144 . -16978.23 61 34078.46 34477.74
m_nostag_r~3 | 5,144 . -16978.15 63 34082.29 34494.66
m_nostag_r~4 | 5,144 . -16976.05 65 34082.1 34507.56
m_nostag_r~5 | 5,144 . -16977.06 67 34088.11 34526.66
m_nostag_r~6 | 5,144 . -16975.34 69 34088.69 34540.33
m_nostag_r~7 | 5,144 . -16975.51 71 34093.01 34557.75
m_nostag_r~1 | 5,144 . -16977.44 60 34074.87 34467.61
m_nostag_r~2 | 5,144 . -16976.58 62 34077.15 34482.98
m_nostag_r~3 | 5,144 . -16976.45 64 34080.9 34499.82
m_nostag_r~4 | 5,144 . -16975.55 66 34083.11 34515.12
m_nostag_r~5 | 5,144 . -16975.4 68 34086.8 34531.9
m_nostag_r~6 | 5,144 . -16974.99 70 34089.98 34548.17
m_nostag_r~7 | 5,144 . -16974.76 72 34093.53 34564.81
m_nostag_r~1 | 5,144 . -16977.18 61 34076.36 34475.64
m_nostag_r~2 | 5,144 . -16976.32 63 34078.65 34491.02
m_nostag_r~3 | 5,144 . -16976.21 65 34082.43 34507.89
m_nostag_r~4 | 5,144 . -16975.1 67 34084.2 34522.75
m_nostag_r~5 | 5,144 . -16975.2 69 34088.4 34540.05
m_nostag_r~6 | 5,144 . -16974.65 71 34091.31 34556.04
m_nostag_r~7 | 5,144 . -16974.58 73 34095.16 34572.99
m_nostag_r~1 | 5,144 . -16976.54 62 34077.07 34482.9
m_nostag_r~2 | 5,144 . -16975.67 64 34079.35 34498.27
m_nostag_r~3 | 5,144 . -16975.54 66 34083.07 34515.08
m_nostag_r~4 | 5,144 . -16974.65 68 34085.3 34530.4
m_nostag_r~5 | 5,144 . -16974.34 70 34088.68 34546.87
m_nostag_r~6 | 5,144 . -16974.1 72 34092.19 34563.47
m_nostag_r~7 | 5,144 . -16972.89 74 34093.77 34578.15
m_nostag_r~1 | 5,144 . -16975.76 63 34077.52 34489.89
m_nostag_r~2 | 5,144 . -16974.89 65 34079.78 34505.24
m_nostag_r~3 | 5,144 . -16974.74 67 34083.47 34522.03
m_nostag_r~4 | 5,144 . -16973.71 69 34085.43 34537.07
m_nostag_r~5 | 5,144 . -16973.64 71 34089.29 34554.02
m_nostag_r~6 | 5,144 . -16973.19 73 34092.39 34570.22
m_nostag_r~7 | 5,144 . -16973.14 75 34096.29 34587.2
m_nostag_r~1 | 5,144 . -16975.36 64 34078.73 34497.65
m_nostag_r~2 | 5,144 . -16974.5 66 34081 34513.01
m_nostag_r~3 | 5,144 . -16974.34 68 34084.68 34529.78
m_nostag_r~4 | 5,144 . -16973.37 70 34086.74 34544.93
m_nostag_r~5 | 5,144 . -16973.16 72 34090.31 34561.59
m_nostag_r~6 | 5,144 . -16972.93 74 34093.86 34578.24
m_nostag_r~7 | 5,144 . -16972.84 76 34097.68 34595.15
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_1=r(S)
.
. ** to order AICs
. *https://www.statalist.org/forums/forum/general-stata-discussion/general/1665263-sorting-matrix-including-rownames
. mata :
------------------------------------------------- mata (type end to exit) ---------------------------------------------------------------------------------------------------------------------------------------------
:
: void st_sort_matrix(
> //argumento de la matriz
> string scalar matname,
> //argumento de las columnas
> real rowvector columns
> )
> {
> string matrix rownames
> real colvector sort_order
> // defino una base
> //Y = st_matrix(matname)
> //[.,(1, 2, 3, 4, 6, 5)]
> //ordeno las columnas
> rownames = st_matrixrowstripe(matname) //[.,(1, 2, 3, 4, 6, 5)]
> sort_order = order(st_matrix(matname), (columns))
> st_replacematrix(matname, st_matrix(matname)[sort_order,.])
> st_matrixrowstripe(matname, rownames[sort_order,.])
> }
:
: end
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
. //mata: mata drop st_sort_matrix()
.
. mata : st_sort_matrix("stats_1", 5) // 5 AIC, 6 BIC
.
. global st_rownames : rownames stats_1
.
. //matrix colname stats_1 = mod N ll0 ll df AIC BIC
.
. *di "$st_rownames"
. esttab matrix(stats_1) using "testreg_aic_bic_mrl_23_1_pris.csv", replace
(output written to testreg_aic_bic_mrl_23_1_pris.csv)
. esttab matrix(stats_1) using "testreg_aic_bic_mrl_23_1_pris.html", replace
(output written to testreg_aic_bic_mrl_23_1_pris.html)
.
. *weibull: Log cumulative hazard is linear in log t: ln𝐻(𝑡)=𝑘 ln〖𝑡−〖k ln〗𝜆 〗
. *Splines generalize to (almost) any baseline hazard shape.
. *Stable estimates on the log cumulative hazard scale.
. *ln𝐻(𝑡)=𝑠(ln〖𝑡)−〖k ln〗𝜆 〗
.
. *corey979 (https://stats.stackexchange.com/users/72352/corey979), How to compare models on the basis of AIC?, URL (version: 2016-08-30): https://stats.stackexchange.com/q/232494
| stats_1 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m_nostag_rp6_tvc_1 | 5144 | . | -16977.44 | 60 | 34074.87 | 34467.61 |
| m_nostag_rp5_tvc_1 | 5144 | . | -16979.1 | 59 | 34076.21 | 34462.4 |
| m_nostag_rp7_tvc_1 | 5144 | . | -16977.18 | 61 | 34076.36 | 34475.64 |
| m_nostag_rp8_tvc_1 | 5144 | . | -16976.54 | 62 | 34077.07 | 34482.9 |
| m_nostag_rp6_tvc_2 | 5144 | . | -16976.58 | 62 | 34077.15 | 34482.98 |
| m_nostag_rp9_tvc_1 | 5144 | . | -16975.76 | 63 | 34077.52 | 34489.89 |
| m_nostag_rp5_tvc_2 | 5144 | . | -16978.23 | 61 | 34078.46 | 34477.74 |
| m_nostag_rp7_tvc_2 | 5144 | . | -16976.32 | 63 | 34078.65 | 34491.02 |
| m_nostag_rp10_tvc_1 | 5144 | . | -16975.36 | 64 | 34078.73 | 34497.65 |
| m_nostag_rp8_tvc_2 | 5144 | . | -16975.67 | 64 | 34079.35 | 34498.27 |
| m_nostag_rp4_tvc_1 | 5144 | . | -16981.68 | 58 | 34079.35 | 34459 |
| m_nostag_rp9_tvc_2 | 5144 | . | -16974.89 | 65 | 34079.78 | 34505.24 |
| m_nostag_rp6_tvc_3 | 5144 | . | -16976.45 | 64 | 34080.9 | 34499.82 |
| m_nostag_rp10_tvc_2 | 5144 | . | -16974.5 | 66 | 34081 | 34513.01 |
| m_nostag_rp4_tvc_2 | 5144 | . | -16980.81 | 60 | 34081.61 | 34474.35 |
| m_nostag_rp3_tvc_1 | 5144 | . | -16983.96 | 57 | 34081.92 | 34455.01 |
| m_nostag_rp5_tvc_4 | 5144 | . | -16976.05 | 65 | 34082.1 | 34507.56 |
| m_nostag_rp5_tvc_3 | 5144 | . | -16978.15 | 63 | 34082.29 | 34494.66 |
| m_nostag_rp7_tvc_3 | 5144 | . | -16976.21 | 65 | 34082.43 | 34507.89 |
| m_nostag_rp8_tvc_3 | 5144 | . | -16975.54 | 66 | 34083.07 | 34515.08 |
| m_nostag_rp6_tvc_4 | 5144 | . | -16975.55 | 66 | 34083.11 | 34515.12 |
| m_nostag_rp9_tvc_3 | 5144 | . | -16974.74 | 67 | 34083.47 | 34522.03 |
| m_nostag_rp7_tvc_4 | 5144 | . | -16975.1 | 67 | 34084.2 | 34522.75 |
| m_nostag_rp3_tvc_2 | 5144 | . | -16983.17 | 59 | 34084.33 | 34470.52 |
| m_nostag_rp10_tvc_3 | 5144 | . | -16974.34 | 68 | 34084.68 | 34529.78 |
| m_nostag_rp4_tvc_3 | 5144 | . | -16980.57 | 62 | 34085.15 | 34490.97 |
| m_nostag_rp8_tvc_4 | 5144 | . | -16974.65 | 68 | 34085.3 | 34530.4 |
| m_nostag_rp9_tvc_4 | 5144 | . | -16973.71 | 69 | 34085.43 | 34537.07 |
| m_nostag_rp10_tvc_4 | 5144 | . | -16973.37 | 70 | 34086.74 | 34544.93 |
| m_nostag_rp6_tvc_5 | 5144 | . | -16975.4 | 68 | 34086.8 | 34531.9 |
| m_nostag_rp4_tvc_4 | 5144 | . | -16979.83 | 64 | 34087.67 | 34506.59 |
| m_nostag_rp4_tvc_6 | 5144 | . | -16975.84 | 68 | 34087.68 | 34532.78 |
| m_nostag_rp3_tvc_5 | 5144 | . | -16978.93 | 65 | 34087.86 | 34513.32 |
| m_nostag_rp3_tvc_3 | 5144 | . | -16982.97 | 61 | 34087.94 | 34487.22 |
| m_nostag_rp5_tvc_5 | 5144 | . | -16977.06 | 67 | 34088.11 | 34526.66 |
| m_nostag_rp7_tvc_5 | 5144 | . | -16975.2 | 69 | 34088.4 | 34540.05 |
| m_nostag_rp8_tvc_5 | 5144 | . | -16974.34 | 70 | 34088.68 | 34546.87 |
| m_nostag_rp5_tvc_6 | 5144 | . | -16975.34 | 69 | 34088.69 | 34540.33 |
| m_nostag_rp3_tvc_6 | 5144 | . | -16977.39 | 67 | 34088.79 | 34527.34 |
| m_nostag_rp2_tvc_5 | 5144 | . | -16980.55 | 64 | 34089.11 | 34508.03 |
| m_nostag_rp9_tvc_5 | 5144 | . | -16973.64 | 71 | 34089.29 | 34554.02 |
| m_nostag_rp4_tvc_5 | 5144 | . | -16978.74 | 66 | 34089.48 | 34521.49 |
| m_nostag_rp2_tvc_3 | 5144 | . | -16984.74 | 60 | 34089.48 | 34482.21 |
| m_nostag_rp6_tvc_6 | 5144 | . | -16974.99 | 70 | 34089.98 | 34548.17 |
| m_nostag_rp2_tvc_4 | 5144 | . | -16983.05 | 62 | 34090.11 | 34495.94 |
| m_nostag_rp2_tvc_6 | 5144 | . | -16979.07 | 66 | 34090.14 | 34522.15 |
| m_nostag_rp10_tvc_5 | 5144 | . | -16973.16 | 72 | 34090.31 | 34561.59 |
| m_nostag_rp3_tvc_4 | 5144 | . | -16982.17 | 63 | 34090.35 | 34502.72 |
| m_nostag_rp7_tvc_6 | 5144 | . | -16974.65 | 71 | 34091.31 | 34556.04 |
| m_nostag_rp8_tvc_6 | 5144 | . | -16974.1 | 72 | 34092.19 | 34563.47 |
| m_nostag_rp9_tvc_6 | 5144 | . | -16973.19 | 73 | 34092.39 | 34570.22 |
| m_nostag_rp4_tvc_7 | 5144 | . | -16976.24 | 70 | 34092.47 | 34550.66 |
| m_nostag_rp3_tvc_7 | 5144 | . | -16977.29 | 69 | 34092.57 | 34544.22 |
| m_nostag_rp1_tvc_5 | 5144 | . | -16983.43 | 63 | 34092.85 | 34505.22 |
| m_nostag_rp1_tvc_3 | 5144 | . | -16987.43 | 59 | 34092.86 | 34479.05 |
| m_nostag_rp5_tvc_7 | 5144 | . | -16975.51 | 71 | 34093.01 | 34557.75 |
| m_nostag_rp6_tvc_7 | 5144 | . | -16974.76 | 72 | 34093.53 | 34564.81 |
| m_nostag_rp1_tvc_4 | 5144 | . | -16985.87 | 61 | 34093.74 | 34493.02 |
| m_nostag_rp8_tvc_7 | 5144 | . | -16972.89 | 74 | 34093.77 | 34578.15 |
| m_nostag_rp1_tvc_6 | 5144 | . | -16981.89 | 65 | 34093.78 | 34519.24 |
| m_nostag_rp10_tvc_6 | 5144 | . | -16972.93 | 74 | 34093.86 | 34578.24 |
| m_nostag_rp2_tvc_7 | 5144 | . | -16979.01 | 68 | 34094.03 | 34539.13 |
| m_nostag_rp7_tvc_7 | 5144 | . | -16974.58 | 73 | 34095.16 | 34572.99 |
| m_nostag_rp9_tvc_7 | 5144 | . | -16973.14 | 75 | 34096.29 | 34587.2 |
| m_nostag_rp10_tvc_7 | 5144 | . | -16972.84 | 76 | 34097.68 | 34595.15 |
| m_nostag_rp1_tvc_7 | 5144 | . | -16981.86 | 67 | 34097.72 | 34536.27 |
| m_nostag_rp2_tvc_1 | 5144 | . | -16997.38 | 56 | 34106.76 | 34473.31 |
| m_nostag_rp2_tvc_2 | 5144 | . | -16996.3 | 58 | 34108.6 | 34488.25 |
| m_nostag_rp1_tvc_2 | 5144 | . | -16998.98 | 57 | 34111.96 | 34485.06 |
| m_nostag_rp1_tvc_1 | 5144 | . | -17041.83 | 55 | 34193.65 | 34553.66 |
In the case of the more flexible parametric models (non-standard), we selected the models that showed the best trade-off between lower complexity and better fit. This is why we also considered the BIC. If a model with fewer parameters had greater or equal AIC (or differences lower than 4) but also had better BIC (<=3), we favoured the model with fewer parameters.
.
. *The per(1000) option multiplies the hazard rate by 1000 as it is easier to interpret the rate per 1000 years than per person per year.
.
. range tt 0 7 28
(70,835 missing values generated)
.
. estimates replay m_nostag_rp5_tvc_1, eform
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp5_tvc_1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -16979.103 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012129 .1269215 11.08 0.000 1.77813 2.276921
mot_egr_late | 1.694649 .0920831 9.71 0.000 1.523448 1.88509
tr_mod2 | 1.21838 .0518237 4.64 0.000 1.120926 1.324306
sex_dum2 | .6071271 .0295114 -10.27 0.000 .5519557 .6678133
edad_ini_cons | .9714466 .0047127 -5.97 0.000 .9622538 .9807274
esc1 | 1.430463 .0886504 5.78 0.000 1.266849 1.615207
esc2 | 1.264177 .0732444 4.05 0.000 1.128471 1.416201
sus_prin2 | 1.156999 .0782323 2.16 0.031 1.013392 1.320956
sus_prin3 | 1.681691 .0916832 9.53 0.000 1.511263 1.871339
sus_prin4 | 1.171035 .0933677 1.98 0.048 1.001619 1.369105
sus_prin5 | 1.590511 .2391156 3.09 0.002 1.18459 2.135528
fr_cons_sus_prin2 | .9674122 .1088583 -0.29 0.768 .7759432 1.206127
fr_cons_sus_prin3 | .9785862 .0894337 -0.24 0.813 .8181015 1.170553
fr_cons_sus_prin4 | 1.003252 .0951179 0.03 0.973 .8331209 1.208126
fr_cons_sus_prin5 | 1.030059 .0934628 0.33 0.744 .8622398 1.230541
cond_ocu2 | 1.049109 .0745566 0.67 0.500 .9127015 1.205904
cond_ocu3 | 1.145624 .3090777 0.50 0.614 .6751445 1.943962
cond_ocu4 | 1.22086 .0890421 2.74 0.006 1.058241 1.408469
cond_ocu5 | 1.058374 .1642321 0.37 0.715 .7808265 1.434575
cond_ocu6 | 1.18939 .0465024 4.44 0.000 1.101652 1.284116
policonsumo | .9916223 .0486138 -0.17 0.864 .9007754 1.091631
num_hij2 | 1.125552 .0447825 2.97 0.003 1.041115 1.216837
tenviv1 | 1.066955 .1350055 0.51 0.609 .8326082 1.367262
tenviv2 | 1.124562 .0968833 1.36 0.173 .9498407 1.331424
tenviv4 | 1.037883 .0510004 0.76 0.449 .9425866 1.142814
tenviv5 | 1.010486 .0383174 0.28 0.783 .9381086 1.088449
mzone2 | 1.45019 .0608432 8.86 0.000 1.335711 1.574481
mzone3 | 1.528339 .0965193 6.72 0.000 1.350404 1.729719
n_off_vio | 1.466697 .0554449 10.13 0.000 1.361955 1.579494
n_off_acq | 2.798992 .0972821 29.61 0.000 2.614672 2.996306
n_off_sud | 1.390827 .0507092 9.05 0.000 1.294906 1.493852
n_off_oth | 1.736197 .0634248 15.10 0.000 1.616233 1.865066
psy_com2 | 1.117981 .0550349 2.27 0.023 1.015154 1.231222
psy_com3 | 1.100229 .0424087 2.48 0.013 1.020171 1.186569
dep2 | 1.036411 .0441261 0.84 0.401 .953436 1.126608
rural2 | .8985623 .0559718 -1.72 0.086 .7952918 1.015243
rural3 | .8605226 .0595623 -2.17 0.030 .751355 .9855517
porc_pobr | 1.568951 .3927089 1.80 0.072 .9606235 2.562508
susini2 | 1.188579 .1083449 1.90 0.058 .9941153 1.421083
susini3 | 1.269722 .0818376 3.70 0.000 1.119041 1.440693
susini4 | 1.180627 .0440216 4.45 0.000 1.097424 1.270139
susini5 | 1.421697 .1319853 3.79 0.000 1.18518 1.705413
ano_nac_corr | .8503175 .0080237 -17.18 0.000 .8347359 .8661899
cohab2 | .8802375 .0591122 -1.90 0.057 .7716805 1.004066
cohab3 | 1.075229 .0859759 0.91 0.364 .9192599 1.25766
cohab4 | .9641082 .0641826 -0.55 0.583 .8461739 1.098479
fis_com2 | 1.058096 .0364718 1.64 0.101 .9889734 1.132049
fis_com3 | .8192466 .0709809 -2.30 0.021 .6912978 .9708768
rc_x1 | .8505878 .0101863 -13.51 0.000 .8308556 .8707887
rc_x2 | .8817925 .0351655 -3.15 0.002 .8154942 .9534808
rc_x3 | 1.277561 .1358823 2.30 0.021 1.037164 1.573679
_rcs1 | 2.201568 .0694739 25.01 0.000 2.069527 2.342033
_rcs2 | 1.066428 .0083328 8.23 0.000 1.050221 1.082886
_rcs3 | 1.034867 .0062318 5.69 0.000 1.022724 1.047153
_rcs4 | 1.015479 .0043482 3.59 0.000 1.006992 1.024037
_rcs5 | 1.010226 .0030941 3.32 0.001 1.00418 1.016309
_rcs_mot_egr_early1 | .892624 .0314331 -3.23 0.001 .8330942 .9564075
_rcs_mot_egr_late1 | .9135289 .0309673 -2.67 0.008 .8548065 .9762854
_cons | 8.6e+138 1.6e+140 16.84 0.000 5.8e+122 1.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_nostag_rp5_tvc_1
(results m_nostag_rp5_tvc_1 are active now)
.
. predict h0, hazard timevar(tt) at(mot_egr_early 0 mot_egr_late 0) zeros ci per(1000)
.
. predict h1, hazard timevar(tt) at(mot_egr_early 1 mot_egr_late 0) zeros ci per(1000)
.
. predict h2, hazard timevar(tt) at(mot_egr_early 0 mot_egr_late 1) zeros ci per(1000)
.
.
. sts gen km=s, by(motivodeegreso_mod_imp_rec)
.
. gen zero=0
.
. estimates replay m_nostag_rp5_tvc_1, eform
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp5_tvc_1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -16979.103 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012129 .1269215 11.08 0.000 1.77813 2.276921
mot_egr_late | 1.694649 .0920831 9.71 0.000 1.523448 1.88509
tr_mod2 | 1.21838 .0518237 4.64 0.000 1.120926 1.324306
sex_dum2 | .6071271 .0295114 -10.27 0.000 .5519557 .6678133
edad_ini_cons | .9714466 .0047127 -5.97 0.000 .9622538 .9807274
esc1 | 1.430463 .0886504 5.78 0.000 1.266849 1.615207
esc2 | 1.264177 .0732444 4.05 0.000 1.128471 1.416201
sus_prin2 | 1.156999 .0782323 2.16 0.031 1.013392 1.320956
sus_prin3 | 1.681691 .0916832 9.53 0.000 1.511263 1.871339
sus_prin4 | 1.171035 .0933677 1.98 0.048 1.001619 1.369105
sus_prin5 | 1.590511 .2391156 3.09 0.002 1.18459 2.135528
fr_cons_sus_prin2 | .9674122 .1088583 -0.29 0.768 .7759432 1.206127
fr_cons_sus_prin3 | .9785862 .0894337 -0.24 0.813 .8181015 1.170553
fr_cons_sus_prin4 | 1.003252 .0951179 0.03 0.973 .8331209 1.208126
fr_cons_sus_prin5 | 1.030059 .0934628 0.33 0.744 .8622398 1.230541
cond_ocu2 | 1.049109 .0745566 0.67 0.500 .9127015 1.205904
cond_ocu3 | 1.145624 .3090777 0.50 0.614 .6751445 1.943962
cond_ocu4 | 1.22086 .0890421 2.74 0.006 1.058241 1.408469
cond_ocu5 | 1.058374 .1642321 0.37 0.715 .7808265 1.434575
cond_ocu6 | 1.18939 .0465024 4.44 0.000 1.101652 1.284116
policonsumo | .9916223 .0486138 -0.17 0.864 .9007754 1.091631
num_hij2 | 1.125552 .0447825 2.97 0.003 1.041115 1.216837
tenviv1 | 1.066955 .1350055 0.51 0.609 .8326082 1.367262
tenviv2 | 1.124562 .0968833 1.36 0.173 .9498407 1.331424
tenviv4 | 1.037883 .0510004 0.76 0.449 .9425866 1.142814
tenviv5 | 1.010486 .0383174 0.28 0.783 .9381086 1.088449
mzone2 | 1.45019 .0608432 8.86 0.000 1.335711 1.574481
mzone3 | 1.528339 .0965193 6.72 0.000 1.350404 1.729719
n_off_vio | 1.466697 .0554449 10.13 0.000 1.361955 1.579494
n_off_acq | 2.798992 .0972821 29.61 0.000 2.614672 2.996306
n_off_sud | 1.390827 .0507092 9.05 0.000 1.294906 1.493852
n_off_oth | 1.736197 .0634248 15.10 0.000 1.616233 1.865066
psy_com2 | 1.117981 .0550349 2.27 0.023 1.015154 1.231222
psy_com3 | 1.100229 .0424087 2.48 0.013 1.020171 1.186569
dep2 | 1.036411 .0441261 0.84 0.401 .953436 1.126608
rural2 | .8985623 .0559718 -1.72 0.086 .7952918 1.015243
rural3 | .8605226 .0595623 -2.17 0.030 .751355 .9855517
porc_pobr | 1.568951 .3927089 1.80 0.072 .9606235 2.562508
susini2 | 1.188579 .1083449 1.90 0.058 .9941153 1.421083
susini3 | 1.269722 .0818376 3.70 0.000 1.119041 1.440693
susini4 | 1.180627 .0440216 4.45 0.000 1.097424 1.270139
susini5 | 1.421697 .1319853 3.79 0.000 1.18518 1.705413
ano_nac_corr | .8503175 .0080237 -17.18 0.000 .8347359 .8661899
cohab2 | .8802375 .0591122 -1.90 0.057 .7716805 1.004066
cohab3 | 1.075229 .0859759 0.91 0.364 .9192599 1.25766
cohab4 | .9641082 .0641826 -0.55 0.583 .8461739 1.098479
fis_com2 | 1.058096 .0364718 1.64 0.101 .9889734 1.132049
fis_com3 | .8192466 .0709809 -2.30 0.021 .6912978 .9708768
rc_x1 | .8505878 .0101863 -13.51 0.000 .8308556 .8707887
rc_x2 | .8817925 .0351655 -3.15 0.002 .8154942 .9534808
rc_x3 | 1.277561 .1358823 2.30 0.021 1.037164 1.573679
_rcs1 | 2.201568 .0694739 25.01 0.000 2.069527 2.342033
_rcs2 | 1.066428 .0083328 8.23 0.000 1.050221 1.082886
_rcs3 | 1.034867 .0062318 5.69 0.000 1.022724 1.047153
_rcs4 | 1.015479 .0043482 3.59 0.000 1.006992 1.024037
_rcs5 | 1.010226 .0030941 3.32 0.001 1.00418 1.016309
_rcs_mot_egr_early1 | .892624 .0314331 -3.23 0.001 .8330942 .9564075
_rcs_mot_egr_late1 | .9135289 .0309673 -2.67 0.008 .8548065 .9762854
_cons | 8.6e+138 1.6e+140 16.84 0.000 5.8e+122 1.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_nostag_rp5_tvc_1
(results m_nostag_rp5_tvc_1 are active now)
.
. // Marginal survival
. predict ms0, meansurv timevar(tt) at(mot_egr_early 0 mot_egr_late 0) ci
.
. predict ms1, meansurv timevar(tt) at(mot_egr_early 1 mot_egr_late 0) ci
.
. predict ms2, meansurv timevar(tt) at(mot_egr_early 0 mot_egr_late 1) ci
.
. twoway (rarea ms0_lci ms0_uci tt, color(gs2%35)) ///
> (rarea ms1_lci ms1_uci tt, color(gs6%35)) ///
> (rarea ms2_lci ms2_uci tt, color(gs10%25)) ///
> (line km _t if motivodeegreso_mod_imp_rec==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%50)) ///
> (line km _t if motivodeegreso_mod_imp_rec==2 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs6%50)) ///
> (line km _t if motivodeegreso_mod_imp_rec==3 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs10%50)) ///
> (line ms0 tt, lcolor(gs2) lwidth(thick)) ///
> (line ms1 tt, lcolor(gs6) lwidth(thick)) ///
> (line ms2 tt, lcolor(gs10) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Probibability of avoiding sentence (standardized)") ///
> legend(order( 4 "Tr. completion" 5 "Early dropout" 6 "Late dropout") ring(0) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(km_vs_standsurv_pre, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5tvc2_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5tvc2_pris.gph saved)
.
. *https://www.pauldickman.com/software/stata/sex-differences/
.
. estimates restore m_nostag_rp5_tvc_1
(results m_nostag_rp5_tvc_1 are active now)
.
. predictnl diff_ms = predict(meansurv timevar(tt)) - ///
> predict(meansurv at(mot_egr_early 1 mot_egr_late 0) timevar(tt)) ///
> if mot_egr_early==0, ci(diff_ms_l diff_ms_u)
(70,841 missing values generated)
note: confidence intervals calculated using Z critical values
.
. predictnl diff_ms2 = predict(meansurv timevar(tt)) - ///
> predict(meansurv at(mot_egr_early 0 mot_egr_late 1) timevar(tt)) ///
> if mot_egr_late==0, ci(diff_ms2_l diff_ms2_u)
(70,851 missing values generated)
note: confidence intervals calculated using Z critical values
.
. predictnl diff_ms3 = predict(meansurv at(mot_egr_early 1 mot_egr_late 0) timevar(tt)) - ///
> predict(meansurv at(mot_egr_early 0 mot_egr_late 1) timevar(tt)) ///
> if mot_egr_late==0, ci(diff_ms3_l diff_ms3_u)
(70,851 missing values generated)
note: confidence intervals calculated using Z critical values
.
.
. twoway (rarea diff_ms_l diff_ms_u tt, color(gs7%35)) ///
> (line diff_ms tt, lcolor(gs7) lwidth(thick)) ///
> (rarea diff_ms2_l diff_ms2_u tt, color(gs2%35)) ///
> (line diff_ms2 tt, lcolor(gs2) lwidth(thick)) ///
> (rarea diff_ms3_l diff_ms3_u tt, color(gs10%25)) ///
> (line diff_ms3 tt, lcolor(gs10) lwidth(thick)) ///
> (line zero tt, lcolor(black%20) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Differences of avoiding sentence (standardized)") ///
> legend(order( 2 "Early vs. tr. completion" 4 "Late dropout vs. tr. completion" 6 "Late vs. early dropout") ring(2) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(surv_diffs, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stddif_s_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stddif_s_pris.gph saved)
. /*
> *https://pclambert.net/software/stpm2_standsurv/standardized_survival/
> *https://pclambert.net/software/stpm2_standsurv/standardized_survival_rmst/
> stpm2_standsurv, at1(male 0 stage2m 0 stage3m 0) ///
> at2(male 1 stage2m = stage2 stage3m = stage3) timevar(temptime) ci contrast(difference)
> */
.
. *REALLY NEEDS DUMMY VARS
. global covs_3b_dum "mot_egr_early mot_egr_late tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 con
> d_ocu3 cond_ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini
> 4 susini5 ano_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3"
.
. estimates replay m_nostag_rp5_tvc_1, eform
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp5_tvc_1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -16979.103 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012129 .1269215 11.08 0.000 1.77813 2.276921
mot_egr_late | 1.694649 .0920831 9.71 0.000 1.523448 1.88509
tr_mod2 | 1.21838 .0518237 4.64 0.000 1.120926 1.324306
sex_dum2 | .6071271 .0295114 -10.27 0.000 .5519557 .6678133
edad_ini_cons | .9714466 .0047127 -5.97 0.000 .9622538 .9807274
esc1 | 1.430463 .0886504 5.78 0.000 1.266849 1.615207
esc2 | 1.264177 .0732444 4.05 0.000 1.128471 1.416201
sus_prin2 | 1.156999 .0782323 2.16 0.031 1.013392 1.320956
sus_prin3 | 1.681691 .0916832 9.53 0.000 1.511263 1.871339
sus_prin4 | 1.171035 .0933677 1.98 0.048 1.001619 1.369105
sus_prin5 | 1.590511 .2391156 3.09 0.002 1.18459 2.135528
fr_cons_sus_prin2 | .9674122 .1088583 -0.29 0.768 .7759432 1.206127
fr_cons_sus_prin3 | .9785862 .0894337 -0.24 0.813 .8181015 1.170553
fr_cons_sus_prin4 | 1.003252 .0951179 0.03 0.973 .8331209 1.208126
fr_cons_sus_prin5 | 1.030059 .0934628 0.33 0.744 .8622398 1.230541
cond_ocu2 | 1.049109 .0745566 0.67 0.500 .9127015 1.205904
cond_ocu3 | 1.145624 .3090777 0.50 0.614 .6751445 1.943962
cond_ocu4 | 1.22086 .0890421 2.74 0.006 1.058241 1.408469
cond_ocu5 | 1.058374 .1642321 0.37 0.715 .7808265 1.434575
cond_ocu6 | 1.18939 .0465024 4.44 0.000 1.101652 1.284116
policonsumo | .9916223 .0486138 -0.17 0.864 .9007754 1.091631
num_hij2 | 1.125552 .0447825 2.97 0.003 1.041115 1.216837
tenviv1 | 1.066955 .1350055 0.51 0.609 .8326082 1.367262
tenviv2 | 1.124562 .0968833 1.36 0.173 .9498407 1.331424
tenviv4 | 1.037883 .0510004 0.76 0.449 .9425866 1.142814
tenviv5 | 1.010486 .0383174 0.28 0.783 .9381086 1.088449
mzone2 | 1.45019 .0608432 8.86 0.000 1.335711 1.574481
mzone3 | 1.528339 .0965193 6.72 0.000 1.350404 1.729719
n_off_vio | 1.466697 .0554449 10.13 0.000 1.361955 1.579494
n_off_acq | 2.798992 .0972821 29.61 0.000 2.614672 2.996306
n_off_sud | 1.390827 .0507092 9.05 0.000 1.294906 1.493852
n_off_oth | 1.736197 .0634248 15.10 0.000 1.616233 1.865066
psy_com2 | 1.117981 .0550349 2.27 0.023 1.015154 1.231222
psy_com3 | 1.100229 .0424087 2.48 0.013 1.020171 1.186569
dep2 | 1.036411 .0441261 0.84 0.401 .953436 1.126608
rural2 | .8985623 .0559718 -1.72 0.086 .7952918 1.015243
rural3 | .8605226 .0595623 -2.17 0.030 .751355 .9855517
porc_pobr | 1.568951 .3927089 1.80 0.072 .9606235 2.562508
susini2 | 1.188579 .1083449 1.90 0.058 .9941153 1.421083
susini3 | 1.269722 .0818376 3.70 0.000 1.119041 1.440693
susini4 | 1.180627 .0440216 4.45 0.000 1.097424 1.270139
susini5 | 1.421697 .1319853 3.79 0.000 1.18518 1.705413
ano_nac_corr | .8503175 .0080237 -17.18 0.000 .8347359 .8661899
cohab2 | .8802375 .0591122 -1.90 0.057 .7716805 1.004066
cohab3 | 1.075229 .0859759 0.91 0.364 .9192599 1.25766
cohab4 | .9641082 .0641826 -0.55 0.583 .8461739 1.098479
fis_com2 | 1.058096 .0364718 1.64 0.101 .9889734 1.132049
fis_com3 | .8192466 .0709809 -2.30 0.021 .6912978 .9708768
rc_x1 | .8505878 .0101863 -13.51 0.000 .8308556 .8707887
rc_x2 | .8817925 .0351655 -3.15 0.002 .8154942 .9534808
rc_x3 | 1.277561 .1358823 2.30 0.021 1.037164 1.573679
_rcs1 | 2.201568 .0694739 25.01 0.000 2.069527 2.342033
_rcs2 | 1.066428 .0083328 8.23 0.000 1.050221 1.082886
_rcs3 | 1.034867 .0062318 5.69 0.000 1.022724 1.047153
_rcs4 | 1.015479 .0043482 3.59 0.000 1.006992 1.024037
_rcs5 | 1.010226 .0030941 3.32 0.001 1.00418 1.016309
_rcs_mot_egr_early1 | .892624 .0314331 -3.23 0.001 .8330942 .9564075
_rcs_mot_egr_late1 | .9135289 .0309673 -2.67 0.008 .8548065 .9762854
_cons | 8.6e+138 1.6e+140 16.84 0.000 5.8e+122 1.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_nostag_rp5_tvc_1
(results m_nostag_rp5_tvc_1 are active now)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 1 mot_egr_late 0) timevar(tt) ci contrast(difference) ///
> atvar(s_tr_comp s_early_drop) contrastvar(sdiff_tr_comp_early_drop)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) ci contrast(difference) ///
> atvar(s_tr_comp0 s_late_drop) contrastvar(sdiff_tr_comp_late_drop)
.
. stpm2_standsurv, at1(mot_egr_early 1 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) ci contrast(difference) ///
> atvar(s_early_drop0 s_late_drop0) contrastvar(sdiff_early_late_drop)
.
. cap noi drop s_tr_comp0 s_early_drop0 s_late_drop0
. twoway (rarea s_tr_comp_lci s_tr_comp_uci tt, color(gs2%35)) ///
> (rarea s_early_drop_lci s_early_drop_uci tt, color(gs6%35)) ///
> (rarea s_late_drop_lci s_late_drop_uci tt, color(gs10%35)) ///
> (line km _t if motivodeegreso_mod_imp_rec==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%35)) ///
> (line km _t if motivodeegreso_mod_imp_rec==2 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs6%35)) ///
> (line km _t if motivodeegreso_mod_imp_rec==3 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs10%50)) ///
> (line s_tr_comp tt, lcolor(gs2) lwidth(thick)) ///
> (line s_early_drop tt, lcolor(gs6) lwidth(thick)) ///
> (line s_late_drop tt, lcolor(gs10) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Probibability of avoiding sentence (standardized)") ///
> legend(order( 4 "Tr. completion" 5 "Early dropout" 6 "Late dropout") ring(0) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(km_vs_standsurv, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_s_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_s_pris.gph saved)
.
.
. twoway (rarea sdiff_tr_comp_early_drop_lci sdiff_tr_comp_early_drop_uci tt, color(gs2%35)) ///
> (line sdiff_tr_comp_early_drop tt, lcolor(gs2)) ///
> (rarea sdiff_tr_comp_late_drop_lci sdiff_tr_comp_late_drop_uci tt, color(gs6%35)) ///
> (line sdiff_tr_comp_late_drop tt, lcolor(gs6)) ///
> (rarea sdiff_early_late_drop_lci sdiff_early_late_drop_uci tt, color(gs10%35)) ///
> (line sdiff_early_late_drop tt, lcolor(gs10)) ///
> (line zero tt, lcolor(black%20) lwidth(thick)) ///
> , ylabel(, format(%3.1f)) ///
> ytitle("Difference in Survival (years)") ///
> xtitle("Years from baseline treatment outcome") ///
> legend(order( 1 "Early vs. Tr. completion" 3 "Late vs. Tr. completion" 5 "Late vs. Early dropout") ring(0) pos(7) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(s_diff, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. gr_edit yaxis1.major.label_format = `"%9.2f"'
.
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdif_s2_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_s2_pris.gph saved)
.
. estimates replay m_nostag_rp5_tvc_1, eform
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp5_tvc_1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -16979.103 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012129 .1269215 11.08 0.000 1.77813 2.276921
mot_egr_late | 1.694649 .0920831 9.71 0.000 1.523448 1.88509
tr_mod2 | 1.21838 .0518237 4.64 0.000 1.120926 1.324306
sex_dum2 | .6071271 .0295114 -10.27 0.000 .5519557 .6678133
edad_ini_cons | .9714466 .0047127 -5.97 0.000 .9622538 .9807274
esc1 | 1.430463 .0886504 5.78 0.000 1.266849 1.615207
esc2 | 1.264177 .0732444 4.05 0.000 1.128471 1.416201
sus_prin2 | 1.156999 .0782323 2.16 0.031 1.013392 1.320956
sus_prin3 | 1.681691 .0916832 9.53 0.000 1.511263 1.871339
sus_prin4 | 1.171035 .0933677 1.98 0.048 1.001619 1.369105
sus_prin5 | 1.590511 .2391156 3.09 0.002 1.18459 2.135528
fr_cons_sus_prin2 | .9674122 .1088583 -0.29 0.768 .7759432 1.206127
fr_cons_sus_prin3 | .9785862 .0894337 -0.24 0.813 .8181015 1.170553
fr_cons_sus_prin4 | 1.003252 .0951179 0.03 0.973 .8331209 1.208126
fr_cons_sus_prin5 | 1.030059 .0934628 0.33 0.744 .8622398 1.230541
cond_ocu2 | 1.049109 .0745566 0.67 0.500 .9127015 1.205904
cond_ocu3 | 1.145624 .3090777 0.50 0.614 .6751445 1.943962
cond_ocu4 | 1.22086 .0890421 2.74 0.006 1.058241 1.408469
cond_ocu5 | 1.058374 .1642321 0.37 0.715 .7808265 1.434575
cond_ocu6 | 1.18939 .0465024 4.44 0.000 1.101652 1.284116
policonsumo | .9916223 .0486138 -0.17 0.864 .9007754 1.091631
num_hij2 | 1.125552 .0447825 2.97 0.003 1.041115 1.216837
tenviv1 | 1.066955 .1350055 0.51 0.609 .8326082 1.367262
tenviv2 | 1.124562 .0968833 1.36 0.173 .9498407 1.331424
tenviv4 | 1.037883 .0510004 0.76 0.449 .9425866 1.142814
tenviv5 | 1.010486 .0383174 0.28 0.783 .9381086 1.088449
mzone2 | 1.45019 .0608432 8.86 0.000 1.335711 1.574481
mzone3 | 1.528339 .0965193 6.72 0.000 1.350404 1.729719
n_off_vio | 1.466697 .0554449 10.13 0.000 1.361955 1.579494
n_off_acq | 2.798992 .0972821 29.61 0.000 2.614672 2.996306
n_off_sud | 1.390827 .0507092 9.05 0.000 1.294906 1.493852
n_off_oth | 1.736197 .0634248 15.10 0.000 1.616233 1.865066
psy_com2 | 1.117981 .0550349 2.27 0.023 1.015154 1.231222
psy_com3 | 1.100229 .0424087 2.48 0.013 1.020171 1.186569
dep2 | 1.036411 .0441261 0.84 0.401 .953436 1.126608
rural2 | .8985623 .0559718 -1.72 0.086 .7952918 1.015243
rural3 | .8605226 .0595623 -2.17 0.030 .751355 .9855517
porc_pobr | 1.568951 .3927089 1.80 0.072 .9606235 2.562508
susini2 | 1.188579 .1083449 1.90 0.058 .9941153 1.421083
susini3 | 1.269722 .0818376 3.70 0.000 1.119041 1.440693
susini4 | 1.180627 .0440216 4.45 0.000 1.097424 1.270139
susini5 | 1.421697 .1319853 3.79 0.000 1.18518 1.705413
ano_nac_corr | .8503175 .0080237 -17.18 0.000 .8347359 .8661899
cohab2 | .8802375 .0591122 -1.90 0.057 .7716805 1.004066
cohab3 | 1.075229 .0859759 0.91 0.364 .9192599 1.25766
cohab4 | .9641082 .0641826 -0.55 0.583 .8461739 1.098479
fis_com2 | 1.058096 .0364718 1.64 0.101 .9889734 1.132049
fis_com3 | .8192466 .0709809 -2.30 0.021 .6912978 .9708768
rc_x1 | .8505878 .0101863 -13.51 0.000 .8308556 .8707887
rc_x2 | .8817925 .0351655 -3.15 0.002 .8154942 .9534808
rc_x3 | 1.277561 .1358823 2.30 0.021 1.037164 1.573679
_rcs1 | 2.201568 .0694739 25.01 0.000 2.069527 2.342033
_rcs2 | 1.066428 .0083328 8.23 0.000 1.050221 1.082886
_rcs3 | 1.034867 .0062318 5.69 0.000 1.022724 1.047153
_rcs4 | 1.015479 .0043482 3.59 0.000 1.006992 1.024037
_rcs5 | 1.010226 .0030941 3.32 0.001 1.00418 1.016309
_rcs_mot_egr_early1 | .892624 .0314331 -3.23 0.001 .8330942 .9564075
_rcs_mot_egr_late1 | .9135289 .0309673 -2.67 0.008 .8548065 .9762854
_cons | 8.6e+138 1.6e+140 16.84 0.000 5.8e+122 1.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_nostag_rp5_tvc_1
(results m_nostag_rp5_tvc_1 are active now)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 1 mot_egr_late 0) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_h0 rmst_h1) contrastvar(rmstdiff_tr_comp_early_drop)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_h00 rmst_h2) contrastvar(rmstdiff_tr_comp_late_drop)
.
. stpm2_standsurv, at1(mot_egr_early 1 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_h11 rmst_h22) contrastvar(rmstdiff_early_late_drop)
.
. cap noi drop rmst_h00 rmst_h11 rmst_h22
. twoway (rarea rmstdiff_tr_comp_early_drop_lci rmstdiff_tr_comp_early_drop_uci tt, color(gs2%35)) ///
> (line rmstdiff_tr_comp_early_drop tt, lcolor(gs2)) ///
> (rarea rmstdiff_tr_comp_late_drop_lci rmstdiff_tr_comp_late_drop_uci tt, color(gs6%35)) ///
> (line rmstdiff_tr_comp_late_drop tt, lcolor(gs6)) ///
> (rarea rmstdiff_early_late_drop_lci rmstdiff_early_late_drop_uci tt, color(gs10%35)) ///
> (line rmstdiff_early_late_drop tt, lcolor(gs10)) ///
> (line zero tt, lcolor(black%20) lwidth(thick)) ///
> , ylabel(, format(%3.1f)) ///
> ytitle("Difference in RMST (years)") ///
> xtitle("Years from baseline treatment outcome") ///
> legend(order( 1 "Early vs. Tr. completion" 3 "Late vs. Tr. completion" 5 "Late vs. Early dropout") ring(0) pos(7) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(RMSTdiff, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdif_rmst_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_pris.gph saved)
=============================================================================
=============================================================================
First we calculated the difference between those patients that had a late dropout vs. those who completed treatment by dropping early dropouts, given that the analysis of stipw is restricted to 2 values and does not allow multi-valued treatments.
Late dropout
. *==============================================
. cap qui noi frame drop late
frame late not found
. frame copy default late
.
. frame change late
.
. *drop early
. drop if motivodeegreso_mod_imp_rec==2
(15,797 observations deleted)
.
. recode motivodeegreso_mod_imp_rec (1=0 "Tr. Completion") (2/3=1 "Late dropout"), gen(tr_outcome)
(55057 differences between motivodeegreso_mod_imp_rec and tr_outcome)
. *==============================================
. *______________________________________________
. *______________________________________________
. * NO STAGGERED ENTRY, BINARY TREATMENT (1-LATE VS. 0-COMPLETION)
.
. global covs_4_dum "motivodeegreso_mod_imp_rec2 tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 con
> d_ocu3 cond_ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini
> 4 susini5 ano_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3"
.
. * tvar must be a binary variable with 1 = treatment/exposure and 0 = control.
.
. *exponential weibull gompertz lognormal loglogistic
. *10481 observations have missing treatment and/or missing confounder values and/or _st = 0.
