. clear all
. cap noi which tabout
C:\Users\CISS Fondecyt\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:\Users\CISS Fondecyt\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:\Users\CISS Fondecyt\ado\plus\p\pathutil.ado
*! version 2.2.0 19nov2020 daniel klein
. if _rc==111 {
. ssc install dirtools
. }
. cap noi which project
C:\Users\CISS Fondecyt\ado\plus\p\project.ado
*! version 1.3.1 22dec2013 picard@netbox.com
. if _rc==111 {
. ssc install project
. }
. cap noi which stipw
C:\Users\CISS Fondecyt\ado\plus\s\stipw.ado
*! Version 1.0.0 17Jan2022
. if _rc==111 {
. ssc install stipw
. }
. cap noi which stpm2
C:\Users\CISS Fondecyt\ado\plus\s\stpm2.ado
*! version 1.7.5 May2021
. if _rc==111 {
. ssc install stpm2
. }
. cap noi which rcsgen
C:\Users\CISS Fondecyt\ado\plus\r\rcsgen.ado
*! version 1.5.9 13FEB2022
. if _rc==111 {
. ssc install rcsgen
. }
. cap noi which matselrc
C:\Users\CISS Fondecyt\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:\Users\CISS Fondecyt\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:\Users\CISS Fondecyt\ado\plus\f\fs.ado
*! NJC 1.0.5 23 November 2006
. if _rc==111 {
. ssc install fs
. }
. cap noi which mkspline2
C:\Users\CISS Fondecyt\ado\plus\m\mkspline2.ado
*! version 1.0.0 MLB 04Apr2009
. if _rc==111 {
. ssc install postrcspline
. }
. cap noi which estwrite
C:\Users\CISS Fondecyt\ado\plus\e\estwrite.ado
*! version 1.2.4 04sep2009
*! version 1.0.1 15may2007 (renamed from -eststo- to -estwrite-; -append- added)
*! version 1.0.0 29apr2005 Ben Jann (ETH Zurich)
. if _rc==111 {
. ssc install estwrite
. }
.
. cap noi ssc install moremata
checking moremata consistency and verifying not already installed...
the following files already exist and are different:
C:\Users\CISS Fondecyt\ado\plus\l\lmoremata.mlib
C:\Users\CISS Fondecyt\ado\plus\l\lmoremata10.mlib
C:\Users\CISS Fondecyt\ado\plus\l\lmoremata11.mlib
C:\Users\CISS Fondecyt\ado\plus\l\lmoremata14.mlib
C:\Users\CISS Fondecyt\ado\plus\m\moremata.hlp
C:\Users\CISS Fondecyt\ado\plus\m\moremata_source.hlp
C:\Users\CISS Fondecyt\ado\plus\m\moremata11_source.hlp
C:\Users\CISS Fondecyt\ado\plus\m\mf_mm_quantile.hlp
C:\Users\CISS Fondecyt\ado\plus\m\mf_mm_ipolate.hlp
C:\Users\CISS Fondecyt\ado\plus\m\mf_mm_collapse.hlp
C:\Users\CISS Fondecyt\ado\plus\m\mf_mm_ebal.sthlp
C:\Users\CISS Fondecyt\ado\plus\m\mf_mm_density.sthlp
C:\Users\CISS Fondecyt\ado\plus\m\mf_mm_hl.hlp
C:\Users\CISS Fondecyt\ado\plus\m\mf_mm_mloc.hlp
no files installed or copied
(no action taken)
.
. cap noi which esttab
C:\Users\CISS Fondecyt\ado\plus\e\esttab.ado
*! version 2.0.9 06feb2016 Ben Jann
*! wrapper for estout
. if _rc==111 {
. ssc install estout
. }
Date created: 23:38:55 4 Apr 2023.
Get the folder
C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)
Fecha: 4 Apr 2023, considerando un SO Windows para el usuario: CISS Fondecyt
Path data= ;
Tiempo: 4 Apr 2023, considerando un SO Windows
The file is located and named as: C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)fiscalia_mariel_oct_2022_match_SENDA.dta
=============================================================================
=============================================================================
We open the files
. use "fiscalia_mariel_feb_2023_match_SENDA.dta", clear
.
. *b) select 10% of the data
. /*
> set seed 2125
> sample 10
> */
.
.
. fs mariel_ags_*.do
mariel_ags_b.do mariel_ags_b_m1.do mariel_ags_b_m2.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
> }
> */
. 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_hijos_mod_joel_bin", "tenencia_de_la_vivienda_mod", "macrozona", "n_off_vio", "n_off_a
> cq", "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")
> */
.
. 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 condic
> ion_ocupacional_cor policonsumo num_hijos_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 d
> g_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 condic
> ion_ocupacional_cor policonsumo num_hijos_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 d
> g_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)
(22,287 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
22,287 failures in single-record/single-failure data
229,620.93 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 3.24035 .001 2.665753 10.75828
subjects with gap 0
time on gap if gap 0
time at risk 229620.93 3.24035 .001 2.665753 10.75828
failures 22287 .3145083 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 | 63,974.7824 .0597892 19276 4.744892 . .
Treatmen | 46,815.0931 .1309407 15797 1.465064 6.881935 .
Treatmen | 118,806.628 .1037484 35781 2.048496 . .
---------+---------------------------------------------------------------------
Total | 229,596.504 .0970442 70854 2.297946 . .
. *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 (cond. sentence)") ///
> 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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\tto_2023.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_hijos_mod_joel_bin", "tenencia_de_la_vivienda_mod", "macrozona", "n_off_vio", "n_off_a
> cq", "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_joe
> l", "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.condici
> on_ocupacional_cor i.policonsumo i.num_hijos_mod_joel_bin i.tenencia_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 = 17,721
Time at risk = 182350.221
Wald chi2(49) = 8659.03
Log pseudolikelihood = -181641.09 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
| Robust
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.640931 .0453189 17.93 0.000 1.554469 1.732203
Treatment non-completion (Late) | 1.526359 .0328619 19.64 0.000 1.463291 1.592145
|
tr_modality |
Residential | 1.220076 .0272364 8.91 0.000 1.167845 1.274643
|
sex_enc |
Women | .7603781 .0165423 -12.59 0.000 .7286373 .7935016
edad_ini_cons | .9869505 .001961 -6.61 0.000 .9831145 .9908016
|
escolaridad_rec |
2-Completed high school or less | .9681199 .0172794 -1.82 0.069 .9348384 1.002586
1-More than high school | .8952306 .023671 -4.19 0.000 .8500179 .9428481
|
sus_principal_mod |
Cocaine hydrochloride | 1.076641 .0300722 2.64 0.008 1.019285 1.137225
Cocaine paste | 1.406312 .0332757 14.41 0.000 1.342582 1.473067
Marijuana | 1.078149 .0383255 2.12 0.034 1.005589 1.155944
Other | 1.140282 .0836116 1.79 0.073 .9876373 1.316518
|
freq_cons_sus_prin |
1 day a week or more | .9198352 .0447506 -1.72 0.086 .8361775 1.011863
2 to 3 days a week | .996672 .0397097 -0.08 0.933 .9218037 1.077621
4 to 6 days a week | 1.008517 .0423414 0.20 0.840 .9288517 1.095014
Daily | 1.030179 .0412302 0.74 0.458 .9524573 1.114242
|
condicion_ocupacional_corr |
Inactive | 1.002871 .0312856 0.09 0.927 .9433891 1.066103
Looking for a job for the first time | .9995578 .1454761 -0.00 0.998 .7514907 1.329512
No activity | 1.095422 .040855 2.44 0.015 1.018204 1.178495
Not seeking for work | 1.153402 .0912059 1.80 0.071 .9878054 1.346759
Unemployed | 1.128021 .0208681 6.51 0.000 1.087853 1.169672
|
1.policonsumo | 1.036007 .0228394 1.60 0.109 .9921956 1.081752
1.num_hijos_mod_joel_bin | 1.17537 .0232028 8.19 0.000 1.130762 1.221738
|
tenencia_de_la_vivienda_mod |
Others | .9741906 .0767373 -0.33 0.740 .8348232 1.136824
Owner/Transferred dwellings/Pays Dividends | .8590365 .0581381 -2.25 0.025 .7523221 .980888
Renting | .8961825 .0611308 -1.61 0.108 .7840323 1.024375
Stays temporarily with a relative | .8652866 .0586741 -2.13 0.033 .7576018 .9882776
|
macrozona |
North | 1.302394 .0277987 12.38 0.000 1.249034 1.358035
South | 1.462253 .0431587 12.87 0.000 1.380064 1.549337
|
n_off_vio |
1 | 1.354563 .0269553 15.25 0.000 1.302749 1.408438
|
n_off_acq |
1 | 1.81426 .0341157 31.68 0.000 1.748611 1.882373
|
n_off_sud |
1 | 1.257937 .0243725 11.84 0.000 1.211064 1.306625
|
n_off_oth |
1 | 1.363302 .0268153 15.76 0.000 1.311745 1.416885
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.072721 .0263818 2.85 0.004 1.02224 1.125695
With psychiatric comorbidity | 1.060482 .0189889 3.28 0.001 1.02391 1.098361
|
dg_trs_cons_sus_or |
Drug dependence | 1.019773 .0196989 1.01 0.311 .9818858 1.059123
|
clas_r |
Mixta | 1.026849 .0294271 0.92 0.355 .9707624 1.086175
Rural | 1.055579 .0331032 1.72 0.085 .992652 1.122496
|
porc_pobr | 1.233906 .1479801 1.75 0.080 .9754369 1.560865
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.09495 .0465753 2.13 0.033 1.007365 1.190149
Cocaine paste | 1.126568 .0391883 3.43 0.001 1.05232 1.206055
Marijuana | 1.078014 .0196357 4.12 0.000 1.040208 1.117195
Other | 1.134184 .0585126 2.44 0.015 1.025109 1.254866
|
ano_nac_corr | .8740911 .0037904 -31.03 0.000 .8666935 .8815519
|
con_quien_vive_joel |
Family of origin | .971674 .0319909 -0.87 0.383 .9109533 1.036442
Others | .9921163 .0400834 -0.20 0.845 .9165843 1.073873
With couple/children | .9562995 .0304117 -1.41 0.160 .8985132 1.017802
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.026027 .0169354 1.56 0.120 .9933652 1.059762
One or more | .8962302 .033986 -2.89 0.004 .8320339 .9653795
|
edad_al_ing_1 | .85001 .0037614 -36.72 0.000 .8426696 .8574144
-------------------------------------------------------------------------------------------------------------
. 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| -0.03742 25.74 1 0.0000
3.motivode~c| -0.02670 12.72 1 0.0004
1b.tr_moda~y| . . 1 .
2.tr_modal~y| 0.00113 0.02 1 0.8748
1b.sex_enc | . . 1 .
2.sex_enc | -0.00388 0.28 1 0.5963
edad_ini_c~s| -0.00174 0.06 1 0.8127
1b.escolar~c| . . 1 .
2.escolari~c| 0.00220 0.09 1 0.7629
3.escolari~c| -0.00223 0.09 1 0.7629
1b.sus_pri~d| . . 1 .
2.sus_prin~d| 0.00378 0.26 1 0.6104
3.sus_prin~d| -0.00300 0.17 1 0.6796
4.sus_prin~d| 0.00476 0.41 1 0.5197
5.sus_prin~d| -0.00469 0.42 1 0.5183
1b.freq_co~n| . . 1 .
2.freq_con~n| 0.00374 0.25 1 0.6196
3.freq_con~n| -0.00623 0.70 1 0.4031
4.freq_con~n| -0.00830 1.26 1 0.2626
5.freq_con~n| -0.00937 1.61 1 0.2049
1b.condici~r| . . 1 .
2.condicio~r| -0.00459 0.38 1 0.5402
3.condicio~r| -0.00577 0.63 1 0.4263
4.condicio~r| -0.00443 0.38 1 0.5364
5.condicio~r| 0.00376 0.27 1 0.6037
6.condicio~r| -0.02085 7.87 1 0.0050
0b.policon~o| . . 1 .
1.policons~o| 0.00034 0.00 1 0.9632
0b.num_hij~n| . . 1 .
1.num_hijo~n| 0.00150 0.04 1 0.8370
1b.tenenci~d| . . 1 .
2.tenencia~d| 0.00332 0.21 1 0.6457
3.tenencia~d| 0.00875 1.47 1 0.2249
4.tenencia~d| 0.00864 1.42 1 0.2326
5.tenencia~d| 0.00888 1.52 1 0.2177
1b.macrozona| . . 1 .