. forvalues i=1/10 {
2. forvalues j=1/7 {
3. qui noi stipw (logit tr_outcome tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 cond_ocu3 cond_
> ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini4 susini5 an
> o_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3), distribution(rp) df(`i') dftvc(`j') genw(rpdf`i'_m_nostag_tvcdf`j') ipwtype(stabilised) vce(mestimation) eform
4. estimates store m_stipw_nostag_rp`i'_tvcdf`j'
5. }
6. }
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -13000.281
Iteration 1: log pseudolikelihood = -12979.557
Iteration 2: log pseudolikelihood = -12979.434
Iteration 3: log pseudolikelihood = -12979.434
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12979.434 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.550212 .1040325 6.53 0.000 1.359152 1.768129
_rcs1 | 2.209891 .0947841 18.49 0.000 2.031712 2.403697
_rcs_tr_outcome1 | .9189338 .0411583 -1.89 0.059 .8417044 1.003249
_cons | .0333881 .0021217 -53.50 0.000 .0294781 .0378166
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12973.542
Iteration 1: log pseudolikelihood = -12965.894
Iteration 2: log pseudolikelihood = -12965.868
Iteration 3: log pseudolikelihood = -12965.868
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12965.868 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.560221 .1047623 6.62 0.000 1.367828 1.779675
_rcs1 | 2.209891 .0947841 18.49 0.000 2.031712 2.403697
_rcs_tr_outcome1 | .9274847 .0421218 -1.66 0.097 .848495 1.013828
_rcs_tr_outcome2 | 1.058801 .0118416 5.11 0.000 1.035844 1.082266
_cons | .0333881 .0021217 -53.50 0.000 .0294781 .0378166
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12971.107
Iteration 1: log pseudolikelihood = -12964.51
Iteration 2: log pseudolikelihood = -12964.471
Iteration 3: log pseudolikelihood = -12964.471
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12964.471 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55952 .1047235 6.62 0.000 1.367199 1.778894
_rcs1 | 2.209891 .0947841 18.49 0.000 2.031712 2.403697
_rcs_tr_outcome1 | .9291189 .0421794 -1.62 0.105 .85002 1.015578
_rcs_tr_outcome2 | 1.055878 .010875 5.28 0.000 1.034777 1.077409
_rcs_tr_outcome3 | 1.017321 .0079637 2.19 0.028 1.001831 1.03305
_cons | .0333881 .0021217 -53.50 0.000 .0294781 .0378166
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12969.095
Iteration 1: log pseudolikelihood = -12964.354
Iteration 2: log pseudolikelihood = -12964.339
Iteration 3: log pseudolikelihood = -12964.339
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12964.339 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55951 .1047236 6.62 0.000 1.36719 1.778885
_rcs1 | 2.209891 .0947841 18.49 0.000 2.031712 2.403697
_rcs_tr_outcome1 | .9290672 .0421852 -1.62 0.105 .8499581 1.015539
_rcs_tr_outcome2 | 1.055293 .0109118 5.20 0.000 1.034121 1.076898
_rcs_tr_outcome3 | 1.018573 .0081541 2.30 0.022 1.002716 1.03468
_rcs_tr_outcome4 | 1.005372 .0058541 0.92 0.357 .9939637 1.016912
_cons | .0333881 .0021217 -53.50 0.000 .0294781 .0378166
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12968.497
Iteration 1: log pseudolikelihood = -12964.082
Iteration 2: log pseudolikelihood = -12964.07
Iteration 3: log pseudolikelihood = -12964.07
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12964.07 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.559441 .1047206 6.62 0.000 1.367126 1.77881
_rcs1 | 2.209891 .0947841 18.49 0.000 2.031712 2.403697
_rcs_tr_outcome1 | .9291139 .0421975 -1.62 0.105 .8499826 1.015612
_rcs_tr_outcome2 | 1.054886 .0108744 5.18 0.000 1.033787 1.076416
_rcs_tr_outcome3 | 1.019287 .0082932 2.35 0.019 1.003162 1.035672
_rcs_tr_outcome4 | 1.007796 .0060874 1.29 0.199 .995935 1.019798
_rcs_tr_outcome5 | 1.004559 .0044493 1.03 0.304 .9958761 1.013317
_cons | .0333881 .0021217 -53.50 0.000 .0294781 .0378166
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12967.875
Iteration 1: log pseudolikelihood = -12963.582
Iteration 2: log pseudolikelihood = -12963.565
Iteration 3: log pseudolikelihood = -12963.565
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12963.565 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.559469 .1047222 6.62 0.000 1.367151 1.778841
_rcs1 | 2.209891 .0947841 18.49 0.000 2.031712 2.403697
_rcs_tr_outcome1 | .928927 .0421891 -1.62 0.105 .8498115 1.015408
_rcs_tr_outcome2 | 1.0544 .0110534 5.05 0.000 1.032956 1.076288
_rcs_tr_outcome3 | 1.018563 .0084463 2.22 0.027 1.002143 1.035253
_rcs_tr_outcome4 | 1.010646 .0062605 1.71 0.087 .9984493 1.022991
_rcs_tr_outcome5 | 1.003918 .0046714 0.84 0.401 .9948036 1.013115
_rcs_tr_outcome6 | 1.00496 .0037037 1.34 0.179 .9977271 1.012245
_cons | .0333881 .0021217 -53.50 0.000 .0294781 .0378166
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12967.782
Iteration 1: log pseudolikelihood = -12963.443
Iteration 2: log pseudolikelihood = -12963.423
Iteration 3: log pseudolikelihood = -12963.423
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12963.423 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55947 .1047222 6.62 0.000 1.367152 1.778842
_rcs1 | 2.209891 .0947841 18.49 0.000 2.031712 2.403697
_rcs_tr_outcome1 | .9288728 .0421882 -1.62 0.104 .8497591 1.015352
_rcs_tr_outcome2 | 1.05404 .0111503 4.98 0.000 1.032411 1.076123
_rcs_tr_outcome3 | 1.018208 .0085883 2.14 0.032 1.001514 1.035181
_rcs_tr_outcome4 | 1.012539 .0064403 1.96 0.050 .9999947 1.025241
_rcs_tr_outcome5 | 1.003599 .0047429 0.76 0.447 .9943457 1.012938
_rcs_tr_outcome6 | 1.005602 .0038193 1.47 0.141 .998144 1.013116
_rcs_tr_outcome7 | 1.002638 .0031739 0.83 0.405 .9964364 1.008878
_cons | .0333881 .0021217 -53.50 0.000 .0294781 .0378166
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12972.364
Iteration 1: log pseudolikelihood = -12965.903
Iteration 2: log pseudolikelihood = -12965.885
Iteration 3: log pseudolikelihood = -12965.885
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12965.885 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556244 .105118 6.55 0.000 1.363272 1.776532
_rcs1 | 2.243662 .1075315 16.86 0.000 2.0425 2.464636
_rcs2 | 1.051678 .0116649 4.54 0.000 1.029062 1.074791
_rcs_tr_outcome1 | .9118238 .0448417 -1.88 0.061 .8280385 1.004087
_cons | .0334614 .0021371 -53.19 0.000 .0295244 .0379235
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12973.505
Iteration 1: log pseudolikelihood = -12965.096
Iteration 2: log pseudolikelihood = -12965.061
Iteration 3: log pseudolikelihood = -12965.061
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12965.061 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.557465 .1045792 6.60 0.000 1.365409 1.776536
_rcs1 | 2.22457 .1068429 16.65 0.000 2.024716 2.444151
_rcs2 | 1.026418 .0289162 0.93 0.355 .9712791 1.084686
_rcs_tr_outcome1 | .9213646 .0463404 -1.63 0.103 .8348722 1.016818
_rcs_tr_outcome2 | 1.03155 .0312638 1.02 0.305 .9720584 1.094682
_cons | .0334471 .0021256 -53.47 0.000 .0295301 .0378837
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12971.203
Iteration 1: log pseudolikelihood = -12963.723
Iteration 2: log pseudolikelihood = -12963.673
Iteration 3: log pseudolikelihood = -12963.673
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12963.673 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556771 .1045382 6.59 0.000 1.36479 1.775756
_rcs1 | 2.224456 .1067556 16.66 0.000 2.024758 2.44385
_rcs2 | 1.026246 .0288461 0.92 0.357 .9712373 1.084369
_rcs_tr_outcome1 | .9230557 .0463673 -1.59 0.111 .8365077 1.018558
_rcs_tr_outcome2 | 1.028923 .0307315 0.95 0.340 .9704194 1.090953
_rcs_tr_outcome3 | 1.01569 .0081466 1.94 0.052 .9998481 1.031783
_cons | .0334469 .0021255 -53.47 0.000 .02953 .0378834
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12969.058
Iteration 1: log pseudolikelihood = -12963.556
Iteration 2: log pseudolikelihood = -12963.532
Iteration 3: log pseudolikelihood = -12963.532
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12963.532 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556756 .1045405 6.59 0.000 1.364772 1.775747
_rcs1 | 2.22457 .1068429 16.65 0.000 2.024716 2.444151
_rcs2 | 1.026418 .0289162 0.93 0.355 .9712791 1.084686
_rcs_tr_outcome1 | .9229367 .0464117 -1.59 0.111 .8363105 1.018536
_rcs_tr_outcome2 | 1.028262 .0307228 0.93 0.351 .969776 1.090276
_rcs_tr_outcome3 | 1.015958 .0086079 1.87 0.062 .9992265 1.03297
_rcs_tr_outcome4 | 1.005372 .0058541 0.92 0.357 .9939637 1.016912
_cons | .0334471 .0021256 -53.47 0.000 .0295301 .0378837
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12968.443
Iteration 1: log pseudolikelihood = -12963.273
Iteration 2: log pseudolikelihood = -12963.251
Iteration 3: log pseudolikelihood = -12963.251
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12963.251 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556678 .1045396 6.59 0.000 1.364696 1.775668
_rcs1 | 2.224696 .1069292 16.64 0.000 2.024688 2.444463
_rcs2 | 1.026607 .028965 0.93 0.352 .9713774 1.084976
_rcs_tr_outcome1 | .9229265 .0464544 -1.59 0.111 .8362246 1.018618
_rcs_tr_outcome2 | 1.027771 .0306443 0.92 0.358 .969431 1.089623
_rcs_tr_outcome3 | 1.015853 .0090482 1.77 0.077 .9982727 1.033743
_rcs_tr_outcome4 | 1.007493 .0060939 1.23 0.217 .99562 1.019508
_rcs_tr_outcome5 | 1.004596 .0044499 1.04 0.301 .9959124 1.013356
_cons | .0334474 .0021256 -53.46 0.000 .0295302 .0378841
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12967.839
Iteration 1: log pseudolikelihood = -12962.784
Iteration 2: log pseudolikelihood = -12962.757
Iteration 3: log pseudolikelihood = -12962.757
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12962.757 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556715 .1045391 6.59 0.000 1.364734 1.775704
_rcs1 | 2.22457 .1068429 16.65 0.000 2.024716 2.444151
_rcs2 | 1.026418 .0289162 0.93 0.355 .9712791 1.084686
_rcs_tr_outcome1 | .9227974 .0464139 -1.60 0.110 .8361679 1.018402
_rcs_tr_outcome2 | 1.027564 .0305791 0.91 0.361 .9693447 1.089281
_rcs_tr_outcome3 | 1.014641 .0094159 1.57 0.117 .996353 1.033265
_rcs_tr_outcome4 | 1.010018 .0062935 1.60 0.110 .9977576 1.022428
_rcs_tr_outcome5 | 1.003918 .0046714 0.84 0.401 .9948036 1.013115
_rcs_tr_outcome6 | 1.00496 .0037037 1.34 0.179 .9977271 1.012245
_cons | .0334471 .0021256 -53.47 0.000 .0295301 .0378837
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12967.74
Iteration 1: log pseudolikelihood = -12962.641
Iteration 2: log pseudolikelihood = -12962.611
Iteration 3: log pseudolikelihood = -12962.611
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12962.611 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556713 .1045399 6.59 0.000 1.36473 1.775703
_rcs1 | 2.224619 .1068763 16.64 0.000 2.024705 2.444272
_rcs2 | 1.026491 .0289353 0.93 0.354 .9713167 1.084799
_rcs_tr_outcome1 | .9227221 .0464245 -1.60 0.110 .8360742 1.01835
_rcs_tr_outcome2 | 1.027234 .0305266 0.90 0.366 .9691121 1.088842
_rcs_tr_outcome3 | 1.01376 .0097972 1.41 0.157 .9947385 1.033145
_rcs_tr_outcome4 | 1.011636 .0065079 1.80 0.072 .9989607 1.024472
_rcs_tr_outcome5 | 1.003516 .0047433 0.74 0.458 .9942626 1.012856
_rcs_tr_outcome6 | 1.005612 .0038195 1.47 0.141 .9981542 1.013126
_rcs_tr_outcome7 | 1.002634 .0031739 0.83 0.406 .9964327 1.008874
_cons | .0334472 .0021256 -53.47 0.000 .0295302 .0378839
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12970.088
Iteration 1: log pseudolikelihood = -12962.922
Iteration 2: log pseudolikelihood = -12962.891
Iteration 3: log pseudolikelihood = -12962.891
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12962.891 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.5559 .1049792 6.55 0.000 1.363169 1.77588
_rcs1 | 2.250943 .1077154 16.95 0.000 2.049423 2.472279
_rcs2 | 1.048828 .0102603 4.87 0.000 1.02891 1.069132
_rcs3 | 1.020706 .0076551 2.73 0.006 1.005812 1.035821
_rcs_tr_outcome1 | .9113404 .0446299 -1.90 0.058 .8279343 1.003149
_cons | .0334477 .002134 -53.26 0.000 .029516 .037903
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12971.317
Iteration 1: log pseudolikelihood = -12962.242
Iteration 2: log pseudolikelihood = -12962.177
Iteration 3: log pseudolikelihood = -12962.177
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12962.177 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556773 .1045336 6.59 0.000 1.364801 1.775749
_rcs1 | 2.233669 .106204 16.90 0.000 2.034918 2.451832
_rcs2 | 1.026206 .0254005 1.05 0.296 .9776099 1.077217
_rcs3 | 1.019207 .0080583 2.41 0.016 1.003535 1.035124
_rcs_tr_outcome1 | .9199282 .0453817 -1.69 0.091 .8351464 1.013317
_rcs_tr_outcome2 | 1.028257 .0277286 1.03 0.301 .9753209 1.084066
_cons | .0334384 .0021247 -53.48 0.000 .0295228 .0378732
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12970.931
Iteration 1: log pseudolikelihood = -12961.808
Iteration 2: log pseudolikelihood = -12961.723
Iteration 3: log pseudolikelihood = -12961.723
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12961.723 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55873 .1047862 6.60 0.000 1.366308 1.778252
_rcs1 | 2.243386 .1129933 16.04 0.000 2.032504 2.476149
_rcs2 | 1.025357 .022687 1.13 0.258 .9818419 1.070801
_rcs3 | 1.032727 .022176 1.50 0.134 .9901648 1.077119
_rcs_tr_outcome1 | .9152467 .0480675 -1.69 0.092 .8257227 1.014477
_rcs_tr_outcome2 | 1.029766 .0251286 1.20 0.229 .9816739 1.080214
_rcs_tr_outcome3 | .9850822 .0225151 -0.66 0.511 .9419272 1.030214
_cons | .033405 .0021255 -53.42 0.000 .0294884 .0378417
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12968.932
Iteration 1: log pseudolikelihood = -12961.827
Iteration 2: log pseudolikelihood = -12961.771
Iteration 3: log pseudolikelihood = -12961.771
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12961.771 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.558454 .1047337 6.60 0.000 1.366125 1.777861
_rcs1 | 2.241818 .1123922 16.10 0.000 2.032011 2.473289
_rcs2 | 1.025351 .0229878 1.12 0.264 .981271 1.07141
_rcs3 | 1.03087 .0216654 1.45 0.148 .9892696 1.07422
_rcs_tr_outcome1 | .9158026 .0479091 -1.68 0.093 .826556 1.014685
_rcs_tr_outcome2 | 1.030199 .0252291 1.21 0.224 .9819189 1.080853
_rcs_tr_outcome3 | .9876824 .0221732 -0.55 0.581 .945166 1.032111
_rcs_tr_outcome4 | .9989479 .0073376 -0.14 0.886 .9846696 1.013433
_cons | .0334106 .0021251 -53.44 0.000 .0294948 .0378464
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12968.325
Iteration 1: log pseudolikelihood = -12961.33
Iteration 2: log pseudolikelihood = -12961.271
Iteration 3: log pseudolikelihood = -12961.271
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12961.271 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55873 .104796 6.60 0.000 1.366291 1.778273
_rcs1 | 2.243684 .1130178 16.04 0.000 2.032757 2.476498
_rcs2 | 1.025271 .0226039 1.13 0.258 .9819114 1.070545
_rcs3 | 1.033135 .0221585 1.52 0.129 .9906057 1.077491
_rcs_tr_outcome1 | .9150902 .0480918 -1.69 0.091 .825524 1.014374
_rcs_tr_outcome2 | 1.030942 .0246375 1.28 0.202 .9837668 1.080379
_rcs_tr_outcome3 | .9873059 .0217444 -0.58 0.562 .9455945 1.030857
_rcs_tr_outcome4 | .9957353 .0099449 -0.43 0.669 .9764332 1.015419
_rcs_tr_outcome5 | 1.0038 .0044741 0.85 0.395 .9950691 1.012608
_cons | .0334036 .0021255 -53.42 0.000 .0294869 .0378404
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12967.699
Iteration 1: log pseudolikelihood = -12960.88
Iteration 2: log pseudolikelihood = -12960.816
Iteration 3: log pseudolikelihood = -12960.816
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12960.816 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55868 .1047848 6.60 0.000 1.36626 1.778199
_rcs1 | 2.243386 .1129933 16.04 0.000 2.032504 2.476149
_rcs2 | 1.025357 .022687 1.13 0.258 .9818419 1.070801
_rcs3 | 1.032727 .022176 1.50 0.134 .9901648 1.077119
_rcs_tr_outcome1 | .9150576 .0480732 -1.69 0.091 .8255245 1.014301
_rcs_tr_outcome2 | 1.031015 .0246399 1.28 0.201 .9838351 1.080457
_rcs_tr_outcome3 | .9880538 .0210282 -0.56 0.572 .9476871 1.03014
_rcs_tr_outcome4 | .9955823 .0117239 -0.38 0.707 .972867 1.018828
_rcs_tr_outcome5 | 1.000636 .0051433 0.12 0.902 .9906063 1.010768
_rcs_tr_outcome6 | 1.00496 .0037037 1.34 0.179 .9977271 1.012245
_cons | .033405 .0021255 -53.42 0.000 .0294884 .0378417
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12967.603
Iteration 1: log pseudolikelihood = -12960.728
Iteration 2: log pseudolikelihood = -12960.661
Iteration 3: log pseudolikelihood = -12960.661
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12960.661 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.558697 .1047881 6.60 0.000 1.366272 1.778223
_rcs1 | 2.243483 .1130163 16.04 0.000 2.032559 2.476295
_rcs2 | 1.025376 .0226755 1.13 0.257 .9818817 1.070796
_rcs3 | 1.032827 .0221763 1.50 0.133 .9902641 1.077219
_rcs_tr_outcome1 | .9149654 .0480755 -1.69 0.091 .8254286 1.014215
_rcs_tr_outcome2 | 1.031286 .0245325 1.30 0.195 .9843069 1.080507
_rcs_tr_outcome3 | .9885428 .0204074 -0.56 0.577 .9493433 1.029361
_rcs_tr_outcome4 | .9954799 .0128687 -0.35 0.726 .9705745 1.021024
_rcs_tr_outcome5 | .9980751 .0059742 -0.32 0.748 .9864342 1.009853
_rcs_tr_outcome6 | 1.004732 .0038581 1.23 0.219 .9971987 1.012322
_rcs_tr_outcome7 | 1.002699 .0031749 0.85 0.395 .9964951 1.008941
_cons | .0334047 .0021255 -53.42 0.000 .0294881 .0378415
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12965.093
Iteration 1: log pseudolikelihood = -12960.678
Iteration 2: log pseudolikelihood = -12960.663
Iteration 3: log pseudolikelihood = -12960.663
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12960.663 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.552809 .1053689 6.49 0.000 1.359434 1.773691
_rcs1 | 2.244761 .1071024 16.95 0.000 2.04436 2.464806
_rcs2 | 1.048151 .0107166 4.60 0.000 1.027355 1.069367
_rcs3 | 1.018941 .0076426 2.50 0.012 1.004072 1.034031
_rcs4 | 1.014488 .0064369 2.27 0.023 1.00195 1.027183
_rcs_tr_outcome1 | .9139486 .0446231 -1.84 0.065 .8305433 1.00573
_cons | .0335019 .0021467 -53.00 0.000 .0295479 .037985
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12966.339
Iteration 1: log pseudolikelihood = -12959.937
Iteration 2: log pseudolikelihood = -12959.901
Iteration 3: log pseudolikelihood = -12959.901
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12959.901 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.553735 .1049047 6.53 0.000 1.361149 1.773569
_rcs1 | 2.227003 .1061422 16.80 0.000 2.028389 2.445064
_rcs2 | 1.024735 .0265837 0.94 0.346 .973934 1.078185
_rcs3 | 1.016506 .0086046 1.93 0.053 .99978 1.033511
_rcs4 | 1.014411 .0064047 2.27 0.023 1.001935 1.027041
_rcs_tr_outcome1 | .9228094 .045714 -1.62 0.105 .8374237 1.016901
_rcs_tr_outcome2 | 1.029423 .0293069 1.02 0.308 .9735556 1.088496
_cons | .0334921 .0021371 -53.23 0.000 .0295547 .037954
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12965.89
Iteration 1: log pseudolikelihood = -12959.336
Iteration 2: log pseudolikelihood = -12959.292
Iteration 3: log pseudolikelihood = -12959.292
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12959.292 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556189 .1051165 6.55 0.000 1.36322 1.776474
_rcs1 | 2.239219 .1126626 16.02 0.000 2.028943 2.471288
_rcs2 | 1.023672 .0230803 1.04 0.299 .9794203 1.069923
_rcs3 | 1.032562 .02145 1.54 0.123 .991365 1.075471
_rcs4 | 1.017745 .0080072 2.24 0.025 1.002172 1.03356
_rcs_tr_outcome1 | .9169429 .0480944 -1.65 0.098 .8273629 1.016222
_rcs_tr_outcome2 | 1.030674 .0261006 1.19 0.233 .9807663 1.083121
_rcs_tr_outcome3 | .98173 .0217831 -0.83 0.406 .939951 1.025366
_cons | .0334498 .0021364 -53.20 0.000 .0295141 .0379103
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12965.078
Iteration 1: log pseudolikelihood = -12954.966
Iteration 2: log pseudolikelihood = -12954.808
Iteration 3: log pseudolikelihood = -12954.808
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12954.808 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556521 .1045661 6.59 0.000 1.364494 1.775571
_rcs1 | 2.239656 .1113019 16.23 0.000 2.031796 2.468781
_rcs2 | 1.021944 .0275648 0.80 0.421 .9693207 1.077423
_rcs3 | 1.021347 .0192327 1.12 0.262 .9843391 1.059747
_rcs4 | 1.048033 .0208884 2.35 0.019 1.007882 1.089784
_rcs_tr_outcome1 | .9167199 .0475617 -1.68 0.094 .8280837 1.014844
_rcs_tr_outcome2 | 1.032633 .0298276 1.11 0.266 .9757962 1.092781
_rcs_tr_outcome3 | .9972832 .0204053 -0.13 0.894 .9580808 1.03809
_rcs_tr_outcome4 | .9592941 .0199186 -2.00 0.045 .9210381 .9991391
_cons | .0334522 .0021268 -53.44 0.000 .0295329 .0378916
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12965.832
Iteration 1: log pseudolikelihood = -12956.821
Iteration 2: log pseudolikelihood = -12956.703
Iteration 3: log pseudolikelihood = -12956.703
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.703 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554922 .1047504 6.55 0.000 1.362591 1.7744
_rcs1 | 2.236217 .1110099 16.21 0.000 2.028891 2.464728
_rcs2 | 1.02227 .0260765 0.86 0.388 .9724176 1.074678
_rcs3 | 1.024934 .019964 1.26 0.206 .9865433 1.06482
_rcs4 | 1.038194 .0187892 2.07 0.038 1.002013 1.075681
_rcs_tr_outcome1 | .919 .0476143 -1.63 0.103 .8302597 1.017225
_rcs_tr_outcome2 | 1.032453 .0281315 1.17 0.241 .9787624 1.089088
_rcs_tr_outcome3 | .9999269 .0211215 -0.00 0.997 .9593748 1.042193
_rcs_tr_outcome4 | .9694811 .018009 -1.67 0.095 .9348189 1.005429
_rcs_tr_outcome5 | .9906124 .0081763 -1.14 0.253 .974716 1.006768
_cons | .0334738 .002133 -53.31 0.000 .0295438 .0379266
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12964.038
Iteration 1: log pseudolikelihood = -12954.645
Iteration 2: log pseudolikelihood = -12954.497
Iteration 3: log pseudolikelihood = -12954.497
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12954.497 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55612 .1045768 6.58 0.000 1.364079 1.775198
_rcs1 | 2.238825 .1111594 16.23 0.000 2.031222 2.467647
_rcs2 | 1.021898 .0272451 0.81 0.417 .9698695 1.076717
_rcs3 | 1.022146 .0193469 1.16 0.247 .9849218 1.060778
_rcs4 | 1.046191 .0206141 2.29 0.022 1.006558 1.087384
_rcs_tr_outcome1 | .9171063 .0475397 -1.67 0.095 .8285072 1.01518
_rcs_tr_outcome2 | 1.032331 .0291884 1.13 0.260 .9766792 1.091154
_rcs_tr_outcome3 | 1.006065 .0206972 0.29 0.769 .9663064 1.04746
_rcs_tr_outcome4 | .9713379 .0167379 -1.69 0.091 .93908 1.004704
_rcs_tr_outcome5 | .975703 .0130119 -1.84 0.065 .9505306 1.001542
_rcs_tr_outcome6 | 1.001273 .0040169 0.32 0.751 .9934313 1.009177
_cons | .0334573 .0021278 -53.42 0.000 .0295364 .0378987
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12963.94
Iteration 1: log pseudolikelihood = -12954.718
Iteration 2: log pseudolikelihood = -12954.56
Iteration 3: log pseudolikelihood = -12954.56
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12954.56 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.555951 .1045645 6.58 0.000 1.363932 1.775003
_rcs1 | 2.238414 .111139 16.23 0.000 2.030849 2.467194
_rcs2 | 1.022084 .0272685 0.82 0.413 .9700125 1.076952
_rcs3 | 1.021913 .0193871 1.14 0.253 .9846132 1.060627
_rcs4 | 1.045721 .0207324 2.25 0.024 1.005865 1.087155
_rcs_tr_outcome1 | .917247 .0475433 -1.67 0.096 .8286409 1.015328
_rcs_tr_outcome2 | 1.031874 .0290634 1.11 0.265 .9764543 1.090439
_rcs_tr_outcome3 | 1.007909 .0206681 0.38 0.701 .968203 1.049242
_rcs_tr_outcome4 | .978457 .015175 -1.40 0.160 .9491621 1.008656
_rcs_tr_outcome5 | .9713079 .0146213 -1.93 0.053 .9430692 1.000392
_rcs_tr_outcome6 | .9936453 .0065018 -0.97 0.330 .9809835 1.006471
_rcs_tr_outcome7 | 1.001636 .0031977 0.51 0.609 .9953884 1.007923
_cons | .03346 .0021279 -53.42 0.000 .0295389 .0379017
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12963.659
Iteration 1: log pseudolikelihood = -12958.983
Iteration 2: log pseudolikelihood = -12958.966
Iteration 3: log pseudolikelihood = -12958.966
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12958.966 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.550873 .1055587 6.45 0.000 1.357188 1.772199
_rcs1 | 2.24196 .1064121 17.01 0.000 2.042803 2.460532
_rcs2 | 1.047756 .0105758 4.62 0.000 1.027231 1.06869
_rcs3 | 1.018545 .0078814 2.37 0.018 1.003214 1.03411
_rcs4 | 1.014955 .0063256 2.38 0.017 1.002633 1.027429
_rcs5 | 1.011048 .0048674 2.28 0.022 1.001553 1.020633
_rcs_tr_outcome1 | .9155811 .0443938 -1.82 0.069 .8325774 1.00686
_cons | .0335325 .0021535 -52.87 0.000 .0295665 .0380304
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12964.892
Iteration 1: log pseudolikelihood = -12958.285
Iteration 2: log pseudolikelihood = -12958.245
Iteration 3: log pseudolikelihood = -12958.245
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12958.245 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.551806 .1051111 6.49 0.000 1.358881 1.77212
_rcs1 | 2.224821 .105715 16.83 0.000 2.026979 2.441973
_rcs2 | 1.025089 .0263833 0.96 0.336 .9746615 1.078126
_rcs3 | 1.01546 .0094371 1.65 0.099 .9971313 1.034126
_rcs4 | 1.014677 .0062818 2.35 0.019 1.002439 1.027064
_rcs5 | 1.010921 .0048396 2.27 0.023 1.00148 1.020451
_rcs_tr_outcome1 | .9241429 .0455209 -1.60 0.109 .8390951 1.017811
_rcs_tr_outcome2 | 1.028587 .0292065 0.99 0.321 .972907 1.087454
_cons | .0335226 .0021442 -53.09 0.000 .0295728 .0379998
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12964.258
Iteration 1: log pseudolikelihood = -12957.969
Iteration 2: log pseudolikelihood = -12957.933
Iteration 3: log pseudolikelihood = -12957.933
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12957.933 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.553626 .1052993 6.50 0.000 1.360363 1.774344
_rcs1 | 2.234014 .1113541 16.13 0.000 2.026086 2.463281
_rcs2 | 1.024168 .0235645 1.04 0.299 .9790086 1.071411
_rcs3 | 1.026971 .0203859 1.34 0.180 .9877832 1.067715
_rcs4 | 1.019229 .0098107 1.98 0.048 1.000181 1.03864
_rcs5 | 1.01109 .0048871 2.28 0.022 1.001557 1.020714
_rcs_tr_outcome1 | .9196811 .0476786 -1.62 0.106 .8308237 1.018042
_rcs_tr_outcome2 | 1.029194 .0266342 1.11 0.266 .978294 1.082743
_rcs_tr_outcome3 | .9864375 .0214269 -0.63 0.530 .945323 1.02934
_cons | .0334912 .0021441 -53.05 0.000 .0295418 .0379687
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.749
Iteration 1: log pseudolikelihood = -12952.277
Iteration 2: log pseudolikelihood = -12952.122
Iteration 3: log pseudolikelihood = -12952.122
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12952.122 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554816 .1048148 6.55 0.000 1.362376 1.774439
_rcs1 | 2.235899 .1101372 16.34 0.000 2.030127 2.462528
_rcs2 | 1.021854 .0280454 0.79 0.431 .9683379 1.078327
_rcs3 | 1.011834 .019173 0.62 0.535 .9749452 1.050119
_rcs4 | 1.047357 .0178286 2.72 0.007 1.01299 1.08289
_rcs5 | 1.023846 .0081804 2.95 0.003 1.007937 1.040005
_rcs_tr_outcome1 | .9184903 .0471819 -1.66 0.098 .8305183 1.015781
_rcs_tr_outcome2 | 1.032414 .0304616 1.08 0.280 .9744043 1.093878
_rcs_tr_outcome3 | 1.000891 .0203083 0.04 0.965 .961868 1.041496
_rcs_tr_outcome4 | .9583875 .0171989 -2.37 0.018 .9252642 .9926965
_cons | .0334812 .0021344 -53.28 0.000 .0295487 .0379371
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12963.924
Iteration 1: log pseudolikelihood = -12952.19
Iteration 2: log pseudolikelihood = -12951.984
Iteration 3: log pseudolikelihood = -12951.984
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.984 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.555177 .1046724 6.56 0.000 1.362979 1.774478
_rcs1 | 2.237507 .1106835 16.28 0.000 2.030756 2.465307
_rcs2 | 1.021133 .0268429 0.80 0.426 .9698547 1.075123
_rcs3 | 1.014498 .0204943 0.71 0.476 .9751152 1.055472
_rcs4 | 1.040521 .0192579 2.15 0.032 1.003452 1.078959
_rcs5 | 1.034008 .0148796 2.32 0.020 1.005252 1.063587
_rcs_tr_outcome1 | .9176466 .0474172 -1.66 0.096 .8292618 1.015452
_rcs_tr_outcome2 | 1.033054 .0291676 1.15 0.249 .9774398 1.091833
_rcs_tr_outcome3 | 1.004721 .0218798 0.22 0.829 .9627392 1.048533
_rcs_tr_outcome4 | .9685494 .018856 -1.64 0.101 .9322885 1.006221
_rcs_tr_outcome5 | .9715193 .0146263 -1.92 0.055 .9432711 1.000614
_cons | .0334796 .002133 -53.32 0.000 .0295494 .0379325
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.579
Iteration 1: log pseudolikelihood = -12951.793
Iteration 2: log pseudolikelihood = -12951.617
Iteration 3: log pseudolikelihood = -12951.617
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.617 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554801 .1047214 6.55 0.000 1.362521 1.774215
_rcs1 | 2.236354 .1100478 16.36 0.000 2.030739 2.462788
_rcs2 | 1.021264 .0269937 0.80 0.426 .9697041 1.075565
_rcs3 | 1.014169 .0201994 0.71 0.480 .9753416 1.054542
_rcs4 | 1.041506 .0188922 2.24 0.025 1.005128 1.0792
_rcs5 | 1.031768 .0129828 2.49 0.013 1.006633 1.05753
_rcs_tr_outcome1 | .9180565 .0471564 -1.66 0.096 .8301319 1.015294
_rcs_tr_outcome2 | 1.032559 .0291478 1.14 0.256 .9769823 1.091298
_rcs_tr_outcome3 | 1.007018 .0222845 0.32 0.752 .9642747 1.051656
_rcs_tr_outcome4 | .977522 .0173635 -1.28 0.201 .9440757 1.012153
_rcs_tr_outcome5 | .9670387 .0136996 -2.37 0.018 .9405573 .9942657
_rcs_tr_outcome6 | .9899199 .0074394 -1.35 0.178 .9754459 1.004609
_cons | .0334854 .0021344 -53.29 0.000 .0295529 .0379413
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12963.468
Iteration 1: log pseudolikelihood = -12952.129
Iteration 2: log pseudolikelihood = -12951.91
Iteration 3: log pseudolikelihood = -12951.91
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.91 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554664 .1046839 6.55 0.000 1.36245 1.773996
_rcs1 | 2.236157 .1103155 16.31 0.000 2.030067 2.46317
_rcs2 | 1.021381 .0268978 0.80 0.422 .9699996 1.075484
_rcs3 | 1.01448 .020309 0.72 0.473 .9754461 1.055076
_rcs4 | 1.040659 .0190251 2.18 0.029 1.004031 1.078623
_rcs5 | 1.031926 .0144333 2.25 0.025 1.004022 1.060606
_rcs_tr_outcome1 | .9181236 .047303 -1.66 0.097 .8299389 1.015678
_rcs_tr_outcome2 | 1.032205 .0287916 1.14 0.256 .9772893 1.090207
_rcs_tr_outcome3 | 1.008109 .0228013 0.36 0.721 .9643953 1.053804
_rcs_tr_outcome4 | .9845405 .0162005 -0.95 0.344 .9532946 1.016811
_rcs_tr_outcome5 | .9687151 .0132998 -2.32 0.021 .9429956 .995136
_rcs_tr_outcome6 | .9807299 .0114729 -1.66 0.096 .9584993 1.003476
_rcs_tr_outcome7 | .9960406 .0042886 -0.92 0.357 .9876705 1.004482
_cons | .0334876 .0021342 -53.30 0.000 .0295554 .0379429
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.001
Iteration 1: log pseudolikelihood = -12958.241
Iteration 2: log pseudolikelihood = -12958.227
Iteration 3: log pseudolikelihood = -12958.227
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12958.227 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.550613 .1055054 6.45 0.000 1.357021 1.771822
_rcs1 | 2.24083 .1067399 16.94 0.000 2.041093 2.460114
_rcs2 | 1.047267 .0107823 4.49 0.000 1.026346 1.068614
_rcs3 | 1.016237 .0081235 2.01 0.044 1.00044 1.032285
_rcs4 | 1.017013 .0063399 2.71 0.007 1.004662 1.029515
_rcs5 | 1.010496 .0049597 2.13 0.033 1.000822 1.020264
_rcs6 | 1.008484 .0037028 2.30 0.021 1.001253 1.015768
_rcs_tr_outcome1 | .915718 .0445756 -1.81 0.070 .8323897 1.007388
_cons | .0335389 .0021535 -52.87 0.000 .0295729 .0380368
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12963.229
Iteration 1: log pseudolikelihood = -12957.538
Iteration 2: log pseudolikelihood = -12957.502
Iteration 3: log pseudolikelihood = -12957.502
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12957.502 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.551546 .1050456 6.49 0.000 1.358736 1.771717
_rcs1 | 2.223547 .106473 16.69 0.000 2.024357 2.442336
_rcs2 | 1.02452 .0269949 0.92 0.358 .9729542 1.078819
_rcs3 | 1.012657 .0101551 1.25 0.210 .992948 1.032758
_rcs4 | 1.016516 .0062744 2.65 0.008 1.004292 1.028888
_rcs5 | 1.010328 .0049346 2.10 0.035 1.000702 1.020046
_rcs6 | 1.008387 .0036764 2.29 0.022 1.001207 1.015618
_rcs_tr_outcome1 | .9243545 .04594 -1.58 0.113 .8385601 1.018927
_rcs_tr_outcome2 | 1.028795 .030102 0.97 0.332 .9714557 1.089518
_cons | .033529 .002144 -53.10 0.000 .0295795 .0380059
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.671
Iteration 1: log pseudolikelihood = -12957.08
Iteration 2: log pseudolikelihood = -12957.043
Iteration 3: log pseudolikelihood = -12957.043
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12957.043 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.553739 .1052292 6.51 0.000 1.360597 1.7743
_rcs1 | 2.233758 .1118093 16.06 0.000 2.025022 2.46401
_rcs2 | 1.02253 .0238978 0.95 0.340 .976748 1.070458
_rcs3 | 1.025102 .0199032 1.28 0.202 .9868248 1.064863
_rcs4 | 1.023288 .0113434 2.08 0.038 1.001295 1.045764
_rcs5 | 1.011629 .0054612 2.14 0.032 1.000982 1.022389
_rcs6 | 1.008462 .0036859 2.31 0.021 1.001263 1.015712
_rcs_tr_outcome1 | .9193785 .0479217 -1.61 0.107 .8300922 1.018269
_rcs_tr_outcome2 | 1.030384 .0274596 1.12 0.261 .977946 1.085635
_rcs_tr_outcome3 | .9839951 .0216449 -0.73 0.463 .9424733 1.027346
_cons | .0334916 .0021432 -53.08 0.000 .0295438 .037967
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.935
Iteration 1: log pseudolikelihood = -12952.294
Iteration 2: log pseudolikelihood = -12952.159
Iteration 3: log pseudolikelihood = -12952.159
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12952.159 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554672 .1046756 6.55 0.000 1.362472 1.773985
_rcs1 | 2.235221 .1109258 16.21 0.000 2.028049 2.463557
_rcs2 | 1.021847 .0281338 0.78 0.432 .9681676 1.078503
_rcs3 | 1.007373 .0192861 0.38 0.701 .9702731 1.045891
_rcs4 | 1.042407 .0161326 2.68 0.007 1.011262 1.074511
_rcs5 | 1.031517 .0125916 2.54 0.011 1.007131 1.056494
_rcs6 | 1.010818 .0038858 2.80 0.005 1.003231 1.018463
_rcs_tr_outcome1 | .9186505 .0475487 -1.64 0.101 .8300279 1.016735
_rcs_tr_outcome2 | 1.032345 .0308183 1.07 0.286 .9736758 1.09455
_rcs_tr_outcome3 | 1.000486 .0204456 0.02 0.981 .9612058 1.041372
_rcs_tr_outcome4 | .9591236 .0186406 -2.15 0.032 .9232758 .9963632
_cons | .0334845 .0021325 -53.33 0.000 .0295551 .0379362
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.908
Iteration 1: log pseudolikelihood = -12952.093
Iteration 2: log pseudolikelihood = -12951.913
Iteration 3: log pseudolikelihood = -12951.913
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.913 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554438 .1046636 6.55 0.000 1.36226 1.773726
_rcs1 | 2.234519 .1104316 16.27 0.000 2.028229 2.46179
_rcs2 | 1.021332 .0277262 0.78 0.437 .9684098 1.077146
_rcs3 | 1.007267 .0204919 0.36 0.722 .9678941 1.048242
_rcs4 | 1.041092 .0178591 2.35 0.019 1.006671 1.07669
_rcs5 | 1.032501 .0131334 2.51 0.012 1.007078 1.058566
_rcs6 | 1.015817 .0067026 2.38 0.017 1.002765 1.02904
_rcs_tr_outcome1 | .9189064 .0473905 -1.64 0.101 .8305628 1.016647
_rcs_tr_outcome2 | 1.032578 .0302658 1.09 0.274 .97493 1.093635
_rcs_tr_outcome3 | 1.00726 .0215016 0.34 0.735 .9659866 1.050296
_rcs_tr_outcome4 | .9640904 .018333 -1.92 0.054 .9288197 1.0007
_rcs_tr_outcome5 | .9793066 .0118406 -1.73 0.084 .9563724 1.002791
_cons | .0334911 .0021338 -53.31 0.000 .0295595 .0379457
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.305
Iteration 1: log pseudolikelihood = -12951.559
Iteration 2: log pseudolikelihood = -12951.386
Iteration 3: log pseudolikelihood = -12951.386
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.386 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554886 .1046423 6.56 0.000 1.362741 1.774122
_rcs1 | 2.235009 .1104646 16.27 0.000 2.028659 2.462349
_rcs2 | 1.02069 .0277056 0.75 0.451 .9678073 1.076462
_rcs3 | 1.00644 .0213852 0.30 0.763 .9653865 1.049239
_rcs4 | 1.040273 .0191654 2.14 0.032 1.00338 1.078523
_rcs5 | 1.033013 .0147261 2.28 0.023 1.004549 1.062282
_rcs6 | 1.020488 .0099651 2.08 0.038 1.001142 1.040207
_rcs_tr_outcome1 | .9184874 .0474231 -1.65 0.100 .8300881 1.016301
_rcs_tr_outcome2 | 1.033026 .0300573 1.12 0.264 .9757635 1.09365
_rcs_tr_outcome3 | 1.012046 .0230833 0.52 0.600 .9677996 1.058315
_rcs_tr_outcome4 | .9715196 .0188826 -1.49 0.137 .9352064 1.009243
_rcs_tr_outcome5 | .971835 .0145726 -1.91 0.057 .9436888 1.000821
_rcs_tr_outcome6 | .984784 .0102788 -1.47 0.142 .9648426 1.005138
_cons | .0334865 .0021332 -53.32 0.000 .0295559 .0379398
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.573
Iteration 1: log pseudolikelihood = -12952.007
Iteration 2: log pseudolikelihood = -12951.826
Iteration 3: log pseudolikelihood = -12951.826
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.826 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554379 .1046411 6.55 0.000 1.36224 1.773617
_rcs1 | 2.234206 .1105804 16.24 0.000 2.027653 2.461801
_rcs2 | 1.021015 .0275461 0.77 0.441 .9684287 1.076458
_rcs3 | 1.007519 .0211597 0.36 0.721 .9668885 1.049856
_rcs4 | 1.039621 .0186932 2.16 0.031 1.003621 1.076912
_rcs5 | 1.03253 .014182 2.33 0.020 1.005104 1.060703
_rcs6 | 1.018459 .0089597 2.08 0.038 1.001049 1.036172
_rcs_tr_outcome1 | .9189734 .0475432 -1.63 0.102 .8303591 1.017044
_rcs_tr_outcome2 | 1.032266 .0296361 1.11 0.269 .9757848 1.092017
_rcs_tr_outcome3 | 1.013154 .0235771 0.56 0.574 .9679816 1.060434
_rcs_tr_outcome4 | .9780922 .0178814 -1.21 0.226 .9436658 1.013775
_rcs_tr_outcome5 | .9719139 .0135918 -2.04 0.042 .9456361 .9989218
_rcs_tr_outcome6 | .9816906 .0103874 -1.75 0.081 .9615414 1.002262
_rcs_tr_outcome7 | .9915684 .0064653 -1.30 0.194 .9789773 1.004321
_cons | .0334934 .002134 -53.31 0.000 .0295613 .0379484
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.216
Iteration 1: log pseudolikelihood = -12957.239
Iteration 2: log pseudolikelihood = -12957.225
Iteration 3: log pseudolikelihood = -12957.225
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12957.225 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.550046 .105599 6.43 0.000 1.356298 1.77147
_rcs1 | 2.239219 .1068815 16.89 0.000 2.039235 2.458815
_rcs2 | 1.047082 .0108984 4.42 0.000 1.025938 1.068662
_rcs3 | 1.014646 .0083715 1.76 0.078 .9983704 1.031187
_rcs4 | 1.018906 .0064125 2.98 0.003 1.006415 1.031552
_rcs5 | 1.008794 .0048932 1.81 0.071 .9992494 1.018431
_rcs6 | 1.011201 .0039662 2.84 0.005 1.003457 1.019005
_rcs7 | 1.003379 .0031613 1.07 0.284 .9972023 1.009594
_rcs_tr_outcome1 | .9164971 .0447243 -1.79 0.074 .8329006 1.008484
_cons | .033548 .0021559 -52.83 0.000 .0295778 .038051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.436
Iteration 1: log pseudolikelihood = -12956.509
Iteration 2: log pseudolikelihood = -12956.473
Iteration 3: log pseudolikelihood = -12956.473
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.473 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.550998 .1051287 6.48 0.000 1.35805 1.77136
_rcs1 | 2.221637 .1067409 16.61 0.000 2.021977 2.441012
_rcs2 | 1.02402 .0271567 0.90 0.371 .9721534 1.078654
_rcs3 | 1.01053 .0108467 0.98 0.329 .9894927 1.032014
_rcs4 | 1.018184 .0063317 2.90 0.004 1.00585 1.03067
_rcs5 | 1.008574 .0048776 1.77 0.077 .9990597 1.01818
_rcs6 | 1.011097 .0039361 2.83 0.005 1.003412 1.018841
_rcs7 | 1.003307 .0031447 1.05 0.292 .9971624 1.00949
_rcs_tr_outcome1 | .9252973 .0461969 -1.56 0.120 .8390422 1.02042
_rcs_tr_outcome2 | 1.029343 .0306156 0.97 0.331 .9710531 1.091132
_cons | .0335379 .0021462 -53.05 0.000 .0295845 .0380197
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.894
Iteration 1: log pseudolikelihood = -12956.06
Iteration 2: log pseudolikelihood = -12956.02
Iteration 3: log pseudolikelihood = -12956.02
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.02 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.553211 .1052978 6.50 0.000 1.359955 1.77393
_rcs1 | 2.231826 .1119136 16.01 0.000 2.022914 2.462314
_rcs2 | 1.021733 .0241537 0.91 0.363 .9754725 1.070187
_rcs3 | 1.022239 .0194371 1.16 0.247 .9848442 1.061054
_rcs4 | 1.025781 .0123363 2.12 0.034 1.001885 1.050247
_rcs5 | 1.010892 .0061067 1.79 0.073 .9989939 1.022932
_rcs6 | 1.011446 .0040068 2.87 0.004 1.003623 1.01933
_rcs7 | 1.003379 .0031428 1.08 0.282 .997238 1.009558
_rcs_tr_outcome1 | .9203203 .0480964 -1.59 0.112 .8307203 1.019584
_rcs_tr_outcome2 | 1.031014 .0281229 1.12 0.263 .9773414 1.087633
_rcs_tr_outcome3 | .9840378 .0217143 -0.73 0.466 .9423858 1.027531
_cons | .0335002 .0021452 -53.04 0.000 .0295489 .0379798
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.262
Iteration 1: log pseudolikelihood = -12951.198
Iteration 2: log pseudolikelihood = -12951.041
Iteration 3: log pseudolikelihood = -12951.041
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.041 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554279 .1047983 6.54 0.000 1.361872 1.77387
_rcs1 | 2.233877 .1113004 16.13 0.000 2.026045 2.463028
_rcs2 | 1.021668 .0282031 0.78 0.437 .9678597 1.078468
_rcs3 | 1.003743 .0193189 0.19 0.846 .9665843 1.042331
_rcs4 | 1.039377 .0147098 2.73 0.006 1.010943 1.068612
_rcs5 | 1.033037 .0143314 2.34 0.019 1.005327 1.061512
_rcs6 | 1.02011 .0064068 3.17 0.002 1.00763 1.032745
_rcs7 | 1.003897 .0030963 1.26 0.207 .9978464 1.009984
_rcs_tr_outcome1 | .9193151 .0478217 -1.62 0.106 .8302061 1.017988
_rcs_tr_outcome2 | 1.032819 .0312277 1.07 0.286 .9733922 1.095874
_rcs_tr_outcome3 | 1.000418 .0206343 0.02 0.984 .9607822 1.041689
_rcs_tr_outcome4 | .9584195 .019257 -2.11 0.035 .9214099 .9969156
_cons | .0334911 .0021352 -53.28 0.000 .0295571 .0379486
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.548
Iteration 1: log pseudolikelihood = -12950.24
Iteration 2: log pseudolikelihood = -12950.031
Iteration 3: log pseudolikelihood = -12950.031
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12950.031 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554491 .1047436 6.55 0.000 1.362175 1.773957
_rcs1 | 2.234083 .1110981 16.16 0.000 2.02661 2.462796
_rcs2 | 1.020398 .0271648 0.76 0.448 .9685216 1.075054
_rcs3 | 1.005232 .0212227 0.25 0.805 .9644855 1.047701
_rcs4 | 1.033881 .0156116 2.21 0.027 1.003731 1.064936
_rcs5 | 1.033042 .0130191 2.58 0.010 1.007837 1.058876
_rcs6 | 1.029094 .0109385 2.70 0.007 1.007877 1.050758
_rcs7 | 1.007914 .0036887 2.15 0.031 1.00071 1.015169
_rcs_tr_outcome1 | .9189232 .0477717 -1.63 0.104 .8299045 1.01749
_rcs_tr_outcome2 | 1.033789 .0302852 1.13 0.257 .9761027 1.094884
_rcs_tr_outcome3 | 1.006163 .0218719 0.28 0.777 .9641956 1.049958
_rcs_tr_outcome4 | .9678447 .0177812 -1.78 0.075 .9336143 1.00333
_rcs_tr_outcome5 | .9720232 .0136975 -2.01 0.044 .9455439 .9992441
_cons | .033493 .0021355 -53.27 0.000 .0295584 .0379512
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.566
Iteration 1: log pseudolikelihood = -12950.38
Iteration 2: log pseudolikelihood = -12950.183
Iteration 3: log pseudolikelihood = -12950.183
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12950.183 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554417 .1047782 6.54 0.000 1.362043 1.773962
_rcs1 | 2.233545 .1108367 16.19 0.000 2.026539 2.461696
_rcs2 | 1.020911 .027573 0.77 0.444 .968274 1.076409
_rcs3 | 1.002352 .0215949 0.11 0.913 .960908 1.045584
_rcs4 | 1.037299 .0172402 2.20 0.028 1.004053 1.071645
_rcs5 | 1.032137 .0142093 2.30 0.022 1.004659 1.060365
_rcs6 | 1.02748 .0098226 2.84 0.005 1.008407 1.046913
_rcs7 | 1.009802 .005933 1.66 0.097 .9982408 1.021498
_rcs_tr_outcome1 | .9192078 .0476723 -1.62 0.104 .8303637 1.017558
_rcs_tr_outcome2 | 1.03299 .0303265 1.11 0.269 .9752292 1.094172
_rcs_tr_outcome3 | 1.012629 .0227849 0.56 0.577 .9689417 1.058286
_rcs_tr_outcome4 | .9717118 .0177635 -1.57 0.116 .9375123 1.007159
_rcs_tr_outcome5 | .9704432 .0143677 -2.03 0.043 .9426876 .999016
_rcs_tr_outcome6 | .9859427 .0086998 -1.60 0.109 .9690379 1.003142
_cons | .033494 .0021359 -53.26 0.000 .0295588 .0379531
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.327
Iteration 1: log pseudolikelihood = -12949.483
Iteration 2: log pseudolikelihood = -12949.225
Iteration 3: log pseudolikelihood = -12949.225