2.macrozona | -0.00590 0.64 1 0.4252
3.macrozona | -0.00883 1.49 1 0.2229
1b.n_off_vio| . . 1 .
2.n_off_vio | -0.00821 1.32 1 0.2504
1b.n_off_acq| . . 1 .
2.n_off_acq | -0.06884 96.17 1 0.0000
1b.n_off_sud| . . 1 .
2.n_off_sud | -0.00684 0.93 1 0.3353
1b.n_off_oth| . . 1 .
2.n_off_oth | -0.00567 0.63 1 0.4264
1b.dg_cie_~c| . . 1 .
2.dg_cie_1~c| 0.00624 0.73 1 0.3928
3.dg_cie_1~c| -0.00886 1.42 1 0.2331
1b.dg_trs_~r| . . 1 .
2.dg_trs_c~r| 0.00759 1.04 1 0.3068
1b.clas_r | . . 1 .
2.clas_r | 0.00191 0.07 1 0.7941
3.clas_r | 0.01683 5.29 1 0.0215
porc_pobr | -0.02147 8.25 1 0.0041
1b.sus_ini~v| . . 1 .
2.sus_ini_~v| -0.00000 0.00 1 0.9999
3.sus_ini_~v| 0.00120 0.03 1 0.8658
4.sus_ini_~v| 0.00006 0.00 1 0.9933
5.sus_ini_~v| -0.00738 1.07 1 0.3008
ano_nac_corr| -0.01267 2.88 1 0.0897
1b.con_qui~l| . . 1 .
2.con_quie~l| -0.00073 0.01 1 0.9193
3.con_quie~l| -0.00876 1.44 1 0.2299
4.con_quie~l| 0.00740 1.03 1 0.3097
1b.fis_co~10| . . 1 .
2.fis_com~10| 0.00970 1.72 1 0.1903
3.fis_com~10| -0.00349 0.23 1 0.6350
edad_al_in~1| -0.01797 5.85 1 0.0156
------------+---------------------------------------------------
global test | 233.93 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.csv", replace
(output written to mat_scho_test_02_2023_1.csv)
. esttab matrix(mat_scho_test) using "mat_scho_test_02_2023_1.html", replace
(output written to mat_scho_test_02_2023_1.html)
.
Chi^2(49)= 233.93, p= 0
| mat_scho_test | ||||
| rho | chi2 | df | p | |
| 1b.motivodeegreso_mod_imp_rec | . | . | 1 | . |
| 2.motivodeegreso_mod_imp_rec | -.0374184 | 25.74029 | 1 | 3.91e-07 |
| 3.motivodeegreso_mod_imp_rec | -.0267036 | 12.72001 | 1 | .0003618 |
| 1b.tr_modality | . | . | 1 | . |
| 2.tr_modality | .001129 | .024845 | 1 | .8747539 |
| 1b.sex_enc | . | . | 1 | . |
| 2.sex_enc | -.0038832 | .2806434 | 1 | .5962798 |
| edad_ini_cons | -.001741 | .0561268 | 1 | .8127259 |
| 1b.escolaridad_rec | . | . | 1 | . |
| 2.escolaridad_rec | .0021994 | .0909986 | 1 | .7629115 |
| 3.escolaridad_rec | -.0022341 | .0910049 | 1 | .7629034 |
| 1b.sus_principal_mod | . | . | 1 | . |
| 2.sus_principal_mod | .0037818 | .2595933 | 1 | .6103997 |
| 3.sus_principal_mod | -.0030047 | .1705644 | 1 | .6796107 |
| 4.sus_principal_mod | .0047565 | .4145809 | 1 | .5196535 |
| 5.sus_principal_mod | -.0046852 | .4172157 | 1 | .5183296 |
| 1b.freq_cons_sus_prin | . | . | 1 | . |
| 2.freq_cons_sus_prin | .0037354 | .2463901 | 1 | .6196285 |
| 3.freq_cons_sus_prin | -.0062337 | .6990185 | 1 | .4031137 |
| 4.freq_cons_sus_prin | -.0083032 | 1.255146 | 1 | .262572 |
| 5.freq_cons_sus_prin | -.0093651 | 1.606805 | 1 | .2049415 |
| 1b.condicion_ocupacional_corr | . | . | 1 | . |
| 2.condicion_ocupacional_corr | -.0045918 | .3751419 | 1 | .5402147 |
| 3.condicion_ocupacional_corr | -.0057676 | .6329778 | 1 | .4262651 |
| 4.condicion_ocupacional_corr | -.0044324 | .3823335 | 1 | .536357 |
| 5.condicion_ocupacional_corr | .0037616 | .2693866 | 1 | .6037436 |
| 6.condicion_ocupacional_corr | -.0208453 | 7.874662 | 1 | .0050132 |
| 0b.policonsumo | . | . | 1 | . |
| 1.policonsumo | .0003398 | .0021318 | 1 | .9631737 |
| 0b.num_hijos_mod_joel_bin | . | . | 1 | . |
| 1.num_hijos_mod_joel_bin | .0015038 | .0423289 | 1 | .8369941 |
| 1b.tenencia_de_la_vivienda_mod | . | . | 1 | . |
| 2.tenencia_de_la_vivienda_mod | .0033167 | .2114201 | 1 | .6456566 |
| 3.tenencia_de_la_vivienda_mod | .0087547 | 1.472739 | 1 | .224914 |
| 4.tenencia_de_la_vivienda_mod | .0086379 | 1.424873 | 1 | .2326029 |
| 5.tenencia_de_la_vivienda_mod | .0088807 | 1.519263 | 1 | .217731 |
| 1b.macrozona | . | . | 1 | . |
| 2.macrozona | -.0059023 | .6358334 | 1 | .4252236 |
| 3.macrozona | -.0088319 | 1.485805 | 1 | .2228685 |
| 1b.n_off_vio | . | . | 1 | . |
| 2.n_off_vio | -.0082141 | 1.321189 | 1 | .2503788 |
| 1b.n_off_acq | . | . | 1 | . |
| 2.n_off_acq | -.0688426 | 96.16944 | 1 | 1.05e-22 |
| 1b.n_off_sud | . | . | 1 | . |
| 2.n_off_sud | -.0068392 | .9282351 | 1 | .335322 |
| 1b.n_off_oth | . | . | 1 | . |
| 2.n_off_oth | -.0056687 | .6325493 | 1 | .4264218 |
| 1b.dg_cie_10_rec | . | . | 1 | . |
| 2.dg_cie_10_rec | .0062403 | .7301724 | 1 | .3928273 |
| 3.dg_cie_10_rec | -.0088647 | 1.42202 | 1 | .2330712 |
| 1b.dg_trs_cons_sus_or | . | . | 1 | . |
| 2.dg_trs_cons_sus_or | .0075934 | 1.044501 | 1 | .306777 |
| 1b.clas_r | . | . | 1 | . |
| 2.clas_r | .0019084 | .0681312 | 1 | .7940775 |
| 3.clas_r | .0168254 | 5.289495 | 1 | .0214544 |
| porc_pobr | -.0214663 | 8.252284 | 1 | .0040701 |
| 1b.sus_ini_mod_mvv | . | . | 1 | . |
| 2.sus_ini_mod_mvv | -1.29e-06 | 3.09e-08 | 1 | .9998598 |
| 3.sus_ini_mod_mvv | .0012046 | .0285646 | 1 | .8657882 |
| 4.sus_ini_mod_mvv | .0000609 | .0000698 | 1 | .9933323 |
| 5.sus_ini_mod_mvv | -.0073829 | 1.070489 | 1 | .3008351 |
| ano_nac_corr | -.0126697 | 2.879508 | 1 | .0897134 |
| 1b.con_quien_vive_joel | . | . | 1 | . |
| 2.con_quien_vive_joel | -.0007326 | .0102736 | 1 | .9192657 |
| 3.con_quien_vive_joel | -.0087611 | 1.441696 | 1 | .2298651 |
| 4.con_quien_vive_joel | .0073995 | 1.031906 | 1 | .3097114 |
| 1b.fis_comorbidity_icd_10 | . | . | 1 | . |
| 2.fis_comorbidity_icd_10 | .0096988 | 1.715056 | 1 | .1903307 |
| 3.fis_comorbidity_icd_10 | -.0034896 | .2252921 | 1 | .6350368 |
| edad_al_ing_1 | -.0179697 | 5.845509 | 1 | .0156169 |
. // 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 = -186538.49
Iteration 1: log likelihood = -181995.43
Iteration 2: log likelihood = -181627.59
Iteration 3: log likelihood = -181624.79
Iteration 4: log likelihood = -181624.79
Refining estimates:
Iteration 0: log likelihood = -181624.79
Cox regression -- Breslow method for ties
No. of subjects = 60,247 Number of obs = 60,247
No. of failures = 17,721
Time at risk = 182350.221
LR chi2(51) = 9827.42
Log likelihood = -181624.79 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.640439 .0448334 18.11 0.000 1.554879 1.730707
Treatment non-completion (Late) | 1.525179 .0329473 19.54 0.000 1.461952 1.591141
|
tr_modality |
Residential | 1.219938 .0262569 9.24 0.000 1.169546 1.272502
|
sex_enc |
Women | .7600303 .0163312 -12.77 0.000 .7286865 .7927223
edad_ini_cons | .9868919 .0019513 -6.67 0.000 .9830748 .9907238
|
escolaridad_rec |
2-Completed high school or less | .9644452 .0168686 -2.07 0.038 .9319437 .9980802
1-More than high school | .8860692 .0234044 -4.58 0.000 .8413646 .933149
|
sus_principal_mod |
Cocaine hydrochloride | 1.068642 .0298079 2.38 0.017 1.011788 1.128691
Cocaine paste | 1.394623 .0326951 14.19 0.000 1.331992 1.4602
Marijuana | 1.077399 .0379003 2.12 0.034 1.005619 1.154303
Other | 1.147458 .0829531 1.90 0.057 .9958664 1.322126
|
freq_cons_sus_prin |
1 day a week or more | .9202213 .0450232 -1.70 0.089 .8360765 1.012835
2 to 3 days a week | .9968658 .0395665 -0.08 0.937 .9222565 1.077511
4 to 6 days a week | 1.008652 .0420346 0.21 0.836 .9295405 1.094496
Daily | 1.03039 .0409298 0.75 0.451 .9532119 1.113816
|
condicion_ocupacional_corr |
Inactive | 1.017792 .0318219 0.56 0.573 .9572944 1.082112
Looking for a job for the first time | 1.010183 .1424586 0.07 0.943 .7662344 1.331799
No activity | 1.103993 .039917 2.74 0.006 1.028465 1.185068
Not seeking for work | 1.161547 .0890163 1.95 0.051 .9995493 1.3498
Unemployed | 1.131996 .0207391 6.77 0.000 1.092069 1.173382
|
1.policonsumo | 1.027219 .0224342 1.23 0.219 .9841769 1.072144
1.num_hijos_mod_joel_bin | 1.165045 .0227519 7.82 0.000 1.121294 1.210502
|
tenencia_de_la_vivienda_mod |
Others | .9769365 .0741258 -0.31 0.758 .8419394 1.133579
Owner/Transferred dwellings/Pays Dividends | .8656333 .0566668 -2.20 0.028 .7613984 .984138
Renting | .8982897 .0593165 -1.62 0.104 .7892403 1.022406
Stays temporarily with a relative | .8691363 .0569356 -2.14 0.032 .7644114 .9882087
|
macrozona |
North | 1.303954 .0274016 12.63 0.000 1.251339 1.358781
South | 1.463329 .0421302 13.22 0.000 1.383042 1.548277
|
n_off_vio |
1 | 1.355668 .0258742 15.94 0.000 1.305892 1.407341
|
n_off_acq |
1 | 1.815862 .0324717 33.36 0.000 1.753321 1.880634
|
n_off_sud |
1 | 1.256652 .023308 12.32 0.000 1.211789 1.303175
|
n_off_oth |
1 | 1.36086 .0257499 16.28 0.000 1.311316 1.412277
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.071388 .0257286 2.87 0.004 1.022129 1.123021
With psychiatric comorbidity | 1.058108 .0187995 3.18 0.001 1.021896 1.095604
|
dg_trs_cons_sus_or |
Drug dependence | 1.020128 .0195508 1.04 0.298 .9825196 1.059176
|
clas_r |
Mixta | 1.028003 .0287026 0.99 0.323 .9732586 1.085827
Rural | 1.05393 .0324271 1.71 0.088 .992252 1.119441
|
porc_pobr | 1.235832 .1463111 1.79 0.074 .9799071 1.558597
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.095338 .0454923 2.19 0.028 1.009707 1.188231
Cocaine paste | 1.123555 .0372927 3.51 0.000 1.052789 1.199077
Marijuana | 1.082281 .0193464 4.42 0.000 1.045019 1.120871
Other | 1.130788 .0562395 2.47 0.013 1.025762 1.246567
|
ano_nac_corr | .8744412 .003747 -31.31 0.000 .867128 .881816
|
con_quien_vive_joel |
Family of origin | .970114 .0310493 -0.95 0.343 .9111279 1.032919
Others | .990901 .0389977 -0.23 0.816 .9173404 1.07036
With couple/children | .9521645 .0296225 -1.58 0.115 .8958402 1.01203
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.02699 .0166776 1.64 0.101 .9948174 1.060204
One or more | .902069 .0336794 -2.76 0.006 .8384158 .9705546
|
rc_x1 | .8511987 .0048085 -28.52 0.000 .8418262 .8606755
rc_x2 | 1.02893 .0186499 1.57 0.116 .9930189 1.06614
rc_x3 | .8949623 .0414469 -2.40 0.017 .8173055 .9799976
-------------------------------------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 60,247 -186538.5 -181624.8 51 363351.6 363810.9
-----------------------------------------------------------------------------
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 = -186538.49
Iteration 1: log likelihood = -181928.16
Iteration 2: log likelihood = -181641.45
Iteration 3: log likelihood = -181641.09
Iteration 4: log likelihood = -181641.09
Refining estimates:
Iteration 0: log likelihood = -181641.09
Cox regression -- Breslow method for ties
No. of subjects = 60,247 Number of obs = 60,247
No. of failures = 17,721
Time at risk = 182350.221
LR chi2(49) = 9794.80
Log likelihood = -181641.09 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.640931 .0448572 18.12 0.000 1.555327 1.731248
Treatment non-completion (Late) | 1.526359 .0329805 19.57 0.000 1.463068 1.592387
|
tr_modality |
Residential | 1.220076 .0262527 9.24 0.000 1.169692 1.272631
|
sex_enc |
Women | .7603781 .0163252 -12.76 0.000 .7290451 .7930577
edad_ini_cons | .9869505 .0019395 -6.68 0.000 .9831565 .9907592
|
escolaridad_rec |
2-Completed high school or less | .96812 .0168784 -1.86 0.063 .9355978 1.001773
1-More than high school | .8952306 .0234928 -4.22 0.000 .8503496 .9424803
|
sus_principal_mod |
Cocaine hydrochloride | 1.076641 .0300379 2.65 0.008 1.019349 1.137154
Cocaine paste | 1.406312 .0329083 14.57 0.000 1.34327 1.472313
Marijuana | 1.078149 .0379647 2.14 0.033 1.00625 1.155186
Other | 1.140282 .0825024 1.81 0.070 .9895225 1.314011
|
freq_cons_sus_prin |
1 day a week or more | .9198355 .0450045 -1.71 0.088 .8357256 1.01241
2 to 3 days a week | .9966722 .0395584 -0.08 0.933 .9220781 1.077301
4 to 6 days a week | 1.008517 .0420278 0.20 0.839 .9294181 1.094347
Daily | 1.030179 .0409185 0.75 0.454 .9530225 1.113582
|
condicion_ocupacional_corr |
Inactive | 1.002871 .0312071 0.09 0.927 .9435342 1.065939
Looking for a job for the first time | .9995583 .140908 -0.00 0.997 .7582528 1.317657
No activity | 1.095422 .0395151 2.53 0.012 1.020648 1.175674
Not seeking for work | 1.153403 .0883395 1.86 0.062 .9926294 1.340216
Unemployed | 1.128021 .0206456 6.58 0.000 1.088274 1.169221
|
1.policonsumo | 1.036006 .0226231 1.62 0.105 .9926016 1.081309
1.num_hijos_mod_joel_bin | 1.17537 .0226999 8.37 0.000 1.131711 1.220714
|
tenencia_de_la_vivienda_mod |
Others | .9741898 .0739177 -0.34 0.730 .8395716 1.130393
Owner/Transferred dwellings/Pays Dividends | .8590368 .0562188 -2.32 0.020 .755624 .9766024
Renting | .8961828 .0591747 -1.66 0.097 .7873939 1.020002
Stays temporarily with a relative | .8652868 .0566772 -2.21 0.027 .7610365 .9838179
|
macrozona |
North | 1.302394 .0273481 12.58 0.000 1.249881 1.357114
South | 1.462253 .0420898 13.20 0.000 1.382042 1.547119
|
n_off_vio |
1 | 1.354563 .0258581 15.90 0.000 1.304819 1.406204
|
n_off_acq |
1 | 1.814259 .0324601 33.29 0.000 1.751741 1.879008
|
n_off_sud |
1 | 1.257937 .0233224 12.38 0.000 1.213046 1.304489
|
n_off_oth |
1 | 1.363302 .025795 16.38 0.000 1.313671 1.414808
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.072721 .0257597 2.92 0.003 1.023403 1.124417
With psychiatric comorbidity | 1.060483 .0188368 3.31 0.001 1.024198 1.098052
|
dg_trs_cons_sus_or |
Drug dependence | 1.019773 .0195358 1.02 0.307 .9821937 1.05879
|
clas_r |
Mixta | 1.026849 .0286656 0.95 0.343 .9721744 1.084598
Rural | 1.05558 .0324759 1.76 0.079 .993809 1.12119
|
porc_pobr | 1.233905 .1460345 1.78 0.076 .9784549 1.556047
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.09495 .0454602 2.18 0.029 1.009378 1.187776
Cocaine paste | 1.126568 .0373791 3.59 0.000 1.055638 1.202265
Marijuana | 1.078014 .0192615 4.20 0.000 1.040916 1.116435
Other | 1.134184 .056436 2.53 0.011 1.028794 1.25037
|
ano_nac_corr | .8740912 .003746 -31.40 0.000 .86678 .881464
|
con_quien_vive_joel |
Family of origin | .971675 .0311249 -0.90 0.370 .9125469 1.034634
Others | .9921169 .0390539 -0.20 0.841 .9184509 1.071691
With couple/children | .9563003 .0297413 -1.44 0.151 .8997494 1.016406
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.026027 .0166611 1.58 0.114 .9938858 1.059207
One or more | .89623 .033458 -2.93 0.003 .8329952 .9642651
|
rc_x1 | .85001 .0037112 -37.22 0.000 .8427673 .857315
-------------------------------------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 60,247 -186538.5 -181641.1 49 363380.2 363821.5
-----------------------------------------------------------------------------
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) = 32.62
(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): 16.31
.
. * 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 orthogonal to survival time, a mathematically handy assumption that is often demonstrably a
> nd seriously in error, and the actual data generation process for survival is often too unknown or too messy to simulate.) So in this context, relia
> nce on LR tests or IC statistics is a fallback position.
. *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 = 17,721
Time at risk = 182350.221
Wald chi2(51) = 8492.18
Log pseudolikelihood = -181624.78 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
| Robust
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.640439 .0452561 17.94 0.000 1.554094 1.731581
Treatment non-completion (Late) | 1.525179 .032817 19.62 0.000 1.462197 1.590875
|
tr_modality |
Residential | 1.219938 .0271833 8.92 0.000 1.167806 1.274397
|
sex_enc |
Women | .7600303 .016544 -12.61 0.000 .7282866 .7931576
edad_ini_cons | .9868919 .0019858 -6.56 0.000 .9830075 .9907917
|
escolaridad_rec |
2-Completed high school or less | .9644452 .017251 -2.02 0.043 .9312197 .9988561
1-More than high school | .8860691 .0235803 -4.55 0.000 .8410372 .9335122
|
sus_principal_mod |
Cocaine hydrochloride | 1.068642 .0297847 2.38 0.017 1.011831 1.128643
Cocaine paste | 1.394624 .0330222 14.05 0.000 1.33138 1.460871
Marijuana | 1.077399 .0382165 2.10 0.036 1.00504 1.154967
Other | 1.147458 .0841178 1.88 0.061 .993887 1.324758
|
freq_cons_sus_prin |
1 day a week or more | .920221 .0447254 -1.71 0.087 .8366066 1.012192
2 to 3 days a week | .9968656 .0396698 -0.08 0.937 .9220691 1.077729
4 to 6 days a week | 1.008652 .0422916 0.21 0.837 .9290761 1.095043
Daily | 1.030389 .0411931 0.75 0.454 .9527344 1.114374
|
condicion_ocupacional_corr |
Inactive | 1.017791 .0319372 0.56 0.574 .9570817 1.082352
Looking for a job for the first time | 1.010183 .1466964 0.07 0.944 .7599597 1.342794
No activity | 1.103993 .0412022 2.65 0.008 1.026121 1.187775
Not seeking for work | 1.161546 .0919193 1.89 0.058 .9946642 1.356428
Unemployed | 1.131996 .020945 6.70 0.000 1.09168 1.173801
|
1.policonsumo | 1.02722 .0226427 1.22 0.223 .9837858 1.072571
1.num_hijos_mod_joel_bin | 1.165044 .0232495 7.65 0.000 1.120356 1.211515
|
tenencia_de_la_vivienda_mod |
Others | .9769374 .0769648 -0.30 0.767 .8371583 1.140055
Owner/Transferred dwellings/Pays Dividends | .865633 .0586139 -2.13 0.033 .7580488 .988486
Renting | .8982894 .061293 -1.57 0.116 .7858437 1.026825
Stays temporarily with a relative | .8691362 .0589573 -2.07 0.039 .7609342 .992724
|
macrozona |
North | 1.303954 .0278299 12.44 0.000 1.250533 1.359656
South | 1.46333 .0432088 12.89 0.000 1.381046 1.550516
|
n_off_vio |
1 | 1.355668 .026913 15.33 0.000 1.303932 1.409456
|
n_off_acq |
1 | 1.815863 .0340551 31.81 0.000 1.750328 1.883851
|
n_off_sud |
1 | 1.256652 .0243204 11.80 0.000 1.209878 1.305235
|
n_off_oth |
1 | 1.36086 .0267298 15.69 0.000 1.309466 1.414271
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.071388 .0263108 2.81 0.005 1.021041 1.124218
With psychiatric comorbidity | 1.058108 .0189394 3.16 0.002 1.021631 1.095887
|
dg_trs_cons_sus_or |
Drug dependence | 1.020128 .0196981 1.03 0.302 .9822417 1.059475
|
clas_r |
Mixta | 1.028003 .0294128 0.97 0.334 .9719416 1.087298
Rural | 1.053929 .0330184 1.68 0.094 .9911612 1.120672
|
porc_pobr | 1.235833 .1481063 1.77 0.077 .9771224 1.563042
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.095338 .0464997 2.15 0.032 1.007888 1.190374
Cocaine paste | 1.123555 .0389931 3.36 0.001 1.049671 1.202639
Marijuana | 1.082281 .0196885 4.35 0.000 1.044372 1.121566
Other | 1.130789 .0582405 2.39 0.017 1.022212 1.250898
|
ano_nac_corr | .8744412 .0037876 -30.98 0.000 .8670491 .8818963
|
con_quien_vive_joel |
Family of origin | .9701131 .0319238 -0.92 0.356 .9095186 1.034745
Others | .9909004 .0400519 -0.23 0.821 .9154291 1.072594
With couple/children | .9521637 .0303269 -1.54 0.124 .8945412 1.013498
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.02699 .0169321 1.62 0.106 .9943345 1.060719
One or more | .9020691 .0342091 -2.72 0.007 .8374517 .9716724
|
rc_x1 | .8511987 .0049331 -27.80 0.000 .8415848 .8609225
rc_x2 | 1.02893 .0189988 1.54 0.122 .9923589 1.066849
rc_x3 | .8949629 .0419738 -2.37 0.018 .8163636 .9811298
-------------------------------------------------------------------------------------------------------------
. 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| -0.03739 25.65 1 0.0000
3.motivode~c| -0.02656 12.57 1 0.0004
1b.tr_moda~y| . . 1 .
2.tr_modal~y| 0.00135 0.04 1 0.8508
1b.sex_enc | . . 1 .
2.sex_enc | -0.00390 0.28 1 0.5950
edad_ini_c~s| -0.00176 0.06 1 0.8087
1b.escolar~c| . . 1 .
2.escolari~c| 0.00220 0.09 1 0.7631
3.escolari~c| -0.00252 0.12 1 0.7336
1b.sus_pri~d| . . 1 .