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12949.225 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.555163 .1044807 6.57 0.000 1.363294 1.774036
_rcs1 | 2.235745 .1107256 16.25 0.000 2.028927 2.463645
_rcs2 | 1.021859 .0279451 0.79 0.429 .9685298 1.078125
_rcs3 | .9999968 .0216053 -0.00 1.000 .9585353 1.043252
_rcs4 | 1.042111 .0182924 2.35 0.019 1.006869 1.078588
_rcs5 | 1.028039 .0149167 1.91 0.057 .9992142 1.057695
_rcs6 | 1.031081 .0114537 2.76 0.006 1.008874 1.053776
_rcs7 | 1.004807 .0094259 0.51 0.609 .9865017 1.023453
_rcs_tr_outcome1 | .9181315 .0474948 -1.65 0.099 .8296069 1.016102
_rcs_tr_outcome2 | 1.031493 .0302438 1.06 0.290 .9738872 1.092506
_rcs_tr_outcome3 | 1.018211 .0236148 0.78 0.436 .9729632 1.065564
_rcs_tr_outcome4 | .9716225 .0181401 -1.54 0.123 .9367112 1.007835
_rcs_tr_outcome5 | .9762267 .0148968 -1.58 0.115 .9474618 1.005865
_rcs_tr_outcome6 | .9752892 .0114491 -2.13 0.033 .9531056 .9979892
_rcs_tr_outcome7 | .9978409 .0098799 -0.22 0.827 .9786633 1.017394
_cons | .0334805 .0021287 -53.42 0.000 .0295578 .0379239
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.409
Iteration 1: log pseudolikelihood = -12956.876
Iteration 2: log pseudolikelihood = -12956.859
Iteration 3: log pseudolikelihood = -12956.859
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.859 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.549768 .1057593 6.42 0.000 1.355748 1.771555
_rcs1 | 2.238545 .1068581 16.88 0.000 2.038606 2.458093
_rcs2 | 1.047053 .0109363 4.40 0.000 1.025836 1.068709
_rcs3 | 1.013517 .0085408 1.59 0.111 .9969146 1.030395
_rcs4 | 1.019503 .0063728 3.09 0.002 1.007089 1.03207
_rcs5 | 1.007945 .0048414 1.65 0.099 .9985006 1.017479
_rcs6 | 1.011158 .0041295 2.72 0.007 1.003096 1.019284
_rcs7 | 1.00782 .0033639 2.33 0.020 1.001248 1.014434
_rcs8 | 1.001625 .0032257 0.50 0.614 .9953226 1.007967
_rcs_tr_outcome1 | .916805 .0447717 -1.78 0.075 .8331228 1.008893
_cons | .0335529 .0021592 -52.75 0.000 .0295769 .0380632
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.658
Iteration 1: log pseudolikelihood = -12956.16
Iteration 2: log pseudolikelihood = -12956.121
Iteration 3: log pseudolikelihood = -12956.121
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.121 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.550724 .1052893 6.46 0.000 1.357502 1.771448
_rcs1 | 2.221165 .1069222 16.58 0.000 2.021183 2.440933
_rcs2 | 1.024254 .0271893 0.90 0.367 .9723263 1.078955
_rcs3 | 1.009199 .0112571 0.82 0.412 .9873752 1.031506
_rcs4 | 1.018591 .0063032 2.98 0.003 1.006312 1.03102
_rcs5 | 1.007663 .004835 1.59 0.112 .9982307 1.017184
_rcs6 | 1.011044 .004103 2.71 0.007 1.003034 1.019118
_rcs7 | 1.00771 .0033434 2.31 0.021 1.001178 1.014284
_rcs8 | 1.001607 .0032017 0.50 0.615 .9953516 1.007902
_rcs_tr_outcome1 | .9255102 .0463306 -1.55 0.122 .8390164 1.020921
_rcs_tr_outcome2 | 1.029078 .0308035 0.96 0.338 .9704407 1.091257
_cons | .0335427 .0021496 -52.98 0.000 .0295835 .0380318
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.093
Iteration 1: log pseudolikelihood = -12955.73
Iteration 2: log pseudolikelihood = -12955.686
Iteration 3: log pseudolikelihood = -12955.686
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12955.686 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.552918 .1054535 6.48 0.000 1.359397 1.773989
_rcs1 | 2.231164 .1119861 15.99 0.000 2.022126 2.461811
_rcs2 | 1.021805 .0242537 0.91 0.363 .9753573 1.070464
_rcs3 | 1.020155 .0190904 1.07 0.286 .9834167 1.058267
_rcs4 | 1.026396 .0126711 2.11 0.035 1.001859 1.051533
_rcs5 | 1.010885 .0069176 1.58 0.114 .9974177 1.024535
_rcs6 | 1.011856 .0043669 2.73 0.006 1.003334 1.020452
_rcs7 | 1.007894 .0033665 2.35 0.019 1.001317 1.014514
_rcs8 | 1.001645 .0031963 0.52 0.606 .9953999 1.007929
_rcs_tr_outcome1 | .9206177 .0481873 -1.58 0.114 .8308553 1.020078
_rcs_tr_outcome2 | 1.030791 .0283885 1.10 0.271 .9766259 1.087961
_rcs_tr_outcome3 | .9843422 .0217948 -0.71 0.476 .9425388 1.028
_cons | .0335053 .0021484 -52.96 0.000 .0295484 .0379922
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.489
Iteration 1: log pseudolikelihood = -12951.256
Iteration 2: log pseudolikelihood = -12951.1
Iteration 3: log pseudolikelihood = -12951.1
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.1 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.553543 .1049114 6.52 0.000 1.360947 1.773394
_rcs1 | 2.232218 .1112774 16.11 0.000 2.024435 2.461328
_rcs2 | 1.021984 .0280357 0.79 0.428 .9684864 1.078437
_rcs3 | 1.002053 .0193793 0.11 0.916 .9647813 1.040765
_rcs4 | 1.035023 .0133368 2.67 0.008 1.009211 1.061496
_rcs5 | 1.030962 .0141988 2.21 0.027 1.003505 1.05917
_rcs6 | 1.025428 .009159 2.81 0.005 1.007633 1.043537
_rcs7 | 1.011104 .0038365 2.91 0.004 1.003613 1.018652
_rcs8 | 1.001826 .0031165 0.59 0.558 .9957365 1.007953
_rcs_tr_outcome1 | .9201719 .0479177 -1.60 0.110 .8308888 1.019049
_rcs_tr_outcome2 | 1.032545 .03124 1.06 0.290 .9730954 1.095626
_rcs_tr_outcome3 | 1.000126 .0208722 0.01 0.995 .9600426 1.041883
_rcs_tr_outcome4 | .9600018 .0191703 -2.04 0.041 .9231544 .99832
_cons | .0335031 .0021386 -53.20 0.000 .029563 .0379682
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.759
Iteration 1: log pseudolikelihood = -12949.972
Iteration 2: log pseudolikelihood = -12949.717
Iteration 3: log pseudolikelihood = -12949.717
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12949.717 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554076 .1048209 6.54 0.000 1.361631 1.773719
_rcs1 | 2.233373 .1113236 16.12 0.000 2.025502 2.462577
_rcs2 | 1.020519 .0267948 0.77 0.439 .9693308 1.074411
_rcs3 | 1.004715 .0217631 0.22 0.828 .9629533 1.048289
_rcs4 | 1.029341 .0142145 2.09 0.036 1.001854 1.057581
_rcs5 | 1.028067 .0129579 2.20 0.028 1.002981 1.05378
_rcs6 | 1.032802 .0122559 2.72 0.007 1.009058 1.057105
_rcs7 | 1.018986 .0071497 2.68 0.007 1.005069 1.033096
_rcs8 | 1.003221 .0029773 1.08 0.279 .9974024 1.009073
_rcs_tr_outcome1 | .9192222 .0479424 -1.61 0.106 .8298999 1.018158
_rcs_tr_outcome2 | 1.0337 .030204 1.13 0.257 .9761645 1.094627
_rcs_tr_outcome3 | 1.004709 .0222406 0.21 0.832 .9620499 1.049259
_rcs_tr_outcome4 | .9701496 .0183108 -1.61 0.108 .9349169 1.00671
_rcs_tr_outcome5 | .9708411 .0144545 -1.99 0.047 .9429203 .9995888
_cons | .0335006 .0021381 -53.21 0.000 .0295615 .0379646
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.583
Iteration 1: log pseudolikelihood = -12950.012
Iteration 2: log pseudolikelihood = -12949.773
Iteration 3: log pseudolikelihood = -12949.773
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12949.773 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554263 .104931 6.53 0.000 1.361628 1.774151
_rcs1 | 2.233202 .1107042 16.21 0.000 2.026433 2.461069
_rcs2 | 1.02098 .0272259 0.78 0.436 .9689888 1.075761
_rcs3 | 1.001121 .022064 0.05 0.959 .9587973 1.045313
_rcs4 | 1.032591 .0163419 2.03 0.043 1.001053 1.065123
_rcs5 | 1.028396 .0129254 2.23 0.026 1.003373 1.054044
_rcs6 | 1.030432 .0112465 2.75 0.006 1.008624 1.052713
_rcs7 | 1.020258 .008346 2.45 0.014 1.004031 1.036748
_rcs8 | 1.005372 .003744 1.44 0.150 .9980607 1.012737
_rcs_tr_outcome1 | .9192849 .0476448 -1.62 0.104 .8304891 1.017575
_rcs_tr_outcome2 | 1.033062 .0302509 1.11 0.267 .9754405 1.094087
_rcs_tr_outcome3 | 1.011504 .022998 0.50 0.615 .9674179 1.057598
_rcs_tr_outcome4 | .9737002 .0181588 -1.43 0.153 .9387522 1.009949
_rcs_tr_outcome5 | .9704538 .0144432 -2.02 0.044 .9425546 .9991788
_rcs_tr_outcome6 | .9841031 .0093349 -1.69 0.091 .9659762 1.00257
_cons | .0334977 .0021391 -53.18 0.000 .0295568 .037964
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.047
Iteration 1: log pseudolikelihood = -12949.78
Iteration 2: log pseudolikelihood = -12949.512
Iteration 3: log pseudolikelihood = -12949.512
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12949.512 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554004 .1050818 6.52 0.000 1.361111 1.774232
_rcs1 | 2.232962 .1099999 16.31 0.000 2.027447 2.459309
_rcs2 | 1.022472 .0276116 0.82 0.411 .9697615 1.078047
_rcs3 | .9979369 .0219584 -0.09 0.925 .9558141 1.041916
_rcs4 | 1.037237 .0169873 2.23 0.026 1.004471 1.071072
_rcs5 | 1.024779 .0132878 1.89 0.059 .9990634 1.051156
_rcs6 | 1.030782 .0118992 2.63 0.009 1.007721 1.054369
_rcs7 | 1.020247 .0079297 2.58 0.010 1.004823 1.035908
_rcs8 | 1.004648 .0058699 0.79 0.427 .9932092 1.016219
_rcs_tr_outcome1 | .9194464 .0473205 -1.63 0.103 .8312244 1.017032
_rcs_tr_outcome2 | 1.031054 .0301484 1.05 0.296 .9736258 1.09187
_rcs_tr_outcome3 | 1.017263 .0237441 0.73 0.463 .9717741 1.064882
_rcs_tr_outcome4 | .9744396 .0176071 -1.43 0.152 .9405342 1.009567
_rcs_tr_outcome5 | .9764978 .0142356 -1.63 0.103 .9489914 1.004801
_rcs_tr_outcome6 | .9764252 .0107203 -2.17 0.030 .9556383 .9976642
_rcs_tr_outcome7 | .9922187 .0073636 -1.05 0.293 .9778907 1.006757
_cons | .0335014 .0021418 -53.12 0.000 .0295558 .0379737
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.016
Iteration 1: log pseudolikelihood = -12957.259
Iteration 2: log pseudolikelihood = -12957.24
Iteration 3: log pseudolikelihood = -12957.24
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12957.24 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55031 .1057022 6.43 0.000 1.356384 1.771963
_rcs1 | 2.239886 .1069992 16.88 0.000 2.03969 2.459732
_rcs2 | 1.04696 .0108824 4.41 0.000 1.025847 1.068508
_rcs3 | 1.012963 .0086732 1.50 0.133 .9961062 1.030106
_rcs4 | 1.019183 .0064341 3.01 0.003 1.00665 1.031872
_rcs5 | 1.008891 .0049011 1.82 0.068 .9993311 1.018543
_rcs6 | 1.009636 .0041275 2.35 0.019 1.001578 1.017758
_rcs7 | 1.009558 .0035113 2.74 0.006 1.0027 1.016464
_rcs8 | 1.004677 .0030021 1.56 0.118 .99881 1.010578
_rcs9 | 1.002045 .0030794 0.66 0.506 .9960278 1.008099
_rcs_tr_outcome1 | .9161271 .0447464 -1.79 0.073 .8324928 1.008164
_cons | .033544 .0021574 -52.79 0.000 .0295713 .0380504
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12963.297
Iteration 1: log pseudolikelihood = -12956.542
Iteration 2: log pseudolikelihood = -12956.501
Iteration 3: log pseudolikelihood = -12956.501
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.501 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.551281 .1052231 6.47 0.000 1.358169 1.771852
_rcs1 | 2.222508 .106978 16.59 0.000 2.022421 2.44239
_rcs2 | 1.024187 .0270602 0.90 0.366 .9725001 1.078621
_rcs3 | 1.00845 .0116007 0.73 0.465 .985967 1.031445
_rcs4 | 1.018023 .006386 2.85 0.004 1.005584 1.030617
_rcs5 | 1.008564 .0049011 1.75 0.079 .9990033 1.018216
_rcs6 | 1.00948 .0041055 2.32 0.020 1.001465 1.017558
_rcs7 | 1.009458 .0034847 2.73 0.006 1.002651 1.016311
_rcs8 | 1.004589 .0029858 1.54 0.123 .998754 1.010458
_rcs9 | 1.002038 .0030531 0.67 0.504 .9960716 1.00804
_rcs_tr_outcome1 | .9248174 .0462572 -1.56 0.118 .8384572 1.020073
_rcs_tr_outcome2 | 1.029103 .0307922 0.96 0.338 .9704865 1.091259
_cons | .0335336 .0021475 -53.02 0.000 .0295779 .0380183
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.725
Iteration 1: log pseudolikelihood = -12956.095
Iteration 2: log pseudolikelihood = -12956.046
Iteration 3: log pseudolikelihood = -12956.046
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.046 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.553512 .1053966 6.49 0.000 1.360084 1.77445
_rcs1 | 2.232734 .1120625 16.00 0.000 2.023553 2.463539
_rcs2 | 1.021605 .0240794 0.91 0.364 .9754841 1.069907
_rcs3 | 1.019087 .0186992 1.03 0.303 .9830888 1.056404
_rcs4 | 1.026339 .0130182 2.05 0.040 1.001138 1.052174
_rcs5 | 1.012569 .0075933 1.67 0.096 .9977956 1.027562
_rcs6 | 1.010885 .0047013 2.33 0.020 1.001713 1.020142
_rcs7 | 1.009818 .0035592 2.77 0.006 1.002866 1.016818
_rcs8 | 1.004683 .0029882 1.57 0.116 .9988427 1.010556
_rcs9 | 1.002108 .0030438 0.69 0.488 .9961602 1.008092
_rcs_tr_outcome1 | .9198262 .0481102 -1.60 0.110 .830204 1.019123
_rcs_tr_outcome2 | 1.030807 .0283559 1.10 0.270 .9767025 1.08791
_rcs_tr_outcome3 | .9839474 .0217017 -0.73 0.463 .9423191 1.027415
_cons | .0334956 .0021465 -53.00 0.000 .029542 .0379783
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.08
Iteration 1: log pseudolikelihood = -12951.312
Iteration 2: log pseudolikelihood = -12951.139
Iteration 3: log pseudolikelihood = -12951.139
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12951.139 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554516 .104835 6.54 0.000 1.362044 1.774187
_rcs1 | 2.234539 .1114431 16.12 0.000 2.02645 2.463995
_rcs2 | 1.02199 .0280442 0.79 0.428 .9684761 1.07846
_rcs3 | 1.000078 .0190757 0.00 0.997 .9633802 1.038173
_rcs4 | 1.03141 .0127199 2.51 0.012 1.006779 1.056645
_rcs5 | 1.031293 .0136876 2.32 0.020 1.004811 1.058472
_rcs6 | 1.02753 .0106822 2.61 0.009 1.006805 1.048681
_rcs7 | 1.017206 .005591 3.10 0.002 1.006307 1.028223
_rcs8 | 1.005894 .0029805 1.98 0.047 1.00007 1.011753
_rcs9 | 1.002336 .0029417 0.80 0.427 .9965869 1.008118
_rcs_tr_outcome1 | .9189683 .0478472 -1.62 0.105 .8298158 1.017699
_rcs_tr_outcome2 | 1.032657 .0313406 1.06 0.290 .9730221 1.095948
_rcs_tr_outcome3 | 1.000392 .0207466 0.02 0.985 .9605448 1.041892
_rcs_tr_outcome4 | .9586505 .0191901 -2.11 0.035 .9217668 .99701
_cons | .0334873 .0021356 -53.26 0.000 .0295527 .0379458
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.578
Iteration 1: log pseudolikelihood = -12950.564
Iteration 2: log pseudolikelihood = -12950.281
Iteration 3: log pseudolikelihood = -12950.281
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12950.281 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554586 .1047357 6.55 0.000 1.362283 1.774034
_rcs1 | 2.234584 .1113524 16.14 0.000 2.026656 2.463844
_rcs2 | 1.020677 .026779 0.78 0.435 .9695178 1.074536
_rcs3 | 1.002909 .021806 0.13 0.894 .9610681 1.046572
_rcs4 | 1.027144 .01322 2.08 0.037 1.001557 1.053385
_rcs5 | 1.02671 .013215 2.05 0.041 1.001133 1.05294
_rcs6 | 1.030249 .0114687 2.68 0.007 1.008015 1.052975
_rcs7 | 1.024958 .0097631 2.59 0.010 1.006 1.044273
_rcs8 | 1.010249 .0041518 2.48 0.013 1.002144 1.018419
_rcs9 | 1.002789 .0028719 0.97 0.331 .9971764 1.008434
_rcs_tr_outcome1 | .9186456 .0478669 -1.63 0.103 .8294599 1.017421
_rcs_tr_outcome2 | 1.03356 .0302921 1.13 0.260 .9758612 1.094669
_rcs_tr_outcome3 | 1.005121 .0223458 0.23 0.818 .9622649 1.049887
_rcs_tr_outcome4 | .969035 .0185059 -1.65 0.100 .9334346 1.005993
_rcs_tr_outcome5 | .9722068 .0144867 -1.89 0.059 .944224 1.001019
_cons | .0334916 .0021357 -53.26 0.000 .0295567 .0379504
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.38
Iteration 1: log pseudolikelihood = -12950.554
Iteration 2: log pseudolikelihood = -12950.316
Iteration 3: log pseudolikelihood = -12950.316
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12950.316 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.55456 .1048273 6.54 0.000 1.362101 1.774213
_rcs1 | 2.234195 .1112945 16.14 0.000 2.026372 2.463332
_rcs2 | 1.021112 .0269897 0.79 0.429 .9695605 1.075405
_rcs3 | 1.000565 .0227506 0.02 0.980 .9569538 1.046164
_rcs4 | 1.028575 .0151538 1.91 0.056 .9992987 1.058709
_rcs5 | 1.028115 .0129296 2.20 0.027 1.003083 1.053772
_rcs6 | 1.029548 .0119179 2.52 0.012 1.006452 1.053174
_rcs7 | 1.023613 .0091925 2.60 0.009 1.005753 1.041789
_rcs8 | 1.011321 .0065386 1.74 0.082 .9985866 1.024218
_rcs9 | 1.003712 .002921 1.27 0.203 .9980036 1.009454
_rcs_tr_outcome1 | .9188608 .0478514 -1.62 0.104 .8297013 1.017601
_rcs_tr_outcome2 | 1.032933 .0301966 1.11 0.268 .9754122 1.093845
_rcs_tr_outcome3 | 1.010751 .0234081 0.46 0.644 .965898 1.057687
_rcs_tr_outcome4 | .973968 .0186054 -1.38 0.167 .9381763 1.011125
_rcs_tr_outcome5 | .9695068 .014644 -2.05 0.040 .9412258 .9986375
_rcs_tr_outcome6 | .9868534 .0101594 -1.29 0.199 .9671409 1.006968
_cons | .0334919 .0021371 -53.23 0.000 .0295546 .0379536
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.619
Iteration 1: log pseudolikelihood = -12950.171
Iteration 2: log pseudolikelihood = -12949.85
Iteration 3: log pseudolikelihood = -12949.85
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12949.85 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.555056 .104622 6.56 0.000 1.362945 1.774245
_rcs1 | 2.235512 .1108331 16.23 0.000 2.028504 2.463646
_rcs2 | 1.022067 .0272695 0.82 0.413 .9699928 1.076936
_rcs3 | .9971806 .0225143 -0.13 0.900 .9540154 1.042299
_rcs4 | 1.033478 .0163186 2.09 0.037 1.001984 1.065962
_rcs5 | 1.02567 .0127841 2.03 0.042 1.000917 1.051035
_rcs6 | 1.028797 .0123182 2.37 0.018 1.004935 1.053226
_rcs7 | 1.025958 .0097054 2.71 0.007 1.007111 1.045158
_rcs8 | 1.009156 .0077059 1.19 0.233 .9941657 1.024373
_rcs9 | 1.003237 .0041852 0.77 0.439 .9950673 1.011473
_rcs_tr_outcome1 | .9181975 .0475586 -1.65 0.099 .8295597 1.016306
_rcs_tr_outcome2 | 1.031655 .0300398 1.07 0.284 .9744268 1.092245
_rcs_tr_outcome3 | 1.016258 .024034 0.68 0.495 .9702271 1.064472
_rcs_tr_outcome4 | .9747873 .0178804 -1.39 0.164 .9403649 1.01047
_rcs_tr_outcome5 | .9748109 .0144912 -1.72 0.086 .9468183 1.003631
_rcs_tr_outcome6 | .9763802 .0113672 -2.05 0.040 .9543532 .9989157
_rcs_tr_outcome7 | .9957547 .0085874 -0.49 0.622 .979065 1.012729
_cons | .0334833 .002132 -53.35 0.000 .0295549 .0379339
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.075
Iteration 1: log pseudolikelihood = -12956.496
Iteration 2: log pseudolikelihood = -12956.479
Iteration 3: log pseudolikelihood = -12956.479
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12956.479 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.550127 .1055349 6.44 0.000 1.356489 1.771406
_rcs1 | 2.239312 .10699 16.87 0.000 2.039135 2.459141
_rcs2 | 1.046754 .0107954 4.43 0.000 1.025808 1.068128
_rcs3 | 1.012757 .0087174 1.47 0.141 .9958142 1.029988
_rcs4 | 1.018248 .0065018 2.83 0.005 1.005584 1.031071
_rcs5 | 1.010466 .0048803 2.16 0.031 1.000946 1.020077
_rcs6 | 1.007665 .0040503 1.90 0.057 .9997572 1.015634
_rcs7 | 1.00995 .0035783 2.79 0.005 1.002961 1.016988
_rcs8 | 1.0076 .0030391 2.51 0.012 1.001661 1.013574
_rcs9 | 1.002909 .0030134 0.97 0.334 .9970199 1.008832
_rcs10 | 1.002884 .0026068 1.11 0.268 .997788 1.008007
_rcs_tr_outcome1 | .9164472 .04476 -1.79 0.074 .8327874 1.008511
_cons | .0335468 .0021547 -52.85 0.000 .0295787 .0380472
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12962.291
Iteration 1: log pseudolikelihood = -12955.793
Iteration 2: log pseudolikelihood = -12955.753
Iteration 3: log pseudolikelihood = -12955.753
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12955.753 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.551051 .1050629 6.48 0.000 1.358215 1.771265
_rcs1 | 2.22201 .1069805 16.58 0.000 2.021921 2.441899
_rcs2 | 1.024202 .0269216 0.91 0.363 .9727728 1.07835
_rcs3 | 1.008196 .0117602 0.70 0.484 .9854075 1.031511
_rcs4 | 1.016911 .0064788 2.63 0.008 1.004291 1.029688
_rcs5 | 1.010103 .0048853 2.08 0.038 1.000573 1.019724
_rcs6 | 1.007456 .0040285 1.86 0.063 .9995908 1.015382
_rcs7 | 1.009862 .0035538 2.79 0.005 1.002921 1.016851
_rcs8 | 1.007492 .0030193 2.49 0.013 1.001592 1.013427
_rcs9 | 1.002858 .0029946 0.96 0.339 .9970064 1.008745
_rcs10 | 1.002842 .0025895 1.10 0.272 .9977791 1.00793
_rcs_tr_outcome1 | .9251086 .0462648 -1.56 0.120 .8387335 1.020379
_rcs_tr_outcome2 | 1.028848 .0306957 0.95 0.340 .9704105 1.090804
_cons | .0335371 .0021451 -53.08 0.000 .0295857 .0380162
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.718
Iteration 1: log pseudolikelihood = -12955.303
Iteration 2: log pseudolikelihood = -12955.258
Iteration 3: log pseudolikelihood = -12955.258
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12955.258 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.553394 .1052386 6.50 0.000 1.360238 1.773978
_rcs1 | 2.232639 .1121873 15.98 0.000 2.023237 2.463714
_rcs2 | 1.021462 .0238297 0.91 0.363 .9758089 1.069252
_rcs3 | 1.018773 .0185002 1.02 0.306 .9831506 1.055685
_rcs4 | 1.025621 .0132128 1.96 0.050 1.000048 1.051847
_rcs5 | 1.014739 .0080486 1.84 0.065 .9990862 1.030638
_rcs6 | 1.009542 .0051177 1.87 0.061 .9995609 1.019622
_rcs7 | 1.010514 .0037361 2.83 0.005 1.003218 1.017863
_rcs8 | 1.00774 .003054 2.54 0.011 1.001772 1.013744
_rcs9 | 1.002914 .0029933 0.97 0.330 .9970646 1.008798
_rcs10 | 1.002936 .0025883 1.14 0.256 .9978755 1.008022
_rcs_tr_outcome1 | .9199107 .0481605 -1.59 0.111 .8301993 1.019316
_rcs_tr_outcome2 | 1.03063 .0281802 1.10 0.270 .9768515 1.087369
_rcs_tr_outcome3 | .9833007 .0217989 -0.76 0.447 .9414905 1.026968
_cons | .0334972 .0021438 -53.07 0.000 .0295482 .0379739
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.109
Iteration 1: log pseudolikelihood = -12950.628
Iteration 2: log pseudolikelihood = -12950.446
Iteration 3: log pseudolikelihood = -12950.446
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12950.446 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554012 .1047154 6.54 0.000 1.361749 1.77342
_rcs1 | 2.233301 .1113556 16.11 0.000 2.025374 2.462575
_rcs2 | 1.021964 .0277783 0.80 0.424 .9689448 1.077885
_rcs3 | .9996266 .0189927 -0.02 0.984 .9630861 1.037553
_rcs4 | 1.027753 .0124195 2.27 0.023 1.003697 1.052385
_rcs5 | 1.030597 .0126816 2.45 0.014 1.006039 1.055755
_rcs6 | 1.026642 .0112804 2.39 0.017 1.00477 1.048991
_rcs7 | 1.021497 .0074196 2.93 0.003 1.007058 1.036143
_rcs8 | 1.011507 .0037247 3.11 0.002 1.004233 1.018833
_rcs9 | 1.003557 .0029229 1.22 0.223 .9978445 1.009302
_rcs10 | 1.00295 .002555 1.16 0.248 .9979546 1.00797
_rcs_tr_outcome1 | .9196485 .0478635 -1.61 0.108 .8304639 1.018411
_rcs_tr_outcome2 | 1.032492 .0311412 1.06 0.289 .9732249 1.095367
_rcs_tr_outcome3 | 1.000152 .0208133 0.01 0.994 .9601794 1.041788
_rcs_tr_outcome4 | .9590081 .0190882 -2.10 0.035 .9223163 .9971595
_cons | .0334951 .0021342 -53.30 0.000 .0295627 .0379506
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.628
Iteration 1: log pseudolikelihood = -12949.739
Iteration 2: log pseudolikelihood = -12949.455
Iteration 3: log pseudolikelihood = -12949.455
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12949.455 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554438 .1046242 6.55 0.000 1.362328 1.773638
_rcs1 | 2.233972 .1113028 16.13 0.000 2.026136 2.463128
_rcs2 | 1.020586 .0266891 0.78 0.436 .9695938 1.074259
_rcs3 | 1.001721 .0216146 0.08 0.936 .9602404 1.044993
_rcs4 | 1.024182 .0126656 1.93 0.053 .9996561 1.049309
_rcs5 | 1.02606 .0128847 2.05 0.040 1.001115 1.051627
_rcs6 | 1.026887 .0106338 2.56 0.010 1.006255 1.047942
_rcs7 | 1.027742 .0105739 2.66 0.008 1.007225 1.048677
_rcs8 | 1.018205 .0069637 2.64 0.008 1.004647 1.031945
_rcs9 | 1.006096 .003095 1.98 0.048 1.000048 1.01218
_rcs10 | 1.002881 .0025604 1.13 0.260 .9978754 1.007912
_rcs_tr_outcome1 | .9189648 .0478764 -1.62 0.105 .8297606 1.017759
_rcs_tr_outcome2 | 1.033519 .0302364 1.13 0.260 .9759243 1.094514
_rcs_tr_outcome3 | 1.005791 .0223016 0.26 0.795 .9630167 1.050465
_rcs_tr_outcome4 | .9684341 .0184244 -1.69 0.092 .9329878 1.005227
_rcs_tr_outcome5 | .9721533 .0145107 -1.89 0.058 .9441249 1.001014
_cons | .0334938 .0021337 -53.31 0.000 .0295623 .0379482
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.451
Iteration 1: log pseudolikelihood = -12950.009
Iteration 2: log pseudolikelihood = -12949.773
Iteration 3: log pseudolikelihood = -12949.773
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12949.773 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554241 .1047043 6.55 0.000 1.361995 1.773622
_rcs1 | 2.233174 .1107243 16.20 0.000 2.02637 2.461085
_rcs2 | 1.021303 .027062 0.80 0.426 .9696166 1.075745
_rcs3 | .9984169 .0225022 -0.07 0.944 .9552733 1.043509
_rcs4 | 1.026827 .0144992 1.87 0.061 .9987984 1.055641
_rcs5 | 1.027996 .0128904 2.20 0.028 1.003039 1.053574
_rcs6 | 1.025624 .0115061 2.26 0.024 1.003319 1.048426
_rcs7 | 1.025305 .0094446 2.71 0.007 1.00696 1.043985
_rcs8 | 1.017971 .0079802 2.27 0.023 1.00245 1.033732
_rcs9 | 1.007593 .0046883 1.63 0.104 .9984457 1.016824
_rcs10 | 1.00333 .0025273 1.32 0.187 .9983891 1.008296
_rcs_tr_outcome1 | .9194197 .0476276 -1.62 0.105 .8306537 1.017671
_rcs_tr_outcome2 | 1.032625 .0302377 1.10 0.273 .9750283 1.093623
_rcs_tr_outcome3 | 1.012077 .0233331 0.52 0.603 .9673625 1.058858
_rcs_tr_outcome4 | .9718727 .0187609 -1.48 0.139 .9357889 1.009348
_rcs_tr_outcome5 | .9716385 .0147614 -1.89 0.058 .9431332 1.001005
_rcs_tr_outcome6 | .9864633 .0100224 -1.34 0.180 .967014 1.006304
_cons | .0334963 .0021351 -53.28 0.000 .0295625 .0379537
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -12961.281
Iteration 1: log pseudolikelihood = -12948.651
Iteration 2: log pseudolikelihood = -12948.314
Iteration 3: log pseudolikelihood = -12948.314
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -12948.314 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.554964 .1044182 6.57 0.000 1.363204 1.773699
_rcs1 | 2.235253 .110726 16.24 0.000 2.028437 2.463157
_rcs2 | 1.02223 .0272874 0.82 0.410 .9701231 1.077136
_rcs3 | .9951327 .0224495 -0.22 0.829 .952091 1.04012
_rcs4 | 1.032074 .0158289 2.06 0.040 1.001511 1.063569
_rcs5 | 1.026253 .0122264 2.18 0.030 1.002568 1.050499
_rcs6 | 1.023999 .0120078 2.02 0.043 1.000732 1.047806
_rcs7 | 1.029653 .0114027 2.64 0.008 1.007545 1.052246
_rcs8 | 1.016683 .0072595 2.32 0.020 1.002554 1.031011
_rcs9 | 1.002903 .0075468 0.39 0.700 .9882201 1.017804
_rcs10 | 1.003181 .0027997 1.14 0.255 .997709 1.008684
_rcs_tr_outcome1 | .9184104 .0475045 -1.65 0.100 .8298673 1.016401
_rcs_tr_outcome2 | 1.031352 .0300374 1.06 0.289 .9741285 1.091937
_rcs_tr_outcome3 | 1.017312 .0240264 0.73 0.467 .971294 1.06551
_rcs_tr_outcome4 | .9723622 .0181302 -1.50 0.133 .9374691 1.008554
_rcs_tr_outcome5 | .9759533 .0148612 -1.60 0.110 .9472562 1.00552
_rcs_tr_outcome6 | .9756286 .011399 -2.11 0.035 .9535407 .9982281
_rcs_tr_outcome7 | .9996913 .0095722 -0.03 0.974 .9811052 1.01863
_cons | .0334832 .002128 -53.45 0.000 .0295617 .0379249
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
.
. *https://core.ac.uk/download/pdf/6990318.pdf
.
. *The following options are not permitted with streg models:
. *bknots, bknotstvc, df, dftvc, failconvlininit, knots, knotstvc knscale, noorthorg, eform, alleq, keepcons, showcons, lininit
. *forvalues j=1/7 {
. local vars "exponential weibull gompertz lognormal loglogistic"
. local varslab "exp wei gom logn llog"
. forvalues i = 1/5 {
2. local v : word `i' of `vars'
3. local v2 : word `i' of `varslab'
4.
. qui noi stipw (logit tr_outcome tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 cond_ocu3 cond_ocu
> 4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini4 susini5 ano_n
> ac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3), distribution(`v') genw(`v2'_m2_nostag) ipwtype(stabilised) vce(mestimation)
5. estimates store m_stipw_nostag_`v2'
6. }
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=exp_m2_nostag]
Iteration 0: log pseudolikelihood = -13152.455
Iteration 1: log pseudolikelihood = -13113.05
Iteration 2: log pseudolikelihood = -13112.6
Iteration 3: log pseudolikelihood = -13112.6
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 46,864
Wald chi2(1) = 37.20
Log pseudolikelihood = -13112.6 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.478055 .0946898 6.10 0.000 1.303645 1.675798
_cons | .0107004 .0006473 -75.01 0.000 .009504 .0120474
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=wei_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -13152.455
Iteration 1: log pseudolikelihood = -13026.559
Iteration 2: log pseudolikelihood = -13025.222
Iteration 3: log pseudolikelihood = -13025.222
Fitting full model:
Iteration 0: log pseudolikelihood = -13025.222
Iteration 1: log pseudolikelihood = -12983.711
Iteration 2: log pseudolikelihood = -12983.217
Iteration 3: log pseudolikelihood = -12983.217
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 46,864
Wald chi2(1) = 39.62
Log pseudolikelihood = -12983.217 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.49316 .0951055 6.29 0.000 1.317922 1.691698
_cons | .0153205 .0009617 -66.57 0.000 .0135469 .0173262
-------------+----------------------------------------------------------------
/ln_p | -.2720937 .0191384 -14.22 0.000 -.3096043 -.2345831
-------------+----------------------------------------------------------------
p | .7617829 .0145793 .7337372 .7909005
1/p | 1.31271 .0251232 1.264381 1.362886
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=gom_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -13153.291
Iteration 1: log pseudolikelihood = -13021.946
Iteration 2: log pseudolikelihood = -13017.675
Iteration 3: log pseudolikelihood = -13017.671
Iteration 4: log pseudolikelihood = -13017.671
Fitting full model:
Iteration 0: log pseudolikelihood = -13017.671
Iteration 1: log pseudolikelihood = -12976.257
Iteration 2: log pseudolikelihood = -12975.765
Iteration 3: log pseudolikelihood = -12975.765
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 46,864
Wald chi2(1) = 39.68
Log pseudolikelihood = -12975.765 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.492462 .0948687 6.30 0.000 1.317639 1.69048
_cons | .016275 .001052 -63.71 0.000 .0143384 .0184731
-------------+----------------------------------------------------------------
/gamma | -.1796905 .0126793 -14.17 0.000 -.2045415 -.1548394
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=logn_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -23469.299
Iteration 1: log pseudolikelihood = -15955.326
Iteration 2: log pseudolikelihood = -13444.85
Iteration 3: log pseudolikelihood = -13070.178
Iteration 4: log pseudolikelihood = -13015.775
Iteration 5: log pseudolikelihood = -13014.489
Iteration 6: log pseudolikelihood = -13014.484
Iteration 7: log pseudolikelihood = -13014.484
Fitting full model:
Iteration 0: log pseudolikelihood = -13014.484
Iteration 1: log pseudolikelihood = -12969.819
Iteration 2: log pseudolikelihood = -12968.484
Iteration 3: log pseudolikelihood = -12968.479
Iteration 4: log pseudolikelihood = -12968.479
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 46,864
Wald chi2(1) = 45.05
Log pseudolikelihood = -12968.479 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5451425 .0492775 -6.71 0.000 .4566324 .6508086
_cons | 729.399 101.0268 47.59 0.000 555.9911 956.891
-------------+----------------------------------------------------------------
/lnsigma | 1.112855 .0206769 53.82 0.000 1.072329 1.153381
-------------+----------------------------------------------------------------
sigma | 3.043033 .0629205 2.922177 3.168888
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
8202 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -27905.896
Iteration 2: log likelihood = -27839.632
Iteration 3: log likelihood = -27839.102
Iteration 4: log likelihood = -27839.102
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -30380.507
Iteration 1: log likelihood = -30380.507
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=llog_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -13191.168
Iteration 1: log pseudolikelihood = -13026.101
Iteration 2: log pseudolikelihood = -13022.401
Iteration 3: log pseudolikelihood = -13022.401
Fitting full model:
Iteration 0: log pseudolikelihood = -13022.401
Iteration 1: log pseudolikelihood = -12981.176
Iteration 2: log pseudolikelihood = -12979.749
Iteration 3: log pseudolikelihood = -12979.746
Iteration 4: log pseudolikelihood = -12979.746
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 46,864
Wald chi2(1) = 39.41
Log pseudolikelihood = -12979.746 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5851019 .0499515 -6.28 0.000 .4949513 .6916725
_cons | 215.2466 24.21836 47.74 0.000 172.6489 268.3545
-------------+----------------------------------------------------------------
/lngamma | .2517319 .019297 13.05 0.000 .2139104 .2895533
-------------+----------------------------------------------------------------
gamma | 1.286251 .0248208 1.238512 1.335831
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
. *}
.
. qui count if _d == 1
. // we count the amount of cases with the event in the strata
. //we call the estimates stored, and the results...
. estimates stat m_stipw_nostag_*, n(`r(N)')
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
m_stipw_no~1 | 3,433 . -12979.43 4 25966.87 25991.43
m_stipw_no~2 | 3,433 . -12965.87 5 25941.74 25972.44
m_stipw_no~3 | 3,433 . -12964.47 6 25940.94 25977.79
m_stipw_no~4 | 3,433 . -12964.34 7 25942.68 25985.67
m_stipw_no~5 | 3,433 . -12964.07 8 25944.14 25993.27
m_stipw_no~6 | 3,433 . -12963.56 9 25945.13 26000.4
m_stipw_no~7 | 3,433 . -12963.42 10 25946.85 26008.26
m_stipw_no~1 | 3,433 . -12965.89 5 25941.77 25972.48
m_stipw_no~2 | 3,433 . -12965.06 6 25942.12 25978.97
m_stipw_no~3 | 3,433 . -12963.67 7 25941.35 25984.33
m_stipw_no~4 | 3,433 . -12963.53 8 25943.06 25992.19
m_stipw_no~5 | 3,433 . -12963.25 9 25944.5 25999.77
m_stipw_no~6 | 3,433 . -12962.76 10 25945.51 26006.93
m_stipw_no~7 | 3,433 . -12962.61 11 25947.22 26014.78
m_stipw_no~1 | 3,433 . -12962.89 6 25937.78 25974.63
m_stipw_no~2 | 3,433 . -12962.18 7 25938.35 25981.34
m_stipw_no~3 | 3,433 . -12961.72 8 25939.45 25988.57
m_stipw_no~4 | 3,433 . -12961.77 9 25941.54 25996.81
m_stipw_no~5 | 3,433 . -12961.27 10 25942.54 26003.95
m_stipw_no~6 | 3,433 . -12960.82 11 25943.63 26011.19
m_stipw_no~7 | 3,433 . -12960.66 12 25945.32 26019.02
m_stipw_no~1 | 3,433 . -12960.66 7 25935.33 25978.31
m_stipw_no~2 | 3,433 . -12959.9 8 25935.8 25984.93
m_stipw_no~3 | 3,433 . -12959.29 9 25936.58 25991.85
m_stipw_no~4 | 3,433 . -12954.81 10 25929.62 25991.03
m_stipw_no~5 | 3,433 . -12956.7 11 25935.41 26002.96
m_stipw_no~6 | 3,433 . -12954.5 12 25932.99 26006.69
m_stipw_no~7 | 3,433 . -12954.56 13 25935.12 26014.96
m_stipw_no~1 | 3,433 . -12958.97 8 25933.93 25983.06
m_stipw_no~2 | 3,433 . -12958.25 9 25934.49 25989.76
m_stipw_no~3 | 3,433 . -12957.93 10 25935.87 25997.28
m_stipw_no~4 | 3,433 . -12952.12 11 25926.24 25993.8
m_stipw_no~5 | 3,433 . -12951.98 12 25927.97 26001.66
m_stipw_no~6 | 3,433 . -12951.62 13 25929.23 26009.07
m_stipw_no~7 | 3,433 . -12951.91 14 25931.82 26017.8
m_stipw_no~1 | 3,433 . -12958.23 9 25934.45 25989.73
m_stipw_no~2 | 3,433 . -12957.5 10 25935 25996.42
m_stipw_no~3 | 3,433 . -12957.04 11 25936.09 26003.64
m_stipw_no~4 | 3,433 . -12952.16 12 25928.32 26002.01
m_stipw_no~5 | 3,433 . -12951.91 13 25929.83 26009.66
m_stipw_no~6 | 3,433 . -12951.39 14 25930.77 26016.75
m_stipw_no~7 | 3,433 . -12951.83 15 25933.65 26025.77
m_stipw_no~1 | 3,433 . -12957.23 10 25934.45 25995.86
m_stipw_no~2 | 3,433 . -12956.47 11 25934.95 26002.5
m_stipw_no~3 | 3,433 . -12956.02 12 25936.04 26009.74
m_stipw_no~4 | 3,433 . -12951.04 13 25928.08 26007.92
m_stipw_no~5 | 3,433 . -12950.03 14 25928.06 26014.04
m_stipw_no~6 | 3,433 . -12950.18 15 25930.37 26022.48
m_stipw_no~7 | 3,433 . -12949.22 16 25930.45 26028.71
m_stipw_no~1 | 3,433 . -12956.86 11 25935.72 26003.27
m_stipw_no~2 | 3,433 . -12956.12 12 25936.24 26009.94
m_stipw_no~3 | 3,433 . -12955.69 13 25937.37 26017.21
m_stipw_no~4 | 3,433 . -12951.1 14 25930.2 26016.18
m_stipw_no~5 | 3,433 . -12949.72 15 25929.43 26021.55
m_stipw_no~6 | 3,433 . -12949.77 16 25931.55 26029.8
m_stipw_no~7 | 3,433 . -12949.51 17 25933.02 26037.42
m_stipw_no~1 | 3,433 . -12957.24 12 25938.48 26012.17
m_stipw_no~2 | 3,433 . -12956.5 13 25939 26018.84
m_stipw_no~3 | 3,433 . -12956.05 14 25940.09 26026.07
m_stipw_no~4 | 3,433 . -12951.14 15 25932.28 26024.4
m_stipw_no~5 | 3,433 . -12950.28 16 25932.56 26030.82
m_stipw_no~6 | 3,433 . -12950.32 17 25934.63 26039.03
m_stipw_no~7 | 3,433 . -12949.85 18 25935.7 26046.24
m_stipw_no~1 | 3,433 . -12956.48 13 25938.96 26018.79
m_stipw_no~2 | 3,433 . -12955.75 14 25939.51 26025.48
m_stipw_no~3 | 3,433 . -12955.26 15 25940.52 26032.63
m_stipw_no~4 | 3,433 . -12950.45 16 25932.89 26031.15
m_stipw_no~5 | 3,433 . -12949.46 17 25932.91 26037.31
m_stipw_no~6 | 3,433 . -12949.77 18 25935.55 26046.09
m_stipw_no~7 | 3,433 . -12948.31 19 25934.63 26051.31
m_stipw_no~p | 3,433 -13152.45 -13112.6 2 26229.2 26241.48
m_stipw_no~i | 3,433 -13025.22 -12983.22 3 25972.43 25990.86
m_stipw_no~m | 3,433 -13017.67 -12975.77 3 25957.53 25975.95
m_stipw_no~n | 3,433 -13014.48 -12968.48 3 25942.96 25961.38
m_stipw_no~g | 3,433 -13022.4 -12979.75 3 25965.49 25983.91
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_2=r(S)
. mata : st_sort_matrix("stats_2", 5) // 5 AIC, 6 BIC
. esttab matrix(stats_2) using "testreg_aic_bic_mrl_23_2_pris.csv", replace
(output written to testreg_aic_bic_mrl_23_2_pris.csv)
. esttab matrix(stats_2) using "testreg_aic_bic_mrl_23_2_pris.html", replace
(output written to testreg_aic_bic_mrl_23_2_pris.html)
.
. *m_stipw_nostag_rp1_tvcdf2
| stats_2 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m_stipw_nostag_rp5_tvcdf4 | 3433 | . | -12952.12 | 11 | 25926.24 | 25993.8 |
| m_stipw_nostag_rp5_tvcdf5 | 3433 | . | -12951.98 | 12 | 25927.97 | 26001.66 |
| m_stipw_nostag_rp7_tvcdf5 | 3433 | . | -12950.03 | 14 | 25928.06 | 26014.04 |
| m_stipw_nostag_rp7_tvcdf4 | 3433 | . | -12951.04 | 13 | 25928.08 | 26007.92 |
| m_stipw_nostag_rp6_tvcdf4 | 3433 | . | -12952.16 | 12 | 25928.32 | 26002.01 |
| m_stipw_nostag_rp5_tvcdf6 | 3433 | . | -12951.62 | 13 | 25929.23 | 26009.07 |
| m_stipw_nostag_rp8_tvcdf5 | 3433 | . | -12949.72 | 15 | 25929.43 | 26021.55 |
| m_stipw_nostag_rp4_tvcdf4 | 3433 | . | -12954.81 | 10 | 25929.62 | 25991.03 |
| m_stipw_nostag_rp6_tvcdf5 | 3433 | . | -12951.91 | 13 | 25929.83 | 26009.66 |
| m_stipw_nostag_rp8_tvcdf4 | 3433 | . | -12951.1 | 14 | 25930.2 | 26016.18 |
| m_stipw_nostag_rp7_tvcdf6 | 3433 | . | -12950.18 | 15 | 25930.37 | 26022.48 |
| m_stipw_nostag_rp7_tvcdf7 | 3433 | . | -12949.22 | 16 | 25930.45 | 26028.71 |
| m_stipw_nostag_rp6_tvcdf6 | 3433 | . | -12951.39 | 14 | 25930.77 | 26016.75 |
| m_stipw_nostag_rp8_tvcdf6 | 3433 | . | -12949.77 | 16 | 25931.55 | 26029.8 |
| m_stipw_nostag_rp5_tvcdf7 | 3433 | . | -12951.91 | 14 | 25931.82 | 26017.8 |
| m_stipw_nostag_rp9_tvcdf4 | 3433 | . | -12951.14 | 15 | 25932.28 | 26024.4 |
| m_stipw_nostag_rp9_tvcdf5 | 3433 | . | -12950.28 | 16 | 25932.56 | 26030.82 |
| m_stipw_nostag_rp10_tvcdf4 | 3433 | . | -12950.45 | 16 | 25932.89 | 26031.15 |
| m_stipw_nostag_rp10_tvcdf5 | 3433 | . | -12949.46 | 17 | 25932.91 | 26037.31 |
| m_stipw_nostag_rp4_tvcdf6 | 3433 | . | -12954.5 | 12 | 25932.99 | 26006.69 |
| m_stipw_nostag_rp8_tvcdf7 | 3433 | . | -12949.51 | 17 | 25933.02 | 26037.42 |
| m_stipw_nostag_rp6_tvcdf7 | 3433 | . | -12951.83 | 15 | 25933.65 | 26025.77 |
| m_stipw_nostag_rp5_tvcdf1 | 3433 | . | -12958.97 | 8 | 25933.93 | 25983.06 |
| m_stipw_nostag_rp7_tvcdf1 | 3433 | . | -12957.23 | 10 | 25934.45 | 25995.86 |
| m_stipw_nostag_rp6_tvcdf1 | 3433 | . | -12958.23 | 9 | 25934.45 | 25989.73 |
| m_stipw_nostag_rp5_tvcdf2 | 3433 | . | -12958.25 | 9 | 25934.49 | 25989.76 |
| m_stipw_nostag_rp10_tvcdf7 | 3433 | . | -12948.31 | 19 | 25934.63 | 26051.31 |
| m_stipw_nostag_rp9_tvcdf6 | 3433 | . | -12950.32 | 17 | 25934.63 | 26039.03 |
| m_stipw_nostag_rp7_tvcdf2 | 3433 | . | -12956.47 | 11 | 25934.95 | 26002.5 |
| m_stipw_nostag_rp6_tvcdf2 | 3433 | . | -12957.5 | 10 | 25935 | 25996.42 |
| m_stipw_nostag_rp4_tvcdf7 | 3433 | . | -12954.56 | 13 | 25935.12 | 26014.96 |
| m_stipw_nostag_rp4_tvcdf1 | 3433 | . | -12960.66 | 7 | 25935.33 | 25978.31 |
| m_stipw_nostag_rp4_tvcdf5 | 3433 | . | -12956.7 | 11 | 25935.41 | 26002.96 |
| m_stipw_nostag_rp10_tvcdf6 | 3433 | . | -12949.77 | 18 | 25935.55 | 26046.09 |
| m_stipw_nostag_rp9_tvcdf7 | 3433 | . | -12949.85 | 18 | 25935.7 | 26046.24 |
| m_stipw_nostag_rp8_tvcdf1 | 3433 | . | -12956.86 | 11 | 25935.72 | 26003.27 |
| m_stipw_nostag_rp4_tvcdf2 | 3433 | . | -12959.9 | 8 | 25935.8 | 25984.93 |
| m_stipw_nostag_rp5_tvcdf3 | 3433 | . | -12957.93 | 10 | 25935.87 | 25997.28 |
| m_stipw_nostag_rp7_tvcdf3 | 3433 | . | -12956.02 | 12 | 25936.04 | 26009.74 |
| m_stipw_nostag_rp6_tvcdf3 | 3433 | . | -12957.04 | 11 | 25936.09 | 26003.64 |
| m_stipw_nostag_rp8_tvcdf2 | 3433 | . | -12956.12 | 12 | 25936.24 | 26009.94 |
| m_stipw_nostag_rp4_tvcdf3 | 3433 | . | -12959.29 | 9 | 25936.58 | 25991.85 |
| m_stipw_nostag_rp8_tvcdf3 | 3433 | . | -12955.69 | 13 | 25937.37 | 26017.21 |
| m_stipw_nostag_rp3_tvcdf1 | 3433 | . | -12962.89 | 6 | 25937.78 | 25974.63 |
| m_stipw_nostag_rp3_tvcdf2 | 3433 | . | -12962.18 | 7 | 25938.35 | 25981.34 |
| m_stipw_nostag_rp9_tvcdf1 | 3433 | . | -12957.24 | 12 | 25938.48 | 26012.17 |
| m_stipw_nostag_rp10_tvcdf1 | 3433 | . | -12956.48 | 13 | 25938.96 | 26018.79 |
| m_stipw_nostag_rp9_tvcdf2 | 3433 | . | -12956.5 | 13 | 25939 | 26018.84 |
| m_stipw_nostag_rp3_tvcdf3 | 3433 | . | -12961.72 | 8 | 25939.45 | 25988.57 |
| m_stipw_nostag_rp10_tvcdf2 | 3433 | . | -12955.75 | 14 | 25939.51 | 26025.48 |
| m_stipw_nostag_rp9_tvcdf3 | 3433 | . | -12956.05 | 14 | 25940.09 | 26026.07 |
| m_stipw_nostag_rp10_tvcdf3 | 3433 | . | -12955.26 | 15 | 25940.52 | 26032.63 |
| m_stipw_nostag_rp1_tvcdf3 | 3433 | . | -12964.47 | 6 | 25940.94 | 25977.79 |
| m_stipw_nostag_rp2_tvcdf3 | 3433 | . | -12963.67 | 7 | 25941.35 | 25984.33 |
| m_stipw_nostag_rp3_tvcdf4 | 3433 | . | -12961.77 | 9 | 25941.54 | 25996.81 |
| m_stipw_nostag_rp1_tvcdf2 | 3433 | . | -12965.87 | 5 | 25941.74 | 25972.44 |
| m_stipw_nostag_rp2_tvcdf1 | 3433 | . | -12965.89 | 5 | 25941.77 | 25972.48 |
| m_stipw_nostag_rp2_tvcdf2 | 3433 | . | -12965.06 | 6 | 25942.12 | 25978.97 |
| m_stipw_nostag_rp3_tvcdf5 | 3433 | . | -12961.27 | 10 | 25942.54 | 26003.95 |
| m_stipw_nostag_rp1_tvcdf4 | 3433 | . | -12964.34 | 7 | 25942.68 | 25985.67 |
| m_stipw_nostag_logn | 3433 | -13014.48 | -12968.48 | 3 | 25942.96 | 25961.38 |
| m_stipw_nostag_rp2_tvcdf4 | 3433 | . | -12963.53 | 8 | 25943.06 | 25992.19 |
| m_stipw_nostag_rp3_tvcdf6 | 3433 | . | -12960.82 | 11 | 25943.63 | 26011.19 |
| m_stipw_nostag_rp1_tvcdf5 | 3433 | . | -12964.07 | 8 | 25944.14 | 25993.27 |
| m_stipw_nostag_rp2_tvcdf5 | 3433 | . | -12963.25 | 9 | 25944.5 | 25999.77 |
| m_stipw_nostag_rp1_tvcdf6 | 3433 | . | -12963.56 | 9 | 25945.13 | 26000.4 |
| m_stipw_nostag_rp3_tvcdf7 | 3433 | . | -12960.66 | 12 | 25945.32 | 26019.02 |
| m_stipw_nostag_rp2_tvcdf6 | 3433 | . | -12962.76 | 10 | 25945.51 | 26006.93 |
| m_stipw_nostag_rp1_tvcdf7 | 3433 | . | -12963.42 | 10 | 25946.85 | 26008.26 |
| m_stipw_nostag_rp2_tvcdf7 | 3433 | . | -12962.61 | 11 | 25947.22 | 26014.78 |
| m_stipw_nostag_gom | 3433 | -13017.67 | -12975.77 | 3 | 25957.53 | 25975.95 |
| m_stipw_nostag_llog | 3433 | -13022.4 | -12979.75 | 3 | 25965.49 | 25983.91 |
| m_stipw_nostag_rp1_tvcdf1 | 3433 | . | -12979.43 | 4 | 25966.87 | 25991.43 |
| m_stipw_nostag_wei | 3433 | -13025.22 | -12983.22 | 3 | 25972.43 | 25990.86 |
| m_stipw_nostag_exp | 3433 | -13152.45 | -13112.6 | 2 | 26229.2 | 26241.48 |
. estimates replay m_stipw_nostag_rp4_tvcdf4, eform
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_stipw_nostag_rp4_tvcdf4
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -12954.808 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.556521 .1045661 6.59 0.000 1.364494 1.775571
_rcs1 | 2.239656 .1113019 16.23 0.000 2.031796 2.468781
_rcs2 | 1.021944 .0275648 0.80 0.421 .9693207 1.077423
_rcs3 | 1.021347 .0192327 1.12 0.262 .9843391 1.059747
_rcs4 | 1.048033 .0208884 2.35 0.019 1.007882 1.089784
_rcs_tr_outcome1 | .9167199 .0475617 -1.68 0.094 .8280837 1.014844
_rcs_tr_outcome2 | 1.032633 .0298276 1.11 0.266 .9757962 1.092781
_rcs_tr_outcome3 | .9972832 .0204053 -0.13 0.894 .9580808 1.03809
_rcs_tr_outcome4 | .9592941 .0199186 -2.00 0.045 .9210381 .9991391
_cons | .0334522 .0021268 -53.44 0.000 .0295329 .0378916
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_stipw_nostag_rp4_tvcdf4
(results m_stipw_nostag_rp4_tvcdf4 are active now)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) ci contrast(difference) ///
> atvar(s_comp_a s_late_a) contrastvar(sdiff_comp_vs_late)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_comp_a rmst_late_a) contrastvar(rmstdiff_comp_vs_late)
.