2.sus_prin~d| 0.00348 0.22 1 0.6408
3.sus_prin~d| -0.00350 0.23 1 0.6313
4.sus_prin~d| 0.00476 0.41 1 0.5201
5.sus_prin~d| -0.00448 0.38 1 0.5369
1b.freq_co~n| . . 1 .
2.freq_con~n| 0.00374 0.25 1 0.6194
3.freq_con~n| -0.00623 0.70 1 0.4039
4.freq_con~n| -0.00830 1.25 1 0.2635
5.freq_con~n| -0.00942 1.62 1 0.2030
1b.condici~r| . . 1 .
2.condicio~r| -0.00401 0.29 1 0.5912
3.condicio~r| -0.00566 0.61 1 0.4362
4.condicio~r| -0.00402 0.31 1 0.5748
5.condicio~r| 0.00391 0.29 1 0.5898
6.condicio~r| -0.02052 7.63 1 0.0058
0b.policon~o| . . 1 .
1.policons~o| -0.00003 0.00 1 0.9971
0b.num_hij~n| . . 1 .
1.num_hijo~n| 0.00090 0.02 1 0.9020
1b.tenenci~d| . . 1 .
2.tenencia~d| 0.00345 0.23 1 0.6327
3.tenencia~d| 0.00903 1.57 1 0.2103
4.tenencia~d| 0.00876 1.47 1 0.2257
5.tenencia~d| 0.00916 1.62 1 0.2034
1b.macrozona| . . 1 .
2.macrozona | -0.00597 0.65 1 0.4203
3.macrozona | -0.00881 1.48 1 0.2238
1b.n_off_vio| . . 1 .
2.n_off_vio | -0.00818 1.30 1 0.2537
1b.n_off_acq| . . 1 .
2.n_off_acq | -0.06877 95.48 1 0.0000
1b.n_off_sud| . . 1 .
2.n_off_sud | -0.00684 0.93 1 0.3361
1b.n_off_oth| . . 1 .
2.n_off_oth | -0.00568 0.63 1 0.4262
1b.dg_cie_~c| . . 1 .
2.dg_cie_1~c| 0.00632 0.75 1 0.3876
3.dg_cie_1~c| -0.00904 1.48 1 0.2242
1b.dg_trs_~r| . . 1 .
2.dg_trs_c~r| 0.00755 1.03 1 0.3098
1b.clas_r | . . 1 .
2.clas_r | 0.00212 0.08 1 0.7725
3.clas_r | 0.01671 5.20 1 0.0225
porc_pobr | -0.02133 8.14 1 0.0043
1b.sus_ini~v| . . 1 .
2.sus_ini_~v| 0.00007 0.00 1 0.9920
3.sus_ini_~v| 0.00128 0.03 1 0.8577
4.sus_ini_~v| 0.00020 0.00 1 0.9777
5.sus_ini_~v| -0.00760 1.13 1 0.2879
ano_nac_corr| -0.01246 2.78 1 0.0956
1b.con_qui~l| . . 1 .
2.con_quie~l| -0.00081 0.01 1 0.9113
3.con_quie~l| -0.00890 1.49 1 0.2223
4.con_quie~l| 0.00718 0.97 1 0.3240
1b.fis_co~10| . . 1 .
2.fis_com~10| 0.00988 1.78 1 0.1827
3.fis_com~10| -0.00324 0.19 1 0.6594
rc_x1 | -0.01237 2.86 1 0.0906
rc_x2 | 0.00064 0.01 1 0.9303
rc_x3 | -0.00172 0.05 1 0.8171
------------+---------------------------------------------------
global test | 233.36 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.csv", replace
(output written to mat_scho_test_02_2023_2.csv)
. esttab matrix(mat_scho_test2) using "mat_scho_test_02_2023_2.html", replace
(output written to mat_scho_test_02_2023_2.html)
.
Chi^2(51)= 233.36, p= 0
| mat_scho_test2 | ||||
| rho | chi2 | df | p | |
| 1b.motivodeegreso_mod_imp_rec | . | . | 1 | . |
| 2.motivodeegreso_mod_imp_rec | -.0373892 | 25.64816 | 1 | 4.10e-07 |
| 3.motivodeegreso_mod_imp_rec | -.0265629 | 12.57406 | 1 | .0003911 |
| 1b.tr_modality | . | . | 1 | . |
| 2.tr_modality | .0013502 | .0354031 | 1 | .8507536 |
| 1b.sex_enc | . | . | 1 | . |
| 2.sex_enc | -.0038953 | .2826048 | 1 | .594999 |
| edad_ini_cons | -.0017575 | .0586238 | 1 | .8086844 |
| 1b.escolaridad_rec | . | . | 1 | . |
| 2.escolaridad_rec | .0022003 | .0908316 | 1 | .7631226 |
| 3.escolaridad_rec | -.0025231 | .1158055 | 1 | .733629 |
| 1b.sus_principal_mod | . | . | 1 | . |
| 2.sus_principal_mod | .0034774 | .2177106 | 1 | .6407899 |
| 3.sus_principal_mod | -.0035017 | .2303312 | 1 | .6312783 |
| 4.sus_principal_mod | .0047639 | .4137015 | 1 | .5200967 |
| 5.sus_principal_mod | -.0044835 | .3812455 | 1 | .5369374 |
| 1b.freq_cons_sus_prin | . | . | 1 | . |
| 2.freq_cons_sus_prin | .003742 | .2467536 | 1 | .6193703 |
| 3.freq_cons_sus_prin | -.0062306 | .6966382 | 1 | .4039156 |
| 4.freq_cons_sus_prin | -.0082979 | 1.250424 | 1 | .2634715 |
| 5.freq_cons_sus_prin | -.0094162 | 1.620754 | 1 | .2029866 |
| 1b.condicion_ocupacional_corr | . | . | 1 | . |
| 2.condicion_ocupacional_corr | -.0040145 | .2884793 | 1 | .5911967 |
| 3.condicion_ocupacional_corr | -.00566 | .6063844 | 1 | .4361524 |
| 4.condicion_ocupacional_corr | -.0040222 | .3146451 | 1 | .5748437 |
| 5.condicion_ocupacional_corr | .0039066 | .290588 | 1 | .589844 |
| 6.condicion_ocupacional_corr | -.0205195 | 7.625588 | 1 | .0057546 |
| 0b.policonsumo | . | . | 1 | . |
| 1.policonsumo | -.000027 | .0000134 | 1 | .9970799 |
| 0b.num_hijos_mod_joel_bin | . | . | 1 | . |
| 1.num_hijos_mod_joel_bin | .0009016 | .0151763 | 1 | .9019549 |
| 1b.tenencia_de_la_vivienda_mod | . | . | 1 | . |
| 2.tenencia_de_la_vivienda_mod | .0034468 | .2284231 | 1 | .6326955 |
| 3.tenencia_de_la_vivienda_mod | .0090349 | 1.569624 | 1 | .2102617 |
| 4.tenencia_de_la_vivienda_mod | .0087646 | 1.467982 | 1 | .2256644 |
| 5.tenencia_de_la_vivienda_mod | .0091609 | 1.617766 | 1 | .2034036 |
| 1b.macrozona | . | . | 1 | . |
| 2.macrozona | -.005967 | .6493775 | 1 | .4203353 |
| 3.macrozona | -.0088102 | 1.479633 | 1 | .2238319 |
| 1b.n_off_vio | . | . | 1 | . |
| 2.n_off_vio | -.0081752 | 1.302609 | 1 | .2537372 |
| 1b.n_off_acq | . | . | 1 | . |
| 2.n_off_acq | -.0687722 | 95.48148 | 1 | 1.49e-22 |
| 1b.n_off_sud | . | . | 1 | . |
| 2.n_off_sud | -.0068371 | .9253466 | 1 | .336075 |
| 1b.n_off_oth | . | . | 1 | . |
| 2.n_off_oth | -.0056803 | .6330851 | 1 | .4262259 |
| 1b.dg_cie_10_rec | . | . | 1 | . |
| 2.dg_cie_10_rec | .0063188 | .7463822 | 1 | .3876241 |
| 3.dg_cie_10_rec | -.0090387 | 1.4772 | 1 | .2242131 |
| 1b.dg_trs_cons_sus_or | . | . | 1 | . |
| 2.dg_trs_cons_sus_or | .0075504 | 1.03153 | 1 | .3097995 |
| 1b.clas_r | . | . | 1 | . |
| 2.clas_r | .0021177 | .0836179 | 1 | .7724531 |
| 3.clas_r | .0167096 | 5.204354 | 1 | .0225304 |
| porc_pobr | -.0213262 | 8.139026 | 1 | .0043323 |
| 1b.sus_ini_mod_mvv | . | . | 1 | . |
| 2.sus_ini_mod_mvv | .0000735 | .0001002 | 1 | .9920134 |
| 3.sus_ini_mod_mvv | .0012812 | .0321345 | 1 | .8577329 |
| 4.sus_ini_mod_mvv | .000204 | .0007792 | 1 | .9777304 |
| 5.sus_ini_mod_mvv | -.0075995 | 1.129245 | 1 | .2879365 |
| ano_nac_corr | -.0124565 | 2.776709 | 1 | .0956445 |
| 1b.con_quien_vive_joel | . | . | 1 | . |
| 2.con_quien_vive_joel | -.0008052 | .0124011 | 1 | .9113306 |
| 3.con_quien_vive_joel | -.0088996 | 1.48937 | 1 | .2223142 |
| 4.con_quien_vive_joel | .0071772 | .9725941 | 1 | .3240341 |
| 1b.fis_comorbidity_icd_10 | . | . | 1 | . |
| 2.fis_comorbidity_icd_10 | .0098774 | 1.775174 | 1 | .1827431 |
| 3.fis_comorbidity_icd_10 | -.0032407 | .1942804 | 1 | .6593777 |
| rc_x1 | -.0123671 | 2.862899 | 1 | .090644 |
| rc_x2 | .0006444 | .0076495 | 1 | .9303049 |
| rc_x3 | -.0017187 | .0535143 | 1 | .8170572 |
=============================================================================
=============================================================================
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_hijos_mod_joel_bin", "tenencia_de_la_vivienda_mod", "macrozona", "n_off_vio", "n_off_a
> cq", "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_joe
> l", "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_ges_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
> }
> */
.
. *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_prin
> 2 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 tenvi
> v4 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_
> nac_corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3"
.
. 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.condic
> ion_ocupacional_cor i.policonsumo i.num_hijos_mod_joel_bin i.tenencia_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 rc_x1 rc_x2 r
> c_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 = -55301.54
Iteration 1: log likelihood = -54841.734
Iteration 2: log likelihood = -54836.006
Iteration 3: log likelihood = -54836.004
Log likelihood = -54836.004 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.688395 .0489105 18.08 0.000 1.595203 1.787032
mot_egr_late | 1.564295 .036858 18.99 0.000 1.493697 1.638229
tr_mod2 | 1.221993 .0262987 9.32 0.000 1.171521 1.27464
sex_dum2 | .7556634 .0162373 -13.04 0.000 .7244998 .7881675
edad_ini_cons | .9866096 .0019518 -6.81 0.000 .9827916 .9904425
esc1 | 1.132862 .0299225 4.72 0.000 1.075707 1.193053
esc2 | 1.091226 .0260063 3.66 0.000 1.041427 1.143406
sus_prin2 | 1.064241 .0296671 2.23 0.026 1.007655 1.124006
sus_prin3 | 1.39742 .0327362 14.28 0.000 1.334709 1.463078
sus_prin4 | 1.072597 .0377145 1.99 0.046 1.001168 1.149123
sus_prin5 | 1.138599 .0823228 1.80 0.073 .9881605 1.311941
fr_cons_sus_prin2 | .9198352 .0450049 -1.71 0.088 .8357245 1.012411
fr_cons_sus_prin3 | .9957537 .0395248 -0.11 0.915 .9212234 1.076314
fr_cons_sus_prin4 | 1.007375 .0419845 0.18 0.860 .9283577 1.093117
fr_cons_sus_prin5 | 1.029893 .0409155 0.74 0.458 .952743 1.113291
cond_ocu2 | 1.017431 .031808 0.55 0.580 .9569602 1.081723
cond_ocu3 | .9925914 .1399845 -0.05 0.958 .7528806 1.308624
cond_ocu4 | 1.105573 .0399879 2.77 0.006 1.029911 1.186792
cond_ocu5 | 1.164049 .0891882 1.98 0.047 1.001736 1.352663
cond_ocu6 | 1.133824 .0207692 6.86 0.000 1.093839 1.17527
policonsumo | 1.020961 .0222809 0.95 0.342 .9782124 1.065579
num_hij2 | 1.169709 .0228451 8.03 0.000 1.12578 1.215353
tenviv1 | 1.153672 .0755266 2.18 0.029 1.014746 1.311618
tenviv2 | 1.126247 .0493426 2.71 0.007 1.033573 1.22723
tenviv4 | 1.035496 .0236979 1.52 0.127 .9900752 1.083
tenviv5 | 1.002188 .017965 0.12 0.903 .9675886 1.038024
mzone2 | 1.309283 .0275059 12.83 0.000 1.256467 1.364319
mzone3 | 1.475277 .0424182 13.52 0.000 1.394438 1.560802
n_off_vio | 1.360168 .0259913 16.10 0.000 1.310168 1.412076
n_off_acq | 1.826207 .0327165 33.62 0.000 1.763196 1.891469
n_off_sud | 1.260411 .0233976 12.47 0.000 1.215376 1.307114
n_off_oth | 1.36728 .0259107 16.51 0.000 1.317428 1.419019
psy_com2 | 1.066365 .0255889 2.68 0.007 1.017373 1.117717
psy_com3 | 1.058946 .0188144 3.22 0.001 1.022705 1.096471
dep2 | 1.02081 .0195623 1.07 0.282 .9831794 1.05988
rural2 | 1.030959 .0287671 1.09 0.275 .9760901 1.088911
rural3 | 1.058415 .0325492 1.85 0.065 .9965046 1.124172
porc_pobr | 1.173038 .1387882 1.35 0.177 .9302547 1.479184
susini2 | 1.095672 .0455054 2.20 0.028 1.010017 1.188592
susini3 | 1.12619 .0373826 3.58 0.000 1.055254 1.201895
susini4 | 1.084632 .0193838 4.55 0.000 1.047298 1.123297
susini5 | 1.131215 .0562675 2.48 0.013 1.026138 1.247052
ano_nac_corr | .8944821 .0037918 -26.31 0.000 .8870812 .9019448
cohab2 | .9691307 .0310125 -0.98 0.327 .9102142 1.031861
cohab3 | .9889351 .038914 -0.28 0.777 .9155319 1.068223
cohab4 | .9508018 .0295674 -1.62 0.105 .8945815 1.010555
fis_com2 | 1.030794 .0167362 1.87 0.062 .998508 1.064124
fis_com3 | .9035943 .0337355 -2.72 0.007 .8398352 .9721939
rc_x1 | .8699671 .0048872 -24.80 0.000 .8604409 .8795988
rc_x2 | 1.029208 .0186527 1.59 0.112 .9932906 1.066423
rc_x3 | .8955324 .041475 -2.38 0.017 .8178232 .9806254
_rcs1 | 2.479151 .0366332 61.44 0.000 2.408381 2.552001
_rcs_mot_egr_early1 | .9270318 .0165422 -4.25 0.000 .89517 .9600276
_rcs_mot_egr_late1 | .9523993 .0156915 -2.96 0.003 .9221358 .983656
_cons | 1.52e+96 1.30e+97 25.94 0.000 8.20e+88 2.8e+103
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54721.17
Iteration 1: log likelihood = -54573.405
Iteration 2: log likelihood = -54572.446
Iteration 3: log likelihood = -54572.446
Log likelihood = -54572.446 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.712172 .0496326 18.55 0.000 1.617606 1.812266
mot_egr_late | 1.57087 .0370394 19.15 0.000 1.499926 1.64517
tr_mod2 | 1.216556 .0261807 9.11 0.000 1.16631 1.268967
sex_dum2 | .7585611 .0162969 -12.86 0.000 .7272828 .7911846
edad_ini_cons | .9867716 .0019514 -6.73 0.000 .9829544 .9906036
esc1 | 1.131119 .0298766 4.66 0.000 1.074052 1.191218
esc2 | 1.09048 .0259887 3.63 0.000 1.040714 1.142625
sus_prin2 | 1.062941 .0296267 2.19 0.029 1.006431 1.122623
sus_prin3 | 1.391205 .0325922 14.09 0.000 1.32877 1.456574
sus_prin4 | 1.072567 .0377113 1.99 0.046 1.001143 1.149086
sus_prin5 | 1.133267 .0819298 1.73 0.084 .9835456 1.30578
fr_cons_sus_prin2 | .9208012 .0450516 -1.69 0.092 .8366032 1.013473
fr_cons_sus_prin3 | .9963126 .0395447 -0.09 0.926 .9217444 1.076913
fr_cons_sus_prin4 | 1.008247 .0420185 0.20 0.844 .9291656 1.094058
fr_cons_sus_prin5 | 1.030285 .0409255 0.75 0.453 .9531158 1.113703
cond_ocu2 | 1.017642 .0318129 0.56 0.576 .9571613 1.081944
cond_ocu3 | .9894426 .1395372 -0.08 0.940 .7504971 1.304464
cond_ocu4 | 1.107385 .0400386 2.82 0.005 1.031627 1.188707
cond_ocu5 | 1.163546 .0891528 1.98 0.048 1.001297 1.352084
cond_ocu6 | 1.13197 .0207361 6.77 0.000 1.092049 1.173351
policonsumo | 1.022132 .0223068 1.00 0.316 .9793329 1.066801
num_hij2 | 1.167066 .0227886 7.91 0.000 1.123245 1.212597
tenviv1 | 1.148217 .0751759 2.11 0.035 1.009937 1.305431
tenviv2 | 1.124422 .0492637 2.68 0.007 1.031897 1.225244
tenviv4 | 1.036501 .0237196 1.57 0.117 .991039 1.084049
tenviv5 | 1.002713 .0179769 0.15 0.880 .9680903 1.038573
mzone2 | 1.302632 .0273758 12.58 0.000 1.250066 1.357408
mzone3 | 1.468485 .0422143 13.37 0.000 1.388034 1.553599
n_off_vio | 1.355556 .025896 15.92 0.000 1.305739 1.407273
n_off_acq | 1.814938 .032499 33.29 0.000 1.752346 1.879766
n_off_sud | 1.258554 .0233557 12.39 0.000 1.2136 1.305173
n_off_oth | 1.36148 .0257906 16.29 0.000 1.311859 1.412979
psy_com2 | 1.068458 .0256432 2.76 0.006 1.019362 1.119919
psy_com3 | 1.057895 .0187935 3.17 0.002 1.021695 1.095378
dep2 | 1.019824 .0195437 1.02 0.306 .9822299 1.058858
rural2 | 1.029913 .0287399 1.06 0.291 .9750968 1.087811
rural3 | 1.056325 .0324891 1.78 0.075 .9945291 1.121961
porc_pobr | 1.187752 .140531 1.45 0.146 .9419206 1.497743
susini2 | 1.096224 .0455302 2.21 0.027 1.010523 1.189195
susini3 | 1.124071 .0373089 3.52 0.000 1.053274 1.199626
susini4 | 1.083088 .019355 4.47 0.000 1.04581 1.121696
susini5 | 1.127894 .0560939 2.42 0.016 1.02314 1.243373
ano_nac_corr | .8826619 .0037545 -29.34 0.000 .8753338 .8900513
cohab2 | .9703574 .0310543 -0.94 0.347 .9113616 1.033172
cohab3 | .9914365 .0390199 -0.22 0.827 .9178343 1.070941
cohab4 | .9522215 .0296159 -1.57 0.115 .8959092 1.012073
fis_com2 | 1.02981 .0167215 1.81 0.070 .997552 1.06311
fis_com3 | .9033162 .0337248 -2.72 0.006 .8395773 .971894
rc_x1 | .859036 .0048331 -27.01 0.000 .8496153 .8685612
rc_x2 | 1.028485 .0186391 1.55 0.121 .9925937 1.065673
rc_x3 | .8964878 .0415127 -2.36 0.018 .8187075 .9816576
_rcs1 | 2.458393 .036058 61.33 0.000 2.388727 2.530091
_rcs_mot_egr_early1 | .9711559 .0179299 -1.59 0.113 .9366422 1.006941
_rcs_mot_egr_early2 | 1.128553 .0104687 13.04 0.000 1.10822 1.149259
_rcs_mot_egr_late1 | 1.01323 .0172355 0.77 0.440 .9800061 1.047581
_rcs_mot_egr_late2 | 1.133837 .0081003 17.58 0.000 1.118071 1.149825
_cons | 6.5e+107 5.6e+108 28.98 0.000 3.3e+100 1.3e+115
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54578.443
Iteration 1: log likelihood = -54524.427
Iteration 2: log likelihood = -54524.229
Iteration 3: log likelihood = -54524.229
Log likelihood = -54524.229 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.717514 .0498039 18.65 0.000 1.622622 1.817955
mot_egr_late | 1.572244 .0370831 19.19 0.000 1.501216 1.646631
tr_mod2 | 1.216212 .026173 9.10 0.000 1.165981 1.268607
sex_dum2 | .7591414 .0163094 -12.83 0.000 .7278393 .7917897
edad_ini_cons | .9867979 .0019514 -6.72 0.000 .9829805 .99063
esc1 | 1.130417 .0298579 4.64 0.000 1.073385 1.190478
esc2 | 1.089881 .0259748 3.61 0.000 1.040142 1.141998
sus_prin2 | 1.064102 .0296601 2.23 0.026 1.007528 1.123852
sus_prin3 | 1.391398 .0325997 14.10 0.000 1.328948 1.456782
sus_prin4 | 1.074223 .0377715 2.04 0.042 1.002686 1.150865
sus_prin5 | 1.133221 .0819354 1.73 0.084 .98349 1.305747
fr_cons_sus_prin2 | .920939 .0450585 -1.68 0.092 .8367283 1.013625
fr_cons_sus_prin3 | .9967601 .0395619 -0.08 0.935 .9221594 1.077396
fr_cons_sus_prin4 | 1.009102 .0420538 0.22 0.828 .9299547 1.094986
fr_cons_sus_prin5 | 1.031065 .040956 0.77 0.441 .9538377 1.114545
cond_ocu2 | 1.017773 .0318167 0.56 0.573 .9572857 1.082083
cond_ocu3 | .9939684 .1401774 -0.04 0.966 .753927 1.310436
cond_ocu4 | 1.106688 .0400107 2.80 0.005 1.030983 1.187953
cond_ocu5 | 1.16249 .089075 1.96 0.049 1.000383 1.350865
cond_ocu6 | 1.132193 .02074 6.78 0.000 1.092264 1.173581
policonsumo | 1.023416 .0223369 1.06 0.289 .98056 1.068146
num_hij2 | 1.166385 .0227747 7.88 0.000 1.12259 1.211887
tenviv1 | 1.147288 .0751161 2.10 0.036 1.009118 1.304377