. sts gen km_a=s, by(tr_outcome)
.
. twoway (rarea s_comp_a_lci s_comp_a_uci tt, color(gs7%35)) ///
> (rarea s_late_a_lci s_late_a_uci tt, color(gs2%35)) ///
> (line km_a _t if tr_outcome==0 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs7%35)) ///
> (line km_a _t if tr_outcome==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%35)) ///
> (line s_comp_a tt, lcolor(gs7) lwidth(thick)) ///
> (line s_late_a tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Probibability of avoiding sentence (standardized)") ///
> legend(order(5 "Tr. completion" 6 "Late dropout") ring(0) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(km_vs_standsurv_fin_a, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_a_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_a_pris.gph saved)
.
.
. twoway (rarea rmst_comp_a_lci rmst_comp_a_uci tt, color(gs7%35)) ///
> (rarea rmst_late_a_lci rmst_late_a_uci tt, color(gs2%35)) ///
> (line rmst_comp_a tt, lcolor(gs7) lwidth(thick)) ///
> (line rmst_late_a tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Restricted Mean Survival Times (standardized)") ///
> legend(order(1 "Tr. completion" 2 "Late dropout") ring(0) pos(5) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(rmst_std_fin_a, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdiff_rmst_a_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdiff_rmst_a_pris.gph saved)
Early dropout
. *==============================================
. cap qui noi frame drop early
frame early not found
. frame copy default early
.
. frame change early
.
. *drop late
. drop if motivodeegreso_mod_imp_rec==3
(35,781 observations deleted)
.
. recode motivodeegreso_mod_imp_rec (1=0 "Tr. Completion") (2/3=1 "Early dropout"), gen(tr_outcome)
(35073 differences between motivodeegreso_mod_imp_rec and tr_outcome)
. *==============================================
. *______________________________________________
. *______________________________________________
. * NO STAGGERED ENTRY, BINARY TREATMENT (1-EARLY VS. 0-COMPLETION)
.
. * tvar must be a binary variable with 1 = treatment/exposure and 0 = control.
.
. forvalues i=1/10 {
2. forvalues j=1/7 {
3. qui noi stipw (logit tr_outcome tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 cond_ocu3 cond_
> ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini4 susini5 an
> o_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3), distribution(rp) df(`i') dftvc(`j') genw(rpdf`i'_m2_nostag_tvcdf`j') ipwtype(stabilised) vce(mestimation) eform
4. estimates store m2_stipw_nostag_rp`i'_tvcdf`j'
5. }
6. }
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8959.8675
Iteration 1: log pseudolikelihood = -8946.1781
Iteration 2: log pseudolikelihood = -8946.1254
Iteration 3: log pseudolikelihood = -8946.1254
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8946.1254 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.528216 .283059 2.29 0.022 1.062976 2.19708
_rcs1 | 1.971751 .2030934 6.59 0.000 1.611302 2.412832
_rcs_tr_outcome1 | 1.027705 .1079349 0.26 0.795 .8365092 1.262601
_cons | .0382478 .0069675 -17.92 0.000 .0267637 .0546597
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8947.5504
Iteration 1: log pseudolikelihood = -8940.9535
Iteration 2: log pseudolikelihood = -8940.9192
Iteration 3: log pseudolikelihood = -8940.9192
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8940.9192 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.541951 .2856829 2.34 0.019 1.072421 2.217052
_rcs1 | 1.971751 .2030934 6.59 0.000 1.611302 2.412832
_rcs_tr_outcome1 | 1.035544 .1090972 0.33 0.740 .8423495 1.273048
_rcs_tr_outcome2 | 1.048691 .017278 2.89 0.004 1.015368 1.083108
_cons | .0382478 .0069675 -17.92 0.000 .0267637 .0546597
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8948.0514
Iteration 1: log pseudolikelihood = -8940.7314
Iteration 2: log pseudolikelihood = -8940.6961
Iteration 3: log pseudolikelihood = -8940.6961
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8940.6961 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.541889 .2856656 2.34 0.019 1.072386 2.216946
_rcs1 | 1.971751 .2030934 6.59 0.000 1.611302 2.412832
_rcs_tr_outcome1 | 1.03382 .1089221 0.32 0.752 .8409371 1.270945
_rcs_tr_outcome2 | 1.05091 .0185748 2.81 0.005 1.015127 1.087954
_rcs_tr_outcome3 | .995158 .0129359 -0.37 0.709 .9701245 1.020838
_cons | .0382478 .0069675 -17.92 0.000 .0267637 .0546597
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8948.0132
Iteration 1: log pseudolikelihood = -8940.6254
Iteration 2: log pseudolikelihood = -8940.5882
Iteration 3: log pseudolikelihood = -8940.5882
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8940.5882 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.541735 .2856372 2.34 0.019 1.072279 2.216725
_rcs1 | 1.971751 .2030934 6.59 0.000 1.611302 2.412832
_rcs_tr_outcome1 | 1.033823 .1089146 0.32 0.752 .8409513 1.270929
_rcs_tr_outcome2 | 1.050188 .0180675 2.85 0.004 1.015366 1.086203
_rcs_tr_outcome3 | 1.000689 .0132015 0.05 0.958 .9751463 1.026901
_rcs_tr_outcome4 | .9941868 .0092274 -0.63 0.530 .976265 1.012438
_cons | .0382478 .0069675 -17.92 0.000 .0267637 .0546597
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8956.1984
Iteration 1: log pseudolikelihood = -8940.3326
Iteration 2: log pseudolikelihood = -8940.0734
Iteration 3: log pseudolikelihood = -8940.0732
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8940.0732 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.541552 .285602 2.34 0.019 1.072153 2.216458
_rcs1 | 1.971751 .2030934 6.59 0.000 1.611302 2.412832
_rcs_tr_outcome1 | 1.034733 .1090114 0.32 0.746 .8416905 1.27205
_rcs_tr_outcome2 | 1.048959 .0171479 2.92 0.003 1.015883 1.083113
_rcs_tr_outcome3 | 1.006299 .0129585 0.49 0.626 .9812183 1.03202
_rcs_tr_outcome4 | .9914259 .0093473 -0.91 0.361 .9732739 1.009917
_rcs_tr_outcome5 | 1.004034 .0071078 0.57 0.570 .990199 1.018062
_cons | .0382478 .0069675 -17.92 0.000 .0267637 .0546597
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8955.8689
Iteration 1: log pseudolikelihood = -8939.7431
Iteration 2: log pseudolikelihood = -8939.4739
Iteration 3: log pseudolikelihood = -8939.4737
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8939.4737 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.541415 .2855746 2.34 0.020 1.072061 2.216256
_rcs1 | 1.971751 .2030934 6.59 0.000 1.611302 2.412832
_rcs_tr_outcome1 | 1.035116 .1090518 0.33 0.743 .8420021 1.272522
_rcs_tr_outcome2 | 1.048289 .0164857 3.00 0.003 1.016471 1.081104
_rcs_tr_outcome3 | 1.010751 .0125796 0.86 0.390 .9863936 1.03571
_rcs_tr_outcome4 | .9888243 .009617 -1.16 0.248 .9701538 1.007854
_rcs_tr_outcome5 | 1.001864 .0077119 0.24 0.809 .9868618 1.017093
_rcs_tr_outcome6 | 1.004069 .0063659 0.64 0.522 .9916698 1.016624
_cons | .0382478 .0069675 -17.92 0.000 .0267637 .0546597
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8957.6133
Iteration 1: log pseudolikelihood = -8939.3088
Iteration 2: log pseudolikelihood = -8938.7557
Iteration 3: log pseudolikelihood = -8938.7551
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8938.7551 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.54134 .2855583 2.34 0.020 1.072012 2.216141
_rcs1 | 1.971751 .2030933 6.59 0.000 1.611302 2.412832
_rcs_tr_outcome1 | 1.03551 .1090966 0.33 0.740 .8423168 1.273013
_rcs_tr_outcome2 | 1.048028 .0158815 3.10 0.002 1.017358 1.079622
_rcs_tr_outcome3 | 1.014858 .0121258 1.23 0.217 .991368 1.038904
_rcs_tr_outcome4 | .9860862 .0101329 -1.36 0.173 .9664248 1.006148
_rcs_tr_outcome5 | 1.002113 .0076732 0.28 0.783 .9871865 1.017266
_rcs_tr_outcome6 | 1.002262 .0064421 0.35 0.725 .9897154 1.014969
_rcs_tr_outcome7 | 1.004145 .0054519 0.76 0.446 .9935159 1.014887
_cons | .0382478 .0069675 -17.92 0.000 .0267637 .0546597
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.9807
Iteration 1: log pseudolikelihood = -8935.8387
Iteration 2: log pseudolikelihood = -8935.8378
Iteration 3: log pseudolikelihood = -8935.8378
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8935.8378 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.532236 .2866509 2.28 0.023 1.061898 2.210897
_rcs1 | 1.97338 .2265928 5.92 0.000 1.575693 2.471438
_rcs2 | 1.049665 .0195349 2.60 0.009 1.012067 1.08866
_rcs_tr_outcome1 | 1.034953 .1221424 0.29 0.771 .8212282 1.304299
_cons | .0384946 .0071006 -17.66 0.000 .0268158 .0552598
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.7398
Iteration 1: log pseudolikelihood = -8935.8349
Iteration 2: log pseudolikelihood = -8935.8339
Iteration 3: log pseudolikelihood = -8935.8339
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8935.8339 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531958 .2876857 2.27 0.023 1.06023 2.213572
_rcs1 | 1.973687 .2260597 5.94 0.000 1.576828 2.470427
_rcs2 | 1.050751 .0365585 1.42 0.155 .9814861 1.124904
_rcs_tr_outcome1 | 1.034528 .1206834 0.29 0.771 .8230857 1.300288
_rcs_tr_outcome2 | .9980398 .0384268 -0.05 0.959 .9254962 1.07627
_cons | .0384973 .0071116 -17.63 0.000 .0268033 .0552934
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8937.3186
Iteration 1: log pseudolikelihood = -8935.5769
Iteration 2: log pseudolikelihood = -8935.5747
Iteration 3: log pseudolikelihood = -8935.5747
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8935.5747 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531875 .2876606 2.27 0.023 1.060185 2.213424
_rcs1 | 1.973739 .2261543 5.93 0.000 1.576731 2.47071
_rcs2 | 1.050932 .0365584 1.43 0.153 .9816673 1.125085
_rcs_tr_outcome1 | 1.032736 .1205203 0.28 0.783 .8215882 1.298149
_rcs_tr_outcome2 | 1.00007 .0389563 0.00 0.999 .9265587 1.079413
_rcs_tr_outcome3 | .9915878 .0131251 -0.64 0.523 .9661939 1.017649
_cons | .0384978 .0071116 -17.63 0.000 .0268037 .0552937
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8937.2025
Iteration 1: log pseudolikelihood = -8935.5067
Iteration 2: log pseudolikelihood = -8935.5028
Iteration 3: log pseudolikelihood = -8935.5028
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8935.5028 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531744 .2876399 2.27 0.023 1.06009 2.213247
_rcs1 | 1.973687 .2260597 5.94 0.000 1.576828 2.470427
_rcs2 | 1.050751 .0365585 1.42 0.155 .9814861 1.124904
_rcs_tr_outcome1 | 1.032809 .1204816 0.28 0.782 .8217194 1.298124
_rcs_tr_outcome2 | .9998213 .0385908 -0.00 0.996 .9269749 1.078392
_rcs_tr_outcome3 | .994763 .0137612 -0.38 0.704 .9681539 1.022103
_rcs_tr_outcome4 | .9941868 .0092274 -0.63 0.530 .976265 1.012438
_cons | .0384973 .0071116 -17.63 0.000 .0268033 .0552934
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8945.3971
Iteration 1: log pseudolikelihood = -8935.2034
Iteration 2: log pseudolikelihood = -8934.9761
Iteration 3: log pseudolikelihood = -8934.9759
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8934.9759 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531561 .2876019 2.27 0.023 1.059968 2.212972
_rcs1 | 1.973706 .2260936 5.94 0.000 1.576794 2.470529
_rcs2 | 1.050817 .0365657 1.42 0.154 .9815386 1.124985
_rcs_tr_outcome1 | 1.0337 .1206015 0.28 0.776 .8224039 1.299284
_rcs_tr_outcome2 | .9987979 .0380514 -0.03 0.975 .9269349 1.076232
_rcs_tr_outcome3 | .9988235 .0138831 -0.08 0.933 .9719804 1.026408
_rcs_tr_outcome4 | .9908201 .0093493 -0.98 0.328 .9726642 1.009315
_rcs_tr_outcome5 | 1.004125 .0071096 0.58 0.561 .990287 1.018157
_cons | .0384975 .0071116 -17.63 0.000 .0268034 .0552935
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8945.0582
Iteration 1: log pseudolikelihood = -8934.5857
Iteration 2: log pseudolikelihood = -8934.3885
Iteration 3: log pseudolikelihood = -8934.3884
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8934.3884 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531426 .2875769 2.27 0.023 1.059874 2.212778
_rcs1 | 1.973687 .2260597 5.94 0.000 1.576828 2.470427
_rcs2 | 1.050751 .0365585 1.42 0.155 .9814861 1.124904
_rcs_tr_outcome1 | 1.034101 .120633 0.29 0.774 .8227466 1.29975
_rcs_tr_outcome2 | .9983762 .0376666 -0.04 0.966 .9272144 1.074999
_rcs_tr_outcome3 | 1.002416 .0137705 0.18 0.861 .9757865 1.029773
_rcs_tr_outcome4 | .9873373 .0096582 -1.30 0.193 .9685879 1.00645
_rcs_tr_outcome5 | 1.001863 .0077119 0.24 0.809 .9868617 1.017093
_rcs_tr_outcome6 | 1.004069 .0063659 0.64 0.522 .9916697 1.016624
_cons | .0384973 .0071116 -17.63 0.000 .0268033 .0552934
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8946.7833
Iteration 1: log pseudolikelihood = -8933.9895
Iteration 2: log pseudolikelihood = -8933.6594
Iteration 3: log pseudolikelihood = -8933.6589
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8933.6589 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531351 .2875583 2.27 0.023 1.059828 2.212657
_rcs1 | 1.973704 .2260905 5.94 0.000 1.576797 2.47052
_rcs2 | 1.050811 .0365656 1.42 0.154 .9815328 1.124978
_rcs_tr_outcome1 | 1.034478 .1206941 0.29 0.771 .8230197 1.300266
_rcs_tr_outcome2 | .9983007 .0372846 -0.05 0.964 .9278347 1.074118
_rcs_tr_outcome3 | 1.005321 .0137358 0.39 0.698 .9787563 1.032606
_rcs_tr_outcome4 | .9840943 .0102073 -1.55 0.122 .9642903 1.004305
_rcs_tr_outcome5 | 1.001927 .0076725 0.25 0.802 .987001 1.017078
_rcs_tr_outcome6 | 1.00229 .0064426 0.36 0.722 .9897422 1.014997
_rcs_tr_outcome7 | 1.004135 .0054518 0.76 0.447 .993506 1.014877
_cons | .0384975 .0071116 -17.63 0.000 .0268034 .0552935
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8939.2909
Iteration 1: log pseudolikelihood = -8935.124
Iteration 2: log pseudolikelihood = -8935.1106
Iteration 3: log pseudolikelihood = -8935.1106
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8935.1106 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.53175 .2876678 2.27 0.023 1.060058 2.213332
_rcs1 | 1.971371 .2327675 5.75 0.000 1.564097 2.484695
_rcs2 | 1.051707 .0203058 2.61 0.009 1.012652 1.092269
_rcs3 | .9970058 .0256805 -0.12 0.907 .9479224 1.048631
_rcs_tr_outcome1 | 1.034696 .1205487 0.29 0.770 .8234597 1.300119
_cons | .0385081 .0071162 -17.62 0.000 .0268072 .0553162
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8938.7147
Iteration 1: log pseudolikelihood = -8935.1151
Iteration 2: log pseudolikelihood = -8935.1052
Iteration 3: log pseudolikelihood = -8935.1052
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8935.1052 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.53142 .2880792 2.27 0.023 1.059187 2.214195
_rcs1 | 1.971773 .233679 5.73 0.000 1.563073 2.487337
_rcs2 | 1.052999 .0373914 1.45 0.146 .9822055 1.128896
_rcs3 | .9972114 .0266777 -0.10 0.917 .9462712 1.050894
_rcs_tr_outcome1 | 1.034199 .1208572 0.29 0.774 .8224925 1.300397
_rcs_tr_outcome2 | .9976443 .0407335 -0.06 0.954 .9209191 1.080762
_cons | .0385114 .0071213 -17.61 0.000 .0268034 .0553336
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8939.4532
Iteration 1: log pseudolikelihood = -8935.0969
Iteration 2: log pseudolikelihood = -8935.0708
Iteration 3: log pseudolikelihood = -8935.0708
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8935.0708 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531327 .2881307 2.26 0.024 1.05903 2.214257
_rcs1 | 1.972597 .2434592 5.50 0.000 1.548754 2.512431
_rcs2 | 1.053295 .0370572 1.48 0.140 .9831116 1.128488
_rcs3 | .9999477 .0614985 -0.00 0.999 .8863943 1.128048
_rcs_tr_outcome1 | 1.033377 .1295812 0.26 0.793 .8082053 1.321283
_rcs_tr_outcome2 | .9977356 .0392906 -0.06 0.954 .9236244 1.077794
_rcs_tr_outcome3 | .9952101 .0625514 -0.08 0.939 .8798622 1.12568
_cons | .0385116 .0071288 -17.59 0.000 .0267934 .055355
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8939.9883
Iteration 1: log pseudolikelihood = -8934.9684
Iteration 2: log pseudolikelihood = -8934.9328
Iteration 3: log pseudolikelihood = -8934.9328
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8934.9328 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531169 .2879301 2.27 0.023 1.059151 2.213543
_rcs1 | 1.972303 .2426432 5.52 0.000 1.549724 2.510112
_rcs2 | 1.053298 .0372478 1.47 0.142 .9827666 1.128892
_rcs3 | .9989047 .0606951 -0.02 0.986 .8867551 1.125238
_rcs_tr_outcome1 | 1.033537 .1292366 0.26 0.792 .8088899 1.320575
_rcs_tr_outcome2 | .997014 .0388837 -0.08 0.939 .9236433 1.076213
_rcs_tr_outcome3 | .9995615 .0609059 -0.01 0.994 .8870408 1.126355
_rcs_tr_outcome4 | .9947488 .0179471 -0.29 0.770 .9601878 1.030554
_cons | .0385119 .0071255 -17.60 0.000 .0267982 .0553458
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8947.4893
Iteration 1: log pseudolikelihood = -8934.6667
Iteration 2: log pseudolikelihood = -8934.4249
Iteration 3: log pseudolikelihood = -8934.4247
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8934.4247 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.53099 .2879389 2.26 0.024 1.05897 2.213406
_rcs1 | 1.972406 .2429686 5.51 0.000 1.549323 2.511022
_rcs2 | 1.053319 .0371867 1.47 0.141 .9828989 1.128784
_rcs3 | .9992533 .0611485 -0.01 0.990 .8863128 1.126586
_rcs_tr_outcome1 | 1.034375 .129361 0.27 0.787 .8095155 1.321695
_rcs_tr_outcome2 | .995954 .0382752 -0.11 0.916 .9236918 1.07387
_rcs_tr_outcome3 | 1.003022 .0580538 0.05 0.958 .895455 1.12351
_rcs_tr_outcome4 | .9920317 .025899 -0.31 0.759 .9425473 1.044114
_rcs_tr_outcome5 | 1.003973 .0073198 0.54 0.587 .9897282 1.018422
_cons | .0385119 .0071266 -17.60 0.000 .0267966 .055349
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8947.2707
Iteration 1: log pseudolikelihood = -8934.1086
Iteration 2: log pseudolikelihood = -8933.8486
Iteration 3: log pseudolikelihood = -8933.8484
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8933.8484 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.530857 .288039 2.26 0.024 1.058709 2.213568
_rcs1 | 1.972597 .2434592 5.50 0.000 1.548754 2.512431
_rcs2 | 1.053295 .0370572 1.48 0.140 .9831116 1.128488
_rcs3 | .9999477 .0614985 -0.00 0.999 .8863943 1.128048
_rcs_tr_outcome1 | 1.034672 .1297376 0.27 0.786 .8092275 1.322924
_rcs_tr_outcome2 | .9954886 .0378288 -0.12 0.905 .9240393 1.072462
_rcs_tr_outcome3 | 1.0057 .0547364 0.10 0.917 .9039426 1.118913
_rcs_tr_outcome4 | .9888502 .0319646 -0.35 0.729 .928144 1.053527
_rcs_tr_outcome5 | 1.00187 .011149 0.17 0.867 .9802552 1.023962
_rcs_tr_outcome6 | 1.004069 .0063659 0.64 0.522 .9916698 1.016624
_cons | .0385116 .0071288 -17.59 0.000 .0267934 .055355
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8949.0468
Iteration 1: log pseudolikelihood = -8933.6216
Iteration 2: log pseudolikelihood = -8933.1409
Iteration 3: log pseudolikelihood = -8933.1403
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8933.1403 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.530784 .2880804 2.26 0.024 1.058584 2.213619
_rcs1 | 1.972679 .2436536 5.50 0.000 1.548535 2.512996
_rcs2 | 1.053277 .0370007 1.48 0.140 .9831968 1.128351
_rcs3 | 1.000246 .061589 0.00 0.997 .8865336 1.128544
_rcs_tr_outcome1 | 1.035028 .1299221 0.27 0.784 .8092914 1.32373
_rcs_tr_outcome2 | .9954373 .0374735 -0.12 0.903 .9246347 1.071662
_rcs_tr_outcome3 | 1.008158 .0522779 0.16 0.875 .9107298 1.116008
_rcs_tr_outcome4 | .9857318 .0348258 -0.41 0.684 .9197841 1.056408
_rcs_tr_outcome5 | 1.002096 .0143752 0.15 0.884 .9743131 1.03067
_rcs_tr_outcome6 | 1.002243 .0068465 0.33 0.743 .988914 1.015752
_rcs_tr_outcome7 | 1.004149 .0054531 0.76 0.446 .9935178 1.014894
_cons | .0385115 .0071298 -17.59 0.000 .0267919 .0553575
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.8475
Iteration 1: log pseudolikelihood = -8924.3369
Iteration 2: log pseudolikelihood = -8924.273
Iteration 3: log pseudolikelihood = -8924.273
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8924.273 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.520713 .2896237 2.20 0.028 1.046968 2.208822
_rcs1 | 1.956511 .2303315 5.70 0.000 1.553367 2.464283
_rcs2 | 1.056883 .0237065 2.47 0.014 1.011426 1.104384
_rcs3 | .9867359 .0294414 -0.45 0.654 .9306867 1.046161
_rcs4 | 1.028747 .0189351 1.54 0.124 .9922966 1.066537
_rcs_tr_outcome1 | 1.052849 .1273634 0.43 0.670 .8306077 1.334554
_cons | .0386502 .0071645 -17.55 0.000 .0268761 .0555823
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.0405
Iteration 1: log pseudolikelihood = -8924.3248
Iteration 2: log pseudolikelihood = -8924.2701
Iteration 3: log pseudolikelihood = -8924.2701
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8924.2701 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.520441 .2896714 2.20 0.028 1.046647 2.20871
_rcs1 | 1.956759 .2320344 5.66 0.000 1.550962 2.46873
_rcs2 | 1.057843 .0400921 1.48 0.138 .9821111 1.139414
_rcs3 | .9869356 .0319443 -0.41 0.685 .9262706 1.051574
_rcs4 | 1.028782 .0184537 1.58 0.114 .9932416 1.065593
_rcs_tr_outcome1 | 1.052515 .1293764 0.42 0.677 .8271753 1.339242
_rcs_tr_outcome2 | .9982238 .0466503 -0.04 0.970 .9108533 1.093975
_cons | .0386529 .0071667 -17.55 0.000 .0268756 .055591
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.4433
Iteration 1: log pseudolikelihood = -8924.0567
Iteration 2: log pseudolikelihood = -8923.9669
Iteration 3: log pseudolikelihood = -8923.9669
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8923.9669 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.520299 .2911311 2.19 0.029 1.044545 2.212741
_rcs1 | 1.958819 .2414175 5.46 0.000 1.538462 2.494031
_rcs2 | 1.05832 .039601 1.51 0.130 .9834816 1.138854
_rcs3 | .9943819 .0600603 -0.09 0.926 .8833666 1.119349
_rcs4 | 1.031273 .0188069 1.69 0.091 .9950628 1.0688
_rcs_tr_outcome1 | 1.050338 .1368265 0.38 0.706 .8136613 1.355858
_rcs_tr_outcome2 | .9986383 .0445212 -0.03 0.976 .915082 1.089824
_rcs_tr_outcome3 | .9864495 .0563041 -0.24 0.811 .8820441 1.103213
_cons | .0386516 .0071927 -17.48 0.000 .0268391 .0556632
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8928.1343
Iteration 1: log pseudolikelihood = -8907.4386
Iteration 2: log pseudolikelihood = -8905.454
Iteration 3: log pseudolikelihood = -8905.4502
Iteration 4: log pseudolikelihood = -8905.4502
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8905.4502 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.534542 .2878939 2.28 0.022 1.062394 2.216521
_rcs1 | 1.988049 .2331298 5.86 0.000 1.57983 2.501749
_rcs2 | 1.06632 .0460312 1.49 0.137 .9798115 1.160466
_rcs3 | .980473 .0511895 -0.38 0.706 .885106 1.086115
_rcs4 | 1.076502 .0406322 1.95 0.051 .9997388 1.159159
_rcs_tr_outcome1 | 1.025348 .1223568 0.21 0.834 .811513 1.295528
_rcs_tr_outcome2 | .9848711 .0457833 -0.33 0.743 .899104 1.07882
_rcs_tr_outcome3 | 1.020619 .0549444 0.38 0.705 .9184163 1.134194
_rcs_tr_outcome4 | .9235346 .0358988 -2.05 0.041 .8557877 .9966446
_cons | .0384271 .0070917 -17.66 0.000 .0267639 .0551729
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8934.0251
Iteration 1: log pseudolikelihood = -8907.8716
Iteration 2: log pseudolikelihood = -8906.3281
Iteration 3: log pseudolikelihood = -8906.3238
Iteration 4: log pseudolikelihood = -8906.3238
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8906.3238 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531488 .2872714 2.27 0.023 1.060347 2.211969
_rcs1 | 1.983358 .2317638 5.86 0.000 1.577374 2.493834
_rcs2 | 1.066117 .0455871 1.50 0.134 .9804094 1.159317
_rcs3 | .9807947 .0516261 -0.37 0.713 .884654 1.087384
_rcs4 | 1.07262 .0386021 1.95 0.051 .9995683 1.151011
_rcs_tr_outcome1 | 1.030446 .1224803 0.25 0.801 .8163003 1.300769
_rcs_tr_outcome2 | .982598 .0448425 -0.38 0.700 .8985244 1.074538
_rcs_tr_outcome3 | 1.034986 .0568559 0.63 0.531 .9293397 1.152642
_rcs_tr_outcome4 | .9305304 .0325449 -2.06 0.040 .8688808 .9965543
_rcs_tr_outcome5 | .979826 .0140918 -1.42 0.156 .9525922 1.007838
_cons | .0384659 .0070928 -17.67 0.000 .0267992 .0552117
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8934.2943
Iteration 1: log pseudolikelihood = -8905.6682
Iteration 2: log pseudolikelihood = -8903.6034
Iteration 3: log pseudolikelihood = -8903.5983
Iteration 4: log pseudolikelihood = -8903.5983
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8903.5983 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.534677 .2879478 2.28 0.022 1.062449 2.216797
_rcs1 | 1.988967 .2332523 5.86 0.000 1.580537 2.502941
_rcs2 | 1.066696 .0462343 1.49 0.136 .9798207 1.161274
_rcs3 | .9798837 .0507542 -0.39 0.695 .88529 1.084585
_rcs4 | 1.077159 .0405515 1.97 0.048 1.00054 1.159644
_rcs_tr_outcome1 | 1.0259 .1224327 0.21 0.830 .811935 1.29625
_rcs_tr_outcome2 | .9808316 .0453767 -0.42 0.676 .895808 1.073925
_rcs_tr_outcome3 | 1.043918 .0551039 0.81 0.415 .9413156 1.157704
_rcs_tr_outcome4 | .9419051 .0291708 -1.93 0.053 .8864321 1.00085
_rcs_tr_outcome5 | .9548653 .0243248 -1.81 0.070 .9083603 1.003751
_rcs_tr_outcome6 | 1.00145 .0064956 0.22 0.823 .9887993 1.014262
_cons | .0384204 .0070914 -17.66 0.000 .0267579 .0551661
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8937.089
Iteration 1: log pseudolikelihood = -8906.5355
Iteration 2: log pseudolikelihood = -8904.1323
Iteration 3: log pseudolikelihood = -8904.1273
Iteration 4: log pseudolikelihood = -8904.1273
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8904.1273 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.533459 .2876182 2.28 0.023 1.061743 2.214751
_rcs1 | 1.986966 .2327954 5.86 0.000 1.579293 2.499875
_rcs2 | 1.066349 .0459252 1.49 0.136 .9800318 1.160269
_rcs3 | .9804706 .0512805 -0.38 0.706 .8849425 1.086311
_rcs4 | 1.07562 .0404477 1.94 0.053 .9991949 1.157891
_rcs_tr_outcome1 | 1.02812 .1225527 0.23 0.816 .8139166 1.298697
_rcs_tr_outcome2 | .9801323 .045048 -0.44 0.662 .8956998 1.072524
_rcs_tr_outcome3 | 1.047791 .0545582 0.90 0.370 .9461344 1.16037
_rcs_tr_outcome4 | .9522892 .0275161 -1.69 0.091 .8998573 1.007776
_rcs_tr_outcome5 | .9507733 .0268036 -1.79 0.073 .8996641 1.004786
_rcs_tr_outcome6 | .9843965 .011117 -1.39 0.164 .962847 1.006428
_rcs_tr_outcome7 | 1.004096 .0054496 0.75 0.451 .9934721 1.014834
_cons | .0384363 .0070912 -17.66 0.000 .0267734 .0551799
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8925.9085
Iteration 1: log pseudolikelihood = -8918.7095
Iteration 2: log pseudolikelihood = -8918.6529
Iteration 3: log pseudolikelihood = -8918.6529
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8918.6529 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.515353 .2892546 2.18 0.029 1.0424 2.202893
_rcs1 | 1.948164 .2252765 5.77 0.000 1.55309 2.443737
_rcs2 | 1.057214 .0256781 2.29 0.022 1.008065 1.10876
_rcs3 | .9837364 .034482 -0.47 0.640 .9184221 1.053696
_rcs4 | 1.024371 .018731 1.32 0.188 .9883092 1.061749
_rcs5 | 1.020731 .0143557 1.46 0.145 .992979 1.04926
_rcs_tr_outcome1 | 1.061201 .126188 0.50 0.617 .8405831 1.339721
_cons | .0387185 .0071744 -17.55 0.000 .0269273 .0556729
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8924.8584
Iteration 1: log pseudolikelihood = -8918.6597
Iteration 2: log pseudolikelihood = -8918.613
Iteration 3: log pseudolikelihood = -8918.613
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8918.613 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.514346 .2890697 2.17 0.030 1.041697 2.20145
_rcs1 | 1.949035 .2287945 5.68 0.000 1.548454 2.453246
_rcs2 | 1.060694 .0395217 1.58 0.114 .9859934 1.141054
_rcs3 | .9846054 .0379585 -0.40 0.687 .9129493 1.061886
_rcs4 | 1.024502 .0182125 1.36 0.173 .9894213 1.060827
_rcs5 | 1.020884 .0143154 1.47 0.140 .9932087 1.049331
_rcs_tr_outcome1 | 1.059978 .1294693 0.48 0.633 .8343115 1.346684
_rcs_tr_outcome2 | .9934452 .0476023 -0.14 0.891 .9043935 1.091266
_cons | .0387283 .0071765 -17.55 0.000 .0269338 .0556877
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8925.5959
Iteration 1: log pseudolikelihood = -8918.3375
Iteration 2: log pseudolikelihood = -8918.2763
Iteration 3: log pseudolikelihood = -8918.2762
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8918.2762 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.514239 .2904868 2.16 0.031 1.039687 2.205394
_rcs1 | 1.950984 .2370323 5.50 0.000 1.537581 2.475537
_rcs2 | 1.060985 .0396914 1.58 0.114 .9859749 1.141702
_rcs3 | .9912874 .0605779 -0.14 0.886 .8793917 1.117421
_rcs4 | 1.02789 .0205603 1.38 0.169 .9883726 1.068988
_rcs5 | 1.021397 .0144194 1.50 0.134 .9935227 1.050053
_rcs_tr_outcome1 | 1.057827 .1360584 0.44 0.662 .8221153 1.361122
_rcs_tr_outcome2 | .9937621 .0451573 -0.14 0.890 .9090824 1.08633
_rcs_tr_outcome3 | .9865611 .0524849 -0.25 0.799 .888874 1.094984
_cons | .0387264 .0072016 -17.48 0.000 .0268978 .0557567
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8925.1642
Iteration 1: log pseudolikelihood = -8899.339
Iteration 2: log pseudolikelihood = -8897.2884
Iteration 3: log pseudolikelihood = -8897.2839
Iteration 4: log pseudolikelihood = -8897.2839
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8897.2839 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.530428 .285854 2.28 0.023 1.061268 2.206991
_rcs1 | 1.980785 .2232811 6.06 0.000 1.588133 2.470517
_rcs2 | 1.069323 .0481495 1.49 0.137 .9789966 1.167984
_rcs3 | .9658467 .0564063 -0.60 0.552 .8613848 1.082977
_rcs4 | 1.067088 .0335233 2.07 0.039 1.003365 1.134857
_rcs5 | 1.036164 .0167806 2.19 0.028 1.003791 1.069581
_rcs_tr_outcome1 | 1.032671 .1174586 0.28 0.777 .826312 1.290565
_rcs_tr_outcome2 | .9831256 .0469194 -0.36 0.721 .8953353 1.079524
_rcs_tr_outcome3 | 1.028375 .0585506 0.49 0.623 .9197893 1.14978
_rcs_tr_outcome4 | .9233057 .0310732 -2.37 0.018 .8643686 .9862614
_cons | .0384805 .0070698 -17.73 0.000 .0268444 .0551604
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8924.206
Iteration 1: log pseudolikelihood = -8897.7123
Iteration 2: log pseudolikelihood = -8895.2258
Iteration 3: log pseudolikelihood = -8895.2133
Iteration 4: log pseudolikelihood = -8895.2133
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8895.2133 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.534219 .286847 2.29 0.022 1.06351 2.213264
_rcs1 | 1.986007 .2233184 6.10 0.000 1.593186 2.475682
_rcs2 | 1.06898 .0506462 1.41 0.159 .9741848 1.173
_rcs3 | .9633776 .0569445 -0.63 0.528 .8579909 1.081709
_rcs4 | 1.068277 .0373199 1.89 0.059 .9975798 1.143985
_rcs5 | 1.040749 .0289512 1.44 0.151 .9855253 1.099068
_rcs_tr_outcome1 | 1.027306 .1177306 0.24 0.814 .8206367 1.286023
_rcs_tr_outcome2 | .9812709 .0491954 -0.38 0.706 .8894356 1.082588
_rcs_tr_outcome3 | 1.044553 .0631721 0.72 0.471 .9277941 1.176005
_rcs_tr_outcome4 | .9280603 .0335811 -2.06 0.039 .8645222 .9962681
_rcs_tr_outcome5 | .9647219 .0276851 -1.25 0.211 .9119579 1.020539
_cons | .0384306 .0070672 -17.72 0.000 .0268007 .0551073
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8926.4282
Iteration 1: log pseudolikelihood = -8897.7492
Iteration 2: log pseudolikelihood = -8894.4723
Iteration 3: log pseudolikelihood = -8894.4387
Iteration 4: log pseudolikelihood = -8894.4387
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8894.4387 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.534103 .2865941 2.29 0.022 1.063743 2.212443
_rcs1 | 1.987485 .2230305 6.12 0.000 1.595087 2.476416
_rcs2 | 1.071145 .0506661 1.45 0.146 .976305 1.175197
_rcs3 | .9623953 .0558671 -0.66 0.509 .8588972 1.078365
_rcs4 | 1.071139 .0367928 2.00 0.045 1.0014 1.145734
_rcs5 | 1.037385 .0259478 1.47 0.142 .9877551 1.089509
_rcs_tr_outcome1 | 1.026914 .1169517 0.23 0.816 .8214738 1.283732
_rcs_tr_outcome2 | .9775888 .0488744 -0.45 0.650 .8863404 1.078231
_rcs_tr_outcome3 | 1.055617 .0634621 0.90 0.368 .9382817 1.187625
_rcs_tr_outcome4 | .9408636 .0309055 -1.86 0.063 .8821987 1.00343
_rcs_tr_outcome5 | .949746 .0244623 -2.00 0.045 .9029909 .998922
_rcs_tr_outcome6 | .98916 .0145233 -0.74 0.458 .9611006 1.018039
_cons | .0384279 .0070644 -17.73 0.000 .0268018 .055097
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8925.106
Iteration 1: log pseudolikelihood = -8896.6957
Iteration 2: log pseudolikelihood = -8893.9133
Iteration 3: log pseudolikelihood = -8893.9015
Iteration 4: log pseudolikelihood = -8893.9015
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8893.9015 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.533758 .2866654 2.29 0.022 1.06332 2.21233
_rcs1 | 1.986174 .2230951 6.11 0.000 1.593701 2.475299
_rcs2 | 1.069759 .0506705 1.42 0.155 .9749169 1.173827
_rcs3 | .963088 .0564186 -0.64 0.521 .8586215 1.080265
_rcs4 | 1.06918 .0370822 1.93 0.054 .9989158 1.144388
_rcs5 | 1.039423 .0284121 1.41 0.157 .9852018 1.096628
_rcs_tr_outcome1 | 1.028255 .1176459 0.24 0.808 .8216978 1.286736
_rcs_tr_outcome2 | .9777836 .0490281 -0.45 0.654 .8862612 1.078757
_rcs_tr_outcome3 | 1.059557 .0640701 0.96 0.339 .9411375 1.192876
_rcs_tr_outcome4 | .9520198 .0289223 -1.62 0.106 .8969878 1.010428
_rcs_tr_outcome5 | .9491905 .0227236 -2.18 0.029 .9056818 .9947893
_rcs_tr_outcome6 | .9706719 .0224771 -1.29 0.199 .9276024 1.015741
_rcs_tr_outcome7 | .9979633 .0069184 -0.29 0.769 .9844952 1.011616
_cons | .0384328 .0070663 -17.72 0.000 .0268039 .0551068
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8929.721
Iteration 1: log pseudolikelihood = -8915.8387
Iteration 2: log pseudolikelihood = -8915.5735
Iteration 3: log pseudolikelihood = -8915.5734
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8915.5734 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.512203 .2908272 2.15 0.032 1.037306 2.204516
_rcs1 | 1.94391 .2250472 5.74 0.000 1.549288 2.439046
_rcs2 | 1.060743 .0299403 2.09 0.037 1.003655 1.121079
_rcs3 | .9770817 .0400181 -0.57 0.571 .9017132 1.05875
_rcs4 | 1.019045 .0167597 1.15 0.251 .9867201 1.052428
_rcs5 | 1.018532 .0122934 1.52 0.128 .9947198 1.042914
_rcs6 | 1.018141 .0095368 1.92 0.055 .9996193 1.037005
_rcs_tr_outcome1 | 1.06517 .1296988 0.52 0.604 .8390223 1.352272
_cons | .0387635 .0072027 -17.49 0.000 .0269315 .0557937
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8928.6949
Iteration 1: log pseudolikelihood = -8915.7966
Iteration 2: log pseudolikelihood = -8915.5469
Iteration 3: log pseudolikelihood = -8915.5468
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8915.5468 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.511393 .2901832 2.15 0.031 1.037407 2.201941
_rcs1 | 1.944634 .2288129 5.65 0.000 1.544125 2.449026
_rcs2 | 1.063538 .0405003 1.62 0.106 .9870485 1.145954
_rcs3 | .9778703 .0443226 -0.49 0.622 .8947466 1.068716
_rcs4 | 1.019161 .0162819 1.19 0.235 .9877431 1.051577
_rcs5 | 1.018669 .0120361 1.57 0.117 .9953494 1.042534
_rcs6 | 1.018209 .0094696 1.94 0.052 .9998168 1.036939
_rcs_tr_outcome1 | 1.064139 .1337133 0.49 0.621 .8318434 1.361305
_rcs_tr_outcome2 | .9946411 .0498241 -0.11 0.915 .9016283 1.097249
_cons | .0387714 .0071997 -17.50 0.000 .026943 .0557927
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8929.5793
Iteration 1: log pseudolikelihood = -8915.375
Iteration 2: log pseudolikelihood = -8915.1042
Iteration 3: log pseudolikelihood = -8915.104
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8915.104 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.511613 .2916878 2.14 0.032 1.035592 2.206441
_rcs1 | 1.946974 .2374959 5.46 0.000 1.532953 2.472814
_rcs2 | 1.06298 .0413928 1.57 0.117 .9848706 1.147285
_rcs3 | .9849849 .0635188 -0.23 0.815 .8680367 1.117689
_rcs4 | 1.024074 .0219065 1.11 0.266 .9820257 1.067923
_rcs5 | 1.020182 .0131036 1.56 0.120 .9948196 1.04619
_rcs6 | 1.01848 .0093861 1.99 0.047 1.000248 1.037043
_rcs_tr_outcome1 | 1.061638 .1405819 0.45 0.651 .818956 1.376235
_rcs_tr_outcome2 | .9967196 .0476731 -0.07 0.945 .9075279 1.094677
_rcs_tr_outcome3 | .9839141 .0516176 -0.31 0.757 .8877728 1.090467
_cons | .0387656 .0072257 -17.44 0.000 .0269022 .0558606
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8928.8819
Iteration 1: log pseudolikelihood = -8900.0858
Iteration 2: log pseudolikelihood = -8897.8525
Iteration 3: log pseudolikelihood = -8897.8394
Iteration 4: log pseudolikelihood = -8897.8394
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8897.8394 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.525812 .2889923 2.23 0.026 1.052644 2.211671
_rcs1 | 1.973848 .2267976 5.92 0.000 1.575831 2.472395
_rcs2 | 1.072477 .0509717 1.47 0.141 .9770867 1.177181
_rcs3 | .9574585 .0614361 -0.68 0.498 .84431 1.08577
_rcs4 | 1.047589 .026298 1.85 0.064 .9972938 1.100422
_rcs5 | 1.046295 .0237039 2.00 0.046 1.000853 1.093801
_rcs6 | 1.018377 .0085755 2.16 0.031 1.001707 1.035324
_rcs_tr_outcome1 | 1.039127 .1247188 0.32 0.749 .8213058 1.314716
_rcs_tr_outcome2 | .9825179 .0480609 -0.36 0.718 .8926949 1.081379
_rcs_tr_outcome3 | 1.029583 .062085 0.48 0.629 .9148145 1.15875
_rcs_tr_outcome4 | .9289927 .0319442 -2.14 0.032 .8684465 .9937602
_cons | .0385452 .0071232 -17.62 0.000 .0268329 .0553698
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8925.598
Iteration 1: log pseudolikelihood = -8896.4733
Iteration 2: log pseudolikelihood = -8893.4452
Iteration 3: log pseudolikelihood = -8893.4311
Iteration 4: log pseudolikelihood = -8893.4311
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8893.4311 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.528675 .2881224 2.25 0.024 1.05653 2.211813
_rcs1 | 1.979848 .2210097 6.12 0.000 1.590789 2.46406
_rcs2 | 1.077016 .0571629 1.40 0.162 .9706096 1.195089
_rcs3 | .9494606 .0640399 -0.77 0.442 .8318873 1.083651
_rcs4 | 1.051763 .0306346 1.73 0.083 .9934022 1.113553
_rcs5 | 1.045975 .0245405 1.92 0.055 .9989652 1.095196
_rcs6 | 1.022897 .0125206 1.85 0.064 .9986489 1.047734
_rcs_tr_outcome1 | 1.034114 .120223 0.29 0.773 .8233988 1.298753
_rcs_tr_outcome2 | .9755045 .0530773 -0.46 0.649 .8768299 1.085284
_rcs_tr_outcome3 | 1.051425 .0700724 0.75 0.452 .9226772 1.198138
_rcs_tr_outcome4 | .9302091 .0307346 -2.19 0.029 .8718795 .9924411
_rcs_tr_outcome5 | .971602 .0214086 -1.31 0.191 .930535 1.014481
_cons | .0385048 .0070931 -17.68 0.000 .0268357 .0552482
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8928.3939
Iteration 1: log pseudolikelihood = -8899.0044
Iteration 2: log pseudolikelihood = -8887.1028
Iteration 3: log pseudolikelihood = -8886.3055
Iteration 4: log pseudolikelihood = -8886.2963
Iteration 5: log pseudolikelihood = -8886.2963
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8886.2963 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.533495 .2858493 2.29 0.022 1.06418 2.209783
_rcs1 | 1.991154 .2155638 6.36 0.000 1.610471 2.461821
_rcs2 | 1.086369 .0641239 1.40 0.160 .9676858 1.219608
_rcs3 | .9389952 .0630509 -0.94 0.349 .8232042 1.071073
_rcs4 | 1.058129 .0310773 1.92 0.054 .9989386 1.120827
_rcs5 | 1.035523 .0255192 1.42 0.157 .9866951 1.086767
_rcs6 | 1.034447 .0167199 2.10 0.036 1.002191 1.067743
_rcs_tr_outcome1 | 1.02503 .1132637 0.22 0.823 .8254305 1.272895
_rcs_tr_outcome2 | .9649475 .0589603 -0.58 0.559 .8560389 1.087712
_rcs_tr_outcome3 | 1.076417 .0734882 1.08 0.281 .9416039 1.230533
_rcs_tr_outcome4 | .9345025 .0289092 -2.19 0.029 .8795252 .9929164
_rcs_tr_outcome5 | .9674951 .0249754 -1.28 0.201 .919762 1.017705
_rcs_tr_outcome6 | .9706335 .0168471 -1.72 0.086 .938169 1.004221
_cons | .0384454 .007048 -17.77 0.000 .026841 .0550668
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8920.9938
Iteration 1: log pseudolikelihood = -8892.5388
Iteration 2: log pseudolikelihood = -8885.5662
Iteration 3: log pseudolikelihood = -8885.4711
Iteration 4: log pseudolikelihood = -8885.471
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8885.471 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.532202 .286036 2.29 0.022 1.062702 2.209128
_rcs1 | 1.98969 .2161025 6.33 0.000 1.608183 2.461702
_rcs2 | 1.086579 .0633494 1.42 0.154 .9692477 1.218114
_rcs3 | .9397965 .0627707 -0.93 0.353 .8244808 1.071241
_rcs4 | 1.058411 .0306845 1.96 0.050 .999947 1.120293
_rcs5 | 1.035041 .024886 1.43 0.152 .9873969 1.084984
_rcs6 | 1.033365 .0159674 2.12 0.034 1.002539 1.065139
_rcs_tr_outcome1 | 1.027213 .114199 0.24 0.809 .8260936 1.277296
_rcs_tr_outcome2 | .9637197 .0582002 -0.61 0.541 .8561417 1.084815
_rcs_tr_outcome3 | 1.082415 .0729004 1.18 0.240 .9485619 1.235157
_rcs_tr_outcome4 | .9392197 .0291139 -2.02 0.043 .8838563 .998051
_rcs_tr_outcome5 | .9692142 .0222526 -1.36 0.173 .9265666 1.013825
_rcs_tr_outcome6 | .9679263 .0179587 -1.76 0.079 .9333602 1.003772
_rcs_tr_outcome7 | .9835759 .0109278 -1.49 0.136 .9623892 1.005229
_cons | .0384597 .0070547 -17.76 0.000 .0268453 .0550989
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.5713
Iteration 1: log pseudolikelihood = -8917.4331
Iteration 2: log pseudolikelihood = -8916.8707