tenviv2 | 1.125292 .0493043 2.69 0.007 1.03269 1.226197
tenviv4 | 1.037264 .0237376 1.60 0.110 .9917673 1.084848
tenviv5 | 1.003281 .017988 0.18 0.855 .9686373 1.039163
mzone2 | 1.302912 .0273836 12.59 0.000 1.250332 1.357704
mzone3 | 1.46854 .042225 13.36 0.000 1.388069 1.553676
n_off_vio | 1.355531 .0258871 15.93 0.000 1.305731 1.40723
n_off_acq | 1.815404 .0324942 33.31 0.000 1.75282 1.880221
n_off_sud | 1.258181 .0233446 12.38 0.000 1.213248 1.304777
n_off_oth | 1.361066 .0257734 16.28 0.000 1.311477 1.41253
psy_com2 | 1.069812 .0256773 2.81 0.005 1.020651 1.121341
psy_com3 | 1.058047 .0187951 3.18 0.001 1.021844 1.095534
dep2 | 1.019765 .019543 1.02 0.307 .9821721 1.058797
rural2 | 1.02946 .0287296 1.04 0.298 .9746636 1.087338
rural3 | 1.055403 .032466 1.75 0.080 .993651 1.120992
porc_pobr | 1.206809 .1427794 1.59 0.112 .9570428 1.521757
susini2 | 1.096599 .0455463 2.22 0.026 1.010867 1.189602
susini3 | 1.12339 .0372842 3.51 0.000 1.05264 1.198895
susini4 | 1.08286 .0193502 4.45 0.000 1.045591 1.121458
susini5 | 1.12867 .0561354 2.43 0.015 1.023839 1.244234
ano_nac_corr | .879396 .0037526 -30.12 0.000 .8720717 .8867818
cohab2 | .9707978 .0310707 -0.93 0.354 .911771 1.033646
cohab3 | .9925494 .0390643 -0.19 0.849 .9188634 1.072145
cohab4 | .9529137 .0296389 -1.55 0.121 .8965578 1.012812
fis_com2 | 1.028721 .0167044 1.74 0.081 .9964968 1.061988
fis_com3 | .9022569 .0336855 -2.75 0.006 .8385923 .9707549
rc_x1 | .8558978 .0048243 -27.61 0.000 .8464944 .8654058
rc_x2 | 1.028661 .0186422 1.56 0.119 .9927646 1.065856
rc_x3 | .8960826 .0414924 -2.37 0.018 .8183401 .9812106
_rcs1 | 2.452512 .0358889 61.31 0.000 2.38317 2.523871
_rcs_mot_egr_early1 | .971764 .017873 -1.56 0.119 .9373574 1.007434
_rcs_mot_egr_early2 | 1.107488 .0103235 10.95 0.000 1.087439 1.127908
_rcs_mot_egr_early3 | 1.040578 .0061391 6.74 0.000 1.028614 1.05268
_rcs_mot_egr_late1 | 1.012476 .017142 0.73 0.464 .9794301 1.046638
_rcs_mot_egr_late2 | 1.106099 .0081061 13.76 0.000 1.090325 1.122101
_rcs_mot_egr_late3 | 1.042762 .0045792 9.54 0.000 1.033825 1.051775
_cons | 1.1e+111 9.7e+111 29.75 0.000 5.5e+103 2.3e+118
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54572.453
Iteration 1: log likelihood = -54515.735
Iteration 2: log likelihood = -54515.488
Iteration 3: log likelihood = -54515.488
Log likelihood = -54515.488 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.718957 .0498493 18.68 0.000 1.623979 1.81949
mot_egr_late | 1.572776 .0370984 19.20 0.000 1.50172 1.647195
tr_mod2 | 1.216174 .026172 9.09 0.000 1.165945 1.268568
sex_dum2 | .7593898 .0163148 -12.81 0.000 .7280772 .792049
edad_ini_cons | .9868066 .0019514 -6.72 0.000 .9829892 .9906388
esc1 | 1.130206 .0298525 4.63 0.000 1.073185 1.190256
esc2 | 1.089642 .0259693 3.60 0.000 1.039913 1.141748
sus_prin2 | 1.064723 .0296789 2.25 0.024 1.008114 1.124511
sus_prin3 | 1.391909 .0326145 14.11 0.000 1.329432 1.457323
sus_prin4 | 1.074958 .037799 2.06 0.040 1.003369 1.151656
sus_prin5 | 1.133889 .0819871 1.74 0.082 .984064 1.306524
fr_cons_sus_prin2 | .9208408 .0450536 -1.69 0.092 .836639 1.013517
fr_cons_sus_prin3 | .9968834 .0395667 -0.08 0.937 .9222737 1.077529
fr_cons_sus_prin4 | 1.009285 .0420613 0.22 0.824 .9301236 1.095184
fr_cons_sus_prin5 | 1.031162 .0409599 0.77 0.440 .9539278 1.11465
cond_ocu2 | 1.017807 .0318176 0.56 0.572 .9573175 1.082118
cond_ocu3 | .9959608 .1404594 -0.03 0.977 .7554367 1.313066
cond_ocu4 | 1.106225 .0399941 2.79 0.005 1.030551 1.187456
cond_ocu5 | 1.162221 .089056 1.96 0.050 1.000149 1.350556
cond_ocu6 | 1.132301 .0207419 6.78 0.000 1.092368 1.173693
policonsumo | 1.023968 .0223498 1.09 0.278 .9810868 1.068723
num_hij2 | 1.166351 .0227739 7.88 0.000 1.122559 1.211852
tenviv1 | 1.147669 .0751402 2.10 0.035 1.009455 1.304808
tenviv2 | 1.125503 .0493148 2.70 0.007 1.032882 1.22643
tenviv4 | 1.037462 .0237424 1.61 0.108 .9919558 1.085056
tenviv5 | 1.003476 .0179916 0.19 0.847 .9688259 1.039366
mzone2 | 1.303219 .0273907 12.60 0.000 1.250625 1.358024
mzone3 | 1.468706 .0422348 13.37 0.000 1.388217 1.553862
n_off_vio | 1.355613 .0258861 15.93 0.000 1.305815 1.40731
n_off_acq | 1.815381 .0324907 33.32 0.000 1.752805 1.880192
n_off_sud | 1.257979 .0233398 12.37 0.000 1.213055 1.304566
n_off_oth | 1.361064 .0257706 16.28 0.000 1.31148 1.412522
psy_com2 | 1.070113 .0256848 2.82 0.005 1.020937 1.121657
psy_com3 | 1.058081 .0187955 3.18 0.001 1.021876 1.095568
dep2 | 1.01983 .0195444 1.02 0.306 .9822339 1.058865
rural2 | 1.029551 .0287328 1.04 0.297 .9747485 1.087435
rural3 | 1.055314 .032465 1.75 0.080 .9935643 1.120902
porc_pobr | 1.211771 .1433607 1.62 0.104 .960987 1.528
susini2 | 1.096734 .045552 2.22 0.026 1.010991 1.189749
susini3 | 1.123228 .037278 3.50 0.000 1.05249 1.19872
susini4 | 1.082863 .0193504 4.45 0.000 1.045593 1.121461
susini5 | 1.128987 .0561525 2.44 0.015 1.024125 1.244587
ano_nac_corr | .8788072 .0037529 -30.25 0.000 .8714823 .8861937
cohab2 | .9709644 .0310758 -0.92 0.357 .9119279 1.033823
cohab3 | .9925493 .0390638 -0.19 0.849 .9188641 1.072143
cohab4 | .9530076 .0296414 -1.55 0.122 .8966469 1.012911
fis_com2 | 1.028424 .0166998 1.73 0.084 .9962089 1.061682
fis_com3 | .9020414 .0336775 -2.76 0.006 .8383918 .9705231
rc_x1 | .8553191 .0048231 -27.71 0.000 .845918 .8648246
rc_x2 | 1.028738 .0186435 1.56 0.118 .9928386 1.065935
rc_x3 | .8959243 .0414846 -2.37 0.018 .8181964 .9810362
_rcs1 | 2.45164 .035869 61.29 0.000 2.382337 2.52296
_rcs_mot_egr_early1 | .9724193 .0178887 -1.52 0.128 .9379827 1.00812
_rcs_mot_egr_early2 | 1.107191 .0106037 10.63 0.000 1.086602 1.12817
_rcs_mot_egr_early3 | 1.039766 .0065976 6.15 0.000 1.026915 1.052777
_rcs_mot_egr_early4 | 1.015329 .0041334 3.74 0.000 1.00726 1.023463
_rcs_mot_egr_late1 | 1.01318 .0171577 0.77 0.439 .9801035 1.047373
_rcs_mot_egr_late2 | 1.107238 .0084338 13.37 0.000 1.090831 1.123892
_rcs_mot_egr_late3 | 1.039754 .0050544 8.02 0.000 1.029894 1.049707
_rcs_mot_egr_late4 | 1.016689 .0030497 5.52 0.000 1.010729 1.022684
_cons | 4.3e+111 3.7e+112 29.89 0.000 2.1e+104 9.1e+118
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54558.696
Iteration 1: log likelihood = -54509.348
Iteration 2: log likelihood = -54509.164
Iteration 3: log likelihood = -54509.164
Log likelihood = -54509.164 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.719952 .0498823 18.70 0.000 1.624911 1.820551
mot_egr_late | 1.573027 .0371082 19.20 0.000 1.501952 1.647465
tr_mod2 | 1.21628 .0261743 9.10 0.000 1.166046 1.268678
sex_dum2 | .7595976 .0163195 -12.80 0.000 .7282761 .7922661
edad_ini_cons | .9868135 .0019514 -6.71 0.000 .9829962 .9906456
esc1 | 1.130055 .0298486 4.63 0.000 1.073042 1.190098
esc2 | 1.08949 .0259658 3.60 0.000 1.039769 1.141589
sus_prin2 | 1.065061 .0296892 2.26 0.024 1.008432 1.124869
sus_prin3 | 1.392148 .0326219 14.12 0.000 1.329656 1.457576
sus_prin4 | 1.075313 .0378123 2.06 0.039 1.003698 1.152037
sus_prin5 | 1.134449 .0820309 1.74 0.081 .9845445 1.307177
fr_cons_sus_prin2 | .9207101 .0450473 -1.69 0.091 .8365203 1.013373
fr_cons_sus_prin3 | .9969322 .0395686 -0.08 0.938 .9223189 1.077582
fr_cons_sus_prin4 | 1.00932 .0420627 0.22 0.824 .9301553 1.095221
fr_cons_sus_prin5 | 1.031132 .0409587 0.77 0.440 .9538996 1.114617
cond_ocu2 | 1.017817 .0318179 0.56 0.572 .9573272 1.082129
cond_ocu3 | .9965992 .1405493 -0.02 0.981 .7559211 1.313907
cond_ocu4 | 1.105733 .0399767 2.78 0.005 1.030091 1.186928
cond_ocu5 | 1.162181 .089054 1.96 0.050 1.000113 1.350512
cond_ocu6 | 1.132317 .0207422 6.78 0.000 1.092384 1.173709
policonsumo | 1.024225 .0223558 1.10 0.273 .9813328 1.068993
num_hij2 | 1.166364 .0227741 7.88 0.000 1.122571 1.211866
tenviv1 | 1.14858 .0752 2.12 0.034 1.010256 1.305844
tenviv2 | 1.125639 .0493218 2.70 0.007 1.033005 1.22658
tenviv4 | 1.037672 .0237474 1.62 0.106 .9921566 1.085276
tenviv5 | 1.003639 .0179945 0.20 0.839 .9689832 1.039535
mzone2 | 1.30339 .0273949 12.61 0.000 1.250787 1.358204
mzone3 | 1.468669 .042237 13.36 0.000 1.388176 1.55383
n_off_vio | 1.355596 .0258842 15.93 0.000 1.305801 1.407289
n_off_acq | 1.81534 .0324874 33.32 0.000 1.752769 1.880144
n_off_sud | 1.257962 .0233386 12.37 0.000 1.213041 1.304547
n_off_oth | 1.361049 .0257682 16.28 0.000 1.31147 1.412503
psy_com2 | 1.070104 .0256851 2.82 0.005 1.020928 1.121649
psy_com3 | 1.058068 .0187953 3.18 0.001 1.021864 1.095555
dep2 | 1.019855 .0195449 1.03 0.305 .9822578 1.05889
rural2 | 1.029618 .0287352 1.05 0.296 .9748107 1.087507
rural3 | 1.055357 .0324677 1.75 0.080 .9936022 1.12095
porc_pobr | 1.214776 .1437117 1.64 0.100 .9633776 1.531779
susini2 | 1.096895 .0455587 2.23 0.026 1.011139 1.189923
susini3 | 1.12319 .0372767 3.50 0.000 1.052455 1.19868
susini4 | 1.082843 .0193503 4.45 0.000 1.045574 1.121441
susini5 | 1.129284 .056169 2.44 0.015 1.024391 1.244918
ano_nac_corr | .8785073 .0037527 -30.32 0.000 .8711828 .8858933
cohab2 | .9710414 .0310778 -0.92 0.359 .9120012 1.033904
cohab3 | .9923793 .0390569 -0.19 0.846 .9187071 1.071959
cohab4 | .952956 .0296394 -1.55 0.121 .896599 1.012855