Iteration 3: log pseudolikelihood = -8916.87
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8916.87 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.513421 .2918238 2.15 0.032 1.037117 2.208471
_rcs1 | 1.947035 .2267984 5.72 0.000 1.54961 2.446387
_rcs2 | 1.064387 .0351111 1.89 0.059 .9977474 1.135476
_rcs3 | .9742371 .0448285 -0.57 0.571 .8902203 1.066183
_rcs4 | 1.018026 .0159041 1.14 0.253 .9873267 1.049679
_rcs5 | 1.015512 .0129325 1.21 0.227 .9904782 1.041178
_rcs6 | 1.019518 .011927 1.65 0.098 .9964077 1.043165
_rcs7 | 1.006377 .0056323 1.14 0.256 .9953979 1.017477
_rcs_tr_outcome1 | 1.063785 .1323397 0.50 0.619 .8336054 1.357523
_cons | .0387423 .0072053 -17.48 0.000 .0269079 .0557817
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8935.6913
Iteration 1: log pseudolikelihood = -8917.4139
Iteration 2: log pseudolikelihood = -8916.865
Iteration 3: log pseudolikelihood = -8916.8642
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8916.8642 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.51304 .2907936 2.15 0.031 1.038141 2.205181
_rcs1 | 1.947371 .2300402 5.64 0.000 1.544889 2.454708
_rcs2 | 1.065669 .0424899 1.60 0.111 .9855607 1.152288
_rcs3 | .9746569 .0499321 -0.50 0.616 .8815448 1.077604
_rcs4 | 1.018089 .0154794 1.18 0.238 .9881971 1.048884
_rcs5 | 1.015577 .0125628 1.25 0.211 .9912501 1.0405
_rcs6 | 1.019559 .011796 1.67 0.094 .9966995 1.042943
_rcs7 | 1.006396 .0056281 1.14 0.254 .9954252 1.017488
_rcs_tr_outcome1 | 1.063306 .1364519 0.48 0.632 .8268468 1.367386
_rcs_tr_outcome2 | .9974875 .0508509 -0.05 0.961 .9026388 1.102303
_cons | .038746 .0071974 -17.50 0.000 .0269222 .0557628
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.6412
Iteration 1: log pseudolikelihood = -8917.0457
Iteration 2: log pseudolikelihood = -8916.4345
Iteration 3: log pseudolikelihood = -8916.4334
Iteration 4: log pseudolikelihood = -8916.4334
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8916.4334 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.513205 .2922793 2.14 0.032 1.036302 2.209579
_rcs1 | 1.94972 .2386822 5.45 0.000 1.533802 2.478422
_rcs2 | 1.065127 .0443241 1.52 0.129 .9817014 1.155642
_rcs3 | .9813862 .0675653 -0.27 0.785 .8575065 1.123162
_rcs4 | 1.023433 .0226207 1.05 0.295 .9800441 1.068743
_rcs5 | 1.017687 .0150161 1.19 0.235 .9886774 1.047548
_rcs6 | 1.020229 .0115916 1.76 0.078 .9977614 1.043203
_rcs7 | 1.006548 .0056103 1.17 0.242 .9956122 1.017605
_rcs_tr_outcome1 | 1.060816 .1431942 0.44 0.662 .8142177 1.3821
_rcs_tr_outcome2 | .9993913 .0485858 -0.01 0.990 .908561 1.099302
_rcs_tr_outcome3 | .9839585 .0521053 -0.31 0.760 .8869549 1.091571
_cons | .0387412 .007223 -17.44 0.000 .0268827 .0558306
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8935.7627
Iteration 1: log pseudolikelihood = -8899.2385
Iteration 2: log pseudolikelihood = -8897.0598
Iteration 3: log pseudolikelihood = -8897.0537
Iteration 4: log pseudolikelihood = -8897.0537
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8897.0537 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.527781 .2904961 2.23 0.026 1.052475 2.217741
_rcs1 | 1.97807 .2293231 5.88 0.000 1.576012 2.482697
_rcs2 | 1.077216 .0558146 1.44 0.151 .9731928 1.192358
_rcs3 | .9511418 .065955 -0.72 0.470 .8302721 1.089608
_rcs4 | 1.041449 .0230993 1.83 0.067 .9971453 1.087721
_rcs5 | 1.048041 .028446 1.73 0.084 .9937444 1.105303
_rcs6 | 1.031111 .0134457 2.35 0.019 1.005092 1.057804
_rcs7 | 1.00486 .0053146 0.92 0.359 .9944973 1.015331
_rcs_tr_outcome1 | 1.037384 .1288276 0.30 0.768 .8132669 1.323263
_rcs_tr_outcome2 | .9831864 .048875 -0.34 0.733 .8919118 1.083802
_rcs_tr_outcome3 | 1.032776 .0658446 0.51 0.613 .9114599 1.170238
_rcs_tr_outcome4 | .9235471 .0340265 -2.16 0.031 .8592074 .9927048
_cons | .0385099 .0071239 -17.61 0.000 .0267984 .0553395
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8934.8064
Iteration 1: log pseudolikelihood = -8896.0067
Iteration 2: log pseudolikelihood = -8893.0067
Iteration 3: log pseudolikelihood = -8892.9995
Iteration 4: log pseudolikelihood = -8892.9995
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8892.9995 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.53015 .2895358 2.25 0.025 1.056013 2.217169
_rcs1 | 1.983576 .2225217 6.11 0.000 1.592059 2.471373
_rcs2 | 1.083169 .0648673 1.33 0.182 .9632089 1.218068
_rcs3 | .9414184 .0705203 -0.81 0.420 .8128686 1.090297
_rcs4 | 1.044911 .0274747 1.67 0.095 .9924256 1.100172
_rcs5 | 1.046894 .0258323 1.86 0.063 .9974682 1.098768
_rcs6 | 1.034967 .0226789 1.57 0.117 .9914581 1.080385
_rcs7 | 1.007821 .0049258 1.59 0.111 .9982127 1.017522
_rcs_tr_outcome1 | 1.033006 .123699 0.27 0.786 .8169104 1.306266
_rcs_tr_outcome2 | .9751477 .0564617 -0.43 0.664 .8705331 1.092334
_rcs_tr_outcome3 | 1.056893 .0766927 0.76 0.446 .9167784 1.218423
_rcs_tr_outcome4 | .9265319 .0322794 -2.19 0.029 .8653771 .9920084
_rcs_tr_outcome5 | .9684936 .0277175 -1.12 0.263 .9156639 1.024371
_cons | .038475 .0070926 -17.67 0.000 .026808 .0552195
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8946.4478
Iteration 1: log pseudolikelihood = -8904.0288
Iteration 2: log pseudolikelihood = -8889.6632
Iteration 3: log pseudolikelihood = -8887.7172
Iteration 4: log pseudolikelihood = -8887.6581
Iteration 5: log pseudolikelihood = -8887.6581
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8887.6581 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531517 .2871522 2.27 0.023 1.060537 2.211659
_rcs1 | 1.992525 .214095 6.42 0.000 1.614146 2.4596
_rcs2 | 1.098101 .0781707 1.31 0.189 .9550969 1.262516
_rcs3 | .9277366 .0720217 -0.97 0.334 .7967911 1.080202
_rcs4 | 1.054633 .0286932 1.96 0.051 .9998682 1.112397
_rcs5 | 1.038311 .0287395 1.36 0.174 .9834832 1.096195
_rcs6 | 1.034608 .0190357 1.85 0.064 .9979634 1.072598
_rcs7 | 1.013105 .0071768 1.84 0.066 .9991357 1.027269
_rcs_tr_outcome1 | 1.027338 .1149589 0.24 0.810 .8250193 1.279271
_rcs_tr_outcome2 | .9581609 .0676061 -0.61 0.545 .8344093 1.100266
_rcs_tr_outcome3 | 1.085793 .0854919 1.05 0.296 .9305208 1.266975
_rcs_tr_outcome4 | .9307652 .0287714 -2.32 0.020 .8760486 .9888994
_rcs_tr_outcome5 | .963092 .0276675 -1.31 0.191 .9103632 1.018875
_rcs_tr_outcome6 | .9793274 .0144115 -1.42 0.156 .9514848 1.007985
_cons | .0384522 .007053 -17.76 0.000 .0268406 .0550871
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.7973
Iteration 1: log pseudolikelihood = -8906.3749
Iteration 2: log pseudolikelihood = -8882.8785
Iteration 3: log pseudolikelihood = -8880.2383
Iteration 4: log pseudolikelihood = -8880.2065
Iteration 5: log pseudolikelihood = -8880.2064
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8880.2064 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.536683 .2855269 2.31 0.021 1.06764 2.211789
_rcs1 | 2.009543 .2154782 6.51 0.000 1.62864 2.479531
_rcs2 | 1.113114 .0864614 1.38 0.168 .9559216 1.296155
_rcs3 | .919801 .0711759 -1.08 0.280 .7903624 1.070438
_rcs4 | 1.064743 .0279107 2.39 0.017 1.011421 1.120877
_rcs5 | 1.032945 .0296768 1.13 0.259 .976387 1.092779
_rcs6 | 1.041019 .0240589 1.74 0.082 .9949167 1.089258
_rcs7 | 1.006159 .0118337 0.52 0.602 .9832307 1.029622
_rcs_tr_outcome1 | 1.016036 .1112393 0.15 0.884 .8198161 1.25922
_rcs_tr_outcome2 | .9415277 .0745333 -0.76 0.447 .8062137 1.099553
_rcs_tr_outcome3 | 1.103345 .0863663 1.26 0.209 .9464158 1.286295
_rcs_tr_outcome4 | .9261254 .026076 -2.73 0.006 .876402 .9786698
_rcs_tr_outcome5 | .9701515 .0288443 -1.02 0.308 .9152334 1.028365
_rcs_tr_outcome6 | .96277 .0230878 -1.58 0.114 .9185656 1.009102
_rcs_tr_outcome7 | .9979978 .0129364 -0.15 0.877 .9729623 1.023678
_cons | .0383637 .0070097 -17.85 0.000 .0268159 .0548845
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8933.5889
Iteration 1: log pseudolikelihood = -8912.3379
Iteration 2: log pseudolikelihood = -8911.4568
Iteration 3: log pseudolikelihood = -8911.4541
Iteration 4: log pseudolikelihood = -8911.4541
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8911.4541 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.511409 .2915368 2.14 0.032 1.035603 2.205825
_rcs1 | 1.944453 .2224903 5.81 0.000 1.553819 2.433294
_rcs2 | 1.065909 .0389777 1.75 0.081 .9921869 1.145108
_rcs3 | .9735601 .0481458 -0.54 0.588 .883625 1.072649
_rcs4 | 1.014332 .0153491 0.94 0.347 .9846902 1.044866
_rcs5 | 1.014816 .0137078 1.09 0.276 .9883012 1.042041
_rcs6 | 1.017164 .0120947 1.43 0.152 .9937328 1.041148
_rcs7 | 1.016377 .0078464 2.10 0.035 1.001114 1.031872
_rcs8 | .9972455 .006801 -0.40 0.686 .9840045 1.010665
_rcs_tr_outcome1 | 1.067281 .1304827 0.53 0.594 .8398723 1.356264
_cons | .0387632 .0072023 -17.49 0.000 .0269317 .0557925
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.7286
Iteration 1: log pseudolikelihood = -8912.2929
Iteration 2: log pseudolikelihood = -8911.4267
Iteration 3: log pseudolikelihood = -8911.4239
Iteration 4: log pseudolikelihood = -8911.4239
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8911.4239 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.510554 .2906385 2.14 0.032 1.036002 2.202479
_rcs1 | 1.945214 .2267381 5.71 0.000 1.547924 2.444473
_rcs2 | 1.068803 .0443893 1.60 0.109 .9852485 1.159443
_rcs3 | .9745644 .0532381 -0.47 0.637 .8756115 1.0847
_rcs4 | 1.014541 .0150783 0.97 0.331 .9854139 1.044528
_rcs5 | 1.014951 .0133313 1.13 0.259 .9891555 1.041419
_rcs6 | 1.017266 .0119758 1.45 0.146 .9940626 1.041011
_rcs7 | 1.01645 .0077709 2.13 0.033 1.001333 1.031795
_rcs8 | .9972505 .006812 -0.40 0.687 .9839883 1.010691
_rcs_tr_outcome1 | 1.066178 .1350946 0.51 0.613 .8317149 1.366736
_rcs_tr_outcome2 | .9942877 .0505563 -0.11 0.910 .8999767 1.098482
_cons | .0387715 .0071971 -17.51 0.000 .0269467 .0557853
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8933.303
Iteration 1: log pseudolikelihood = -8911.8258
Iteration 2: log pseudolikelihood = -8910.9214
Iteration 3: log pseudolikelihood = -8910.918
Iteration 4: log pseudolikelihood = -8910.918
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8910.918 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.510816 .2921382 2.13 0.033 1.034236 2.207008
_rcs1 | 1.947603 .2351926 5.52 0.000 1.537125 2.467695
_rcs2 | 1.067773 .0469979 1.49 0.136 .9795205 1.163977
_rcs3 | .9811372 .0696817 -0.27 0.789 .8536429 1.127673
_rcs4 | 1.020463 .0243819 0.85 0.397 .9737775 1.069388
_rcs5 | 1.017848 .0172881 1.04 0.298 .9845213 1.052302
_rcs6 | 1.01839 .0119225 1.56 0.120 .9952888 1.042028
_rcs7 | 1.016887 .0078618 2.17 0.030 1.001595 1.032413
_rcs8 | .9973351 .0067613 -0.39 0.694 .9841709 1.010675
_rcs_tr_outcome1 | 1.063595 .1418295 0.46 0.644 .8189721 1.381285
_rcs_tr_outcome2 | .9968481 .048295 -0.07 0.948 .9065469 1.096144
_rcs_tr_outcome3 | .9826268 .0522881 -0.33 0.742 .8853072 1.090645
_cons | .038765 .0072235 -17.44 0.000 .0269045 .0558538
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8933.1483
Iteration 1: log pseudolikelihood = -8896.2958
Iteration 2: log pseudolikelihood = -8893.4923
Iteration 3: log pseudolikelihood = -8893.4883
Iteration 4: log pseudolikelihood = -8893.4883
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8893.4883 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.523386 .2907707 2.21 0.027 1.047949 2.214522
_rcs1 | 1.972713 .2252791 5.95 0.000 1.577098 2.467568
_rcs2 | 1.080792 .0586402 1.43 0.152 .9717595 1.202059
_rcs3 | .9504431 .0687914 -0.70 0.483 .8247412 1.095304
_rcs4 | 1.029365 .0212076 1.40 0.160 .9886273 1.071782
_rcs5 | 1.044419 .0277695 1.63 0.102 .9913853 1.100289
_rcs6 | 1.037344 .0172531 2.20 0.027 1.004074 1.071717
_rcs7 | 1.019283 .0075077 2.59 0.010 1.004674 1.034105
_rcs8 | .997627 .0063635 -0.37 0.710 .9852324 1.010178
_rcs_tr_outcome1 | 1.042905 .1280049 0.34 0.732 .8199161 1.32654
_rcs_tr_outcome2 | .9804173 .0480792 -0.40 0.687 .8905708 1.079328
_rcs_tr_outcome3 | 1.031145 .0672404 0.47 0.638 .9074302 1.171726
_rcs_tr_outcome4 | .9268203 .0317478 -2.22 0.027 .8666386 .9911812
_cons | .0385619 .0071358 -17.59 0.000 .0268315 .0554207
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.4254
Iteration 1: log pseudolikelihood = -8892.2483
Iteration 2: log pseudolikelihood = -8888.0081
Iteration 3: log pseudolikelihood = -8887.957
Iteration 4: log pseudolikelihood = -8887.9569
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8887.9569 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.527582 .2906338 2.23 0.026 1.052101 2.217951
_rcs1 | 1.980819 .220542 6.14 0.000 1.592476 2.463863
_rcs2 | 1.086441 .0682547 1.32 0.187 .9605729 1.228803
_rcs3 | .9405253 .073904 -0.78 0.435 .8062789 1.097124
_rcs4 | 1.031726 .0236097 1.36 0.172 .9864738 1.079053
_rcs5 | 1.045023 .0263912 1.74 0.081 .994556 1.09805
_rcs6 | 1.040595 .0239028 1.73 0.083 .9947848 1.088514
_rcs7 | 1.024868 .0138192 1.82 0.069 .9981373 1.052314
_rcs8 | .9986804 .0059856 -0.22 0.826 .9870175 1.010481
_rcs_tr_outcome1 | 1.03577 .1242329 0.29 0.770 .8187813 1.310263
_rcs_tr_outcome2 | .9732048 .0557471 -0.47 0.635 .8698527 1.088837
_rcs_tr_outcome3 | 1.054547 .0779505 0.72 0.472 .9123184 1.218949
_rcs_tr_outcome4 | .9278864 .0310139 -2.24 0.025 .8690486 .9907077
_rcs_tr_outcome5 | .965528 .0288037 -1.18 0.240 .9106925 1.023665
_cons | .038502 .0071092 -17.64 0.000 .026811 .055291
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8937.5496
Iteration 1: log pseudolikelihood = -8894.1285
Iteration 2: log pseudolikelihood = -8883.4278
Iteration 3: log pseudolikelihood = -8880.9755
Iteration 4: log pseudolikelihood = -8880.879
Iteration 5: log pseudolikelihood = -8880.8788
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8880.8788 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.530306 .2884828 2.26 0.024 1.057586 2.214323
_rcs1 | 1.995769 .2113512 6.53 0.000 1.621692 2.456135
_rcs2 | 1.10827 .086268 1.32 0.187 .9514542 1.290932
_rcs3 | .9217992 .0769124 -0.98 0.329 .7827343 1.085571
_rcs4 | 1.046297 .0278869 1.70 0.090 .9930423 1.102407
_rcs5 | 1.038268 .0246278 1.58 0.113 .9911033 1.087677
_rcs6 | 1.03123 .0210964 1.50 0.133 .9907002 1.073419
_rcs7 | 1.03046 .0130928 2.36 0.018 1.005116 1.056444
_rcs8 | 1.002994 .0057947 0.52 0.605 .9917008 1.014416
_rcs_tr_outcome1 | 1.026022 .1137838 0.23 0.817 .8255816 1.275126
_rcs_tr_outcome2 | .9504242 .0698418 -0.69 0.489 .8229378 1.09766
_rcs_tr_outcome3 | 1.086826 .0905599 1.00 0.318 .9230679 1.279636
_rcs_tr_outcome4 | .9255453 .0300802 -2.38 0.017 .8684276 .9864197
_rcs_tr_outcome5 | .9695758 .0253399 -1.18 0.237 .9211612 1.020535
_rcs_tr_outcome6 | .9740957 .013987 -1.83 0.068 .9470639 1.001899
_cons | .0384561 .0070614 -17.74 0.000 .0268328 .0551143
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8940.091
Iteration 1: log pseudolikelihood = -8887.5684
Iteration 2: log pseudolikelihood = -8872.7779
Iteration 3: log pseudolikelihood = -8872.4545
Iteration 4: log pseudolikelihood = -8872.4539
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8872.4539 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531669 .2848068 2.29 0.022 1.063869 2.20517
_rcs1 | 2.008937 .2049374 6.84 0.000 1.644875 2.453578
_rcs2 | 1.131679 .1064961 1.31 0.189 .9410686 1.360897
_rcs3 | .904537 .0814936 -1.11 0.265 .75812 1.079232
_rcs4 | 1.057342 .0265516 2.22 0.026 1.006561 1.110684
_rcs5 | 1.032524 .0266027 1.24 0.214 .9816781 1.086003
_rcs6 | 1.0386 .0254338 1.55 0.122 .9899283 1.089665
_rcs7 | 1.027542 .0117347 2.38 0.017 1.004798 1.050801
_rcs8 | .9966406 .0099164 -0.34 0.735 .9773931 1.016267
_rcs_tr_outcome1 | 1.019773 .1058285 0.19 0.850 .8320871 1.249793
_rcs_tr_outcome2 | .9269826 .0864015 -0.81 0.416 .7722064 1.112781
_rcs_tr_outcome3 | 1.11619 .1021477 1.20 0.230 .9329126 1.335475
_rcs_tr_outcome4 | .922176 .0264704 -2.82 0.005 .8717273 .9755442
_rcs_tr_outcome5 | .972755 .025937 -1.04 0.300 .9232248 1.024942
_rcs_tr_outcome6 | .9627901 .0215252 -1.70 0.090 .9215124 1.005917
_rcs_tr_outcome7 | .9983457 .010115 -0.16 0.870 .9787163 1.018369
_cons | .0384109 .0069939 -17.90 0.000 .0268824 .0548834
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8933.1693
Iteration 1: log pseudolikelihood = -8907.1371
Iteration 2: log pseudolikelihood = -8905.5256
Iteration 3: log pseudolikelihood = -8905.521
Iteration 4: log pseudolikelihood = -8905.521
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8905.521 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.504849 .2912207 2.11 0.035 1.029834 2.198967
_rcs1 | 1.934392 .2143111 5.96 0.000 1.556825 2.403527
_rcs2 | 1.068412 .0426034 1.66 0.097 .98809 1.155262
_rcs3 | .9719774 .0498496 -0.55 0.579 .8790241 1.07476
_rcs4 | 1.009852 .0144511 0.69 0.493 .9819221 1.038577
_rcs5 | 1.012463 .0130128 0.96 0.335 .9872774 1.038292
_rcs6 | 1.015682 .0097857 1.62 0.106 .9966825 1.035044
_rcs7 | 1.017961 .0114194 1.59 0.113 .9958236 1.040591
_rcs8 | 1.009371 .0052716 1.79 0.074 .9990915 1.019756
_rcs9 | .9914974 .0075172 -1.13 0.260 .976873 1.006341
_rcs_tr_outcome1 | 1.076582 .1275253 0.62 0.533 .8535305 1.357923
_cons | .0388524 .0072208 -17.48 0.000 .026991 .0559263
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.2655
Iteration 1: log pseudolikelihood = -8907.0248
Iteration 2: log pseudolikelihood = -8905.4088
Iteration 3: log pseudolikelihood = -8905.4039
Iteration 4: log pseudolikelihood = -8905.4039
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8905.4039 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.503206 .2907809 2.11 0.035 1.028873 2.196217
_rcs1 | 1.93591 .2198574 5.82 0.000 1.549587 2.418546
_rcs2 | 1.074043 .0463919 1.65 0.098 .9868592 1.168929
_rcs3 | .9739685 .0545578 -0.47 0.638 .872698 1.086991
_rcs4 | 1.010381 .014517 0.72 0.472 .9823254 1.039239
_rcs5 | 1.01268 .0126639 1.01 0.314 .9881605 1.037807
_rcs6 | 1.015895 .0096478 1.66 0.097 .997161 1.034982
_rcs7 | 1.018109 .0113689 1.61 0.108 .9960685 1.040637
_rcs8 | 1.009468 .0052813 1.80 0.072 .9991695 1.019872
_rcs9 | .9914673 .0075272 -1.13 0.259 .9768234 1.006331
_rcs_tr_outcome1 | 1.07434 .1323793 0.58 0.561 .8438343 1.367811
_rcs_tr_outcome2 | .9888308 .0498107 -0.22 0.824 .8958683 1.09144
_cons | .0388678 .0072218 -17.48 0.000 .0270043 .0559431
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.5035
Iteration 1: log pseudolikelihood = -8906.6861
Iteration 2: log pseudolikelihood = -8905.0554
Iteration 3: log pseudolikelihood = -8905.053
Iteration 4: log pseudolikelihood = -8905.053
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8905.053 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.503439 .2922698 2.10 0.036 1.027097 2.200697
_rcs1 | 1.937958 .2278354 5.63 0.000 1.539122 2.440144
_rcs2 | 1.073232 .0500381 1.52 0.130 .9795068 1.175926
_rcs3 | .9788566 .0691606 -0.30 0.762 .8522713 1.124243
_rcs4 | 1.01487 .0251713 0.60 0.552 .9667149 1.065424
_rcs5 | 1.015439 .0172168 0.90 0.366 .9822491 1.04975
_rcs6 | 1.017137 .0106557 1.62 0.105 .996465 1.038238
_rcs7 | 1.018673 .0112902 1.67 0.095 .9967833 1.041044
_rcs8 | 1.0096 .0053047 1.82 0.069 .9992565 1.020051
_rcs9 | .9915346 .0074825 -1.13 0.260 .9769771 1.006309
_rcs_tr_outcome1 | 1.072023 .1390134 0.54 0.592 .8314301 1.382238
_rcs_tr_outcome2 | .9903828 .0475513 -0.20 0.840 .9014348 1.088108
_rcs_tr_outcome3 | .9859598 .0514522 -0.27 0.786 .8901012 1.092142
_cons | .0388617 .0072476 -17.41 0.000 .0269635 .0560104
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.6405
Iteration 1: log pseudolikelihood = -8893.1389
Iteration 2: log pseudolikelihood = -8889.4957
Iteration 3: log pseudolikelihood = -8889.4925
Iteration 4: log pseudolikelihood = -8889.4925
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8889.4925 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.516483 .2915853 2.17 0.030 1.04033 2.210571
_rcs1 | 1.963537 .2183149 6.07 0.000 1.57906 2.441628
_rcs2 | 1.085719 .0613608 1.46 0.146 .9718756 1.212898
_rcs3 | .9501572 .0689142 -0.70 0.481 .824249 1.095299
_rcs4 | 1.018133 .0212135 0.86 0.388 .9773927 1.060571
_rcs5 | 1.037395 .0246437 1.55 0.122 .9902018 1.086838
_rcs6 | 1.039335 .0185502 2.16 0.031 1.003606 1.076336
_rcs7 | 1.027321 .0124898 2.22 0.027 1.00313 1.052094
_rcs8 | 1.008817 .0047477 1.87 0.062 .9995548 1.018166
_rcs9 | .9927179 .0069934 -1.04 0.300 .9791054 1.00652
_rcs_tr_outcome1 | 1.050831 .1254223 0.42 0.678 .8316438 1.327787
_rcs_tr_outcome2 | .9764986 .0483145 -0.48 0.631 .8862504 1.075937
_rcs_tr_outcome3 | 1.030136 .0663784 0.46 0.645 .9079172 1.168808
_rcs_tr_outcome4 | .9308199 .0303046 -2.20 0.028 .8732793 .9921519
_cons | .0386541 .0071701 -17.54 0.000 .0268722 .0556018
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8932.0097
Iteration 1: log pseudolikelihood = -8889.741
Iteration 2: log pseudolikelihood = -8884.2273
Iteration 3: log pseudolikelihood = -8884.1642
Iteration 4: log pseudolikelihood = -8884.1641
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8884.1641 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.520289 .2920201 2.18 0.029 1.04334 2.21527
_rcs1 | 1.971254 .2144436 6.24 0.000 1.592738 2.439725
_rcs2 | 1.092181 .0720978 1.34 0.182 .959631 1.243039
_rcs3 | .9397135 .0755236 -0.77 0.439 .8027595 1.100032
_rcs4 | 1.020041 .0220488 0.92 0.359 .9777285 1.064184
_rcs5 | 1.039227 .0253418 1.58 0.115 .9907264 1.090102
_rcs6 | 1.041038 .0203503 2.06 0.040 1.001907 1.081698
_rcs7 | 1.032433 .0215945 1.53 0.127 .9909648 1.075637
_rcs8 | 1.011636 .0053666 2.18 0.029 1.001172 1.022209
_rcs9 | .9930104 .0067719 -1.03 0.304 .9798261 1.006372
_rcs_tr_outcome1 | 1.044291 .1228187 0.37 0.713 .8293004 1.315016
_rcs_tr_outcome2 | .969214 .0559695 -0.54 0.588 .8654961 1.085361
_rcs_tr_outcome3 | 1.053676 .0785597 0.70 0.483 .9104238 1.219469
_rcs_tr_outcome4 | .9300813 .0309994 -2.17 0.030 .8712655 .9928674
_rcs_tr_outcome5 | .9675738 .0292384 -1.09 0.275 .9119316 1.026611
_cons | .038599 .0071502 -17.57 0.000 .0268471 .0554952
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8934.3238
Iteration 1: log pseudolikelihood = -8887.6872
Iteration 2: log pseudolikelihood = -8877.2458
Iteration 3: log pseudolikelihood = -8876.2304
Iteration 4: log pseudolikelihood = -8876.2148
Iteration 5: log pseudolikelihood = -8876.2147
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8876.2147 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.522127 .2901272 2.20 0.028 1.047626 2.211543
_rcs1 | 1.983535 .2050704 6.62 0.000 1.61971 2.429084
_rcs2 | 1.114465 .0916737 1.32 0.188 .948524 1.309437
_rcs3 | .9187734 .0810232 -0.96 0.337 .772937 1.092126
_rcs4 | 1.031585 .0248755 1.29 0.197 .9839644 1.081511
_rcs5 | 1.038111 .0229979 1.69 0.091 .9940009 1.084179
_rcs6 | 1.03054 .0194637 1.59 0.111 .9930891 1.069403
_rcs7 | 1.034851 .0186798 1.90 0.058 .9988796 1.072118
_rcs8 | 1.019753 .0079818 2.50 0.012 1.004229 1.035518
_rcs9 | .9949357 .0063936 -0.79 0.429 .982483 1.007546
_rcs_tr_outcome1 | 1.036422 .1140203 0.33 0.745 .8353977 1.28582
_rcs_tr_outcome2 | .9477896 .0696376 -0.73 0.465 .8206746 1.094593
_rcs_tr_outcome3 | 1.088419 .0947164 0.97 0.330 .9177471 1.290831
_rcs_tr_outcome4 | .9287423 .0292059 -2.35 0.019 .8732281 .9877857
_rcs_tr_outcome5 | .9679391 .0244871 -1.29 0.198 .9211157 1.017143
_rcs_tr_outcome6 | .970404 .0160726 -1.81 0.070 .939408 1.002423
_cons | .0385676 .0071058 -17.67 0.000 .0268778 .0553415
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8935.43
Iteration 1: log pseudolikelihood = -8880.7072
Iteration 2: log pseudolikelihood = -8870.5287
Iteration 3: log pseudolikelihood = -8870.3578
Iteration 4: log pseudolikelihood = -8870.3577
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8870.3577 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.524488 .2891617 2.22 0.026 1.051163 2.210946
_rcs1 | 2.000725 .1994098 6.96 0.000 1.645695 2.432348
_rcs2 | 1.144006 .1191769 1.29 0.197 .9327269 1.403144
_rcs3 | .8965231 .0897866 -1.09 0.275 .7367393 1.090961
_rcs4 | 1.044764 .0243776 1.88 0.061 .9980611 1.093653
_rcs5 | 1.034155 .0235524 1.47 0.140 .9890084 1.081363
_rcs6 | 1.031431 .0211873 1.51 0.132 .9907294 1.073805
_rcs7 | 1.036589 .0216976 1.72 0.086 .9949233 1.08
_rcs8 | 1.013064 .008765 1.50 0.134 .9960298 1.03039
_rcs9 | .9939894 .0072604 -0.83 0.409 .9798607 1.008322
_rcs_tr_outcome1 | 1.027787 .1071389 0.26 0.793 .8378612 1.260766
_rcs_tr_outcome2 | .9202041 .0902319 -0.85 0.396 .7593088 1.115193
_rcs_tr_outcome3 | 1.122724 .1141172 1.14 0.255 .9199283 1.370224
_rcs_tr_outcome4 | .9220531 .0262747 -2.85 0.004 .8719673 .9750159
_rcs_tr_outcome5 | .9750937 .0246852 -1.00 0.319 .9278923 1.024696
_rcs_tr_outcome6 | .9640638 .022822 -1.55 0.122 .9203552 1.009848
_rcs_tr_outcome7 | .9935012 .0111454 -0.58 0.561 .971895 1.015588
_cons | .0385062 .007066 -17.75 0.000 .026874 .0551734
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.2468
Iteration 1: log pseudolikelihood = -8911.5453
Iteration 2: log pseudolikelihood = -8910.0023
Iteration 3: log pseudolikelihood = -8909.9996
Iteration 4: log pseudolikelihood = -8909.9996
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8909.9996 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.509107 .2914879 2.13 0.033 1.033495 2.203595
_rcs1 | 1.940155 .2190866 5.87 0.000 1.554951 2.420784
_rcs2 | 1.065675 .0370476 1.83 0.067 .9954812 1.140818
_rcs3 | .9766767 .0443455 -0.52 0.603 .8935163 1.067577
_rcs4 | 1.003747 .0152714 0.25 0.806 .9742577 1.034129
_rcs5 | 1.013124 .0126174 1.05 0.295 .9886932 1.038158
_rcs6 | 1.013113 .0100587 1.31 0.189 .9935888 1.033021
_rcs7 | 1.014557 .0108064 1.36 0.175 .9935965 1.03596
_rcs8 | 1.01708 .008366 2.06 0.040 1.000814 1.03361
_rcs9 | 1.002142 .004633 0.46 0.643 .9931026 1.011264
_rcs10 | .9935982 .0064337 -0.99 0.321 .9810682 1.006288
_rcs_tr_outcome1 | 1.06975 .1279249 0.56 0.573 .8462375 1.352298
_cons | .0388019 .0072173 -17.47 0.000 .026948 .0558703
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8935.3801
Iteration 1: log pseudolikelihood = -8911.3573
Iteration 2: log pseudolikelihood = -8909.9012
Iteration 3: log pseudolikelihood = -8909.8984
Iteration 4: log pseudolikelihood = -8909.8984
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8909.8984 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.507574 .2911479 2.13 0.034 1.032504 2.201231
_rcs1 | 1.94164 .2242846 5.74 0.000 1.54826 2.434969
_rcs2 | 1.071079 .0424314 1.73 0.083 .9910615 1.157557
_rcs3 | .9783674 .0490413 -0.44 0.663 .8868189 1.079367
_rcs4 | 1.004457 .0159892 0.28 0.780 .973603 1.03629
_rcs5 | 1.013262 .0123549 1.08 0.280 .9893334 1.037768
_rcs6 | 1.013346 .0097888 1.37 0.170 .9943413 1.032715
_rcs7 | 1.014685 .0107422 1.38 0.168 .993848 1.03596
_rcs8 | 1.017223 .0083178 2.09 0.037 1.00105 1.033657
_rcs9 | 1.002183 .0046443 0.47 0.638 .9931213 1.011327
_rcs10 | .9935652 .0064419 -1.00 0.319 .9810191 1.006272
_rcs_tr_outcome1 | 1.067663 .1324206 0.53 0.598 .8372614 1.361468
_rcs_tr_outcome2 | .9895751 .0490417 -0.21 0.833 .8979757 1.090518
_cons | .0388165 .0072191 -17.47 0.000 .0269594 .0558886
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8935.4591
Iteration 1: log pseudolikelihood = -8910.9695
Iteration 2: log pseudolikelihood = -8909.492
Iteration 3: log pseudolikelihood = -8909.4891
Iteration 4: log pseudolikelihood = -8909.4891
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8909.4891 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.507821 .2926463 2.12 0.034 1.030728 2.205748
_rcs1 | 1.943819 .2325817 5.55 0.000 1.537475 2.457558
_rcs2 | 1.069922 .0456444 1.58 0.113 .9840985 1.16323
_rcs3 | .9836335 .0633822 -0.26 0.798 .8669312 1.116046
_rcs4 | 1.009413 .029784 0.32 0.751 .9526933 1.069509
_rcs5 | 1.01657 .0168305 0.99 0.321 .9841123 1.050098
_rcs6 | 1.01492 .0117982 1.27 0.203 .9920578 1.03831
_rcs7 | 1.015504 .0105616 1.48 0.139 .9950127 1.036416
_rcs8 | 1.017605 .0084377 2.10 0.035 1.001201 1.034278
_rcs9 | 1.002253 .0046056 0.49 0.624 .9932668 1.011321
_rcs10 | .9936348 .006416 -0.99 0.323 .9811389 1.00629
_rcs_tr_outcome1 | 1.065232 .1392411 0.48 0.629 .8244795 1.376285
_rcs_tr_outcome2 | .9914684 .0465022 -0.18 0.855 .9043895 1.086932
_rcs_tr_outcome3 | .9846906 .0523802 -0.29 0.772 .8871978 1.092897
_cons | .0388101 .0072453 -17.40 0.000 .0269177 .0559567
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8935.4588
Iteration 1: log pseudolikelihood = -8896.5913
Iteration 2: log pseudolikelihood = -8893.2497
Iteration 3: log pseudolikelihood = -8893.2464
Iteration 4: log pseudolikelihood = -8893.2464
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8893.2464 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.521173 .2911839 2.19 0.028 1.0453 2.213687
_rcs1 | 1.970127 .2222604 6.01 0.000 1.579303 2.457665
_rcs2 | 1.083654 .0579311 1.50 0.133 .975857 1.203358
_rcs3 | .9541826 .0640789 -0.70 0.485 .8365046 1.088415
_rcs4 | 1.00956 .0253036 0.38 0.704 .9611642 1.060392
_rcs5 | 1.036596 .0225626 1.65 0.099 .9933045 1.081775
_rcs6 | 1.036962 .0204738 1.84 0.066 .9976006 1.077876
_rcs7 | 1.030075 .0142427 2.14 0.032 1.002535 1.058372
_rcs8 | 1.020391 .0080748 2.55 0.011 1.004687 1.036341
_rcs9 | 1.001904 .0044963 0.42 0.672 .9931303 1.010756
_rcs10 | .9947523 .0058505 -0.89 0.371 .9833513 1.006286
_rcs_tr_outcome1 | 1.043786 .125134 0.36 0.721 .8252121 1.320255
_rcs_tr_outcome2 | .97738 .0481133 -0.46 0.642 .887486 1.076379
_rcs_tr_outcome3 | 1.029096 .0661239 0.45 0.655 .907324 1.167211
_rcs_tr_outcome4 | .9296159 .0309896 -2.19 0.029 .8708191 .9923826
_cons | .0385983 .0071571 -17.55 0.000 .026837 .055514
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8935.3414
Iteration 1: log pseudolikelihood = -8893.1782
Iteration 2: log pseudolikelihood = -8887.9943
Iteration 3: log pseudolikelihood = -8887.9144
Iteration 4: log pseudolikelihood = -8887.9142
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8887.9142 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.525425 .2912712 2.21 0.027 1.049201 2.217803
_rcs1 | 1.978395 .2178378 6.20 0.000 1.594368 2.454921
_rcs2 | 1.090054 .0684799 1.37 0.170 .9637704 1.232885
_rcs3 | .9438671 .0704278 -0.77 0.439 .8154502 1.092507
_rcs4 | 1.01049 .0245465 0.43 0.668 .9635069 1.059764
_rcs5 | 1.038539 .0243997 1.61 0.107 .9918007 1.08748
_rcs6 | 1.037868 .0200264 1.93 0.054 .9993496 1.07787
_rcs7 | 1.033667 .0215441 1.59 0.112 .9922926 1.076767
_rcs8 | 1.025346 .0139731 1.84 0.066 .9983212 1.053101
_rcs9 | 1.003246 .0041775 0.78 0.436 .995092 1.011468
_rcs10 | .9949194 .0056096 -0.90 0.366 .9839853 1.005975
_rcs_tr_outcome1 | 1.036733 .1219013 0.31 0.759 .8233427 1.305428
_rcs_tr_outcome2 | .9706267 .0555371 -0.52 0.602 .8676575 1.085816
_rcs_tr_outcome3 | 1.052004 .0764241 0.70 0.485 .9123905 1.212981
_rcs_tr_outcome4 | .9292597 .0312592 -2.18 0.029 .8699688 .9925916
_rcs_tr_outcome5 | .9668153 .0294889 -1.11 0.269 .9107118 1.026375
_cons | .0385379 .007133 -17.59 0.000 .0268126 .0553907
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.749
Iteration 1: log pseudolikelihood = -8892.4519
Iteration 2: log pseudolikelihood = -8881.7993
Iteration 3: log pseudolikelihood = -8880.217
Iteration 4: log pseudolikelihood = -8880.1864
Iteration 5: log pseudolikelihood = -8880.1864
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8880.1864 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.527887 .2890828 2.24 0.025 1.054484 2.21382
_rcs1 | 1.991427 .2071767 6.62 0.000 1.624091 2.441848
_rcs2 | 1.112629 .0889092 1.34 0.182 .9513313 1.301276
_rcs3 | .9217081 .0768468 -0.98 0.328 .7827536 1.08533
_rcs4 | 1.020216 .0266595 0.77 0.444 .9692797 1.073829
_rcs5 | 1.040194 .0230492 1.78 0.075 .9959855 1.086365
_rcs6 | 1.027847 .0195411 1.44 0.149 .9902517 1.066869
_rcs7 | 1.029413 .0182619 1.63 0.102 .9942354 1.065835
_rcs8 | 1.032553 .0144939 2.28 0.022 1.004533 1.061355
_rcs9 | 1.009478 .005458 1.74 0.081 .9988368 1.020232
_rcs10 | .9955656 .0055349 -0.80 0.424 .9847763 1.006473
_rcs_tr_outcome1 | 1.028188 .1124327 0.25 0.799 .8298384 1.273948
_rcs_tr_outcome2 | .9502466 .0688178 -0.70 0.481 .8245016 1.095169
_rcs_tr_outcome3 | 1.086318 .0913629 0.98 0.325 .9212302 1.28099
_rcs_tr_outcome4 | .9278224 .030119 -2.31 0.021 .8706291 .9887729
_rcs_tr_outcome5 | .9673959 .0258506 -1.24 0.215 .9180335 1.019412
_rcs_tr_outcome6 | .9692895 .0168431 -1.80 0.073 .9368334 1.00287
_cons | .0384999 .007083 -17.70 0.000 .0268448 .0552154
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -8936.6982
Iteration 1: log pseudolikelihood = -8886.1339
Iteration 2: log pseudolikelihood = -8875.9822
Iteration 3: log pseudolikelihood = -8875.6979
Iteration 4: log pseudolikelihood = -8875.6965
Iteration 5: log pseudolikelihood = -8875.6965
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -8875.6965 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.528427 .287622 2.25 0.024 1.056973 2.210168
_rcs1 | 2.004723 .2006572 6.95 0.000 1.647615 2.439232
_rcs2 | 1.141277 .1171394 1.29 0.198 .9333069 1.395588
_rcs3 | .8987834 .0883035 -1.09 0.277 .7413552 1.089642
_rcs4 | 1.03101 .0259248 1.21 0.225 .9814303 1.083095
_rcs5 | 1.038387 .0225289 1.74 0.083 .9951566 1.083495
_rcs6 | 1.027393 .0214753 1.29 0.196 .9861532 1.070358
_rcs7 | 1.033632 .0230775 1.48 0.138 .9893766 1.079868
_rcs8 | 1.028801 .0122653 2.38 0.017 1.00504 1.053123
_rcs9 | 1.004262 .0084489 0.51 0.613 .987838 1.020959
_rcs10 | .9958908 .0055613 -0.74 0.461 .9850503 1.006851
_rcs_tr_outcome1 | 1.023375 .1069048 0.22 0.825 .833904 1.255897
_rcs_tr_outcome2 | .9248835 .0880627 -0.82 0.412 .767432 1.114639
_rcs_tr_outcome3 | 1.120282 .1124544 1.13 0.258 .9202029 1.363865
_rcs_tr_outcome4 | .9230275 .0276147 -2.68 0.007 .8704599 .9787697
_rcs_tr_outcome5 | .9724697 .026485 -1.03 0.305 .9219212 1.02579
_rcs_tr_outcome6 | .963669 .0230706 -1.55 0.122 .9194959 1.009964
_rcs_tr_outcome7 | .9929624 .0117555 -0.60 0.551 .9701873 1.016272
_cons | .0384622 .0070392 -17.80 0.000 .026869 .0550576
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
.
. *https://core.ac.uk/download/pdf/6990318.pdf
.
. *The following options are not permitted with streg models:
. *bknots, bknotstvc, df, dftvc, failconvlininit, knots, knotstvc knscale, noorthorg, eform, alleq, keepcons, showcons, lininit
. *forvalues j=1/7 {
. local vars "exponential weibull gompertz lognormal loglogistic"
. local varslab "exp wei gom logn llog"
. forvalues i = 1/5 {
2. local v : word `i' of `vars'
3. local v2 : word `i' of `varslab'
4. qui noi stipw (logit tr_outcome tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 cond_ocu3 cond_
> ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini4 susini5 an
> o_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3), distribution(`v') genw(`v2'_m2_nostag) ipwtype(stabilised) vce(mestimation)
5. estimates store m2_stipw_nostag_`v2'
6. }
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=exp_m2_nostag]
Iteration 0: log pseudolikelihood = -9148.668
Iteration 1: log pseudolikelihood = -9113.2853
Iteration 2: log pseudolikelihood = -9112.9226
Iteration 3: log pseudolikelihood = -9112.9226
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 29,848
Wald chi2(1) = 6.10
Log pseudolikelihood = -9112.9226 Prob > chi2 = 0.0135
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.496847 .2444073 2.47 0.013 1.086906 2.061403
_cons | .0123673 .0019758 -27.50 0.000 .0090424 .0169148
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=wei_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -9148.668
Iteration 1: log pseudolikelihood = -8991.3425
Iteration 2: log pseudolikelihood = -8988.474
Iteration 3: log pseudolikelihood = -8988.4728
Iteration 4: log pseudolikelihood = -8988.4728
Fitting full model:
Iteration 0: log pseudolikelihood = -8988.4728
Iteration 1: log pseudolikelihood = -8947.0345
Iteration 2: log pseudolikelihood = -8946.5325
Iteration 3: log pseudolikelihood = -8946.5324
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 29,848
Wald chi2(1) = 7.51
Log pseudolikelihood = -8946.5324 Prob > chi2 = 0.0061
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.548238 .2470091 2.74 0.006 1.132492 2.116606
_cons | .0193581 .0038433 -19.87 0.000 .013118 .0285665
-------------+----------------------------------------------------------------
/ln_p | -.3661123 .0692198 -5.29 0.000 -.5017807 -.2304439
-------------+----------------------------------------------------------------
p | .6934249 .0479988 .6054516 .794181
1/p | 1.442117 .0998231 1.259159 1.65166
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=gom_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -9149.5952
Iteration 1: log pseudolikelihood = -9018.1402
Iteration 2: log pseudolikelihood = -9012.0497
Iteration 3: log pseudolikelihood = -9012.0372
Iteration 4: log pseudolikelihood = -9012.0372
Fitting full model:
Iteration 0: log pseudolikelihood = -9012.0372
Iteration 1: log pseudolikelihood = -8969.5158
Iteration 2: log pseudolikelihood = -8968.9852
Iteration 3: log pseudolikelihood = -8968.9851
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 29,848
Wald chi2(1) = 7.72
Log pseudolikelihood = -8968.9851 Prob > chi2 = 0.0055
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.557284 .2482161 2.78 0.005 1.139448 2.128341
_cons | .0203359 .0041443 -19.11 0.000 .0136394 .0303201
-------------+----------------------------------------------------------------
/gamma | -.2241965 .0373639 -6.00 0.000 -.2974284 -.1509647
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=logn_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -16061.257
Iteration 1: log pseudolikelihood = -9150.744
Iteration 2: log pseudolikelihood = -8997.2209
Iteration 3: log pseudolikelihood = -8979.6385
Iteration 4: log pseudolikelihood = -8979.5634
Iteration 5: log pseudolikelihood = -8979.5634
Fitting full model:
Iteration 0: log pseudolikelihood = -8979.5634
Iteration 1: log pseudolikelihood = -8941.2767
Iteration 2: log pseudolikelihood = -8939.6112
Iteration 3: log pseudolikelihood = -8939.6041
Iteration 4: log pseudolikelihood = -8939.6041
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 29,848
Wald chi2(1) = 7.32
Log pseudolikelihood = -8939.6041 Prob > chi2 = 0.0068
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5040207 .1275951 -2.71 0.007 .3068759 .8278162
_cons | 902.7814 312.9514 19.63 0.000 457.6268 1780.958
-------------+----------------------------------------------------------------
/lnsigma | 1.197002 .0678138 17.65 0.000 1.064089 1.329914
-------------+----------------------------------------------------------------
sigma | 3.310177 .2244757 2.898198 3.78072
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
5234 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -12240.752
Iteration 2: log likelihood = -12032.309
Iteration 3: log likelihood = -12026.884
Iteration 4: log likelihood = -12026.862
Iteration 5: log likelihood = -12026.862
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -20529.655
Iteration 1: log likelihood = -20529.655
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=llog_m2_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -9143.7253
Iteration 1: log pseudolikelihood = -9005.9222
Iteration 2: log pseudolikelihood = -8987.5896
Iteration 3: log pseudolikelihood = -8987.2469
Iteration 4: log pseudolikelihood = -8987.2468
Fitting full model:
Iteration 0: log pseudolikelihood = -8987.2468
Iteration 1: log pseudolikelihood = -8947.7877
Iteration 2: log pseudolikelihood = -8945.3698
Iteration 3: log pseudolikelihood = -8945.3665
Iteration 4: log pseudolikelihood = -8945.3665
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 29,848
Wald chi2(1) = 8.51
Log pseudolikelihood = -8945.3665 Prob > chi2 = 0.0035
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5279311 .1156238 -2.92 0.004 .343679 .810964
_cons | 258.6234 71.66766 20.05 0.000 150.2414 445.1906
-------------+----------------------------------------------------------------
/lngamma | .3446507 .0696588 4.95 0.000 .208122 .4811794
-------------+----------------------------------------------------------------
gamma | 1.411497 .0983231 1.231363 1.617982
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
. *}
. *
. *Just a workaround: I dropped the colinear variables from the regressions manually. I know this sounds like a solution, but it was an issue because I was looping over subsamples, so I didn't know what would be col
> inear before running.
.
.
. qui count if _d == 1
. // we count the amount of cases with the event in the strata
. //we call the estimates stored, and the results...
. estimates stat m2_stipw_nostag_*, n(`r(N)')
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
m2_stipw_n~1 | 2,378 . -8946.125 4 17900.25 17923.35
m2_stipw_n~2 | 2,378 . -8940.919 5 17891.84 17920.71
m2_stipw_n~3 | 2,378 . -8940.696 6 17893.39 17928.04
m2_stipw_n~4 | 2,378 . -8940.588 7 17895.18 17935.59
m2_stipw_n~5 | 2,378 . -8940.073 8 17896.15 17942.34
m2_stipw_n~6 | 2,378 . -8939.474 9 17896.95 17948.91
m2_stipw_n~7 | 2,378 . -8938.755 10 17897.51 17955.25
m2_stipw_n~1 | 2,378 . -8935.838 5 17881.68 17910.55
m2_stipw_n~2 | 2,378 . -8935.834 6 17883.67 17918.31
m2_stipw_n~3 | 2,378 . -8935.575 7 17885.15 17925.57
m2_stipw_n~4 | 2,378 . -8935.503 8 17887.01 17933.2
m2_stipw_n~5 | 2,378 . -8934.976 9 17887.95 17939.92
m2_stipw_n~6 | 2,378 . -8934.388 10 17888.78 17946.52
m2_stipw_n~7 | 2,378 . -8933.659 11 17889.32 17952.83
m2_stipw_n~1 | 2,378 . -8935.111 6 17882.22 17916.87
m2_stipw_n~2 | 2,378 . -8935.105 7 17884.21 17924.63
m2_stipw_n~3 | 2,378 . -8935.071 8 17886.14 17932.33
m2_stipw_n~4 | 2,378 . -8934.933 9 17887.87 17939.83
m2_stipw_n~5 | 2,378 . -8934.425 10 17888.85 17946.59
m2_stipw_n~6 | 2,378 . -8933.848 11 17889.7 17953.21
m2_stipw_n~7 | 2,378 . -8933.14 12 17890.28 17959.57
m2_stipw_n~1 | 2,378 . -8924.273 7 17862.55 17902.96
m2_stipw_n~2 | 2,378 . -8924.27 8 17864.54 17910.73
m2_stipw_n~3 | 2,378 . -8923.967 9 17865.93 17917.9
m2_stipw_n~4 | 2,378 . -8905.45 10 17830.9 17888.64
m2_stipw_n~5 | 2,378 . -8906.324 11 17834.65 17898.16
m2_stipw_n~6 | 2,378 . -8903.598 12 17831.2 17900.48
m2_stipw_n~7 | 2,378 . -8904.127 13 17834.25 17909.32
m2_stipw_n~1 | 2,378 . -8918.653 8 17853.31 17899.5
m2_stipw_n~2 | 2,378 . -8918.613 9 17855.23 17907.19
m2_stipw_n~3 | 2,378 . -8918.276 10 17856.55 17914.29
m2_stipw_n~4 | 2,378 . -8897.284 11 17816.57 17880.08
m2_stipw_n~5 | 2,378 . -8895.213 12 17814.43 17883.71
m2_stipw_n~6 | 2,378 . -8894.439 13 17814.88 17889.94
m2_stipw_n~7 | 2,378 . -8893.901 14 17815.8 17896.64
m2_stipw_n~1 | 2,378 . -8915.573 9 17849.15 17901.11
m2_stipw_n~2 | 2,378 . -8915.547 10 17851.09 17908.83
m2_stipw_n~3 | 2,378 . -8915.104 11 17852.21 17915.72
m2_stipw_n~4 | 2,378 . -8897.839 12 17819.68 17888.97
m2_stipw_n~5 | 2,378 . -8893.431 13 17812.86 17887.92
m2_stipw_n~6 | 2,378 . -8886.296 14 17800.59 17881.43
m2_stipw_n~7 | 2,378 . -8885.471 15 17800.94 17887.55
m2_stipw_n~1 | 2,378 . -8916.87 10 17853.74 17911.48
m2_stipw_n~2 | 2,378 . -8916.864 11 17855.73 17919.24
m2_stipw_n~3 | 2,378 . -8916.433 12 17856.87 17926.15
m2_stipw_n~4 | 2,378 . -8897.054 13 17820.11 17895.17
m2_stipw_n~5 | 2,378 . -8892.999 14 17814 17894.84
m2_stipw_n~6 | 2,378 . -8887.658 15 17805.32 17891.93
m2_stipw_n~7 | 2,378 . -8880.206 16 17792.41 17884.8
m2_stipw_n~1 | 2,378 . -8911.454 11 17844.91 17908.42
m2_stipw_n~2 | 2,378 . -8911.424 12 17846.85 17916.14
m2_stipw_n~3 | 2,378 . -8910.918 13 17847.84 17922.9
m2_stipw_n~4 | 2,378 . -8893.488 14 17814.98 17895.81
m2_stipw_n~5 | 2,378 . -8887.957 15 17805.91 17892.52
m2_stipw_n~6 | 2,378 . -8880.879 16 17793.76 17886.14
m2_stipw_n~7 | 2,378 . -8872.454 17 17778.91 17877.07
m2_stipw_n~1 | 2,378 . -8905.521 12 17835.04 17904.33
m2_stipw_n~2 | 2,378 . -8905.404 13 17836.81 17911.87
m2_stipw_n~3 | 2,378 . -8905.053 14 17838.11 17918.94
m2_stipw_n~4 | 2,378 . -8889.492 15 17808.98 17895.6
m2_stipw_n~5 | 2,378 . -8884.164 16 17800.33 17892.71
m2_stipw_n~6 | 2,378 . -8876.215 17 17786.43 17884.59
m2_stipw_n~7 | 2,378 . -8870.358 18 17776.72 17880.65
m2_stipw_n~1 | 2,378 . -8910 13 17846 17921.06
m2_stipw_n~2 | 2,378 . -8909.898 14 17847.8 17928.63
m2_stipw_n~3 | 2,378 . -8909.489 15 17848.98 17935.59
m2_stipw_n~4 | 2,378 . -8893.246 16 17818.49 17910.88
m2_stipw_n~5 | 2,378 . -8887.914 17 17809.83 17907.99
m2_stipw_n~6 | 2,378 . -8880.186 18 17796.37 17900.31
m2_stipw_n~7 | 2,378 . -8875.697 19 17789.39 17899.1
m2_stipw_n~p | 2,378 -9148.668 -9112.923 2 18229.85 18241.39
m2_stipw_n~i | 2,378 -8988.473 -8946.532 3 17899.06 17916.39
m2_stipw_n~m | 2,378 -9012.037 -8968.985 3 17943.97 17961.29
m2_stipw_n~n | 2,378 -8979.563 -8939.604 3 17885.21 17902.53
m2_stipw_n~g | 2,378 -8987.247 -8945.367 3 17896.73 17914.06
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_3=r(S)
. mata : st_sort_matrix("stats_3", 5) // 5 AIC, 6 BIC
. esttab matrix(stats_3) using "testreg_aic_bic_mrl_23_3_pris.csv", replace
(output written to testreg_aic_bic_mrl_23_3_pris.csv)
. esttab matrix(stats_3) using "testreg_aic_bic_mrl_23_3_pris.html", replace
(output written to testreg_aic_bic_mrl_23_3_pris.html)
.
. *
.
| stats_3 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m2_stipw_nostag_rp9_tvcdf7 | 2378 | . | -8870.358 | 18 | 17776.72 | 17880.65 |
| m2_stipw_nostag_rp8_tvcdf7 | 2378 | . | -8872.454 | 17 | 17778.91 | 17877.07 |
| m2_stipw_nostag_rp9_tvcdf6 | 2378 | . | -8876.215 | 17 | 17786.43 | 17884.59 |
| m2_stipw_nostag_rp10_tvcdf7 | 2378 | . | -8875.697 | 19 | 17789.39 | 17899.1 |
| m2_stipw_nostag_rp7_tvcdf7 | 2378 | . | -8880.206 | 16 | 17792.41 | 17884.8 |
| m2_stipw_nostag_rp8_tvcdf6 | 2378 | . | -8880.879 | 16 | 17793.76 | 17886.14 |
| m2_stipw_nostag_rp10_tvcdf6 | 2378 | . | -8880.186 | 18 | 17796.37 | 17900.31 |
| m2_stipw_nostag_rp9_tvcdf5 | 2378 | . | -8884.164 | 16 | 17800.33 | 17892.71 |
| m2_stipw_nostag_rp6_tvcdf6 | 2378 | . | -8886.296 | 14 | 17800.59 | 17881.43 |
| m2_stipw_nostag_rp6_tvcdf7 | 2378 | . | -8885.471 | 15 | 17800.94 | 17887.55 |
| m2_stipw_nostag_rp7_tvcdf6 | 2378 | . | -8887.658 | 15 | 17805.32 | 17891.93 |
| m2_stipw_nostag_rp8_tvcdf5 | 2378 | . | -8887.957 | 15 | 17805.91 | 17892.52 |
| m2_stipw_nostag_rp9_tvcdf4 | 2378 | . | -8889.492 | 15 | 17808.98 | 17895.6 |
| m2_stipw_nostag_rp10_tvcdf5 | 2378 | . | -8887.914 | 17 | 17809.83 | 17907.99 |
| m2_stipw_nostag_rp6_tvcdf5 | 2378 | . | -8893.431 | 13 | 17812.86 | 17887.92 |
| m2_stipw_nostag_rp7_tvcdf5 | 2378 | . | -8892.999 | 14 | 17814 | 17894.84 |
| m2_stipw_nostag_rp5_tvcdf5 | 2378 | . | -8895.213 | 12 | 17814.43 | 17883.71 |
| m2_stipw_nostag_rp5_tvcdf6 | 2378 | . | -8894.439 | 13 | 17814.88 | 17889.94 |
| m2_stipw_nostag_rp8_tvcdf4 | 2378 | . | -8893.488 | 14 | 17814.98 | 17895.81 |
| m2_stipw_nostag_rp5_tvcdf7 | 2378 | . | -8893.901 | 14 | 17815.8 | 17896.64 |
| m2_stipw_nostag_rp5_tvcdf4 | 2378 | . | -8897.284 | 11 | 17816.57 | 17880.08 |
| m2_stipw_nostag_rp10_tvcdf4 | 2378 | . | -8893.246 | 16 | 17818.49 | 17910.88 |
| m2_stipw_nostag_rp6_tvcdf4 | 2378 | . | -8897.839 | 12 | 17819.68 | 17888.97 |
| m2_stipw_nostag_rp7_tvcdf4 | 2378 | . | -8897.054 | 13 | 17820.11 | 17895.17 |
| m2_stipw_nostag_rp4_tvcdf4 | 2378 | . | -8905.45 | 10 | 17830.9 | 17888.64 |
| m2_stipw_nostag_rp4_tvcdf6 | 2378 | . | -8903.598 | 12 | 17831.2 | 17900.48 |
| m2_stipw_nostag_rp4_tvcdf7 | 2378 | . | -8904.127 | 13 | 17834.25 | 17909.32 |
| m2_stipw_nostag_rp4_tvcdf5 | 2378 | . | -8906.324 | 11 | 17834.65 | 17898.16 |
| m2_stipw_nostag_rp9_tvcdf1 | 2378 | . | -8905.521 | 12 | 17835.04 | 17904.33 |
| m2_stipw_nostag_rp9_tvcdf2 | 2378 | . | -8905.404 | 13 | 17836.81 | 17911.87 |
| m2_stipw_nostag_rp9_tvcdf3 | 2378 | . | -8905.053 | 14 | 17838.11 | 17918.94 |
| m2_stipw_nostag_rp8_tvcdf1 | 2378 | . | -8911.454 | 11 | 17844.91 | 17908.42 |
| m2_stipw_nostag_rp10_tvcdf1 | 2378 | . | -8910 | 13 | 17846 | 17921.06 |
| m2_stipw_nostag_rp8_tvcdf2 | 2378 | . | -8911.424 | 12 | 17846.85 | 17916.14 |
| m2_stipw_nostag_rp10_tvcdf2 | 2378 | . | -8909.898 | 14 | 17847.8 | 17928.63 |
| m2_stipw_nostag_rp8_tvcdf3 | 2378 | . | -8910.918 | 13 | 17847.84 | 17922.9 |
| m2_stipw_nostag_rp10_tvcdf3 | 2378 | . | -8909.489 | 15 | 17848.98 | 17935.59 |
| m2_stipw_nostag_rp6_tvcdf1 | 2378 | . | -8915.573 | 9 | 17849.15 | 17901.11 |
| m2_stipw_nostag_rp6_tvcdf2 | 2378 | . | -8915.547 | 10 | 17851.09 | 17908.83 |
| m2_stipw_nostag_rp6_tvcdf3 | 2378 | . | -8915.104 | 11 | 17852.21 | 17915.72 |
| m2_stipw_nostag_rp5_tvcdf1 | 2378 | . | -8918.653 | 8 | 17853.31 | 17899.5 |
| m2_stipw_nostag_rp7_tvcdf1 | 2378 | . | -8916.87 | 10 | 17853.74 | 17911.48 |
| m2_stipw_nostag_rp5_tvcdf2 | 2378 | . | -8918.613 | 9 | 17855.23 | 17907.19 |
| m2_stipw_nostag_rp7_tvcdf2 | 2378 | . | -8916.864 | 11 | 17855.73 | 17919.24 |
| m2_stipw_nostag_rp5_tvcdf3 | 2378 | . | -8918.276 | 10 | 17856.55 | 17914.29 |
| m2_stipw_nostag_rp7_tvcdf3 | 2378 | . | -8916.433 | 12 | 17856.87 | 17926.15 |
| m2_stipw_nostag_rp4_tvcdf1 | 2378 | . | -8924.273 | 7 | 17862.55 | 17902.96 |
| m2_stipw_nostag_rp4_tvcdf2 | 2378 | . | -8924.27 | 8 | 17864.54 | 17910.73 |
| m2_stipw_nostag_rp4_tvcdf3 | 2378 | . | -8923.967 | 9 | 17865.93 | 17917.9 |
| m2_stipw_nostag_rp2_tvcdf1 | 2378 | . | -8935.838 | 5 | 17881.68 | 17910.55 |
| m2_stipw_nostag_rp3_tvcdf1 | 2378 | . | -8935.111 | 6 | 17882.22 | 17916.87 |
| m2_stipw_nostag_rp2_tvcdf2 | 2378 | . | -8935.834 | 6 | 17883.67 | 17918.31 |
| m2_stipw_nostag_rp3_tvcdf2 | 2378 | . | -8935.105 | 7 | 17884.21 | 17924.63 |
| m2_stipw_nostag_rp2_tvcdf3 | 2378 | . | -8935.575 | 7 | 17885.15 | 17925.57 |
| m2_stipw_nostag_logn | 2378 | -8979.563 | -8939.604 | 3 | 17885.21 | 17902.53 |
| m2_stipw_nostag_rp3_tvcdf3 | 2378 | . | -8935.071 | 8 | 17886.14 | 17932.33 |
| m2_stipw_nostag_rp2_tvcdf4 | 2378 | . | -8935.503 | 8 | 17887.01 | 17933.2 |
| m2_stipw_nostag_rp3_tvcdf4 | 2378 | . | -8934.933 | 9 | 17887.87 | 17939.83 |
| m2_stipw_nostag_rp2_tvcdf5 | 2378 | . | -8934.976 | 9 | 17887.95 | 17939.92 |
| m2_stipw_nostag_rp2_tvcdf6 | 2378 | . | -8934.388 | 10 | 17888.78 | 17946.52 |
| m2_stipw_nostag_rp3_tvcdf5 | 2378 | . | -8934.425 | 10 | 17888.85 | 17946.59 |
| m2_stipw_nostag_rp2_tvcdf7 | 2378 | . | -8933.659 | 11 | 17889.32 | 17952.83 |
| m2_stipw_nostag_rp3_tvcdf6 | 2378 | . | -8933.848 | 11 | 17889.7 | 17953.21 |
| m2_stipw_nostag_rp3_tvcdf7 | 2378 | . | -8933.14 | 12 | 17890.28 | 17959.57 |
| m2_stipw_nostag_rp1_tvcdf2 | 2378 | . | -8940.919 | 5 | 17891.84 | 17920.71 |
| m2_stipw_nostag_rp1_tvcdf3 | 2378 | . | -8940.696 | 6 | 17893.39 | 17928.04 |
| m2_stipw_nostag_rp1_tvcdf4 | 2378 | . | -8940.588 | 7 | 17895.18 | 17935.59 |
| m2_stipw_nostag_rp1_tvcdf5 | 2378 | . | -8940.073 | 8 | 17896.15 | 17942.34 |
| m2_stipw_nostag_llog | 2378 | -8987.247 | -8945.367 | 3 | 17896.73 | 17914.06 |
| m2_stipw_nostag_rp1_tvcdf6 | 2378 | . | -8939.474 | 9 | 17896.95 | 17948.91 |
| m2_stipw_nostag_rp1_tvcdf7 | 2378 | . | -8938.755 | 10 | 17897.51 | 17955.25 |
| m2_stipw_nostag_wei | 2378 | -8988.473 | -8946.532 | 3 | 17899.06 | 17916.39 |
| m2_stipw_nostag_rp1_tvcdf1 | 2378 | . | -8946.125 | 4 | 17900.25 | 17923.35 |
| m2_stipw_nostag_gom | 2378 | -9012.037 | -8968.985 | 3 | 17943.97 | 17961.29 |
| m2_stipw_nostag_exp | 2378 | -9148.668 | -9112.923 | 2 | 18229.85 | 18241.39 |
. estimates replay m2_stipw_nostag_rp8_tvcdf7, eform
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model m2_stipw_nostag_rp8_tvcdf7
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -8872.4539 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.531669 .2848068 2.29 0.022 1.063869 2.20517
_rcs1 | 2.008937 .2049374 6.84 0.000 1.644875 2.453578
_rcs2 | 1.131679 .1064961 1.31 0.189 .9410686 1.360897
_rcs3 | .904537 .0814936 -1.11 0.265 .75812 1.079232
_rcs4 | 1.057342 .0265516 2.22 0.026 1.006561 1.110684
_rcs5 | 1.032524 .0266027 1.24 0.214 .9816781 1.086003
_rcs6 | 1.0386 .0254338 1.55 0.122 .9899283 1.089665
_rcs7 | 1.027542 .0117347 2.38 0.017 1.004798 1.050801
_rcs8 | .9966406 .0099164 -0.34 0.735 .9773931 1.016267
_rcs_tr_outcome1 | 1.019773 .1058285 0.19 0.850 .8320871 1.249793
_rcs_tr_outcome2 | .9269826 .0864015 -0.81 0.416 .7722064 1.112781
_rcs_tr_outcome3 | 1.11619 .1021477 1.20 0.230 .9329126 1.335475
_rcs_tr_outcome4 | .922176 .0264704 -2.82 0.005 .8717273 .9755442
_rcs_tr_outcome5 | .972755 .025937 -1.04 0.300 .9232248 1.024942
_rcs_tr_outcome6 | .9627901 .0215252 -1.70 0.090 .9215124 1.005917
_rcs_tr_outcome7 | .9983457 .010115 -0.16 0.870 .9787163 1.018369
_cons | .0384109 .0069939 -17.90 0.000 .0268824 .0548834
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m2_stipw_nostag_rp8_tvcdf7
(results m2_stipw_nostag_rp8_tvcdf7 are active now)
.
. sts gen km_b=s, by(tr_outcome)
.
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) ci contrast(difference) ///
> atvar(s_comp_b s_early_b) contrastvar(sdiff_comp_vs_early)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_comp_b rmst_early_b) contrastvar(rmstdiff_comp_vs_early)
.
. * s_tr_comp_early_b s_tr_comp_early_b_lci s_tr_comp_early_b_uci s_late_drop_b s_late_drop_b_lci s_late_drop_b_uci sdiff_tr_comp_early_vs_late sdiff_tr_comp_early_vs_late_lci sdiff_tr_comp_early_vs_late_uci
.
. twoway (rarea s_comp_b_lci s_comp_b_uci tt, color(gs7%35)) ///
> (rarea s_early_b_lci s_early_b_uci tt, color(gs2%35)) ///
> (line km_b _t if tr_outcome==0 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs7%50)) ///
> (line km_b _t if tr_outcome==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%50)) ///
> (line s_comp_b tt, lcolor(gs7) lwidth(thick)) ///
> (line s_early_b tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Probibability of avoiding sentence (standardized)") ///
> legend(order(5 "Tr. completion" 6 "Early dropout") ring(0) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(km_vs_standsurv_fin_b, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_22_b_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_22_b_pris.gph saved)
.
. estimates restore m2_stipw_nostag_rp8_tvcdf7
(results m2_stipw_nostag_rp8_tvcdf7 are active now)
.
. twoway (rarea rmst_comp_b_lci rmst_comp_b_uci tt, color(gs7%35)) ///
> (rarea rmst_early_b_lci rmst_early_b_uci tt, color(gs2%35)) ///
> (line rmst_comp_b tt, lcolor(gs7) lwidth(thick)) ///
> (line rmst_early_b tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Restricted Mean Survival Times (standardized)") ///
> legend(order(1 "Tr. completion" 2 "Early dropout") ring(0) pos(5) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(rmst_std_fin_b, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdif_rmst_b_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_b_pris.gph saved)
Early vs. Late dropout
. *==============================================
. cap qui noi frame drop early_late
frame early_late not found
. frame copy default early_late
.
. frame change early_late
.
. *drop late
. drop if motivodeegreso_mod_imp_rec==1
(19,276 observations deleted)
.
. recode motivodeegreso_mod_imp_rec (3=0 "Late dropout") (2=1 "Early dropout"), gen(tr_outcome)
(51578 differences between motivodeegreso_mod_imp_rec and tr_outcome)
.
. *==============================================
. *______________________________________________
. *______________________________________________
. * NO STAGGERED ENTRY, BINARY TREATMENT (1-EARLY VS. 0-LATE)
.
. * tvar must be a binary variable with 1 = treatment/exposure and 0 = control.
.
. forvalues i=1/10 {
2. forvalues j=1/7 {
3. qui noi stipw (logit tr_outcome tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 cond_ocu3 cond_
> ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini4 susini5 an
> o_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3), distribution(rp) df(`i') dftvc(`j') genw(rpdf`i'_m3_nostag_tvcdf`j') ipwtype(stabilised) vce(mestimation) eform
4. estimates store m3_stipw_nostag_rp`i'_tvcdf`j'
5. }
6. }
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16493.614
Iteration 1: log pseudolikelihood = -16441.789
Iteration 2: log pseudolikelihood = -16441.338
Iteration 3: log pseudolikelihood = -16441.338
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16441.338 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.174815 .0533439 3.55 0.000 1.07478 1.28416
_rcs1 | 2.027621 .0289412 49.52 0.000 1.971683 2.085145
_rcs_tr_outcome1 | .964673 .0235916 -1.47 0.141 .9195251 1.012038
_cons | .0626275 .0017268 -100.48 0.000 .0593329 .0661051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16461.809
Iteration 1: log pseudolikelihood = -16431.809
Iteration 2: log pseudolikelihood = -16431.68
Iteration 3: log pseudolikelihood = -16431.68
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16431.68 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.186584 .0540384 3.76 0.000 1.08526 1.297368
_rcs1 | 2.027621 .0289412 49.52 0.000 1.971683 2.085145
_rcs_tr_outcome1 | .974004 .025475 -1.01 0.314 .9253321 1.025236
_rcs_tr_outcome2 | 1.060072 .0185688 3.33 0.001 1.024295 1.097098
_cons | .0626275 .0017268 -100.48 0.000 .0593329 .0661051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16462.563
Iteration 1: log pseudolikelihood = -16431.772
Iteration 2: log pseudolikelihood = -16431.609
Iteration 3: log pseudolikelihood = -16431.609
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16431.609 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18623 .0539976 3.75 0.000 1.084981 1.296928
_rcs1 | 2.027621 .0289412 49.52 0.000 1.971683 2.085145
_rcs_tr_outcome1 | .9733027 .0254382 -1.04 0.301 .9247002 1.02446
_rcs_tr_outcome2 | 1.061846 .0195983 3.25 0.001 1.024121 1.100961
_rcs_tr_outcome3 | .9980842 .0137035 -0.14 0.889 .971584 1.025307
_cons | .0626275 .0017268 -100.48 0.000 .0593329 .0661051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16458.863
Iteration 1: log pseudolikelihood = -16431.36
Iteration 2: log pseudolikelihood = -16431.23
Iteration 3: log pseudolikelihood = -16431.23
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16431.23 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.185653 .0539435 3.74 0.000 1.084503 1.296237
_rcs1 | 2.027621 .0289412 49.52 0.000 1.971683 2.085145
_rcs_tr_outcome1 | .9729117 .0253577 -1.05 0.292 .9244597 1.023903
_rcs_tr_outcome2 | 1.06045 .0190804 3.26 0.001 1.023705 1.098514
_rcs_tr_outcome3 | 1.003714 .0141977 0.26 0.793 .9762695 1.031931
_rcs_tr_outcome4 | .9933335 .0097013 -0.68 0.493 .9745002 1.012531
_cons | .0626275 .0017268 -100.48 0.000 .0593329 .0661051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16457.52
Iteration 1: log pseudolikelihood = -16431.074
Iteration 2: log pseudolikelihood = -16430.964
Iteration 3: log pseudolikelihood = -16430.964
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16430.964 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.185801 .0539496 3.75 0.000 1.08464 1.296398
_rcs1 | 2.027621 .0289412 49.52 0.000 1.971683 2.085145
_rcs_tr_outcome1 | .9733141 .0253322 -1.04 0.299 .9249091 1.024252
_rcs_tr_outcome2 | 1.059209 .0185061 3.29 0.001 1.023552 1.096109
_rcs_tr_outcome3 | 1.00884 .0143829 0.62 0.537 .9810399 1.037427
_rcs_tr_outcome4 | .9919234 .009879 -0.81 0.416 .9727486 1.011476
_rcs_tr_outcome5 | 1.00095 .0074071 0.13 0.898 .9865366 1.015573
_cons | .0626275 .0017268 -100.48 0.000 .0593329 .0661051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16458.064
Iteration 1: log pseudolikelihood = -16430.98
Iteration 2: log pseudolikelihood = -16430.85
Iteration 3: log pseudolikelihood = -16430.85
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16430.85 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.185827 .0539526 3.75 0.000 1.08466 1.29643
_rcs1 | 2.027621 .0289412 49.52 0.000 1.971683 2.085145
_rcs_tr_outcome1 | .9733761 .0253289 -1.04 0.300 .9249771 1.024308
_rcs_tr_outcome2 | 1.05885 .0185639 3.26 0.001 1.023084 1.095867
_rcs_tr_outcome3 | 1.010388 .0143339 0.73 0.466 .9826811 1.038876
_rcs_tr_outcome4 | .9932799 .009813 -0.68 0.495 .9742319 1.0127
_rcs_tr_outcome5 | .9976628 .0076505 -0.31 0.760 .9827801 1.012771
_rcs_tr_outcome6 | 1.002207 .0063248 0.35 0.727 .9898869 1.01468
_cons | .0626275 .0017268 -100.48 0.000 .0593329 .0661051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16458.599
Iteration 1: log pseudolikelihood = -16430.758
Iteration 2: log pseudolikelihood = -16430.582
Iteration 3: log pseudolikelihood = -16430.582
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16430.582 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.185837 .0539539 3.75 0.000 1.084667 1.296443
_rcs1 | 2.027621 .0289412 49.52 0.000 1.971683 2.085145
_rcs_tr_outcome1 | .973554 .0253261 -1.03 0.303 .92516 1.024479
_rcs_tr_outcome2 | 1.057909 .0179768 3.31 0.001 1.023256 1.093736
_rcs_tr_outcome3 | 1.01436 .0140636 1.03 0.304 .9871666 1.042302
_rcs_tr_outcome4 | .9923185 .0099258 -0.77 0.441 .9730536 1.011965
_rcs_tr_outcome5 | .9970655 .007861 -0.37 0.709 .9817766 1.012592
_rcs_tr_outcome6 | 1.001654 .006628 0.25 0.803 .9887471 1.014729
_rcs_tr_outcome7 | 1.000741 .0056537 0.13 0.896 .9897214 1.011884
_cons | .0626275 .0017268 -100.48 0.000 .0593329 .0661051
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16416.377
Iteration 1: log pseudolikelihood = -16405.066
Iteration 2: log pseudolikelihood = -16405.024
Iteration 3: log pseudolikelihood = -16405.024
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16405.024 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.181718 .0538153 3.67 0.000 1.080813 1.292045
_rcs1 | 2.055055 .0337351 43.88 0.000 1.989988 2.12225
_rcs2 | 1.075182 .0118834 6.56 0.000 1.052141 1.098727
_rcs_tr_outcome1 | .9654853 .0271139 -1.25 0.211 .9137791 1.020117
_cons | .0629575 .0017469 -99.66 0.000 .0596251 .0664762
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16416.409
Iteration 1: log pseudolikelihood = -16404.222
Iteration 2: log pseudolikelihood = -16404.168
Iteration 3: log pseudolikelihood = -16404.168
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16404.168 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180379 .0538716 3.63 0.000 1.079377 1.290832
_rcs1 | 2.061754 .0348454 42.81 0.000 1.994577 2.131194
_rcs2 | 1.085558 .0153555 5.80 0.000 1.055875 1.116075
_rcs_tr_outcome1 | .9578788 .0265124 -1.55 0.120 .9072997 1.011277
_rcs_tr_outcome2 | .9765228 .0219677 -1.06 0.291 .9344023 1.020542
_cons | .0629568 .0017465 -99.68 0.000 .0596251 .0664747
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16416.856
Iteration 1: log pseudolikelihood = -16404.069
Iteration 2: log pseudolikelihood = -16403.984
Iteration 3: log pseudolikelihood = -16403.984
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.984 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.179982 .0538308 3.63 0.000 1.079055 1.290349
_rcs1 | 2.061882 .0348669 42.79 0.000 1.994665 2.131365
_rcs2 | 1.085748 .0153671 5.81 0.000 1.056043 1.116288
_rcs_tr_outcome1 | .9570305 .0264721 -1.59 0.112 .9065276 1.010347
_rcs_tr_outcome2 | .9780216 .0226669 -0.96 0.338 .9345891 1.023472
_rcs_tr_outcome3 | .9929087 .0136605 -0.52 0.605 .9664923 1.020047
_cons | .0629567 .0017465 -99.68 0.000 .059625 .0664745
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16413.464
Iteration 1: log pseudolikelihood = -16403.773
Iteration 2: log pseudolikelihood = -16403.718
Iteration 3: log pseudolikelihood = -16403.718
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.718 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.179452 .0537772 3.62 0.000 1.078623 1.289707
_rcs1 | 2.061754 .0348454 42.81 0.000 1.994577 2.131194
_rcs2 | 1.085558 .0153555 5.80 0.000 1.055875 1.116075
_rcs_tr_outcome1 | .9568046 .0264001 -1.60 0.110 .9064355 1.009973
_rcs_tr_outcome2 | .9773046 .022304 -1.01 0.314 .9345529 1.022012
_rcs_tr_outcome3 | .9951948 .0141525 -0.34 0.735 .9678394 1.023323
_rcs_tr_outcome4 | .9933335 .0097013 -0.68 0.493 .9745002 1.012531
_cons | .0629568 .0017465 -99.68 0.000 .0596251 .0664747
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16412.119
Iteration 1: log pseudolikelihood = -16403.47
Iteration 2: log pseudolikelihood = -16403.435
Iteration 3: log pseudolikelihood = -16403.435
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.435 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.179607 .0537836 3.62 0.000 1.078766 1.289874
_rcs1 | 2.061775 .0348488 42.81 0.000 1.994591 2.131221
_rcs2 | 1.085588 .0153576 5.80 0.000 1.055901 1.116109
_rcs_tr_outcome1 | .957184 .0263782 -1.59 0.112 .9068552 1.010306
_rcs_tr_outcome2 | .9764272 .0218604 -1.07 0.287 .934508 1.020227
_rcs_tr_outcome3 | .997824 .0143532 -0.15 0.880 .970085 1.026356
_rcs_tr_outcome4 | .9910096 .0098682 -0.91 0.364 .9718558 1.010541
_rcs_tr_outcome5 | 1.001071 .0074098 0.14 0.885 .9866525 1.015699
_cons | .0629568 .0017465 -99.68 0.000 .0596251 .0664746
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16412.665
Iteration 1: log pseudolikelihood = -16403.393
Iteration 2: log pseudolikelihood = -16403.338
Iteration 3: log pseudolikelihood = -16403.338
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.338 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.179625 .0537864 3.62 0.000 1.078779 1.289899
_rcs1 | 2.061754 .0348454 42.81 0.000 1.994577 2.131194
_rcs2 | 1.085558 .0153555 5.80 0.000 1.055875 1.116075
_rcs_tr_outcome1 | .9572613 .0263747 -1.59 0.113 .9069388 1.010376
_rcs_tr_outcome2 | .9762678 .0218808 -1.07 0.284 .9343106 1.020109
_rcs_tr_outcome3 | .9984141 .0143113 -0.11 0.912 .9707549 1.026861
_rcs_tr_outcome4 | .9914108 .0097996 -0.87 0.383 .9723889 1.010805
_rcs_tr_outcome5 | .9976628 .0076505 -0.31 0.760 .9827801 1.012771
_rcs_tr_outcome6 | 1.002207 .0063248 0.35 0.727 .9898869 1.01468
_cons | .0629568 .0017465 -99.68 0.000 .0596251 .0664747
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16413.192
Iteration 1: log pseudolikelihood = -16403.162
Iteration 2: log pseudolikelihood = -16403.061
Iteration 3: log pseudolikelihood = -16403.061
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.061 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.179638 .0537876 3.62 0.000 1.078789 1.289914
_rcs1 | 2.061765 .0348472 42.81 0.000 1.994585 2.131208
_rcs2 | 1.085574 .0153566 5.80 0.000 1.055889 1.116093
_rcs_tr_outcome1 | .9574259 .0263733 -1.58 0.114 .9071058 1.010537
_rcs_tr_outcome2 | .975651 .0214284 -1.12 0.262 .9345433 1.018567
_rcs_tr_outcome3 | 1.000722 .0140681 0.05 0.959 .9735255 1.028678
_rcs_tr_outcome4 | .9897393 .0099102 -1.03 0.303 .970505 1.009355
_rcs_tr_outcome5 | .9968173 .0078585 -0.40 0.686 .9815333 1.012339
_rcs_tr_outcome6 | 1.001685 .0066287 0.25 0.799 .9887772 1.014762
_rcs_tr_outcome7 | 1.000729 .0056536 0.13 0.897 .9897094 1.011872
_cons | .0629568 .0017465 -99.68 0.000 .0596251 .0664746
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.324
Iteration 1: log pseudolikelihood = -16404.641
Iteration 2: log pseudolikelihood = -16404.619
Iteration 3: log pseudolikelihood = -16404.619
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16404.619 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.181962 .053821 3.67 0.000 1.081045 1.2923
_rcs1 | 2.054444 .0335688 44.07 0.000 1.989693 2.121303
_rcs2 | 1.072093 .0117101 6.37 0.000 1.049386 1.095292
_rcs3 | 1.011529 .0081009 1.43 0.152 .9957759 1.027532
_rcs_tr_outcome1 | .9669749 .0271284 -1.20 0.231 .9152397 1.021635
_cons | .0629529 .0017472 -99.64 0.000 .05962 .0664721
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.299
Iteration 1: log pseudolikelihood = -16403.829
Iteration 2: log pseudolikelihood = -16403.797
Iteration 3: log pseudolikelihood = -16403.797
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.797 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180738 .053877 3.64 0.000 1.079725 1.291201
_rcs1 | 2.060943 .0345114 43.19 0.000 1.9944 2.129706
_rcs2 | 1.082137 .0151077 5.65 0.000 1.052928 1.112157
_rcs3 | 1.012017 .0080803 1.50 0.135 .9963026 1.027978
_rcs_tr_outcome1 | .9595648 .0263572 -1.50 0.133 .9092716 1.01264
_rcs_tr_outcome2 | .9773223 .021272 -1.05 0.292 .9365067 1.019917
_cons | .0629511 .0017467 -99.67 0.000 .0596191 .0664693
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.551
Iteration 1: log pseudolikelihood = -16402.61
Iteration 2: log pseudolikelihood = -16402.534
Iteration 3: log pseudolikelihood = -16402.534
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16402.534 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180497 .0538747 3.64 0.000 1.079489 1.290956
_rcs1 | 2.061376 .0343781 43.37 0.000 1.995085 2.129869
_rcs2 | 1.078879 .014456 5.67 0.000 1.050915 1.107587
_rcs3 | 1.020035 .0099692 2.03 0.042 1.000682 1.039763
_rcs_tr_outcome1 | .9573648 .0263493 -1.58 0.113 .9070893 1.010427
_rcs_tr_outcome2 | .9842124 .0224306 -0.70 0.485 .9412166 1.029172
_rcs_tr_outcome3 | .9784801 .0164848 -1.29 0.197 .9466982 1.011329
_cons | .0629317 .0017479 -99.58 0.000 .0595974 .0664525
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16408.192
Iteration 1: log pseudolikelihood = -16402.273
Iteration 2: log pseudolikelihood = -16402.226
Iteration 3: log pseudolikelihood = -16402.226
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16402.226 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.17985 .0538154 3.63 0.000 1.078951 1.290185
_rcs1 | 2.061327 .0343767 43.37 0.000 1.99504 2.129818
_rcs2 | 1.078965 .0144802 5.66 0.000 1.050954 1.107722
_rcs3 | 1.019757 .0099453 2.01 0.045 1.000449 1.039437
_rcs_tr_outcome1 | .9569702 .0262723 -1.60 0.109 .9068383 1.009874
_rcs_tr_outcome2 | .983687 .0221673 -0.73 0.465 .9411854 1.028108
_rcs_tr_outcome3 | .9817422 .0168057 -1.08 0.282 .94935 1.01524
_rcs_tr_outcome4 | .9893745 .0099126 -1.07 0.286 .9701357 1.008995
_cons | .0629326 .0017479 -99.58 0.000 .0595984 .0664533
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16406.455
Iteration 1: log pseudolikelihood = -16401.885
Iteration 2: log pseudolikelihood = -16401.861
Iteration 3: log pseudolikelihood = -16401.861
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.861 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180081 .0538285 3.63 0.000 1.079158 1.290443
_rcs1 | 2.061383 .0343752 43.38 0.000 1.995097 2.12987
_rcs2 | 1.0788 .0144383 5.67 0.000 1.050869 1.107472
_rcs3 | 1.02019 .0099667 2.05 0.041 1.000841 1.039912
_rcs_tr_outcome1 | .9573522 .0262498 -1.59 0.112 .9072616 1.010208
_rcs_tr_outcome2 | .983273 .0216844 -0.76 0.444 .9416778 1.026705
_rcs_tr_outcome3 | .9851372 .0165795 -0.89 0.374 .953172 1.018174
_rcs_tr_outcome4 | .9848762 .0104523 -1.44 0.151 .9646016 1.005577
_rcs_tr_outcome5 | 1.000411 .0074072 0.06 0.956 .9859984 1.015035
_cons | .0629312 .0017479 -99.57 0.000 .0595969 .0664521
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16407.053
Iteration 1: log pseudolikelihood = -16401.818
Iteration 2: log pseudolikelihood = -16401.775
Iteration 3: log pseudolikelihood = -16401.775
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.775 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180096 .0538299 3.63 0.000 1.07917 1.29046
_rcs1 | 2.061376 .0343781 43.37 0.000 1.995085 2.129869
_rcs2 | 1.078879 .014456 5.67 0.000 1.050915 1.107587
_rcs3 | 1.020035 .0099692 2.03 0.042 1.000682 1.039763
_rcs_tr_outcome1 | .957437 .0262474 -1.59 0.113 .9073507 1.010288
_rcs_tr_outcome2 | .9831387 .0217268 -0.77 0.442 .941464 1.026658
_rcs_tr_outcome3 | .9865428 .016334 -0.82 0.413 .9550425 1.019082
_rcs_tr_outcome4 | .9844794 .0106388 -1.45 0.148 .963847 1.005554
_rcs_tr_outcome5 | .9956254 .0077003 -0.57 0.571 .9806468 1.010833
_rcs_tr_outcome6 | 1.002207 .0063248 0.35 0.727 .9898869 1.01468
_cons | .0629317 .0017479 -99.58 0.000 .0595974 .0664525
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16407.506
Iteration 1: log pseudolikelihood = -16401.558
Iteration 2: log pseudolikelihood = -16401.471
Iteration 3: log pseudolikelihood = -16401.471
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.471 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180126 .0538337 3.63 0.000 1.079194 1.290499
_rcs1 | 2.061386 .0343745 43.38 0.000 1.995102 2.129872
_rcs2 | 1.078774 .0144326 5.67 0.000 1.050854 1.107435
_rcs3 | 1.020243 .0099688 2.05 0.040 1.000891 1.03997
_rcs_tr_outcome1 | .9575918 .0262449 -1.58 0.114 .90751 1.010437
_rcs_tr_outcome2 | .9828718 .0212949 -0.80 0.425 .9420084 1.025508
_rcs_tr_outcome3 | .9893621 .0159199 -0.66 0.506 .9586465 1.021062
_rcs_tr_outcome4 | .982198 .0109158 -1.62 0.106 .9610348 1.003827
_rcs_tr_outcome5 | .9937631 .0080016 -0.78 0.437 .9782034 1.00957
_rcs_tr_outcome6 | 1.001048 .0066282 0.16 0.874 .9881408 1.014124
_rcs_tr_outcome7 | 1.000806 .005655 0.14 0.887 .9897834 1.011951
_cons | .062931 .0017479 -99.57 0.000 .0595967 .0664519
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.397
Iteration 1: log pseudolikelihood = -16403.955
Iteration 2: log pseudolikelihood = -16403.927
Iteration 3: log pseudolikelihood = -16403.927
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.927 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.181695 .0538179 3.67 0.000 1.080785 1.292027
_rcs1 | 2.054874 .0335549 44.11 0.000 1.990149 2.121704
_rcs2 | 1.070049 .0112747 6.43 0.000 1.048177 1.092376
_rcs3 | 1.016988 .0084635 2.02 0.043 1.000535 1.033712
_rcs4 | .9977847 .0056602 -0.39 0.696 .9867524 1.00894
_rcs_tr_outcome1 | .9664474 .0270568 -1.22 0.223 .9148457 1.02096
_cons | .0629491 .0017472 -99.63 0.000 .059616 .0664685
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.401
Iteration 1: log pseudolikelihood = -16403.181
Iteration 2: log pseudolikelihood = -16403.146
Iteration 3: log pseudolikelihood = -16403.146
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.146 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18054 .0538739 3.64 0.000 1.079533 1.290997
_rcs1 | 2.061201 .0344254 43.31 0.000 1.994821 2.12979
_rcs2 | 1.079823 .0145254 5.71 0.000 1.051725 1.108671
_rcs3 | 1.01769 .0084558 2.11 0.035 1.001252 1.034399
_rcs4 | .9980348 .0056351 -0.35 0.728 .9870512 1.009141
_rcs_tr_outcome1 | .9592457 .0262373 -1.52 0.128 .9091757 1.012073
_rcs_tr_outcome2 | .9779758 .0206896 -1.05 0.292 .9382542 1.019379
_cons | .0629472 .0017468 -99.66 0.000 .059615 .0664656
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.366
Iteration 1: log pseudolikelihood = -16401.841
Iteration 2: log pseudolikelihood = -16401.785
Iteration 3: log pseudolikelihood = -16401.785
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.785 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180251 .0538682 3.63 0.000 1.079256 1.290697
_rcs1 | 2.061757 .0343433 43.44 0.000 1.995532 2.130179
_rcs2 | 1.076239 .0137209 5.76 0.000 1.049679 1.10347
_rcs3 | 1.025949 .0102359 2.57 0.010 1.006082 1.046209
_rcs4 | .9999061 .005725 -0.02 0.987 .9887482 1.01119
_rcs_tr_outcome1 | .9568974 .0262799 -1.60 0.109 .9067514 1.009817
_rcs_tr_outcome2 | .984711 .0215691 -0.70 0.482 .9433311 1.027906
_rcs_tr_outcome3 | .9776447 .0162703 -1.36 0.174 .94627 1.01006
_cons | .062927 .001748 -99.56 0.000 .0595925 .0664481
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.428
Iteration 1: log pseudolikelihood = -16401.855
Iteration 2: log pseudolikelihood = -16401.787
Iteration 3: log pseudolikelihood = -16401.787
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.787 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.179981 .0538249 3.63 0.000 1.079065 1.290336
_rcs1 | 2.06158 .0343538 43.42 0.000 1.995336 2.130024
_rcs2 | 1.076576 .013776 5.77 0.000 1.049911 1.103918
_rcs3 | 1.025275 .010488 2.44 0.015 1.004924 1.046038
_rcs4 | 1.000954 .0068631 0.14 0.889 .9875928 1.014496
_rcs_tr_outcome1 | .9568854 .0262625 -1.61 0.108 .9067717 1.009769
_rcs_tr_outcome2 | .9850211 .021733 -0.68 0.494 .9433331 1.028551
_rcs_tr_outcome3 | .9789709 .0170826 -1.22 0.223 .9460557 1.013031
_rcs_tr_outcome4 | .9923865 .0118398 -0.64 0.522 .9694502 1.015866
_cons | .0629286 .0017479 -99.57 0.000 .0595943 .0664494
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16409.978
Iteration 1: log pseudolikelihood = -16401.566
Iteration 2: log pseudolikelihood = -16401.519
Iteration 3: log pseudolikelihood = -16401.519
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.519 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180135 .05383 3.63 0.000 1.079209 1.2905
_rcs1 | 2.061589 .0343555 43.41 0.000 1.995341 2.130036
_rcs2 | 1.076628 .0137891 5.76 0.000 1.049938 1.103996
_rcs3 | 1.025203 .0104724 2.44 0.015 1.004882 1.045936
_rcs4 | 1.00089 .0068037 0.13 0.896 .9876432 1.014314
_rcs_tr_outcome1 | .9572821 .0262369 -1.59 0.111 .9072155 1.010112
_rcs_tr_outcome2 | .9846586 .0213946 -0.71 0.477 .9436063 1.027497
_rcs_tr_outcome3 | .9820286 .0172251 -1.03 0.301 .9488418 1.016376
_rcs_tr_outcome4 | .9878614 .011719 -1.03 0.303 .9651576 1.011099
_rcs_tr_outcome5 | 1.001109 .0079179 0.14 0.889 .9857097 1.016749
_cons | .0629288 .0017479 -99.57 0.000 .0595945 .0664497
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16410.977
Iteration 1: log pseudolikelihood = -16401.569
Iteration 2: log pseudolikelihood = -16401.496
Iteration 3: log pseudolikelihood = -16401.496
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.496 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180117 .0538304 3.63 0.000 1.07919 1.290483
_rcs1 | 2.061516 .0343548 43.41 0.000 1.99527 2.129962
_rcs2 | 1.076846 .0138591 5.75 0.000 1.050023 1.104355
_rcs3 | 1.02479 .0104806 2.39 0.017 1.004453 1.045539
_rcs4 | 1.001387 .0068655 0.20 0.840 .9880208 1.014934
_rcs_tr_outcome1 | .9574106 .0262348 -1.59 0.112 .9073478 1.010236
_rcs_tr_outcome2 | .9844205 .0214314 -0.72 0.471 .9432993 1.027334
_rcs_tr_outcome3 | .9837185 .0170843 -0.95 0.345 .9507973 1.01778
_rcs_tr_outcome4 | .9863601 .0112462 -1.20 0.228 .9645624 1.00865
_rcs_tr_outcome5 | .9969014 .0087513 -0.35 0.724 .9798959 1.014202
_rcs_tr_outcome6 | 1.002034 .0063617 0.32 0.749 .9896427 1.014581
_cons | .0629297 .0017479 -99.57 0.000 .0595954 .0664505
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16411.288
Iteration 1: log pseudolikelihood = -16401.31
Iteration 2: log pseudolikelihood = -16401.195
Iteration 3: log pseudolikelihood = -16401.195
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.195 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180139 .0538316 3.63 0.000 1.07921 1.290507
_rcs1 | 2.061551 .0343557 43.41 0.000 1.995303 2.129999
_rcs2 | 1.076739 .0138198 5.76 0.000 1.049991 1.104169
_rcs3 | 1.024974 .0104861 2.41 0.016 1.004626 1.045734
_rcs4 | 1.001038 .0068624 0.15 0.880 .9876775 1.014578
_rcs_tr_outcome1 | .957552 .0262339 -1.58 0.113 .9074906 1.010375
_rcs_tr_outcome2 | .9841951 .0209687 -0.75 0.455 .9439435 1.026163
_rcs_tr_outcome3 | .9864082 .0167603 -0.81 0.421 .9540996 1.019811
_rcs_tr_outcome4 | .9837659 .0111292 -1.45 0.148 .9621931 1.005822
_rcs_tr_outcome5 | .9957094 .009165 -0.47 0.640 .9779073 1.013835
_rcs_tr_outcome6 | 1.001424 .0069163 0.21 0.837 .9879592 1.015072
_rcs_tr_outcome7 | 1.00069 .0056564 0.12 0.903 .9896648 1.011838
_cons | .0629294 .0017479 -99.57 0.000 .0595952 .0664502
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16407.478
Iteration 1: log pseudolikelihood = -16403.619
Iteration 2: log pseudolikelihood = -16403.61
Iteration 3: log pseudolikelihood = -16403.61
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16403.61 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.182031 .053831 3.67 0.000 1.081096 1.292389
_rcs1 | 2.054956 .0335652 44.10 0.000 1.990211 2.121807
_rcs2 | 1.068847 .0109759 6.48 0.000 1.04755 1.090577
_rcs3 | 1.020905 .008647 2.44 0.015 1.004097 1.037994
_rcs4 | .9992416 .006035 -0.13 0.900 .987483 1.01114
_rcs5 | 1.00198 .0043346 0.46 0.647 .9935206 1.010512
_rcs_tr_outcome1 | .9668548 .0270903 -1.20 0.229 .9151904 1.021436
_cons | .0629431 .001747 -99.64 0.000 .0596105 .0664619
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16407.471
Iteration 1: log pseudolikelihood = -16402.831
Iteration 2: log pseudolikelihood = -16402.811
Iteration 3: log pseudolikelihood = -16402.811
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16402.811 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180878 .0538858 3.64 0.000 1.079849 1.291359
_rcs1 | 2.061335 .0344249 43.31 0.000 1.994955 2.129923
_rcs2 | 1.078689 .014181 5.76 0.000 1.051249 1.106844
_rcs3 | 1.021866 .0086525 2.55 0.011 1.005048 1.038966
_rcs4 | .9995543 .0060007 -0.07 0.941 .987862 1.011385
_rcs5 | 1.00213 .0043245 0.49 0.622 .9936903 1.010642
_rcs_tr_outcome1 | .9596021 .0262223 -1.51 0.131 .9095593 1.012398
_rcs_tr_outcome2 | .977766 .0204205 -1.08 0.282 .9385506 1.01862
_cons | .062941 .0017465 -99.67 0.000 .0596094 .0664588
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16407.358
Iteration 1: log pseudolikelihood = -16401.464
Iteration 2: log pseudolikelihood = -16401.439
Iteration 3: log pseudolikelihood = -16401.439
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.439 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180565 .0538763 3.64 0.000 1.079555 1.291027
_rcs1 | 2.061871 .0343513 43.43 0.000 1.995631 2.13031
_rcs2 | 1.074971 .0133496 5.82 0.000 1.049122 1.101457
_rcs3 | 1.029588 .0101249 2.97 0.003 1.009934 1.049625
_rcs4 | 1.00254 .0063556 0.40 0.689 .9901608 1.015075
_rcs5 | 1.002572 .0042974 0.60 0.549 .9941848 1.01103
_rcs_tr_outcome1 | .9572818 .0262701 -1.59 0.112 .9071535 1.01018
_rcs_tr_outcome2 | .9842783 .0211984 -0.74 0.462 .943595 1.026716
_rcs_tr_outcome3 | .977829 .0160984 -1.36 0.173 .9467804 1.009896
_cons | .0629213 .0017477 -99.58 0.000 .0595876 .0664416
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16407.751
Iteration 1: log pseudolikelihood = -16401.279
Iteration 2: log pseudolikelihood = -16401.25
Iteration 3: log pseudolikelihood = -16401.25
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.25 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180285 .0538412 3.63 0.000 1.079338 1.290673
_rcs1 | 2.061723 .0343653 43.41 0.000 1.995457 2.13019
_rcs2 | 1.075284 .0133675 5.84 0.000 1.049401 1.101806
_rcs3 | 1.028883 .0106028 2.76 0.006 1.00831 1.049875
_rcs4 | 1.003897 .007159 0.55 0.585 .9899636 1.018027
_rcs5 | 1.003589 .0044904 0.80 0.423 .9948264 1.012429
_rcs_tr_outcome1 | .9571209 .0262568 -1.60 0.110 .9070177 1.009992
_rcs_tr_outcome2 | .9846689 .0211451 -0.72 0.472 .9440853 1.026997
_rcs_tr_outcome3 | .9790583 .016832 -1.23 0.218 .9466179 1.012611
_rcs_tr_outcome4 | .9906561 .0117154 -0.79 0.427 .9679584 1.013886
_cons | .0629204 .0017476 -99.58 0.000 .0595867 .0664407
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16407.504
Iteration 1: log pseudolikelihood = -16401.313
Iteration 2: log pseudolikelihood = -16401.275
Iteration 3: log pseudolikelihood = -16401.275
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.275 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180254 .0538343 3.63 0.000 1.079319 1.290627
_rcs1 | 2.061668 .0343722 43.40 0.000 1.995389 2.130149
_rcs2 | 1.075399 .0133781 5.84 0.000 1.049495 1.101942