fis_com2 | 1.028246 .0166969 1.72 0.086 .9960364 1.061498
fis_com3 | .9019394 .0336737 -2.76 0.006 .8382971 .9704134
rc_x1 | .8550275 .0048222 -27.77 0.000 .8456283 .8645312
rc_x2 | 1.028756 .018644 1.56 0.118 .9928556 1.065954
rc_x3 | .8958874 .0414831 -2.37 0.018 .8181624 .9809964
_rcs1 | 2.451401 .0358701 61.28 0.000 2.382096 2.522723
_rcs_mot_egr_early1 | .9725365 .0178915 -1.51 0.130 .9380945 1.008243
_rcs_mot_egr_early2 | 1.104136 .0105428 10.37 0.000 1.083665 1.124995
_rcs_mot_egr_early3 | 1.043325 .0068323 6.48 0.000 1.03002 1.056803
_rcs_mot_egr_early4 | 1.014803 .0043401 3.44 0.001 1.006332 1.023345
_rcs_mot_egr_early5 | 1.011385 .0030601 3.74 0.000 1.005405 1.0174
_rcs_mot_egr_late1 | 1.013171 .0171591 0.77 0.440 .9800915 1.047366
_rcs_mot_egr_late2 | 1.105392 .008507 13.02 0.000 1.088844 1.122192
_rcs_mot_egr_late3 | 1.040898 .0053379 7.82 0.000 1.030488 1.051412
_rcs_mot_egr_late4 | 1.019187 .0032623 5.94 0.000 1.012813 1.025601
_rcs_mot_egr_late5 | 1.009362 .002198 4.28 0.000 1.005063 1.013679
_cons | 8.6e+111 7.4e+112 29.96 0.000 4.1e+104 1.8e+119
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54555.397
Iteration 1: log likelihood = -54505.799
Iteration 2: log likelihood = -54505.623
Iteration 3: log likelihood = -54505.623
Log likelihood = -54505.623 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.720209 .0498927 18.70 0.000 1.625149 1.82083
mot_egr_late | 1.573127 .0371127 19.20 0.000 1.502044 1.647575
tr_mod2 | 1.216321 .0261752 9.10 0.000 1.166085 1.268721
sex_dum2 | .7597395 .0163226 -12.79 0.000 .7284121 .7924142
edad_ini_cons | .9868119 .0019514 -6.71 0.000 .9829947 .9906439
esc1 | 1.129919 .029845 4.62 0.000 1.072912 1.189954
esc2 | 1.089386 .0259633 3.59 0.000 1.039669 1.14148
sus_prin2 | 1.065305 .0296963 2.27 0.023 1.008662 1.125128
sus_prin3 | 1.392335 .0326271 14.12 0.000 1.329834 1.457774
sus_prin4 | 1.075561 .0378216 2.07 0.038 1.003929 1.152304
sus_prin5 | 1.134973 .0820709 1.75 0.080 .9849958 1.307786
fr_cons_sus_prin2 | .9206531 .0450444 -1.69 0.091 .8364685 1.01331
fr_cons_sus_prin3 | .9970567 .0395736 -0.07 0.941 .9224341 1.077716
fr_cons_sus_prin4 | 1.009378 .0420651 0.22 0.823 .9302087 1.095284
fr_cons_sus_prin5 | 1.03118 .0409606 0.77 0.440 .9539444 1.114669
cond_ocu2 | 1.017763 .0318161 0.56 0.573 .957277 1.082072
cond_ocu3 | .9969827 .1406034 -0.02 0.983 .756212 1.314412
cond_ocu4 | 1.105517 .0399688 2.77 0.006 1.029891 1.186697
cond_ocu5 | 1.162336 .0890659 1.96 0.050 1.000247 1.350693
cond_ocu6 | 1.132292 .0207419 6.78 0.000 1.09236 1.173684
policonsumo | 1.024364 .022359 1.10 0.270 .9814649 1.069137
num_hij2 | 1.166317 .0227732 7.88 0.000 1.122526 1.211817
tenviv1 | 1.148785 .0752136 2.12 0.034 1.010436 1.306078
tenviv2 | 1.125873 .0493326 2.71 0.007 1.033218 1.226837
tenviv4 | 1.037797 .0237505 1.62 0.105 .9922759 1.085407
tenviv5 | 1.003792 .0179972 0.21 0.833 .9691307 1.039693
mzone2 | 1.30351 .0273975 12.61 0.000 1.250903 1.35833
mzone3 | 1.468712 .0422399 13.37 0.000 1.388213 1.553879
n_off_vio | 1.355597 .0258833 15.93 0.000 1.305804 1.407289
n_off_acq | 1.815277 .0324855 33.32 0.000 1.75271 1.880077
n_off_sud | 1.25796 .0233382 12.37 0.000 1.21304 1.304544
n_off_oth | 1.360965 .0257655 16.28 0.000 1.31139 1.412413
psy_com2 | 1.070212 .0256878 2.83 0.005 1.02103 1.121762
psy_com3 | 1.058098 .0187958 3.18 0.001 1.021893 1.095586
dep2 | 1.019872 .0195453 1.03 0.305 .9822749 1.058909
rural2 | 1.029629 .0287357 1.05 0.295 .9748203 1.087518
rural3 | 1.055323 .0324675 1.75 0.080 .9935683 1.120916
porc_pobr | 1.216401 .1439027 1.66 0.098 .9646686 1.533825
susini2 | 1.096899 .0455587 2.23 0.026 1.011143 1.189927
susini3 | 1.123326 .0372813 3.50 0.000 1.052582 1.198825
susini4 | 1.082767 .0193491 4.45 0.000 1.0455 1.121363
susini5 | 1.129295 .0561707 2.44 0.015 1.024398 1.244932
ano_nac_corr | .8783583 .0037525 -30.36 0.000 .8710341 .885744
cohab2 | .9710587 .0310785 -0.92 0.359 .9120172 1.033922
cohab3 | .9923505 .039056 -0.20 0.845 .9186802 1.071929
cohab4 | .9529414 .0296391 -1.55 0.121 .8965851 1.01284
fis_com2 | 1.028127 .0166949 1.71 0.088 .9959205 1.061374
fis_com3 | .9019641 .0336748 -2.76 0.006 .8383197 .9704404
rc_x1 | .8548884 .0048217 -27.80 0.000 .8454901 .8643912
rc_x2 | 1.028743 .0186437 1.56 0.118 .9928429 1.06594
rc_x3 | .895909 .0414842 -2.37 0.018 .8181819 .9810202
_rcs1 | 2.451272 .0358706 61.27 0.000 2.381965 2.522595
_rcs_mot_egr_early1 | .9725895 .0178944 -1.51 0.131 .9381419 1.008302
_rcs_mot_egr_early2 | 1.103336 .0105812 10.25 0.000 1.082791 1.124271
_rcs_mot_egr_early3 | 1.043947 .007034 6.38 0.000 1.030251 1.057825
_rcs_mot_egr_early4 | 1.016007 .004523 3.57 0.000 1.007181 1.024911
_rcs_mot_egr_early5 | 1.013078 .0031725 4.15 0.000 1.006879 1.019315
_rcs_mot_egr_early6 | 1.00539 .0024681 2.19 0.029 1.000564 1.010238
_rcs_mot_egr_late1 | 1.013316 .0171643 0.78 0.435 .9802267 1.047522
_rcs_mot_egr_late2 | 1.105343 .0086329 12.82 0.000 1.088552 1.122393
_rcs_mot_egr_late3 | 1.039454 .0055667 7.23 0.000 1.0286 1.050422
_rcs_mot_egr_late4 | 1.022044 .0033927 6.57 0.000 1.015416 1.028715
_rcs_mot_egr_late5 | 1.010612 .0023118 4.61 0.000 1.006091 1.015154
_rcs_mot_egr_late6 | 1.007791 .0017569 4.45 0.000 1.004354 1.011241
_cons | 1.2e+112 1.0e+113 29.99 0.000 5.8e+104 2.6e+119
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54552.997
Iteration 1: log likelihood = -54504.019
Iteration 2: log likelihood = -54503.833
Iteration 3: log likelihood = -54503.833
Log likelihood = -54503.833 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.720355 .0498979 18.71 0.000 1.625285 1.820986
mot_egr_late | 1.573192 .0371151 19.21 0.000 1.502104 1.647644
tr_mod2 | 1.216379 .0261765 9.10 0.000 1.166141 1.268782
sex_dum2 | .7598259 .0163244 -12.78 0.000 .7284949 .7925044
edad_ini_cons | .9868106 .0019514 -6.71 0.000 .9829934 .9906426
esc1 | 1.129861 .0298437 4.62 0.000 1.072857 1.189894
esc2 | 1.089335 .0259622 3.59 0.000 1.039621 1.141428
sus_prin2 | 1.065475 .0297012 2.28 0.023 1.008824 1.125308
sus_prin3 | 1.392459 .0326305 14.13 0.000 1.329951 1.457905
sus_prin4 | 1.075706 .0378271 2.08 0.038 1.004064 1.152461
sus_prin5 | 1.135238 .0820912 1.75 0.079 .9852238 1.308094
fr_cons_sus_prin2 | .9206797 .0450457 -1.69 0.091 .8364927 1.013339
fr_cons_sus_prin3 | .9971671 .039578 -0.07 0.943 .9225361 1.077836
fr_cons_sus_prin4 | 1.009434 .0420675 0.23 0.822 .930261 1.095346
fr_cons_sus_prin5 | 1.031214 .0409622 0.77 0.439 .9539758 1.114707
cond_ocu2 | 1.017716 .0318145 0.56 0.574 .957233 1.082022
cond_ocu3 | .9970939 .140619 -0.02 0.984 .7562965 1.314559
cond_ocu4 | 1.105353 .0399631 2.77 0.006 1.029738 1.186521
cond_ocu5 | 1.162203 .0890557 1.96 0.050 1.000132 1.350538
cond_ocu6 | 1.132266 .0207415 6.78 0.000 1.092334 1.173657
policonsumo | 1.024378 .0223593 1.10 0.270 .9814784 1.069152
num_hij2 | 1.166299 .0227729 7.88 0.000 1.122508 1.211798
tenviv1 | 1.148898 .0752208 2.12 0.034 1.010535 1.306205
tenviv2 | 1.12603 .0493401 2.71 0.007 1.033361 1.227009
tenviv4 | 1.037872 .0237523 1.62 0.104 .9923467 1.085485
tenviv5 | 1.003891 .017999 0.22 0.829 .9692258 1.039795
mzone2 | 1.303596 .0273994 12.61 0.000 1.250986 1.35842
mzone3 | 1.468758 .0422424 13.37 0.000 1.388255 1.55393
n_off_vio | 1.355563 .0258822 15.93 0.000 1.305772 1.407252
n_off_acq | 1.815282 .0324849 33.32 0.000 1.752716 1.880081
n_off_sud | 1.257933 .0233375 12.37 0.000 1.213014 1.304515
n_off_oth | 1.360933 .0257643 16.28 0.000 1.311362 1.412379
psy_com2 | 1.070292 .0256898 2.83 0.005 1.021107 1.121846
psy_com3 | 1.058123 .0187963 3.18 0.001 1.021917 1.095611
dep2 | 1.019889 .0195457 1.03 0.304 .9822908 1.058927
rural2 | 1.029629 .0287358 1.05 0.295 .9748211 1.087519
rural3 | 1.055275 .0324666 1.75 0.080 .9935226 1.120866
porc_pobr | 1.217148 .1439904 1.66 0.097 .9652615 1.534765
susini2 | 1.096974 .0455619 2.23 0.026 1.011213 1.190009
susini3 | 1.123424 .0372846 3.51 0.000 1.052674 1.19893
susini4 | 1.08272 .0193484 4.45 0.000 1.045454 1.121314
susini5 | 1.129253 .0561693 2.44 0.015 1.02436 1.244888
ano_nac_corr | .8782826 .0037526 -30.38 0.000 .8709584 .8856684
cohab2 | .9710163 .0310772 -0.92 0.358 .9119772 1.033878
cohab3 | .9923098 .0390544 -0.20 0.844 .9186423 1.071885
cohab4 | .9529109 .0296381 -1.55 0.121 .8965565 1.012808
fis_com2 | 1.028066 .0166938 1.70 0.088 .9958615 1.061311
fis_com3 | .9019445 .0336742 -2.76 0.006 .8383013 .9704195
rc_x1 | .8548187 .0048216 -27.81 0.000 .8454206 .8643212
rc_x2 | 1.028719 .0186432 1.56 0.118 .9928201 1.065915
rc_x3 | .8959626 .0414865 -2.37 0.018 .8182312 .9810785
_rcs1 | 2.451174 .0358693 61.27 0.000 2.38187 2.522494
_rcs_mot_egr_early1 | .9725857 .017895 -1.51 0.131 .9381372 1.008299
_rcs_mot_egr_early2 | 1.102919 .0106712 10.12 0.000 1.0822 1.124033
_rcs_mot_egr_early3 | 1.043575 .0072051 6.18 0.000 1.029548 1.057792
_rcs_mot_egr_early4 | 1.01846 .0046785 3.98 0.000 1.009332 1.027671
_rcs_mot_egr_early5 | 1.011983 .0032488 3.71 0.000 1.005636 1.018371
_rcs_mot_egr_early6 | 1.009578 .0025732 3.74 0.000 1.004547 1.014634