_rcs3 | 1.028605 .0107261 2.70 0.007 1.007796 1.049844
_rcs4 | 1.004019 .0075327 0.53 0.593 .9893627 1.018891
_rcs5 | 1.003241 .0052611 0.62 0.537 .992982 1.013605
_rcs_tr_outcome1 | .9572403 .0262436 -1.59 0.111 .9071614 1.010084
_rcs_tr_outcome2 | .9849452 .0211108 -0.71 0.479 .9444258 1.027203
_rcs_tr_outcome3 | .9807841 .0173188 -1.10 0.272 .9474207 1.015323
_rcs_tr_outcome4 | .9879533 .0123154 -0.97 0.331 .9641079 1.012388
_rcs_tr_outcome5 | .9977163 .009047 -0.25 0.801 .9801411 1.015607
_cons | .0629219 .0017475 -99.59 0.000 .0595885 .0664418
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16408.506
Iteration 1: log pseudolikelihood = -16401.264
Iteration 2: log pseudolikelihood = -16401.199
Iteration 3: log pseudolikelihood = -16401.199
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.199 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18026 .053835 3.63 0.000 1.079325 1.290635
_rcs1 | 2.061625 .03437 43.40 0.000 1.995349 2.130101
_rcs2 | 1.075556 .0134311 5.83 0.000 1.049551 1.102205
_rcs3 | 1.028278 .0107188 2.68 0.007 1.007483 1.049503
_rcs4 | 1.00421 .0075034 0.56 0.574 .9896105 1.019024
_rcs5 | 1.003369 .0051906 0.65 0.516 .9932475 1.013594
_rcs_tr_outcome1 | .9573287 .0262361 -1.59 0.112 .9072635 1.010157
_rcs_tr_outcome2 | .984944 .0212295 -0.70 0.482 .9442017 1.027444
_rcs_tr_outcome3 | .9819372 .0173282 -1.03 0.302 .9485553 1.016494
_rcs_tr_outcome4 | .9874612 .0120914 -1.03 0.303 .9640446 1.011447
_rcs_tr_outcome5 | .9940653 .0090856 -0.65 0.515 .9764164 1.012033
_rcs_tr_outcome6 | 1.00039 .0069717 0.06 0.955 .986819 1.014148
_cons | .0629224 .0017475 -99.59 0.000 .059589 .0664423
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16409.488
Iteration 1: log pseudolikelihood = -16401.237
Iteration 2: log pseudolikelihood = -16401.116
Iteration 3: log pseudolikelihood = -16401.116
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.116 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18018 .0538303 3.63 0.000 1.079253 1.290545
_rcs1 | 2.061484 .034355 43.41 0.000 1.995237 2.12993
_rcs2 | 1.075782 .013551 5.80 0.000 1.049548 1.102672
_rcs3 | 1.027554 .0107231 2.60 0.009 1.00675 1.048787
_rcs4 | 1.00477 .0075499 0.63 0.527 .9900805 1.019677
_rcs5 | 1.002409 .0052388 0.46 0.645 .9921933 1.012729
_rcs_tr_outcome1 | .9576361 .0262333 -1.58 0.114 .9075757 1.010458
_rcs_tr_outcome2 | .9843686 .0208243 -0.74 0.456 .9443882 1.026042
_rcs_tr_outcome3 | .9856024 .0171245 -0.83 0.404 .9526041 1.019744
_rcs_tr_outcome4 | .984255 .0119211 -1.31 0.190 .9611653 1.007899
_rcs_tr_outcome5 | .9939171 .0090217 -0.67 0.501 .9763913 1.011757
_rcs_tr_outcome6 | .9996017 .0078423 -0.05 0.959 .9843487 1.015091
_rcs_tr_outcome7 | 1.000246 .0057826 0.04 0.966 .9889767 1.011645
_cons | .0629256 .0017475 -99.60 0.000 .0595922 .0664455
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16408.905
Iteration 1: log pseudolikelihood = -16402.496
Iteration 2: log pseudolikelihood = -16402.472
Iteration 3: log pseudolikelihood = -16402.472
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16402.472 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.182091 .05383 3.67 0.000 1.081157 1.292447
_rcs1 | 2.054698 .0335265 44.13 0.000 1.990027 2.121471
_rcs2 | 1.068527 .010957 6.46 0.000 1.047266 1.090219
_rcs3 | 1.021986 .0087906 2.53 0.011 1.004902 1.039362
_rcs4 | 1.001275 .0062487 0.20 0.838 .9891029 1.013598
_rcs5 | 1.000745 .0044389 0.17 0.867 .9920827 1.009483
_rcs6 | 1.004261 .0037202 1.15 0.251 .996996 1.011579
_rcs_tr_outcome1 | .9675141 .0270962 -1.18 0.238 .9158378 1.022106
_cons | .062943 .0017467 -99.66 0.000 .059611 .0664612
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16408.905
Iteration 1: log pseudolikelihood = -16401.663
Iteration 2: log pseudolikelihood = -16401.627
Iteration 3: log pseudolikelihood = -16401.627
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.627 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180919 .0538831 3.64 0.000 1.079895 1.291395
_rcs1 | 2.061257 .0344257 43.31 0.000 1.994877 2.129847
_rcs2 | 1.078646 .0141185 5.78 0.000 1.051326 1.106676
_rcs3 | 1.023062 .0088115 2.65 0.008 1.005937 1.040479
_rcs4 | 1.00166 .0061998 0.27 0.789 .9895825 1.013886
_rcs5 | 1.000935 .004428 0.21 0.833 .9922938 1.009651
_rcs6 | 1.004411 .0037093 1.19 0.233 .9971669 1.011707
_rcs_tr_outcome1 | .9600564 .026223 -1.49 0.136 .9100118 1.012853
_rcs_tr_outcome2 | .9771355 .0203859 -1.11 0.268 .9379857 1.017919
_cons | .0629407 .0017461 -99.69 0.000 .0596097 .0664578
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16408.785
Iteration 1: log pseudolikelihood = -16400.261
Iteration 2: log pseudolikelihood = -16400.215
Iteration 3: log pseudolikelihood = -16400.215
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16400.215 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180599 .0538749 3.64 0.000 1.079591 1.291058
_rcs1 | 2.061737 .0343374 43.44 0.000 1.995523 2.130147
_rcs2 | 1.074747 .0133014 5.82 0.000 1.048991 1.101136
_rcs3 | 1.030479 .0101333 3.05 0.002 1.010809 1.050533
_rcs4 | 1.005351 .0067598 0.79 0.427 .9921889 1.018688
_rcs5 | 1.001958 .0044012 0.45 0.656 .9933686 1.010621
_rcs6 | 1.004641 .003692 1.26 0.208 .9974311 1.011904
_rcs_tr_outcome1 | .9577666 .0262678 -1.57 0.116 .907642 1.010659
_rcs_tr_outcome2 | .9838193 .0212401 -0.76 0.450 .943058 1.026342
_rcs_tr_outcome3 | .9774461 .0161624 -1.38 0.168 .9462762 1.009643
_cons | .0629207 .0017474 -99.60 0.000 .0595875 .0664405
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16409.004
Iteration 1: log pseudolikelihood = -16400.127
Iteration 2: log pseudolikelihood = -16400.069
Iteration 3: log pseudolikelihood = -16400.069
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16400.069 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180434 .0538472 3.64 0.000 1.079476 1.290834
_rcs1 | 2.061672 .0343662 43.40 0.000 1.995404 2.130141
_rcs2 | 1.074801 .0132373 5.86 0.000 1.049167 1.101061
_rcs3 | 1.030317 .0107192 2.87 0.004 1.009521 1.051542
_rcs4 | 1.005835 .0070998 0.82 0.410 .9920152 1.019847
_rcs5 | 1.002851 .0049035 0.58 0.560 .9932864 1.012508
_rcs6 | 1.004935 .0036916 1.34 0.180 .9977255 1.012197
_rcs_tr_outcome1 | .957593 .026276 -1.58 0.114 .9074533 1.010503
_rcs_tr_outcome2 | .9846253 .0212904 -0.72 0.474 .9437687 1.027251
_rcs_tr_outcome3 | .9776364 .0169408 -1.31 0.192 .9449906 1.01141
_rcs_tr_outcome4 | .9923306 .011639 -0.66 0.512 .9697787 1.015407
_cons | .0629193 .0017473 -99.60 0.000 .0595862 .0664388
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16408.788
Iteration 1: log pseudolikelihood = -16400.088
Iteration 2: log pseudolikelihood = -16400.031
Iteration 3: log pseudolikelihood = -16400.031
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16400.031 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180365 .0538352 3.64 0.000 1.079429 1.29074
_rcs1 | 2.061666 .0343737 43.39 0.000 1.995384 2.13015
_rcs2 | 1.074976 .013216 5.88 0.000 1.049383 1.101194
_rcs3 | 1.030004 .0109664 2.78 0.005 1.008733 1.051723
_rcs4 | 1.00604 .0077523 0.78 0.434 .9909603 1.02135
_rcs5 | 1.003261 .0051799 0.63 0.528 .9931596 1.013465
_rcs6 | 1.005236 .0040055 1.31 0.190 .9974162 1.013118
_rcs_tr_outcome1 | .957498 .0262485 -1.58 0.113 .9074096 1.010351
_rcs_tr_outcome2 | .9849673 .0212498 -0.70 0.483 .9441868 1.027509
_rcs_tr_outcome3 | .9791839 .0173941 -1.18 0.236 .9456787 1.013876
_rcs_tr_outcome4 | .9888986 .0122141 -0.90 0.366 .9652468 1.01313
_rcs_tr_outcome5 | .9963812 .0089297 -0.40 0.686 .979032 1.014038
_cons | .0629186 .0017468 -99.63 0.000 .0595864 .0664371
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16408.941
Iteration 1: log pseudolikelihood = -16400.019
Iteration 2: log pseudolikelihood = -16399.936
Iteration 3: log pseudolikelihood = -16399.936
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.936 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180381 .0538392 3.64 0.000 1.079437 1.290764
_rcs1 | 2.061774 .0344024 43.36 0.000 1.995438 2.130316
_rcs2 | 1.075259 .0132883 5.87 0.000 1.049528 1.101622
_rcs3 | 1.029415 .0110515 2.70 0.007 1.007981 1.051305
_rcs4 | 1.006634 .0080426 0.83 0.408 .9909933 1.022521
_rcs5 | 1.00311 .0053464 0.58 0.560 .9926857 1.013643
_rcs6 | 1.006088 .0045307 1.35 0.178 .9972468 1.015007
_rcs_tr_outcome1 | .9572521 .0262473 -1.59 0.111 .9071663 1.010103
_rcs_tr_outcome2 | .9847393 .0211094 -0.72 0.473 .9442228 1.026994
_rcs_tr_outcome3 | .9815166 .0174577 -1.05 0.294 .9478896 1.016337
_rcs_tr_outcome4 | .9867343 .0125343 -1.05 0.293 .9624709 1.011609
_rcs_tr_outcome5 | .9945698 .0092867 -0.58 0.560 .9765337 1.012939
_rcs_tr_outcome6 | .9961427 .0077211 -0.50 0.618 .9811239 1.011391
_cons | .0629165 .0017471 -99.61 0.000 .0595838 .0664355
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16410.198
Iteration 1: log pseudolikelihood = -16399.71
Iteration 2: log pseudolikelihood = -16399.559
Iteration 3: log pseudolikelihood = -16399.559
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.559 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180354 .0538412 3.64 0.000 1.079406 1.290741
_rcs1 | 2.0618 .0344136 43.35 0.000 1.995442 2.130365
_rcs2 | 1.075756 .0135164 5.81 0.000 1.049588 1.102576
_rcs3 | 1.028228 .0110558 2.59 0.010 1.006786 1.050127
_rcs4 | 1.007967 .0080311 1.00 0.319 .992349 1.023832
_rcs5 | 1.001957 .0053171 0.37 0.713 .9915899 1.012433
_rcs6 | 1.006144 .0044443 1.39 0.166 .9974707 1.014892
_rcs_tr_outcome1 | .9572905 .0262644 -1.59 0.112 .9071729 1.010177
_rcs_tr_outcome2 | .9838427 .0206929 -0.77 0.439 .94411 1.025248
_rcs_tr_outcome3 | .985674 .0172822 -0.82 0.411 .9523769 1.020135
_rcs_tr_outcome4 | .9822323 .0126497 -1.39 0.164 .9577496 1.007341
_rcs_tr_outcome5 | .9958667 .0092459 -0.45 0.656 .977909 1.014154
_rcs_tr_outcome6 | .996742 .0079192 -0.41 0.681 .9813409 1.012385
_rcs_tr_outcome7 | .9965241 .0064333 -0.54 0.590 .9839946 1.009213
_cons | .0629182 .0017473 -99.60 0.000 .0595851 .0664378
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16405.722
Iteration 1: log pseudolikelihood = -16401.611
Iteration 2: log pseudolikelihood = -16401.6
Iteration 3: log pseudolikelihood = -16401.6
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16401.6 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.181813 .0538578 3.67 0.000 1.080831 1.29223
_rcs1 | 2.054166 .0334557 44.20 0.000 1.98963 2.120796
_rcs2 | 1.067495 .0107217 6.50 0.000 1.046686 1.088717
_rcs3 | 1.024418 .0088221 2.80 0.005 1.007272 1.041855
_rcs4 | 1.001617 .0064593 0.25 0.802 .9890364 1.014357
_rcs5 | 1.001301 .0044912 0.29 0.772 .9925366 1.010142
_rcs6 | 1.002575 .0038223 0.67 0.500 .9951115 1.010095
_rcs7 | 1.004956 .0032258 1.54 0.124 .9986535 1.011299
_rcs_tr_outcome1 | .9683798 .0271077 -1.15 0.251 .9166809 1.022994
_cons | .0629485 .0017476 -99.61 0.000 .0596149 .0664686
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16405.723
Iteration 1: log pseudolikelihood = -16400.747
Iteration 2: log pseudolikelihood = -16400.723
Iteration 3: log pseudolikelihood = -16400.723
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16400.723 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180627 .0539112 3.64 0.000 1.079553 1.291163
_rcs1 | 2.06083 .0343657 43.36 0.000 1.994563 2.129299
_rcs2 | 1.077745 .0138802 5.81 0.000 1.050881 1.105297
_rcs3 | 1.025709 .008859 2.94 0.003 1.008492 1.04322
_rcs4 | 1.002019 .0064075 0.32 0.752 .9895384 1.014656
_rcs5 | 1.001533 .0044814 0.34 0.732 .9927878 1.010355
_rcs6 | 1.002741 .0038071 0.72 0.471 .9953074 1.010231
_rcs7 | 1.00509 .0032178 1.59 0.113 .9988031 1.011417
_rcs_tr_outcome1 | .9607938 .0261998 -1.47 0.142 .9107913 1.013541
_rcs_tr_outcome2 | .9767474 .0202052 -1.14 0.255 .937938 1.017163
_cons | .0629461 .001747 -99.64 0.000 .0596135 .0664649
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16405.552
Iteration 1: log pseudolikelihood = -16399.351
Iteration 2: log pseudolikelihood = -16399.321
Iteration 3: log pseudolikelihood = -16399.321
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.321 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18031 .0539024 3.63 0.000 1.079253 1.290829
_rcs1 | 2.061324 .0342858 43.49 0.000 1.995209 2.129631
_rcs2 | 1.073755 .0130459 5.86 0.000 1.048487 1.099631
_rcs3 | 1.03273 .0100899 3.30 0.001 1.013142 1.052696
_rcs4 | 1.006063 .0070529 0.86 0.389 .9923339 1.019982
_rcs5 | 1.002985 .0045097 0.66 0.507 .9941847 1.011863
_rcs6 | 1.003259 .0037765 0.86 0.387 .9958848 1.010689
_rcs7 | 1.00521 .0032056 1.63 0.103 .9989463 1.011512
_rcs_tr_outcome1 | .9584996 .0262466 -1.55 0.122 .9084133 1.011348
_rcs_tr_outcome2 | .9832648 .0209558 -0.79 0.428 .9430382 1.025207
_rcs_tr_outcome3 | .9775847 .0160227 -1.38 0.167 .9466798 1.009498
_cons | .0629261 .0017482 -99.55 0.000 .0595913 .0664475
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16405.735
Iteration 1: log pseudolikelihood = -16399.203
Iteration 2: log pseudolikelihood = -16399.172
Iteration 3: log pseudolikelihood = -16399.172
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.172 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180127 .0538748 3.63 0.000 1.07912 1.290587
_rcs1 | 2.06124 .0343165 43.45 0.000 1.995066 2.129608
_rcs2 | 1.073882 .012996 5.89 0.000 1.04871 1.099658
_rcs3 | 1.032386 .010706 3.07 0.002 1.011615 1.053585
_rcs4 | 1.006453 .0071667 0.90 0.366 .9925037 1.020598
_rcs5 | 1.003896 .0050792 0.77 0.442 .99399 1.013901
_rcs6 | 1.003859 .0038804 1.00 0.319 .996282 1.011493
_rcs7 | 1.005331 .0032008 1.67 0.095 .9990775 1.011624
_rcs_tr_outcome1 | .9583374 .0262552 -1.55 0.120 .9082354 1.011203
_rcs_tr_outcome2 | .9839635 .0209176 -0.76 0.447 .9438083 1.025827
_rcs_tr_outcome3 | .9780669 .016697 -1.30 0.194 .9458829 1.011346
_rcs_tr_outcome4 | .9919846 .0114985 -0.69 0.488 .9697021 1.014779
_cons | .0629247 .0017482 -99.55 0.000 .05959 .0664461
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16405.686
Iteration 1: log pseudolikelihood = -16399.141
Iteration 2: log pseudolikelihood = -16399.107
Iteration 3: log pseudolikelihood = -16399.107
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.107 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180067 .0538693 3.63 0.000 1.079071 1.290517
_rcs1 | 2.061209 .034326 43.43 0.000 1.995018 2.129597
_rcs2 | 1.074016 .0129657 5.91 0.000 1.048902 1.099731
_rcs3 | 1.032071 .0110355 2.95 0.003 1.010667 1.053928
_rcs4 | 1.006754 .0078172 0.87 0.386 .9915484 1.022192
_rcs5 | 1.0041 .005002 0.82 0.411 .9943437 1.013952
_rcs6 | 1.003984 .0044147 0.90 0.366 .9953684 1.012674
_rcs7 | 1.005471 .0032468 1.69 0.091 .9991272 1.011854
_rcs_tr_outcome1 | .9583033 .026244 -1.56 0.120 .9082221 1.011146
_rcs_tr_outcome2 | .9843721 .0208215 -0.74 0.456 .9443971 1.026039
_rcs_tr_outcome3 | .9794889 .0171182 -1.19 0.236 .9465058 1.013621
_rcs_tr_outcome4 | .9884415 .0122018 -0.94 0.346 .9648133 1.012648
_rcs_tr_outcome5 | .9970736 .0089077 -0.33 0.743 .9797669 1.014686
_cons | .0629246 .0017479 -99.57 0.000 .0595903 .0664455
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16406.016
Iteration 1: log pseudolikelihood = -16399.061
Iteration 2: log pseudolikelihood = -16399.025
Iteration 3: log pseudolikelihood = -16399.025
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.025 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180117 .053867 3.63 0.000 1.079124 1.290561
_rcs1 | 2.061351 .0343508 43.41 0.000 1.995112 2.129789
_rcs2 | 1.074095 .0129351 5.94 0.000 1.04904 1.099749
_rcs3 | 1.031984 .0112137 2.90 0.004 1.010238 1.054199
_rcs4 | 1.006688 .0082646 0.81 0.417 .9906191 1.023017
_rcs5 | 1.004284 .0051843 0.83 0.408 .9941738 1.014496
_rcs6 | 1.004842 .0044597 1.09 0.276 .9961391 1.013621
_rcs7 | 1.006096 .0035894 1.70 0.088 .9990855 1.013156
_rcs_tr_outcome1 | .9579545 .0262351 -1.57 0.117 .9078903 1.010779
_rcs_tr_outcome2 | .9846215 .0207929 -0.73 0.463 .9447 1.02623
_rcs_tr_outcome3 | .9807081 .0172726 -1.11 0.269 .9474321 1.015153
_rcs_tr_outcome4 | .9878149 .0124931 -0.97 0.332 .96363 1.012607
_rcs_tr_outcome5 | .9934303 .0090601 -0.72 0.470 .9758305 1.011348
_rcs_tr_outcome6 | .9963143 .0074915 -0.49 0.623 .9817389 1.011106
_cons | .0629208 .0017477 -99.58 0.000 .059587 .0664411
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16405.639
Iteration 1: log pseudolikelihood = -16398.105
Iteration 2: log pseudolikelihood = -16397.998
Iteration 3: log pseudolikelihood = -16397.998
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16397.998 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180382 .053838 3.64 0.000 1.079441 1.290763
_rcs1 | 2.061625 .0343838 43.38 0.000 1.995324 2.13013
_rcs2 | 1.074338 .0130724 5.89 0.000 1.04902 1.100268
_rcs3 | 1.030934 .0112808 2.78 0.005 1.00906 1.053283
_rcs4 | 1.007745 .008458 0.92 0.358 .9913034 1.02446
_rcs5 | 1.004298 .0053481 0.81 0.421 .9938706 1.014835
_rcs6 | 1.004009 .0045542 0.88 0.378 .995122 1.012974
_rcs7 | 1.007876 .0038223 2.07 0.039 1.000413 1.015396
_rcs_tr_outcome1 | .9574962 .0262426 -1.58 0.113 .9074187 1.010337
_rcs_tr_outcome2 | .9847077 .0205674 -0.74 0.461 .9452103 1.025855
_rcs_tr_outcome3 | .9839225 .017376 -0.92 0.359 .9504488 1.018575
_rcs_tr_outcome4 | .9846919 .0128567 -1.18 0.237 .9598131 1.010216
_rcs_tr_outcome5 | .9927983 .0094433 -0.76 0.447 .9744612 1.01148
_rcs_tr_outcome6 | .9976548 .0080021 -0.29 0.770 .9820935 1.013463
_rcs_tr_outcome7 | .9929206 .0067497 -1.05 0.296 .9797792 1.006238
_cons | .0629169 .0017469 -99.62 0.000 .0595846 .0664356
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.556
Iteration 1: log pseudolikelihood = -16400.51
Iteration 2: log pseudolikelihood = -16400.503
Iteration 3: log pseudolikelihood = -16400.503
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16400.503 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.182152 .0538678 3.67 0.000 1.08115 1.292589
_rcs1 | 2.054691 .0334851 44.19 0.000 1.990099 2.12138
_rcs2 | 1.066946 .0105454 6.56 0.000 1.046476 1.087816
_rcs3 | 1.025926 .0087933 2.99 0.003 1.008836 1.043307
_rcs4 | 1.002063 .0066464 0.31 0.756 .989121 1.015175
_rcs5 | 1.002251 .0045162 0.50 0.618 .9934384 1.011142
_rcs6 | 1.000187 .0036633 0.05 0.959 .9930325 1.007393
_rcs7 | 1.00542 .0035252 1.54 0.123 .9985345 1.012353
_rcs8 | 1.003586 .0029494 1.22 0.223 .9978219 1.009383
_rcs_tr_outcome1 | .9678787 .0271202 -1.17 0.244 .9161573 1.02252
_cons | .0629428 .0017472 -99.63 0.000 .0596098 .0664621
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.549
Iteration 1: log pseudolikelihood = -16399.602
Iteration 2: log pseudolikelihood = -16399.584
Iteration 3: log pseudolikelihood = -16399.584
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.584 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180951 .0539182 3.64 0.000 1.079864 1.291501
_rcs1 | 2.061531 .0344259 43.32 0.000 1.99515 2.130121
_rcs2 | 1.077431 .0137649 5.84 0.000 1.050787 1.10475
_rcs3 | 1.02735 .0088292 3.14 0.002 1.01019 1.044801
_rcs4 | 1.002494 .0065979 0.38 0.705 .9896457 1.01551
_rcs5 | 1.002535 .0045014 0.56 0.573 .9937511 1.011397
_rcs6 | 1.000338 .0036498 0.09 0.926 .9932102 1.007517
_rcs7 | 1.005598 .0035116 1.60 0.110 .9987393 1.012504
_rcs8 | 1.003683 .0029414 1.25 0.210 .9979343 1.009465
_rcs_tr_outcome1 | .960107 .0262 -1.49 0.136 .9101051 1.012856
_rcs_tr_outcome2 | .9762199 .0200435 -1.17 0.241 .9377152 1.016306
_cons | .0629401 .0017466 -99.66 0.000 .0596082 .0664582
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.344
Iteration 1: log pseudolikelihood = -16398.127
Iteration 2: log pseudolikelihood = -16398.104
Iteration 3: log pseudolikelihood = -16398.104
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16398.104 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180617 .0539061 3.64 0.000 1.079553 1.291143
_rcs1 | 2.062033 .034347 43.45 0.000 1.995801 2.130463
_rcs2 | 1.073221 .0128681 5.89 0.000 1.048294 1.098741
_rcs3 | 1.034276 .0100125 3.48 0.000 1.014837 1.054087
_rcs4 | 1.006856 .0072795 0.95 0.345 .9926895 1.021226
_rcs5 | 1.00452 .0046277 0.98 0.328 .9954904 1.013631
_rcs6 | 1.00109 .0036166 0.30 0.763 .9940269 1.008204
_rcs7 | 1.005912 .0034858 1.70 0.089 .9991029 1.012767
_rcs8 | 1.003804 .0029287 1.30 0.193 .99808 1.009561
_rcs_tr_outcome1 | .9577535 .0262555 -1.57 0.115 .9076516 1.010621
_rcs_tr_outcome2 | .9828683 .0207259 -0.82 0.413 .9430743 1.024341
_rcs_tr_outcome3 | .9769771 .0159628 -1.43 0.154 .9461862 1.00877
_cons | .0629195 .0017478 -99.57 0.000 .0595856 .06644
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.592
Iteration 1: log pseudolikelihood = -16398.025
Iteration 2: log pseudolikelihood = -16398
Iteration 3: log pseudolikelihood = -16398
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16398 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180461 .0538799 3.63 0.000 1.079444 1.290931
_rcs1 | 2.061955 .0343798 43.40 0.000 1.995661 2.130451
_rcs2 | 1.07334 .012791 5.94 0.000 1.04856 1.098705
_rcs3 | 1.033999 .0106458 3.25 0.001 1.013343 1.055077
_rcs4 | 1.007018 .0072832 0.97 0.334 .9928444 1.021395
_rcs5 | 1.00512 .0051655 0.99 0.320 .9950466 1.015295
_rcs6 | 1.001755 .0039039 0.45 0.653 .9941329 1.009436
_rcs7 | 1.006209 .003488 1.79 0.074 .9993956 1.013068
_rcs8 | 1.003846 .002926 1.32 0.188 .9981278 1.009598
_rcs_tr_outcome1 | .9576342 .0262678 -1.58 0.115 .9075097 1.010527
_rcs_tr_outcome2 | .9835954 .0206617 -0.79 0.431 .9439215 1.024937
_rcs_tr_outcome3 | .9773951 .0165405 -1.35 0.177 .9455081 1.010358
_rcs_tr_outcome4 | .9924565 .0114406 -0.66 0.511 .9702848 1.015135
_cons | .0629185 .0017477 -99.58 0.000 .0595846 .0664389
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.558
Iteration 1: log pseudolikelihood = -16397.927
Iteration 2: log pseudolikelihood = -16397.898
Iteration 3: log pseudolikelihood = -16397.898
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16397.898 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180369 .0538735 3.63 0.000 1.079364 1.290826
_rcs1 | 2.061884 .0343793 43.40 0.000 1.995591 2.13038
_rcs2 | 1.073567 .0127971 5.96 0.000 1.048776 1.098944
_rcs3 | 1.033378 .01099 3.09 0.002 1.012061 1.055144
_rcs4 | 1.007458 .007773 0.96 0.336 .9923381 1.022809
_rcs5 | 1.005518 .0051486 1.07 0.282 .9954777 1.01566
_rcs6 | 1.001814 .0042727 0.42 0.671 .9934745 1.010223
_rcs7 | 1.006278 .0037524 1.68 0.093 .9989507 1.01366
_rcs8 | 1.00392 .0029236 1.34 0.179 .9982063 1.009667
_rcs_tr_outcome1 | .9576376 .0262463 -1.58 0.114 .907553 1.010486
_rcs_tr_outcome2 | .9838631 .020469 -0.78 0.434 .9445516 1.024811
_rcs_tr_outcome3 | .9792754 .0168424 -1.22 0.223 .9468151 1.012849
_rcs_tr_outcome4 | .9879807 .0121514 -0.98 0.326 .9644493 1.012086
_rcs_tr_outcome5 | .9976237 .0089618 -0.26 0.791 .9802127 1.015344
_cons | .0629189 .0017475 -99.59 0.000 .0595854 .0664389
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.749
Iteration 1: log pseudolikelihood = -16397.717
Iteration 2: log pseudolikelihood = -16397.68
Iteration 3: log pseudolikelihood = -16397.68
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16397.68 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180563 .0538859 3.64 0.000 1.079535 1.291046
_rcs1 | 2.062189 .0344173 43.37 0.000 1.995824 2.130761
_rcs2 | 1.07367 .0128157 5.96 0.000 1.048843 1.099084
_rcs3 | 1.033022 .0112162 2.99 0.003 1.011271 1.055241
_rcs4 | 1.007895 .00823 0.96 0.336 .9918926 1.024155
_rcs5 | 1.005263 .005116 1.03 0.302 .9952861 1.015341
_rcs6 | 1.002278 .0042688 0.53 0.593 .993946 1.01068
_rcs7 | 1.007677 .0041666 1.85 0.064 .9995432 1.015876
_rcs8 | 1.004403 .0029794 1.48 0.139 .9985802 1.010259
_rcs_tr_outcome1 | .9570277 .0262465 -1.60 0.109 .9069436 1.009878
_rcs_tr_outcome2 | .9841969 .0203824 -0.77 0.442 .945048 1.024968
_rcs_tr_outcome3 | .9810133 .0169433 -1.11 0.267 .9483608 1.01479
_rcs_tr_outcome4 | .9864536 .0125267 -1.07 0.283 .9622047 1.011314
_rcs_tr_outcome5 | .9945729 .0091172 -0.59 0.553 .9768632 1.012604
_rcs_tr_outcome6 | .9949794 .0075715 -0.66 0.508 .9802495 1.00993
_cons | .0629119 .0017476 -99.57 0.000 .0595782 .0664322
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.08
Iteration 1: log pseudolikelihood = -16396.543
Iteration 2: log pseudolikelihood = -16396.477
Iteration 3: log pseudolikelihood = -16396.477
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16396.477 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.181025 .053893 3.65 0.000 1.079982 1.291521
_rcs1 | 2.062832 .0345022 43.29 0.000 1.996305 2.131575
_rcs2 | 1.073801 .0128711 5.94 0.000 1.048868 1.099326
_rcs3 | 1.03249 .0113504 2.91 0.004 1.010481 1.054978
_rcs4 | 1.008331 .0085777 0.98 0.329 .9916585 1.025284
_rcs5 | 1.005962 .0053181 1.12 0.261 .9955924 1.016439
_rcs6 | 1.00129 .0042368 0.30 0.761 .9930208 1.009629
_rcs7 | 1.008173 .0041227 1.99 0.047 1.000125 1.016286
_rcs8 | 1.006068 .0032313 1.88 0.060 .9997552 1.012422
_rcs_tr_outcome1 | .9561449 .0262897 -1.63 0.103 .9059819 1.009085
_rcs_tr_outcome2 | .9844642 .0201892 -0.76 0.445 .9456788 1.02484
_rcs_tr_outcome3 | .9832452 .0170631 -0.97 0.330 .9503644 1.017264
_rcs_tr_outcome4 | .9839935 .0129018 -1.23 0.218 .9590286 1.009608
_rcs_tr_outcome5 | .9934014 .0093197 -0.71 0.480 .975302 1.011837
_rcs_tr_outcome6 | .9973686 .0078817 -0.33 0.739 .9820397 1.012937
_rcs_tr_outcome7 | .9920787 .0066048 -1.19 0.232 .9792176 1.005109
_cons | .0629045 .001747 -99.60 0.000 .059572 .0664234
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.718
Iteration 1: log pseudolikelihood = -16400.725
Iteration 2: log pseudolikelihood = -16400.717
Iteration 3: log pseudolikelihood = -16400.717
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16400.717 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.181968 .0539387 3.66 0.000 1.08084 1.292558
_rcs1 | 2.054396 .0335095 44.14 0.000 1.989757 2.121134
_rcs2 | 1.066526 .0105194 6.53 0.000 1.046106 1.087344
_rcs3 | 1.026678 .0088209 3.06 0.002 1.009534 1.044113
_rcs4 | 1.003233 .0066754 0.49 0.628 .9902344 1.016402
_rcs5 | 1.00239 .004596 0.52 0.603 .9934224 1.011439
_rcs6 | 1.000228 .0036252 0.06 0.950 .9931475 1.007358
_rcs7 | 1.002509 .0033898 0.74 0.459 .9958875 1.009175
_rcs8 | 1.00538 .0032002 1.69 0.092 .9991274 1.011672
_rcs9 | 1.00334 .0027516 1.22 0.224 .9979612 1.008747
_rcs_tr_outcome1 | .9682723 .0272049 -1.15 0.251 .9163931 1.023088
_cons | .0629466 .0017484 -99.56 0.000 .0596114 .0664685
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.704
Iteration 1: log pseudolikelihood = -16399.825
Iteration 2: log pseudolikelihood = -16399.805
Iteration 3: log pseudolikelihood = -16399.805
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.805 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18078 .0539889 3.63 0.000 1.079566 1.291482
_rcs1 | 2.061217 .0344381 43.29 0.000 1.994812 2.129831
_rcs2 | 1.076939 .0137125 5.82 0.000 1.050395 1.104153
_rcs3 | 1.028218 .0088672 3.23 0.001 1.010985 1.045745
_rcs4 | 1.003691 .0066327 0.56 0.577 .9907748 1.016775
_rcs5 | 1.002703 .0045725 0.59 0.554 .9937814 1.011705
_rcs6 | 1.000381 .0036153 0.11 0.916 .9933207 1.007492
_rcs7 | 1.002681 .0033735 0.80 0.426 .9960904 1.009315
_rcs8 | 1.005528 .0031891 1.74 0.082 .999297 1.011798
_rcs9 | 1.003425 .0027425 1.25 0.211 .9980643 1.008815
_rcs_tr_outcome1 | .9605177 .0262715 -1.47 0.141 .9103822 1.013414
_rcs_tr_outcome2 | .9763119 .0200271 -1.17 0.243 .9378382 1.016364
_cons | .0629438 .0017478 -99.59 0.000 .0596097 .0664645
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.463
Iteration 1: log pseudolikelihood = -16398.345
Iteration 2: log pseudolikelihood = -16398.321
Iteration 3: log pseudolikelihood = -16398.321
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16398.321 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180512 .0539826 3.63 0.000 1.079311 1.291202
_rcs1 | 2.061839 .0343805 43.39 0.000 1.995543 2.130336
_rcs2 | 1.07265 .0128172 5.87 0.000 1.047821 1.098068
_rcs3 | 1.034893 .0099713 3.56 0.000 1.015533 1.054622
_rcs4 | 1.008159 .007328 1.12 0.264 .9938982 1.022624
_rcs5 | 1.00512 .0047981 1.07 0.285 .9957595 1.014568
_rcs6 | 1.001354 .0035977 0.38 0.707 .9943272 1.00843
_rcs7 | 1.003231 .0033412 0.97 0.333 .9967035 1.009801
_rcs8 | 1.005698 .0031697 1.80 0.071 .999505 1.01193
_rcs9 | 1.003598 .0027273 1.32 0.186 .9982665 1.008957
_rcs_tr_outcome1 | .9580306 .0263403 -1.56 0.119 .9077709 1.011073
_rcs_tr_outcome2 | .9829353 .0207192 -0.82 0.414 .9431538 1.024395
_rcs_tr_outcome3 | .9769246 .0159389 -1.43 0.152 .9461791 1.008669
_cons | .0629221 .0017491 -99.50 0.000 .0595856 .0664455
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.805
Iteration 1: log pseudolikelihood = -16398.236
Iteration 2: log pseudolikelihood = -16398.209
Iteration 3: log pseudolikelihood = -16398.209
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16398.209 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180319 .0539545 3.63 0.000 1.079169 1.29095
_rcs1 | 2.061711 .0344075 43.35 0.000 1.995365 2.130264
_rcs2 | 1.072825 .0127581 5.91 0.000 1.048109 1.098124
_rcs3 | 1.03447 .0106031 3.31 0.001 1.013896 1.055462
_rcs4 | 1.008275 .0072681 1.14 0.253 .9941299 1.022621
_rcs5 | 1.005658 .0052304 1.08 0.278 .9954583 1.015962
_rcs6 | 1.002107 .0039926 0.53 0.597 .9943117 1.009963
_rcs7 | 1.003733 .0034405 1.09 0.277 .9970128 1.010499
_rcs8 | 1.005889 .0031612 1.87 0.062 .9997124 1.012104
_rcs9 | 1.003612 .0027235 1.33 0.184 .9982886 1.008965
_rcs_tr_outcome1 | .9579505 .0263477 -1.56 0.118 .9076771 1.011008
_rcs_tr_outcome2 | .9835952 .0206324 -0.79 0.430 .9439765 1.024877
_rcs_tr_outcome3 | .9775931 .0164989 -1.34 0.179 .9457848 1.010471
_rcs_tr_outcome4 | .9920672 .0114343 -0.69 0.490 .9699077 1.014733
_cons | .0629214 .001749 -99.50 0.000 .0595851 .0664446
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.772
Iteration 1: log pseudolikelihood = -16398.141
Iteration 2: log pseudolikelihood = -16398.112
Iteration 3: log pseudolikelihood = -16398.112
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16398.112 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180237 .053946 3.63 0.000 1.079102 1.29085
_rcs1 | 2.061636 .0344038 43.36 0.000 1.995297 2.130181
_rcs2 | 1.073061 .0127628 5.93 0.000 1.048335 1.098369
_rcs3 | 1.033826 .0109793 3.13 0.002 1.012529 1.05557
_rcs4 | 1.008632 .0075925 1.14 0.254 .99386 1.023623
_rcs5 | 1.006073 .0054218 1.12 0.261 .995502 1.016756
_rcs6 | 1.002159 .0040699 0.53 0.595 .9942137 1.010168
_rcs7 | 1.003695 .003855 0.96 0.337 .9961675 1.011279
_rcs8 | 1.005944 .0032484 1.84 0.066 .9995978 1.012331
_rcs9 | 1.003658 .0027199 1.35 0.178 .9983412 1.009003
_rcs_tr_outcome1 | .9579873 .0263246 -1.56 0.118 .9077568 1.010997
_rcs_tr_outcome2 | .9838476 .0204294 -0.78 0.433 .9446106 1.024714
_rcs_tr_outcome3 | .9795116 .0168022 -1.21 0.228 .9471274 1.013003
_rcs_tr_outcome4 | .9877338 .0121607 -1.00 0.316 .9641846 1.011858
_rcs_tr_outcome5 | .9977887 .0088765 -0.25 0.803 .9805418 1.015339
_cons | .0629222 .0017488 -99.52 0.000 .0595863 .0664448
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.886
Iteration 1: log pseudolikelihood = -16398.021
Iteration 2: log pseudolikelihood = -16397.983
Iteration 3: log pseudolikelihood = -16397.983
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16397.983 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180363 .0539582 3.63 0.000 1.079206 1.291001
_rcs1 | 2.061859 .0344402 43.32 0.000 1.99545 2.130477
_rcs2 | 1.073153 .0127537 5.94 0.000 1.048445 1.098443
_rcs3 | 1.033596 .0112197 3.04 0.002 1.011838 1.055822
_rcs4 | 1.008814 .0079766 1.11 0.267 .9933009 1.02457
_rcs5 | 1.005988 .0053254 1.13 0.259 .9956046 1.01648
_rcs6 | 1.002284 .0042585 0.54 0.591 .9939717 1.010665
_rcs7 | 1.004671 .0039128 1.20 0.231 .9970317 1.01237
_rcs8 | 1.006863 .0035622 1.93 0.053 .9999053 1.013869
_rcs9 | 1.003856 .0027178 1.42 0.155 .998543 1.009197
_rcs_tr_outcome1 | .9575015 .0263306 -1.58 0.114 .9072606 1.010525
_rcs_tr_outcome2 | .9841377 .0203457 -0.77 0.439 .945058 1.024833
_rcs_tr_outcome3 | .9810755 .0168946 -1.11 0.267 .9485152 1.014754
_rcs_tr_outcome4 | .9865928 .0125635 -1.06 0.289 .9622735 1.011527
_rcs_tr_outcome5 | .9942004 .0090636 -0.64 0.523 .9765939 1.012124
_rcs_tr_outcome6 | .9957481 .0075702 -0.56 0.575 .9810209 1.010696
_cons | .0629165 .0017488 -99.51 0.000 .0595805 .0664393
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.438
Iteration 1: log pseudolikelihood = -16396.657
Iteration 2: log pseudolikelihood = -16396.597
Iteration 3: log pseudolikelihood = -16396.597
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16396.597 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180809 .0539358 3.64 0.000 1.079691 1.291398
_rcs1 | 2.06232 .0344829 43.29 0.000 1.99583 2.131025
_rcs2 | 1.07326 .0128289 5.91 0.000 1.048408 1.098701
_rcs3 | 1.03279 .0114011 2.92 0.003 1.010685 1.055379
_rcs4 | 1.009148 .0083533 1.10 0.271 .9929083 1.025654
_rcs5 | 1.006803 .0054038 1.26 0.206 .9962677 1.017451
_rcs6 | 1.001897 .0041435 0.46 0.647 .9938088 1.010051
_rcs7 | 1.003946 .003977 0.99 0.320 .9961819 1.011772
_rcs8 | 1.008616 .0036937 2.34 0.019 1.001403 1.015882
_rcs9 | 1.005017 .0028068 1.79 0.073 .9995309 1.010533
_rcs_tr_outcome1 | .9567746 .0263302 -1.61 0.108 .9065354 1.009798
_rcs_tr_outcome2 | .9845925 .0201777 -0.76 0.449 .9458287 1.024945
_rcs_tr_outcome3 | .9837228 .0170243 -0.95 0.343 .9509152 1.017662
_rcs_tr_outcome4 | .9842615 .012892 -1.21 0.226 .9593152 1.009856
_rcs_tr_outcome5 | .9928456 .0093929 -0.76 0.448 .9746056 1.011427
_rcs_tr_outcome6 | .9975789 .0078763 -0.31 0.759 .9822605 1.013136
_rcs_tr_outcome7 | .9917511 .0066003 -1.24 0.213 .9788987 1.004772
_cons | .0629094 .0017479 -99.56 0.000 .0595752 .0664302
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.317
Iteration 1: log pseudolikelihood = -16399.752
Iteration 2: log pseudolikelihood = -16399.74
Iteration 3: log pseudolikelihood = -16399.74
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16399.74 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.182116 .0538935 3.67 0.000 1.081069 1.292608
_rcs1 | 2.054603 .0335123 44.15 0.000 1.989959 2.121347
_rcs2 | 1.066388 .0105756 6.48 0.000 1.045861 1.087319
_rcs3 | 1.026753 .0088525 3.06 0.002 1.009548 1.044251
_rcs4 | 1.004761 .0065424 0.73 0.466 .99202 1.017666
_rcs5 | 1.002072 .0046744 0.44 0.657 .9929525 1.011276
_rcs6 | 1.000794 .0036305 0.22 0.827 .993704 1.007936
_rcs7 | 1.000553 .0032392 0.17 0.864 .9942243 1.006922
_rcs8 | 1.00492 .0031455 1.57 0.117 .9987734 1.011104
_rcs9 | 1.004039 .0030001 1.35 0.177 .9981765 1.009937
_rcs10 | 1.003644 .00259 1.41 0.159 .9985802 1.008733
_rcs_tr_outcome1 | .9680569 .0271515 -1.16 0.247 .9162773 1.022763
_cons | .0629455 .0017477 -99.60 0.000 .0596115 .0664659
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.311
Iteration 1: log pseudolikelihood = -16398.848
Iteration 2: log pseudolikelihood = -16398.823
Iteration 3: log pseudolikelihood = -16398.823
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16398.823 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18092 .0539441 3.64 0.000 1.079786 1.291526
_rcs1 | 2.061439 .0344522 43.28 0.000 1.995008 2.130083
_rcs2 | 1.076823 .0137509 5.80 0.000 1.050206 1.104114
_rcs3 | 1.028364 .0089097 3.23 0.001 1.011049 1.045976
_rcs4 | 1.005258 .0065055 0.81 0.418 .9925885 1.01809
_rcs5 | 1.002397 .0046446 0.52 0.605 .9933349 1.011542
_rcs6 | 1.000981 .0036222 0.27 0.786 .9939072 1.008106
_rcs7 | 1.000704 .0032245 0.22 0.827 .9944043 1.007044
_rcs8 | 1.005084 .0031326 1.63 0.104 .9989629 1.011242
_rcs9 | 1.00417 .002988 1.40 0.162 .9983304 1.010043
_rcs10 | 1.00371 .0025814 1.44 0.150 .9986636 1.008782
_rcs_tr_outcome1 | .9602915 .0262348 -1.48 0.138 .9102246 1.013112
_rcs_tr_outcome2 | .9762298 .0200805 -1.17 0.242 .9376556 1.016391
_cons | .0629428 .0017471 -99.63 0.000 .0596099 .066462
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.059
Iteration 1: log pseudolikelihood = -16397.343
Iteration 2: log pseudolikelihood = -16397.314
Iteration 3: log pseudolikelihood = -16397.314
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16397.314 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180617 .0539345 3.63 0.000 1.079502 1.291204
_rcs1 | 2.061978 .0343823 43.40 0.000 1.99568 2.13048
_rcs2 | 1.072419 .0128526 5.83 0.000 1.047521 1.097907
_rcs3 | 1.034816 .0099625 3.55 0.000 1.015473 1.054527
_rcs4 | 1.009836 .0072245 1.37 0.171 .9957754 1.024096
_rcs5 | 1.005078 .004957 1.03 0.304 .9954096 1.014841
_rcs6 | 1.002248 .0036444 0.62 0.537 .9951302 1.009416
_rcs7 | 1.00139 .0031935 0.44 0.663 .9951505 1.007669
_rcs8 | 1.0054 .0031033 1.74 0.081 .9993358 1.0115
_rcs9 | 1.004348 .0029678 1.47 0.142 .9985479 1.010182
_rcs10 | 1.003854 .0025647 1.51 0.132 .9988398 1.008893
_rcs_tr_outcome1 | .957892 .0263015 -1.57 0.117 .9077046 1.010854
_rcs_tr_outcome2 | .9829655 .0207981 -0.81 0.417 .9430357 1.024586
_rcs_tr_outcome3 | .9767784 .0159701 -1.44 0.151 .9459738 1.008586
_cons | .0629216 .0017484 -99.54 0.000 .0595865 .0664433
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.441
Iteration 1: log pseudolikelihood = -16397.222
Iteration 2: log pseudolikelihood = -16397.19
Iteration 3: log pseudolikelihood = -16397.19
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16397.19 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180432 .0539074 3.63 0.000 1.079366 1.290962
_rcs1 | 2.061872 .03441 43.36 0.000 1.99552 2.130429
_rcs2 | 1.072595 .0127957 5.87 0.000 1.047807 1.09797
_rcs3 | 1.034406 .0105975 3.30 0.001 1.013843 1.055387
_rcs4 | 1.009908 .0071461 1.39 0.164 .9959984 1.024012
_rcs5 | 1.005522 .0052838 1.05 0.295 .9952191 1.015931
_rcs6 | 1.002959 .004075 0.73 0.467 .995004 1.010978
_rcs7 | 1.00201 .0034246 0.59 0.557 .99532 1.008744
_rcs8 | 1.00573 .0031053 1.85 0.064 .9996621 1.011835
_rcs9 | 1.004456 .0029614 1.51 0.132 .9986687 1.010277
_rcs10 | 1.00387 .0025591 1.51 0.130 .9988662 1.008898
_rcs_tr_outcome1 | .95778 .0263087 -1.57 0.116 .9075792 1.010757
_rcs_tr_outcome2 | .9836367 .0207268 -0.78 0.434 .9438403 1.025111
_rcs_tr_outcome3 | .9774025 .0165708 -1.35 0.178 .9454581 1.010426
_rcs_tr_outcome4 | .9919728 .0114597 -0.70 0.485 .9697646 1.01469
_cons | .0629206 .0017483 -99.54 0.000 .0595857 .0664422
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.372
Iteration 1: log pseudolikelihood = -16397.134
Iteration 2: log pseudolikelihood = -16397.099
Iteration 3: log pseudolikelihood = -16397.099
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16397.099 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180347 .0538988 3.63 0.000 1.079297 1.290858
_rcs1 | 2.061808 .0344087 43.36 0.000 1.995459 2.130362
_rcs2 | 1.072816 .0127905 5.90 0.000 1.048037 1.09818
_rcs3 | 1.033846 .0109942 3.13 0.002 1.012521 1.055621
_rcs4 | 1.0101 .0073502 1.38 0.167 .9957956 1.024609
_rcs5 | 1.005903 .0056066 1.06 0.291 .9949737 1.016952
_rcs6 | 1.003185 .0040147 0.79 0.427 .9953469 1.011084
_rcs7 | 1.002111 .0037685 0.56 0.575 .9947516 1.009524
_rcs8 | 1.005852 .0033939 1.73 0.084 .9992219 1.012526
_rcs9 | 1.004557 .0029704 1.54 0.124 .9987518 1.010396
_rcs10 | 1.003898 .002554 1.53 0.126 .9989052 1.008917
_rcs_tr_outcome1 | .9577724 .0262819 -1.57 0.116 .9076215 1.010694
_rcs_tr_outcome2 | .9839415 .0205537 -0.77 0.438 .9444705 1.025062
_rcs_tr_outcome3 | .9792423 .0168976 -1.22 0.224 .9466775 1.012927
_rcs_tr_outcome4 | .9877985 .0121731 -1.00 0.319 .9642254 1.011948
_rcs_tr_outcome5 | .9971665 .0089218 -0.32 0.751 .9798325 1.014807
_cons | .0629207 .001748 -99.56 0.000 .0595863 .0664418
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.481
Iteration 1: log pseudolikelihood = -16396.977
Iteration 2: log pseudolikelihood = -16396.936
Iteration 3: log pseudolikelihood = -16396.936
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16396.936 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.180487 .0539083 3.63 0.000 1.079419 1.291018
_rcs1 | 2.062041 .0344441 43.32 0.000 1.995625 2.130668
_rcs2 | 1.072937 .0127954 5.90 0.000 1.048149 1.098311
_rcs3 | 1.033507 .0112689 3.02 0.003 1.011655 1.055832
_rcs4 | 1.010399 .0076768 1.36 0.173 .9954644 1.025558
_rcs5 | 1.005933 .005591 1.06 0.287 .9950342 1.016951
_rcs6 | 1.002992 .0041942 0.71 0.475 .9948052 1.011246
_rcs7 | 1.002597 .0037172 0.70 0.484 .9953381 1.009909
_rcs8 | 1.006852 .0036987 1.86 0.063 .9996292 1.014128
_rcs9 | 1.005173 .0030921 1.68 0.093 .9991308 1.011252
_rcs10 | 1.004017 .0025451 1.58 0.114 .9990408 1.009017
_rcs_tr_outcome1 | .9573017 .0262855 -1.59 0.112 .9071448 1.010232
_rcs_tr_outcome2 | .9841818 .0204491 -0.77 0.443 .9449075 1.025089
_rcs_tr_outcome3 | .9809285 .0170124 -1.11 0.267 .9481453 1.014845
_rcs_tr_outcome4 | .9864072 .0126076 -1.07 0.284 .9620037 1.01143
_rcs_tr_outcome5 | .9942558 .0091047 -0.63 0.529 .97657 1.012262
_rcs_tr_outcome6 | .9954886 .0074553 -0.60 0.546 .9809832 1.010208
_cons | .0629153 .0017481 -99.55 0.000 .0595808 .0664365
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16403.304
Iteration 1: log pseudolikelihood = -16395.969
Iteration 2: log pseudolikelihood = -16395.905
Iteration 3: log pseudolikelihood = -16395.905
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16395.905 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.18077 .053885 3.64 0.000 1.079743 1.29125
_rcs1 | 2.062345 .034487 43.29 0.000 1.995847 2.131058
_rcs2 | 1.073155 .0128898 5.88 0.000 1.048187 1.098718
_rcs3 | 1.032669 .0114842 2.89 0.004 1.010404 1.055424
_rcs4 | 1.010711 .0080155 1.34 0.179 .9951226 1.026544
_rcs5 | 1.006582 .0055527 1.19 0.234 .9957573 1.017524
_rcs6 | 1.003118 .004214 0.74 0.459 .9948922 1.011411
_rcs7 | 1.001641 .0037626 0.44 0.663 .9942931 1.009042
_rcs8 | 1.007203 .0036219 2.00 0.046 1.00013 1.014327
_rcs9 | 1.006698 .0033078 2.03 0.042 1.000236 1.013202
_rcs10 | 1.004583 .0025428 1.81 0.071 .999612 1.009579
_rcs_tr_outcome1 | .9568236 .0262984 -1.61 0.108 .9066434 1.009781
_rcs_tr_outcome2 | .9843695 .0202893 -0.76 0.445 .9453958 1.02495
_rcs_tr_outcome3 | .9834948 .0171662 -0.95 0.340 .9504187 1.017722
_rcs_tr_outcome4 | .9840888 .0128996 -1.22 0.221 .9591281 1.009699
_rcs_tr_outcome5 | .9931731 .0093001 -0.73 0.464 .9751116 1.011569
_rcs_tr_outcome6 | .9972603 .0078094 -0.35 0.726 .9820711 1.012684
_rcs_tr_outcome7 | .9925332 .0065798 -1.13 0.258 .9797205 1.005513
_cons | .0629112 .0017472 -99.60 0.000 .0595782 .0664306
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
.
. *https://core.ac.uk/download/pdf/6990318.pdf
.
. *The following options are not permitted with streg models:
. *bknots, bknotstvc, df, dftvc, failconvlininit, knots, knotstvc knscale, noorthorg, eform, alleq, keepcons, showcons, lininit
. *forvalues j=1/7 {
. local vars "exponential weibull gompertz lognormal loglogistic"
. local varslab "exp wei gom logn llog"
. forvalues i = 1/5 {
2. local v : word `i' of `vars'
3. local v2 : word `i' of `varslab'
4. qui noi stipw (logit tr_outcome tr_mod2 sex_dum2 edad_ini_cons esc1 esc2 sus_prin2 sus_prin3 sus_prin4 sus_prin5 fr_cons_sus_prin2 fr_cons_sus_prin3 fr_cons_sus_prin4 fr_cons_sus_prin5 cond_ocu2 cond_ocu3 cond_
> ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 tenviv5 mzone2 mzone3 n_off_vio n_off_acq n_off_sud n_off_oth psy_com2 psy_com3 dep2 rural2 rural3 porc_pobr susini2 susini3 susini4 susini5 an
> o_nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3), distribution(`v') genw(`v2'_m3_nostag) ipwtype(stabilised) vce(mestimation)
5. estimates store m3_stipw_nostag_`v2'
6. }
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=exp_m3_nostag]
Iteration 0: log pseudolikelihood = -16697.059
Iteration 1: log pseudolikelihood = -16691.345
Iteration 2: log pseudolikelihood = -16691.338
Iteration 3: log pseudolikelihood = -16691.338
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 43,782
Wald chi2(1) = 7.00
Log pseudolikelihood = -16691.338 Prob > chi2 = 0.0082
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.12852 .0515898 2.64 0.008 1.031804 1.234302
_cons | .0185915 .0005004 -148.05 0.000 .017636 .0195986
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=wei_m3_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -16697.059
Iteration 1: log pseudolikelihood = -16454.641
Iteration 2: log pseudolikelihood = -16450.728
Iteration 3: log pseudolikelihood = -16450.727
Fitting full model:
Iteration 0: log pseudolikelihood = -16450.727
Iteration 1: log pseudolikelihood = -16442.639
Iteration 2: log pseudolikelihood = -16442.624
Iteration 3: log pseudolikelihood = -16442.624
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 43,782
Wald chi2(1) = 10.26
Log pseudolikelihood = -16442.624 Prob > chi2 = 0.0014
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.155023 .0519666 3.20 0.001 1.057532 1.261502
_cons | .0285242 .0009269 -109.46 0.000 .0267642 .0304
-------------+----------------------------------------------------------------
/ln_p | -.3248875 .0167436 -19.40 0.000 -.3577043 -.2920706
-------------+----------------------------------------------------------------
p | .7226087 .0120991 .6992798 .7467158
1/p | 1.383875 .0231711 1.339198 1.430043
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=gom_m3_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -16697.213
Iteration 1: log pseudolikelihood = -16453.553
Iteration 2: log pseudolikelihood = -16443.173
Iteration 3: log pseudolikelihood = -16443.153
Iteration 4: log pseudolikelihood = -16443.153
Fitting full model:
Iteration 0: log pseudolikelihood = -16443.153
Iteration 1: log pseudolikelihood = -16433.525
Iteration 2: log pseudolikelihood = -16433.503
Iteration 3: log pseudolikelihood = -16433.503
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 43,782
Wald chi2(1) = 12.40
Log pseudolikelihood = -16433.503 Prob > chi2 = 0.0004
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.170512 .0523284 3.52 0.000 1.072315 1.277701
_cons | .0304522 .001219 -87.22 0.000 .0281543 .0329376
-------------+----------------------------------------------------------------
/gamma | -.211009 .01392 -15.16 0.000 -.2382916 -.1837264
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=logn_m3_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -26594.847
Iteration 1: log pseudolikelihood = -17316.774
Iteration 2: log pseudolikelihood = -16470.493
Iteration 3: log pseudolikelihood = -16418.826
Iteration 4: log pseudolikelihood = -16416.848
Iteration 5: log pseudolikelihood = -16416.84
Iteration 6: log pseudolikelihood = -16416.84
Fitting full model:
Iteration 0: log pseudolikelihood = -16416.84
Iteration 1: log pseudolikelihood = -16406.826
Iteration 2: log pseudolikelihood = -16406.776
Iteration 3: log pseudolikelihood = -16406.776
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 43,782
Wald chi2(1) = 13.14
Log pseudolikelihood = -16406.776 Prob > chi2 = 0.0003
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .7777846 .0539253 -3.62 0.000 .6789596 .890994
_cons | 305.0337 27.44347 63.58 0.000 255.721 363.8558
-------------+----------------------------------------------------------------
/lnsigma | 1.108097 .0171211 64.72 0.000 1.074541 1.141654
-------------+----------------------------------------------------------------
sigma | 3.028591 .0518528 2.928647 3.131945
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
7805 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -19850.436
Iteration 2: log likelihood = -19657.555
Iteration 3: log likelihood = -19656.805
Iteration 4: log likelihood = -19656.805
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -26951.986
Iteration 1: log likelihood = -26951.986
Fitting weighted survival model to obtain point estimates
failure _d: event == 1
analysis time _t: diff
weight: [pweight=llog_m3_nostag]
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -16672.79
Iteration 1: log pseudolikelihood = -16445.882
Iteration 2: log pseudolikelihood = -16442.954
Iteration 3: log pseudolikelihood = -16442.948
Iteration 4: log pseudolikelihood = -16442.948
Fitting full model:
Iteration 0: log pseudolikelihood = -16442.948
Iteration 1: log pseudolikelihood = -16434.629
Iteration 2: log pseudolikelihood = -16434.582
Iteration 3: log pseudolikelihood = -16434.582
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 43,782
Wald chi2(1) = 10.61
Log pseudolikelihood = -16434.582 Prob > chi2 = 0.0011
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .8128984 .0517061 -3.26 0.001 .7176188 .9208285
_cons | 117.302 7.872465 71.00 0.000 102.8439 133.7925
-------------+----------------------------------------------------------------
/lngamma | .2971735 .0168522 17.63 0.000 .2641438 .3302032
-------------+----------------------------------------------------------------
gamma | 1.346049 .0226839 1.302315 1.391251
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
. *}
. *
. *Just a workaround: I dropped the colinear variables from the regressions manually. I know this sounds like a solution, but it was an issue because I was looping over subsamples, so I didn't know what would be col
> inear before running.
.
.
. qui count if _d == 1
. // we count the amount of cases with the event in the strata
. //we call the estimates stored, and the results...
. estimates stat m3_stipw_nostag_*, n(`r(N)')
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
m3_stipw_n~1 | 4,480 . -16441.34 4 32890.68 32916.31
m3_stipw_n~2 | 4,480 . -16431.68 5 32873.36 32905.4
m3_stipw_n~3 | 4,480 . -16431.61 6 32875.22 32913.66
m3_stipw_n~4 | 4,480 . -16431.23 7 32876.46 32921.31
m3_stipw_n~5 | 4,480 . -16430.96 8 32877.93 32929.19
m3_stipw_n~6 | 4,480 . -16430.85 9 32879.7 32937.37
m3_stipw_n~7 | 4,480 . -16430.58 10 32881.16 32945.24
m3_stipw_n~1 | 4,480 . -16405.02 5 32820.05 32852.08
m3_stipw_n~2 | 4,480 . -16404.17 6 32820.34 32858.78
m3_stipw_n~3 | 4,480 . -16403.98 7 32821.97 32866.82
m3_stipw_n~4 | 4,480 . -16403.72 8 32823.44 32874.69
m3_stipw_n~5 | 4,480 . -16403.43 9 32824.87 32882.54
m3_stipw_n~6 | 4,480 . -16403.34 10 32826.68 32890.75
m3_stipw_n~7 | 4,480 . -16403.06 11 32828.12 32898.6
m3_stipw_n~1 | 4,480 . -16404.62 6 32821.24 32859.68
m3_stipw_n~2 | 4,480 . -16403.8 7 32821.59 32866.45
m3_stipw_n~3 | 4,480 . -16402.53 8 32821.07 32872.33
m3_stipw_n~4 | 4,480 . -16402.23 9 32822.45 32880.12
m3_stipw_n~5 | 4,480 . -16401.86 10 32823.72 32887.8
m3_stipw_n~6 | 4,480 . -16401.78 11 32825.55 32896.03
m3_stipw_n~7 | 4,480 . -16401.47 12 32826.94 32903.83
m3_stipw_n~1 | 4,480 . -16403.93 7 32821.85 32866.71
m3_stipw_n~2 | 4,480 . -16403.15 8 32822.29 32873.55
m3_stipw_n~3 | 4,480 . -16401.79 9 32821.57 32879.24
m3_stipw_n~4 | 4,480 . -16401.79 10 32823.57 32887.65
m3_stipw_n~5 | 4,480 . -16401.52 11 32825.04 32895.52
m3_stipw_n~6 | 4,480 . -16401.5 12 32826.99 32903.88
m3_stipw_n~7 | 4,480 . -16401.2 13 32828.39 32911.69
m3_stipw_n~1 | 4,480 . -16403.61 8 32823.22 32874.48
m3_stipw_n~2 | 4,480 . -16402.81 9 32823.62 32881.29
m3_stipw_n~3 | 4,480 . -16401.44 10 32822.88 32886.95
m3_stipw_n~4 | 4,480 . -16401.25 11 32824.5 32894.98
m3_stipw_n~5 | 4,480 . -16401.28 12 32826.55 32903.44
m3_stipw_n~6 | 4,480 . -16401.2 13 32828.4 32911.69
m3_stipw_n~7 | 4,480 . -16401.12 14 32830.23 32919.93
m3_stipw_n~1 | 4,480 . -16402.47 9 32822.94 32880.61
m3_stipw_n~2 | 4,480 . -16401.63 10 32823.25 32887.33
m3_stipw_n~3 | 4,480 . -16400.21 11 32822.43 32892.91
m3_stipw_n~4 | 4,480 . -16400.07 12 32824.14 32901.03
m3_stipw_n~5 | 4,480 . -16400.03 13 32826.06 32909.36
m3_stipw_n~6 | 4,480 . -16399.94 14 32827.87 32917.57
m3_stipw_n~7 | 4,480 . -16399.56 15 32829.12 32925.23
m3_stipw_n~1 | 4,480 . -16401.6 10 32823.2 32887.27
m3_stipw_n~2 | 4,480 . -16400.72 11 32823.45 32893.93
m3_stipw_n~3 | 4,480 . -16399.32 12 32822.64 32899.53
m3_stipw_n~4 | 4,480 . -16399.17 13 32824.34 32907.64
m3_stipw_n~5 | 4,480 . -16399.11 14 32826.21 32915.92
m3_stipw_n~6 | 4,480 . -16399.03 15 32828.05 32924.16
m3_stipw_n~7 | 4,480 . -16398 16 32828 32930.51
m3_stipw_n~1 | 4,480 . -16400.5 11 32823.01 32893.49
m3_stipw_n~2 | 4,480 . -16399.58 12 32823.17 32900.06
m3_stipw_n~3 | 4,480 . -16398.1 13 32822.21 32905.5
m3_stipw_n~4 | 4,480 . -16398 14 32824 32913.7
m3_stipw_n~5 | 4,480 . -16397.9 15 32825.8 32921.91
m3_stipw_n~6 | 4,480 . -16397.68 16 32827.36 32929.88
m3_stipw_n~7 | 4,480 . -16396.48 17 32826.95 32935.88
m3_stipw_n~1 | 4,480 . -16400.72 12 32825.43 32902.32
m3_stipw_n~2 | 4,480 . -16399.81 13 32825.61 32908.91
m3_stipw_n~3 | 4,480 . -16398.32 14 32824.64 32914.35
m3_stipw_n~4 | 4,480 . -16398.21 15 32826.42 32922.53
m3_stipw_n~5 | 4,480 . -16398.11 16 32828.22 32930.74
m3_stipw_n~6 | 4,480 . -16397.98 17 32829.97 32938.89
m3_stipw_n~7 | 4,480 . -16396.6 18 32829.19 32944.53
m3_stipw_n~1 | 4,480 . -16399.74 13 32825.48 32908.78
m3_stipw_n~2 | 4,480 . -16398.82 14 32825.65 32915.35
m3_stipw_n~3 | 4,480 . -16397.31 15 32824.63 32920.74
m3_stipw_n~4 | 4,480 . -16397.19 16 32826.38 32928.9
m3_stipw_n~5 | 4,480 . -16397.1 17 32828.2 32937.12
m3_stipw_n~6 | 4,480 . -16396.94 18 32829.87 32945.2
m3_stipw_n~7 | 4,480 . -16395.9 19 32829.81 32951.55
m3_stipw_n~p | 4,480 -16697.06 -16691.34 2 33386.68 33399.49
m3_stipw_n~i | 4,480 -16450.73 -16442.62 3 32891.25 32910.47
m3_stipw_n~m | 4,480 -16443.15 -16433.5 3 32873.01 32892.23
m3_stipw_n~n | 4,480 -16416.84 -16406.78 3 32819.55 32838.77
m3_stipw_n~g | 4,480 -16442.95 -16434.58 3 32875.16 32894.39
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_4=r(S)
. mata : st_sort_matrix("stats_4", 5) // 5 AIC, 6 BIC
. esttab matrix(stats_4) using "testreg_aic_bic_mrl_23_4_pris.csv", replace
(output written to testreg_aic_bic_mrl_23_4_pris.csv)
. esttab matrix(stats_4) using "testreg_aic_bic_mrl_23_4_pris.html", replace
(output written to testreg_aic_bic_mrl_23_4_pris.html)
.
| stats_4 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m3_stipw_nostag_logn | 4480 | -16416.84 | -16406.78 | 3 | 32819.55 | 32838.77 |
| m3_stipw_nostag_rp2_tvcdf1 | 4480 | . | -16405.02 | 5 | 32820.05 | 32852.08 |
| m3_stipw_nostag_rp2_tvcdf2 | 4480 | . | -16404.17 | 6 | 32820.34 | 32858.78 |
| m3_stipw_nostag_rp3_tvcdf3 | 4480 | . | -16402.53 | 8 | 32821.07 | 32872.33 |
| m3_stipw_nostag_rp3_tvcdf1 | 4480 | . | -16404.62 | 6 | 32821.24 | 32859.68 |
| m3_stipw_nostag_rp4_tvcdf3 | 4480 | . | -16401.79 | 9 | 32821.57 | 32879.24 |
| m3_stipw_nostag_rp3_tvcdf2 | 4480 | . | -16403.8 | 7 | 32821.59 | 32866.45 |
| m3_stipw_nostag_rp4_tvcdf1 | 4480 | . | -16403.93 | 7 | 32821.85 | 32866.71 |
| m3_stipw_nostag_rp2_tvcdf3 | 4480 | . | -16403.98 | 7 | 32821.97 | 32866.82 |
| m3_stipw_nostag_rp8_tvcdf3 | 4480 | . | -16398.1 | 13 | 32822.21 | 32905.5 |
| m3_stipw_nostag_rp4_tvcdf2 | 4480 | . | -16403.15 | 8 | 32822.29 | 32873.55 |
| m3_stipw_nostag_rp6_tvcdf3 | 4480 | . | -16400.21 | 11 | 32822.43 | 32892.91 |
| m3_stipw_nostag_rp3_tvcdf4 | 4480 | . | -16402.23 | 9 | 32822.45 | 32880.12 |
| m3_stipw_nostag_rp7_tvcdf3 | 4480 | . | -16399.32 | 12 | 32822.64 | 32899.53 |
| m3_stipw_nostag_rp5_tvcdf3 | 4480 | . | -16401.44 | 10 | 32822.88 | 32886.95 |
| m3_stipw_nostag_rp6_tvcdf1 | 4480 | . | -16402.47 | 9 | 32822.94 | 32880.61 |
| m3_stipw_nostag_rp8_tvcdf1 | 4480 | . | -16400.5 | 11 | 32823.01 | 32893.49 |
| m3_stipw_nostag_rp8_tvcdf2 | 4480 | . | -16399.58 | 12 | 32823.17 | 32900.06 |
| m3_stipw_nostag_rp7_tvcdf1 | 4480 | . | -16401.6 | 10 | 32823.2 | 32887.27 |
| m3_stipw_nostag_rp5_tvcdf1 | 4480 | . | -16403.61 | 8 | 32823.22 | 32874.48 |
| m3_stipw_nostag_rp6_tvcdf2 | 4480 | . | -16401.63 | 10 | 32823.25 | 32887.33 |
| m3_stipw_nostag_rp2_tvcdf4 | 4480 | . | -16403.72 | 8 | 32823.44 | 32874.69 |
| m3_stipw_nostag_rp7_tvcdf2 | 4480 | . | -16400.72 | 11 | 32823.45 | 32893.93 |
| m3_stipw_nostag_rp4_tvcdf4 | 4480 | . | -16401.79 | 10 | 32823.57 | 32887.65 |
| m3_stipw_nostag_rp5_tvcdf2 | 4480 | . | -16402.81 | 9 | 32823.62 | 32881.29 |
| m3_stipw_nostag_rp3_tvcdf5 | 4480 | . | -16401.86 | 10 | 32823.72 | 32887.8 |
| m3_stipw_nostag_rp8_tvcdf4 | 4480 | . | -16398 | 14 | 32824 | 32913.7 |
| m3_stipw_nostag_rp6_tvcdf4 | 4480 | . | -16400.07 | 12 | 32824.14 | 32901.03 |
| m3_stipw_nostag_rp7_tvcdf4 | 4480 | . | -16399.17 | 13 | 32824.34 | 32907.64 |
| m3_stipw_nostag_rp5_tvcdf4 | 4480 | . | -16401.25 | 11 | 32824.5 | 32894.98 |
| m3_stipw_nostag_rp10_tvcdf3 | 4480 | . | -16397.31 | 15 | 32824.63 | 32920.74 |
| m3_stipw_nostag_rp9_tvcdf3 | 4480 | . | -16398.32 | 14 | 32824.64 | 32914.35 |
| m3_stipw_nostag_rp2_tvcdf5 | 4480 | . | -16403.43 | 9 | 32824.87 | 32882.54 |
| m3_stipw_nostag_rp4_tvcdf5 | 4480 | . | -16401.52 | 11 | 32825.04 | 32895.52 |
| m3_stipw_nostag_rp9_tvcdf1 | 4480 | . | -16400.72 | 12 | 32825.43 | 32902.32 |
| m3_stipw_nostag_rp10_tvcdf1 | 4480 | . | -16399.74 | 13 | 32825.48 | 32908.78 |
| m3_stipw_nostag_rp3_tvcdf6 | 4480 | . | -16401.78 | 11 | 32825.55 | 32896.03 |
| m3_stipw_nostag_rp9_tvcdf2 | 4480 | . | -16399.81 | 13 | 32825.61 | 32908.91 |
| m3_stipw_nostag_rp10_tvcdf2 | 4480 | . | -16398.82 | 14 | 32825.65 | 32915.35 |
| m3_stipw_nostag_rp8_tvcdf5 | 4480 | . | -16397.9 | 15 | 32825.8 | 32921.91 |
| m3_stipw_nostag_rp6_tvcdf5 | 4480 | . | -16400.03 | 13 | 32826.06 | 32909.36 |
| m3_stipw_nostag_rp7_tvcdf5 | 4480 | . | -16399.11 | 14 | 32826.21 | 32915.92 |
| m3_stipw_nostag_rp10_tvcdf4 | 4480 | . | -16397.19 | 16 | 32826.38 | 32928.9 |
| m3_stipw_nostag_rp9_tvcdf4 | 4480 | . | -16398.21 | 15 | 32826.42 | 32922.53 |
| m3_stipw_nostag_rp5_tvcdf5 | 4480 | . | -16401.28 | 12 | 32826.55 | 32903.44 |
| m3_stipw_nostag_rp2_tvcdf6 | 4480 | . | -16403.34 | 10 | 32826.68 | 32890.75 |
| m3_stipw_nostag_rp3_tvcdf7 | 4480 | . | -16401.47 | 12 | 32826.94 | 32903.83 |
| m3_stipw_nostag_rp8_tvcdf7 | 4480 | . | -16396.48 | 17 | 32826.95 | 32935.88 |
| m3_stipw_nostag_rp4_tvcdf6 | 4480 | . | -16401.5 | 12 | 32826.99 | 32903.88 |
| m3_stipw_nostag_rp8_tvcdf6 | 4480 | . | -16397.68 | 16 | 32827.36 | 32929.88 |
| m3_stipw_nostag_rp6_tvcdf6 | 4480 | . | -16399.94 | 14 | 32827.87 | 32917.57 |
| m3_stipw_nostag_rp7_tvcdf7 | 4480 | . | -16398 | 16 | 32828 | 32930.51 |
| m3_stipw_nostag_rp7_tvcdf6 | 4480 | . | -16399.03 | 15 | 32828.05 | 32924.16 |
| m3_stipw_nostag_rp2_tvcdf7 | 4480 | . | -16403.06 | 11 | 32828.12 | 32898.6 |
| m3_stipw_nostag_rp10_tvcdf5 | 4480 | . | -16397.1 | 17 | 32828.2 | 32937.12 |
| m3_stipw_nostag_rp9_tvcdf5 | 4480 | . | -16398.11 | 16 | 32828.22 | 32930.74 |
| m3_stipw_nostag_rp4_tvcdf7 | 4480 | . | -16401.2 | 13 | 32828.39 | 32911.69 |
| m3_stipw_nostag_rp5_tvcdf6 | 4480 | . | -16401.2 | 13 | 32828.4 | 32911.69 |
| m3_stipw_nostag_rp6_tvcdf7 | 4480 | . | -16399.56 | 15 | 32829.12 | 32925.23 |
| m3_stipw_nostag_rp9_tvcdf7 | 4480 | . | -16396.6 | 18 | 32829.19 | 32944.53 |
| m3_stipw_nostag_rp10_tvcdf7 | 4480 | . | -16395.9 | 19 | 32829.81 | 32951.55 |
| m3_stipw_nostag_rp10_tvcdf6 | 4480 | . | -16396.94 | 18 | 32829.87 | 32945.2 |
| m3_stipw_nostag_rp9_tvcdf6 | 4480 | . | -16397.98 | 17 | 32829.97 | 32938.89 |
| m3_stipw_nostag_rp5_tvcdf7 | 4480 | . | -16401.12 | 14 | 32830.23 | 32919.93 |
| m3_stipw_nostag_gom | 4480 | -16443.15 | -16433.5 | 3 | 32873.01 | 32892.23 |
| m3_stipw_nostag_rp1_tvcdf2 | 4480 | . | -16431.68 | 5 | 32873.36 | 32905.4 |
| m3_stipw_nostag_llog | 4480 | -16442.95 | -16434.58 | 3 | 32875.16 | 32894.39 |
| m3_stipw_nostag_rp1_tvcdf3 | 4480 | . | -16431.61 | 6 | 32875.22 | 32913.66 |
| m3_stipw_nostag_rp1_tvcdf4 | 4480 | . | -16431.23 | 7 | 32876.46 | 32921.31 |
| m3_stipw_nostag_rp1_tvcdf5 | 4480 | . | -16430.96 | 8 | 32877.93 | 32929.19 |
| m3_stipw_nostag_rp1_tvcdf6 | 4480 | . | -16430.85 | 9 | 32879.7 | 32937.37 |
| m3_stipw_nostag_rp1_tvcdf7 | 4480 | . | -16430.58 | 10 | 32881.16 | 32945.24 |
| m3_stipw_nostag_rp1_tvcdf1 | 4480 | . | -16441.34 | 4 | 32890.68 | 32916.31 |
| m3_stipw_nostag_wei | 4480 | -16450.73 | -16442.62 | 3 | 32891.25 | 32910.47 |
| m3_stipw_nostag_exp | 4480 | -16697.06 | -16691.34 | 2 | 33386.68 | 33399.49 |
.
. estimates replay m3_stipw_nostag_rp2_tvcdf1, eform
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Model m3_stipw_nostag_rp2_tvcdf1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -16405.024 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.181718 .0538153 3.67 0.000 1.080813 1.292045
_rcs1 | 2.055055 .0337351 43.88 0.000 1.989988 2.12225
_rcs2 | 1.075182 .0118834 6.56 0.000 1.052141 1.098727
_rcs_tr_outcome1 | .9654853 .0271139 -1.25 0.211 .9137791 1.020117
_cons | .0629575 .0017469 -99.66 0.000 .0596251 .0664762
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m3_stipw_nostag_rp2_tvcdf1 // m3_stipw_nostag_rp5_tvcdf1
(results m3_stipw_nostag_rp2_tvcdf1 are active now)
.
. sts gen km_c=s, by(tr_outcome)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) ci contrast(difference) ///
> atvar(s_late_c s_early_c) contrastvar(sdiff_late_vs_early)
.
. * s_tr_comp_early_b s_tr_comp_early_b_lci s_tr_comp_early_b_uci s_late_drop_b s_late_drop_b_lci s_late_drop_b_uci sdiff_tr_comp_early_vs_late sdiff_tr_comp_early_vs_late_lci sdiff_tr_comp_early_vs_late_uci
.
. twoway (rarea s_late_c_lci s_late_c_uci tt, color(gs7%35)) ///
> (rarea s_early_c_lci s_early_c_uci tt, color(gs2%35)) ///
> (line km_c _t if tr_outcome==0 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs7%50)) ///
> (line km_c _t if tr_outcome==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%50)) ///
> (line s_late_c tt, lcolor(gs7) lwidth(thick)) ///
> (line s_early_c tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Probibability of avoiding sentence (standardized)") ///
> legend(order(5 "Late dropout" 6 "Early dropout") ring(0) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(km_vs_standsurv_fin_c, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_22_c_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_22_c_pris.gph saved)
.
. estimates restore m3_stipw_nostag_rp2_tvcdf1
(results m3_stipw_nostag_rp2_tvcdf1 are active now)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_late_c rmst_early_c) contrastvar(rmstdiff_late_vs_early)
.
. twoway (rarea rmst_late_c_lci rmst_late_c_uci tt, color(gs7%35)) ///
> (rarea rmst_early_c_lci rmst_early_c_uci tt, color(gs2%35)) ///
> (line rmst_late_c tt, lcolor(gs7) lwidth(thick)) ///
> (line rmst_early_c tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Restricted Mean Survival Times (standardized)") ///
> legend(order(3 "Late dropout" 4 "Early dropout") ring(0) pos(5) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(rmst_std_fin_c, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdif_rmst_c_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_c_pris.gph saved)
Summary
. frame change default
. cap gen tt2= round(tt,.01)
.
. frame late: cap gen tt2= round(tt,.01)
. frame late: drop if missing(tt)
(55,043 observations deleted)
. *ERROR: invalid match variables for 1:1 match The variable tt does not uniquely identify the observations in frame default. Perhaps you meant to specify m:1 instead of 1:1.
. frlink m:1 tt2, frame(late)
(70,840 observations in frame default unmatched)
. frget sdiff_comp_vs_late sdiff_comp_vs_late_lci sdiff_comp_vs_late_uci ///
> rmstdiff_comp_vs_late rmstdiff_comp_vs_late_lci rmstdiff_comp_vs_late_uci, from(late)
(70,840 missing values generated)
(70,840 missing values generated)
(70,840 missing values generated)
(70,840 missing values generated)
(70,841 missing values generated)
(70,841 missing values generated)
(6 variables copied from linked frame)
.
. frame early: cap gen tt2= round(tt,.01)
. frame early: drop if missing(tt)
(35,069 observations deleted)
. frlink m:1 tt2, frame(early)
(70,850 observations in frame default unmatched)
. frget sdiff_comp_vs_early sdiff_comp_vs_early_lci sdiff_comp_vs_early_uci ///
> rmstdiff_comp_vs_early rmstdiff_comp_vs_early_lci rmstdiff_comp_vs_early_uci, from(early)
(70,850 missing values generated)
(70,850 missing values generated)
(70,850 missing values generated)
(70,850 missing values generated)
(70,851 missing values generated)
(70,851 missing values generated)
(6 variables copied from linked frame)
.
. frame early_late: cap gen tt2= round(tt,.01)
. frame early_late: drop if missing(tt)
(51,566 observations deleted)
. frlink m:1 tt2, frame(early_late)
(70,842 observations in frame default unmatched)
. frget sdiff_late_vs_early sdiff_late_vs_early_lci sdiff_late_vs_early_uci ///
> rmstdiff_late_vs_early rmstdiff_late_vs_early_lci rmstdiff_late_vs_early_uci, from(early_late)
(70,842 missing values generated)
(70,842 missing values generated)
(70,842 missing values generated)
(70,842 missing values generated)
(70,842 missing values generated)
(70,842 missing values generated)
(6 variables copied from linked frame)
.
. twoway (rarea sdiff_comp_vs_late_lci sdiff_comp_vs_late_uci tt, color(gs2%35)) ///
> (line sdiff_comp_vs_late tt, lcolor(gs2)) ///
> (rarea sdiff_comp_vs_early_lci sdiff_comp_vs_early_uci tt, color(gs6%35)) ///
> (line sdiff_comp_vs_early tt, lcolor(gs6)) ///
> (rarea sdiff_late_vs_early_lci sdiff_late_vs_early_uci tt, color(gs10%35)) ///
> (line sdiff_late_vs_early tt, lcolor(gs10)) ///
> (line zero tt, lcolor(black%20) lwidth(thick)) ///
> , ylabel(, format(%3.1f)) ///
> ytitle("Difference in Survival (years)") ///
> xtitle("Years from baseline treatment outcome") ///
> legend(order( 1 "Late dropout vs. Tr. completion" 3 "Early dropout vs. Tr. completion" 5 "Early vs. late dropout") ring(0) pos(7) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(s_diff_fin_abc, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. gr_edit yaxis1.major.label_format = `"%9.2f"'
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdif_s_abc_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_s_abc_pris.gph saved)
.
. twoway (rarea rmstdiff_comp_vs_late_lci rmstdiff_comp_vs_late_uci tt, color(gs2%35)) ///
> (line rmstdiff_comp_vs_late tt, lcolor(gs2)) ///
> (rarea rmstdiff_comp_vs_early_lci rmstdiff_comp_vs_early_uci tt, color(gs6%35)) ///
> (line rmstdiff_comp_vs_early tt, lcolor(gs6)) ///
> (rarea rmstdiff_late_vs_early_lci rmstdiff_late_vs_early_uci tt, color(gs10%35)) ///
> (line rmstdiff_late_vs_early tt, lcolor(gs10)) ///
> (line zero tt, lcolor(black%20) lwidth(thick)) ///
> , ylabel(, format(%3.1f)) ///
> ytitle("Difference in RMST (years)") ///
> xtitle("Years from baseline treatment outcome") ///
> legend(order( 1 "Late dropout vs. Tr. completion" 3 "Early dropout vs. Tr. completion" 5 "Early vs. late dropout") ring(0) pos(7) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(RMSTdiff_fin_abc, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. gr_edit yaxis1.major.label_format = `"%9.2f"'
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdif_rmst_abc_pris.gph", replace
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_abc_pris.gph saved)
Saved at= 22:10:53 5 Apr 2023
. frame late: cap qui save "mariel_feb_23_2_late.dta", all replace emptyok
. frame early: cap qui save "mariel_feb_23_2_early.dta", all replace emptyok
. frame early_late: cap qui save "mariel_feb_23_2_early_late.dta", all replace emptyok
(saving full_spline)
(saving linear_term)
(saving m_nostag_rp1_tvc_1)
(saving m_nostag_rp1_tvc_2)
(saving m_nostag_rp1_tvc_3)
(saving m_nostag_rp1_tvc_4)
(saving m_nostag_rp1_tvc_5)
(saving m_nostag_rp1_tvc_6)
(saving m_nostag_rp1_tvc_7)
(saving m_nostag_rp2_tvc_1)
(saving m_nostag_rp2_tvc_2)
(saving m_nostag_rp2_tvc_3)
(saving m_nostag_rp2_tvc_4)
(saving m_nostag_rp2_tvc_5)
(saving m_nostag_rp2_tvc_6)
(saving m_nostag_rp2_tvc_7)
(saving m_nostag_rp3_tvc_1)
(saving m_nostag_rp3_tvc_2)
(saving m_nostag_rp3_tvc_3)
(saving m_nostag_rp3_tvc_4)
(saving m_nostag_rp3_tvc_5)
(saving m_nostag_rp3_tvc_6)
(saving m_nostag_rp3_tvc_7)
(saving m_nostag_rp4_tvc_1)
(saving m_nostag_rp4_tvc_2)
(saving m_nostag_rp4_tvc_3)
(saving m_nostag_rp4_tvc_4)
(saving m_nostag_rp4_tvc_5)
(saving m_nostag_rp4_tvc_6)
(saving m_nostag_rp4_tvc_7)
(saving m_nostag_rp5_tvc_1)
(saving m_nostag_rp5_tvc_2)
(saving m_nostag_rp5_tvc_3)
(saving m_nostag_rp5_tvc_4)
(saving m_nostag_rp5_tvc_5)
(saving m_nostag_rp5_tvc_6)
(saving m_nostag_rp5_tvc_7)
(saving m_nostag_rp6_tvc_1)
(saving m_nostag_rp6_tvc_2)
(saving m_nostag_rp6_tvc_3)
(saving m_nostag_rp6_tvc_4)
(saving m_nostag_rp6_tvc_5)
(saving m_nostag_rp6_tvc_6)
(saving m_nostag_rp6_tvc_7)
(saving m_nostag_rp7_tvc_1)
(saving m_nostag_rp7_tvc_2)
(saving m_nostag_rp7_tvc_3)
(saving m_nostag_rp7_tvc_4)
(saving m_nostag_rp7_tvc_5)
(saving m_nostag_rp7_tvc_6)
(saving m_nostag_rp7_tvc_7)
(saving m_nostag_rp8_tvc_1)
(saving m_nostag_rp8_tvc_2)
(saving m_nostag_rp8_tvc_3)
(saving m_nostag_rp8_tvc_4)
(saving m_nostag_rp8_tvc_5)
(saving m_nostag_rp8_tvc_6)
(saving m_nostag_rp8_tvc_7)
(saving m_nostag_rp9_tvc_1)
(saving m_nostag_rp9_tvc_2)
(saving m_nostag_rp9_tvc_3)
(saving m_nostag_rp9_tvc_4)
(saving m_nostag_rp9_tvc_5)
(saving m_nostag_rp9_tvc_6)
(saving m_nostag_rp9_tvc_7)
(saving m_nostag_rp10_tvc_1)
(saving m_nostag_rp10_tvc_2)
(saving m_nostag_rp10_tvc_3)
(saving m_nostag_rp10_tvc_4)
(saving m_nostag_rp10_tvc_5)
(saving m_nostag_rp10_tvc_6)
(saving m_nostag_rp10_tvc_7)
(saving m_stipw_nostag_rp1_tvcdf1)
(saving m_stipw_nostag_rp1_tvcdf2)
(saving m_stipw_nostag_rp1_tvcdf3)
(saving m_stipw_nostag_rp1_tvcdf4)
(saving m_stipw_nostag_rp1_tvcdf5)
(saving m_stipw_nostag_rp1_tvcdf6)
(saving m_stipw_nostag_rp1_tvcdf7)
(saving m_stipw_nostag_rp2_tvcdf1)
(saving m_stipw_nostag_rp2_tvcdf2)
(saving m_stipw_nostag_rp2_tvcdf3)
(saving m_stipw_nostag_rp2_tvcdf4)
(saving m_stipw_nostag_rp2_tvcdf5)
(saving m_stipw_nostag_rp2_tvcdf6)
(saving m_stipw_nostag_rp2_tvcdf7)
(saving m_stipw_nostag_rp3_tvcdf1)
(saving m_stipw_nostag_rp3_tvcdf2)
(saving m_stipw_nostag_rp3_tvcdf3)
(saving m_stipw_nostag_rp3_tvcdf4)
(saving m_stipw_nostag_rp3_tvcdf5)
(saving m_stipw_nostag_rp3_tvcdf6)
(saving m_stipw_nostag_rp3_tvcdf7)
(saving m_stipw_nostag_rp4_tvcdf1)
(saving m_stipw_nostag_rp4_tvcdf2)
(saving m_stipw_nostag_rp4_tvcdf3)
(saving m_stipw_nostag_rp4_tvcdf4)
(saving m_stipw_nostag_rp4_tvcdf5)
(saving m_stipw_nostag_rp4_tvcdf6)
(saving m_stipw_nostag_rp4_tvcdf7)
(saving m_stipw_nostag_rp5_tvcdf1)
(saving m_stipw_nostag_rp5_tvcdf2)
(saving m_stipw_nostag_rp5_tvcdf3)
(saving m_stipw_nostag_rp5_tvcdf4)
(saving m_stipw_nostag_rp5_tvcdf5)
(saving m_stipw_nostag_rp5_tvcdf6)
(saving m_stipw_nostag_rp5_tvcdf7)
(saving m_stipw_nostag_rp6_tvcdf1)
(saving m_stipw_nostag_rp6_tvcdf2)
(saving m_stipw_nostag_rp6_tvcdf3)
(saving m_stipw_nostag_rp6_tvcdf4)
(saving m_stipw_nostag_rp6_tvcdf5)
(saving m_stipw_nostag_rp6_tvcdf6)
(saving m_stipw_nostag_rp6_tvcdf7)
(saving m_stipw_nostag_rp7_tvcdf1)
(saving m_stipw_nostag_rp7_tvcdf2)
(saving m_stipw_nostag_rp7_tvcdf3)
(saving m_stipw_nostag_rp7_tvcdf4)
(saving m_stipw_nostag_rp7_tvcdf5)
(saving m_stipw_nostag_rp7_tvcdf6)
(saving m_stipw_nostag_rp7_tvcdf7)
(saving m_stipw_nostag_rp8_tvcdf1)
(saving m_stipw_nostag_rp8_tvcdf2)
(saving m_stipw_nostag_rp8_tvcdf3)
(saving m_stipw_nostag_rp8_tvcdf4)
(saving m_stipw_nostag_rp8_tvcdf5)
(saving m_stipw_nostag_rp8_tvcdf6)
(saving m_stipw_nostag_rp8_tvcdf7)
(saving m_stipw_nostag_rp9_tvcdf1)
(saving m_stipw_nostag_rp9_tvcdf2)
(saving m_stipw_nostag_rp9_tvcdf3)
(saving m_stipw_nostag_rp9_tvcdf4)
(saving m_stipw_nostag_rp9_tvcdf5)
(saving m_stipw_nostag_rp9_tvcdf6)
(saving m_stipw_nostag_rp9_tvcdf7)
(saving m_stipw_nostag_rp10_tvcdf1)
(saving m_stipw_nostag_rp10_tvcdf2)
(saving m_stipw_nostag_rp10_tvcdf3)
(saving m_stipw_nostag_rp10_tvcdf4)
(saving m_stipw_nostag_rp10_tvcdf5)
(saving m_stipw_nostag_rp10_tvcdf6)
(saving m_stipw_nostag_rp10_tvcdf7)
(saving m_stipw_nostag_exp)
(saving m_stipw_nostag_wei)
(saving m_stipw_nostag_gom)
(saving m_stipw_nostag_logn)
(saving m_stipw_nostag_llog)
(saving m2_stipw_nostag_rp1_tvcdf1)
(saving m2_stipw_nostag_rp1_tvcdf2)
(saving m2_stipw_nostag_rp1_tvcdf3)
(saving m2_stipw_nostag_rp1_tvcdf4)
(saving m2_stipw_nostag_rp1_tvcdf5)
(saving m2_stipw_nostag_rp1_tvcdf6)
(saving m2_stipw_nostag_rp1_tvcdf7)
(saving m2_stipw_nostag_rp2_tvcdf1)
(saving m2_stipw_nostag_rp2_tvcdf2)
(saving m2_stipw_nostag_rp2_tvcdf3)
(saving m2_stipw_nostag_rp2_tvcdf4)
(saving m2_stipw_nostag_rp2_tvcdf5)
(saving m2_stipw_nostag_rp2_tvcdf6)
(saving m2_stipw_nostag_rp2_tvcdf7)
(saving m2_stipw_nostag_rp3_tvcdf1)
(saving m2_stipw_nostag_rp3_tvcdf2)
(saving m2_stipw_nostag_rp3_tvcdf3)
(saving m2_stipw_nostag_rp3_tvcdf4)
(saving m2_stipw_nostag_rp3_tvcdf5)
(saving m2_stipw_nostag_rp3_tvcdf6)
(saving m2_stipw_nostag_rp3_tvcdf7)
(saving m2_stipw_nostag_rp4_tvcdf1)
(saving m2_stipw_nostag_rp4_tvcdf2)
(saving m2_stipw_nostag_rp4_tvcdf3)
(saving m2_stipw_nostag_rp4_tvcdf4)
(saving m2_stipw_nostag_rp4_tvcdf5)
(saving m2_stipw_nostag_rp4_tvcdf6)
(saving m2_stipw_nostag_rp4_tvcdf7)
(saving m2_stipw_nostag_rp5_tvcdf1)
(saving m2_stipw_nostag_rp5_tvcdf2)
(saving m2_stipw_nostag_rp5_tvcdf3)
(saving m2_stipw_nostag_rp5_tvcdf4)
(saving m2_stipw_nostag_rp5_tvcdf5)
(saving m2_stipw_nostag_rp5_tvcdf6)
(saving m2_stipw_nostag_rp5_tvcdf7)
(saving m2_stipw_nostag_rp6_tvcdf1)
(saving m2_stipw_nostag_rp6_tvcdf2)
(saving m2_stipw_nostag_rp6_tvcdf3)
(saving m2_stipw_nostag_rp6_tvcdf4)
(saving m2_stipw_nostag_rp6_tvcdf5)
(saving m2_stipw_nostag_rp6_tvcdf6)
(saving m2_stipw_nostag_rp6_tvcdf7)
(saving m2_stipw_nostag_rp7_tvcdf1)
(saving m2_stipw_nostag_rp7_tvcdf2)
(saving m2_stipw_nostag_rp7_tvcdf3)
(saving m2_stipw_nostag_rp7_tvcdf4)
(saving m2_stipw_nostag_rp7_tvcdf5)
(saving m2_stipw_nostag_rp7_tvcdf6)
(saving m2_stipw_nostag_rp7_tvcdf7)
(saving m2_stipw_nostag_rp8_tvcdf1)
(saving m2_stipw_nostag_rp8_tvcdf2)
(saving m2_stipw_nostag_rp8_tvcdf3)
(saving m2_stipw_nostag_rp8_tvcdf4)
(saving m2_stipw_nostag_rp8_tvcdf5)
(saving m2_stipw_nostag_rp8_tvcdf6)
(saving m2_stipw_nostag_rp8_tvcdf7)
(saving m2_stipw_nostag_rp9_tvcdf1)
(saving m2_stipw_nostag_rp9_tvcdf2)
(saving m2_stipw_nostag_rp9_tvcdf3)
(saving m2_stipw_nostag_rp9_tvcdf4)
(saving m2_stipw_nostag_rp9_tvcdf5)
(saving m2_stipw_nostag_rp9_tvcdf6)
(saving m2_stipw_nostag_rp9_tvcdf7)
(saving m2_stipw_nostag_rp10_tvcdf1)
(saving m2_stipw_nostag_rp10_tvcdf2)
(saving m2_stipw_nostag_rp10_tvcdf3)
(saving m2_stipw_nostag_rp10_tvcdf4)
(saving m2_stipw_nostag_rp10_tvcdf5)
(saving m2_stipw_nostag_rp10_tvcdf6)
(saving m2_stipw_nostag_rp10_tvcdf7)
(saving m2_stipw_nostag_exp)
(saving m2_stipw_nostag_wei)
(saving m2_stipw_nostag_gom)
(saving m2_stipw_nostag_logn)
(saving m2_stipw_nostag_llog)
(saving m3_stipw_nostag_rp1_tvcdf1)
(saving m3_stipw_nostag_rp1_tvcdf2)
(saving m3_stipw_nostag_rp1_tvcdf3)
(saving m3_stipw_nostag_rp1_tvcdf4)
(saving m3_stipw_nostag_rp1_tvcdf5)
(saving m3_stipw_nostag_rp1_tvcdf6)
(saving m3_stipw_nostag_rp1_tvcdf7)
(saving m3_stipw_nostag_rp2_tvcdf1)
(saving m3_stipw_nostag_rp2_tvcdf2)
(saving m3_stipw_nostag_rp2_tvcdf3)
(saving m3_stipw_nostag_rp2_tvcdf4)
(saving m3_stipw_nostag_rp2_tvcdf5)
(saving m3_stipw_nostag_rp2_tvcdf6)
(saving m3_stipw_nostag_rp2_tvcdf7)
(saving m3_stipw_nostag_rp3_tvcdf1)
(saving m3_stipw_nostag_rp3_tvcdf2)
(saving m3_stipw_nostag_rp3_tvcdf3)
(saving m3_stipw_nostag_rp3_tvcdf4)
(saving m3_stipw_nostag_rp3_tvcdf5)
(saving m3_stipw_nostag_rp3_tvcdf6)
(saving m3_stipw_nostag_rp3_tvcdf7)
(saving m3_stipw_nostag_rp4_tvcdf1)
(saving m3_stipw_nostag_rp4_tvcdf2)
(saving m3_stipw_nostag_rp4_tvcdf3)
(saving m3_stipw_nostag_rp4_tvcdf4)
(saving m3_stipw_nostag_rp4_tvcdf5)
(saving m3_stipw_nostag_rp4_tvcdf6)
(saving m3_stipw_nostag_rp4_tvcdf7)
(saving m3_stipw_nostag_rp5_tvcdf1)
(saving m3_stipw_nostag_rp5_tvcdf2)
(saving m3_stipw_nostag_rp5_tvcdf3)
(saving m3_stipw_nostag_rp5_tvcdf4)
(saving m3_stipw_nostag_rp5_tvcdf5)
(saving m3_stipw_nostag_rp5_tvcdf6)
(saving m3_stipw_nostag_rp5_tvcdf7)
(saving m3_stipw_nostag_rp6_tvcdf1)
(saving m3_stipw_nostag_rp6_tvcdf2)
(saving m3_stipw_nostag_rp6_tvcdf3)
(saving m3_stipw_nostag_rp6_tvcdf4)
(saving m3_stipw_nostag_rp6_tvcdf5)
(saving m3_stipw_nostag_rp6_tvcdf6)
(saving m3_stipw_nostag_rp6_tvcdf7)
(saving m3_stipw_nostag_rp7_tvcdf1)
(saving m3_stipw_nostag_rp7_tvcdf2)
(saving m3_stipw_nostag_rp7_tvcdf3)
(saving m3_stipw_nostag_rp7_tvcdf4)
(saving m3_stipw_nostag_rp7_tvcdf5)
(saving m3_stipw_nostag_rp7_tvcdf6)
(saving m3_stipw_nostag_rp7_tvcdf7)
(saving m3_stipw_nostag_rp8_tvcdf1)
(saving m3_stipw_nostag_rp8_tvcdf2)
(saving m3_stipw_nostag_rp8_tvcdf3)
(saving m3_stipw_nostag_rp8_tvcdf4)
(saving m3_stipw_nostag_rp8_tvcdf5)
(saving m3_stipw_nostag_rp8_tvcdf6)
(saving m3_stipw_nostag_rp8_tvcdf7)
(saving m3_stipw_nostag_rp9_tvcdf1)
(saving m3_stipw_nostag_rp9_tvcdf2)
(saving m3_stipw_nostag_rp9_tvcdf3)
(saving m3_stipw_nostag_rp9_tvcdf4)
(saving m3_stipw_nostag_rp9_tvcdf5)
(saving m3_stipw_nostag_rp9_tvcdf6)
(saving m3_stipw_nostag_rp9_tvcdf7)
(saving m3_stipw_nostag_rp10_tvcdf1)
(saving m3_stipw_nostag_rp10_tvcdf2)
(saving m3_stipw_nostag_rp10_tvcdf3)
(saving m3_stipw_nostag_rp10_tvcdf4)
(saving m3_stipw_nostag_rp10_tvcdf5)
(saving m3_stipw_nostag_rp10_tvcdf6)
(saving m3_stipw_nostag_rp10_tvcdf7)
(saving m3_stipw_nostag_exp)
(saving m3_stipw_nostag_wei)
(saving m3_stipw_nostag_gom)
(saving m3_stipw_nostag_logn)
(saving m3_stipw_nostag_llog)
(file mariel_feb_23_2.sters saved)