_rcs_mot_egr_early7 | 1.002344 .0021246 1.10 0.269 .9981883 1.006517
_rcs_mot_egr_late1 | 1.013258 .0171629 0.78 0.437 .9801715 1.047461
_rcs_mot_egr_late2 | 1.104126 .0086742 12.61 0.000 1.087256 1.121259
_rcs_mot_egr_late3 | 1.040393 .0057033 7.22 0.000 1.029275 1.051632
_rcs_mot_egr_late4 | 1.022379 .0035198 6.43 0.000 1.015504 1.029301
_rcs_mot_egr_late5 | 1.012174 .0023528 5.21 0.000 1.007573 1.016796
_rcs_mot_egr_late6 | 1.008426 .0018455 4.58 0.000 1.004815 1.012049
_rcs_mot_egr_late7 | 1.006272 .0015142 4.15 0.000 1.003308 1.009244
_cons | 1.4e+112 1.2e+113 30.01 0.000 6.8e+104 3.1e+119
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54640.529
Iteration 1: log likelihood = -54524.409
Iteration 2: log likelihood = -54523.742
Iteration 3: log likelihood = -54523.742
Log likelihood = -54523.742 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.720801 .0497556 18.77 0.000 1.625994 1.821136
mot_egr_late | 1.576641 .0370447 19.38 0.000 1.505681 1.650945
tr_mod2 | 1.218012 .0262104 9.16 0.000 1.167709 1.270482
sex_dum2 | .7587329 .0163 -12.85 0.000 .7274487 .7913625
edad_ini_cons | .9868388 .0019513 -6.70 0.000 .9830216 .9906707
esc1 | 1.130268 .0298536 4.64 0.000 1.073245 1.190321
esc2 | 1.089865 .025974 3.61 0.000 1.040128 1.141981
sus_prin2 | 1.063538 .0296466 2.21 0.027 1.00699 1.123261
sus_prin3 | 1.391199 .0325971 14.09 0.000 1.328754 1.456578
sus_prin4 | 1.073014 .0377321 2.00 0.045 1.001551 1.149575
sus_prin5 | 1.137308 .0822051 1.78 0.075 .9870814 1.310398
fr_cons_sus_prin2 | .92069 .045046 -1.69 0.091 .8365024 1.01335
fr_cons_sus_prin3 | .9963006 .039544 -0.09 0.926 .9217337 1.0769
fr_cons_sus_prin4 | 1.007716 .0419961 0.18 0.854 .9286774 1.093482
fr_cons_sus_prin5 | 1.030045 .0409155 0.75 0.456 .9528946 1.113443
cond_ocu2 | 1.017865 .031818 0.57 0.571 .9573745 1.082177
cond_ocu3 | .9950381 .1403236 -0.04 0.972 .7547453 1.311834
cond_ocu4 | 1.107215 .0400318 2.82 0.005 1.03147 1.188523
cond_ocu5 | 1.162126 .0890481 1.96 0.050 1.000069 1.350444
cond_ocu6 | 1.131274 .0207242 6.73 0.000 1.091376 1.172631
policonsumo | 1.023414 .0223414 1.06 0.289 .9805495 1.068153
num_hij2 | 1.166232 .0227728 7.88 0.000 1.122441 1.211731
tenviv1 | 1.149908 .0752827 2.13 0.033 1.011431 1.307344
tenviv2 | 1.124788 .0492817 2.68 0.007 1.032229 1.225647
tenviv4 | 1.036253 .0237138 1.56 0.120 .9908015 1.083789
tenviv5 | 1.002575 .0179738 0.14 0.886 .9679586 1.038429
mzone2 | 1.301724 .0273552 12.55 0.000 1.249198 1.356459
mzone3 | 1.465591 .0421349 13.30 0.000 1.385292 1.550545
n_off_vio | 1.355246 .0258867 15.91 0.000 1.305447 1.406945
n_off_acq | 1.813943 .0324701 33.27 0.000 1.751406 1.878713
n_off_sud | 1.258021 .0233438 12.37 0.000 1.21309 1.304616
n_off_oth | 1.360959 .0257766 16.27 0.000 1.311364 1.412429
psy_com2 | 1.068718 .0256492 2.77 0.006 1.019611 1.120191
psy_com3 | 1.058051 .0187958 3.18 0.001 1.021846 1.095539
dep2 | 1.019872 .0195445 1.03 0.305 .9822756 1.058907
rural2 | 1.029286 .0287227 1.03 0.301 .9745026 1.08715
rural3 | 1.055587 .0324642 1.76 0.079 .9938378 1.121172
porc_pobr | 1.194707 .1413819 1.50 0.133 .9473928 1.506582
susini2 | 1.095456 .0454962 2.20 0.028 1.009818 1.188357
susini3 | 1.123204 .0372811 3.50 0.000 1.052461 1.198703
susini4 | 1.082778 .0193505 4.45 0.000 1.045508 1.121376
susini5 | 1.127618 .0560727 2.42 0.016 1.022904 1.243053
ano_nac_corr | .8801303 .0037487 -29.98 0.000 .8728136 .8875083
cohab2 | .9700301 .0310406 -0.95 0.342 .9110602 1.032817
cohab3 | .9910357 .0390017 -0.23 0.819 .9174675 1.070503
cohab4 | .951935 .0296059 -1.58 0.113 .8956416 1.011767
fis_com2 | 1.029497 .0167156 1.79 0.073 .9972507 1.062786
fis_com3 | .9036743 .0337378 -2.71 0.007 .8399108 .9722786
rc_x1 | .856693 .0048229 -27.48 0.000 .8472922 .866198
rc_x2 | 1.028461 .0186383 1.55 0.121 .9925714 1.065647
rc_x3 | .8961907 .0414976 -2.37 0.018 .8184384 .9813294
_rcs1 | 2.639729 .040007 64.05 0.000 2.56247 2.719318
_rcs2 | 1.134521 .0060387 23.71 0.000 1.122747 1.146419
_rcs_mot_egr_early1 | .9058246 .0161854 -5.54 0.000 .8746509 .9381093
_rcs_mot_egr_late1 | .9428105 .0155285 -3.58 0.000 .9128612 .9737425
_cons | 2.1e+110 1.8e+111 29.61 0.000 1.0e+103 4.2e+117
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54642.496
Iteration 1: log likelihood = -54524.262
Iteration 2: log likelihood = -54523.41
Iteration 3: log likelihood = -54523.41
Log likelihood = -54523.41 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.723145 .0499194 18.78 0.000 1.62803 1.823816
mot_egr_late | 1.578437 .0371971 19.37 0.000 1.50719 1.653052
tr_mod2 | 1.218122 .0262135 9.17 0.000 1.167812 1.270598
sex_dum2 | .7587361 .0163002 -12.85 0.000 .7274516 .791366
edad_ini_cons | .9868419 .0019513 -6.70 0.000 .9830248 .9906738
esc1 | 1.130226 .0298525 4.63 0.000 1.073205 1.190277
esc2 | 1.089845 .0259736 3.61 0.000 1.040109 1.14196
sus_prin2 | 1.063636 .0296499 2.21 0.027 1.007083 1.123366
sus_prin3 | 1.391272 .0325992 14.09 0.000 1.328824 1.456655
sus_prin4 | 1.07304 .0377335 2.00 0.045 1.001575 1.149604
sus_prin5 | 1.137758 .0822383 1.79 0.074 .9874704 1.310918
fr_cons_sus_prin2 | .920651 .0450442 -1.69 0.091 .8364669 1.013308
fr_cons_sus_prin3 | .9962811 .0395433 -0.09 0.925 .9217156 1.076879
fr_cons_sus_prin4 | 1.007681 .0419947 0.18 0.854 .9286445 1.093444
fr_cons_sus_prin5 | 1.03002 .0409145 0.74 0.456 .9528712 1.113415
cond_ocu2 | 1.017837 .0318172 0.57 0.572 .9573479 1.082147
cond_ocu3 | .9954021 .1403757 -0.03 0.974 .7550202 1.312316
cond_ocu4 | 1.107185 .0400306 2.82 0.005 1.031442 1.188491
cond_ocu5 | 1.162093 .0890461 1.96 0.050 1.000039 1.350407
cond_ocu6 | 1.131257 .0207239 6.73 0.000 1.091359 1.172613
policonsumo | 1.023521 .0223446 1.06 0.287 .9806504 1.068266
num_hij2 | 1.166219 .0227727 7.87 0.000 1.122429 1.211718
tenviv1 | 1.15008 .0752939 2.14 0.033 1.011583 1.30754
tenviv2 | 1.124819 .0492833 2.68 0.007 1.032257 1.225681
tenviv4 | 1.036243 .0237136 1.56 0.120 .9907925 1.083779
tenviv5 | 1.002569 .0179736 0.14 0.886 .967953 1.038423
mzone2 | 1.301783 .0273563 12.55 0.000 1.249255 1.35652
mzone3 | 1.465515 .0421333 13.29 0.000 1.385218 1.550465
n_off_vio | 1.355289 .0258874 15.92 0.000 1.305488 1.406989
n_off_acq | 1.814024 .0324712 33.27 0.000 1.751485 1.878796
n_off_sud | 1.257982 .0233431 12.37 0.000 1.213052 1.304576
n_off_oth | 1.360958 .0257764 16.27 0.000 1.311364 1.412429
psy_com2 | 1.068754 .0256503 2.77 0.006 1.019644 1.120229
psy_com3 | 1.058057 .018796 3.18 0.001 1.021852 1.095546
dep2 | 1.019878 .0195446 1.03 0.304 .9822821 1.058913
rural2 | 1.029212 .0287209 1.03 0.302 .9744315 1.087072
rural3 | 1.055523 .0324625 1.76 0.079 .9937773 1.121105
porc_pobr | 1.195047 .1414256 1.51 0.132 .9476568 1.507019
susini2 | 1.095322 .0454909 2.19 0.028 1.009694 1.188212
susini3 | 1.123228 .0372823 3.50 0.000 1.052482 1.198729
susini4 | 1.082796 .019351 4.45 0.000 1.045525 1.121395
susini5 | 1.127625 .0560726 2.42 0.016 1.022911 1.243059
ano_nac_corr | .8800987 .0037489 -29.98 0.000 .8727815 .8874772
cohab2 | .9699558 .0310382 -0.95 0.340 .9109906 1.032738
cohab3 | .9909689 .0389989 -0.23 0.818 .917406 1.070431
cohab4 | .9518764 .0296041 -1.59 0.113 .8955866 1.011704
fis_com2 | 1.029475 .0167152 1.79 0.074 .9972301 1.062763
fis_com3 | .9036764 .0337379 -2.71 0.007 .8399126 .972281
rc_x1 | .8566596 .0048229 -27.48 0.000 .8472587 .8661647
rc_x2 | 1.028486 .0186388 1.55 0.121 .9925962 1.065674
rc_x3 | .8961216 .0414946 -2.37 0.018 .818375 .9812542
_rcs1 | 2.658026 .0481102 54.01 0.000 2.565385 2.754013
_rcs2 | 1.145465 .0166784 9.33 0.000 1.113238 1.178625
_rcs_mot_egr_early1 | .8974787 .019138 -5.07 0.000 .8607421 .9357833
_rcs_mot_egr_early2 | .9861557 .0169555 -0.81 0.417 .9534772 1.019954
_rcs_mot_egr_late1 | .9364591 .0188014 -3.27 0.001 .9003247 .9740438
_rcs_mot_egr_late2 | .9907052 .0159995 -0.58 0.563 .9598379 1.022565
_cons | 2.3e+110 1.9e+111 29.62 0.000 1.1e+103 4.5e+117
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54501.686
Iteration 1: log likelihood = -54475.718
Iteration 2: log likelihood = -54475.587
Iteration 3: log likelihood = -54475.587
Log likelihood = -54475.587 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.727719 .0500634 18.87 0.000 1.632331 1.828682
mot_egr_late | 1.579131 .0372196 19.38 0.000 1.507841 1.653791
tr_mod2 | 1.217767 .0262057 9.16 0.000 1.167473 1.270228
sex_dum2 | .7593091 .0163124 -12.82 0.000 .7280011 .7919636
edad_ini_cons | .9868674 .0019514 -6.69 0.000 .9830502 .9906994
esc1 | 1.129542 .0298343 4.61 0.000 1.072556 1.189556
esc2 | 1.089262 .0259601 3.59 0.000 1.039551 1.14135
sus_prin2 | 1.064753 .029682 2.25 0.024 1.008138 1.124547
sus_prin3 | 1.391418 .0326054 14.10 0.000 1.328958 1.456813
sus_prin4 | 1.074641 .0377916 2.05 0.041 1.003066 1.151323
sus_prin5 | 1.137624 .0822376 1.78 0.074 .9873393 1.310784
fr_cons_sus_prin2 | .9208001 .0450516 -1.69 0.092 .8366023 1.013472
fr_cons_sus_prin3 | .9967272 .0395605 -0.08 0.934 .9221293 1.07736
fr_cons_sus_prin4 | 1.008532 .0420298 0.20 0.838 .9294296 1.094367
fr_cons_sus_prin5 | 1.030797 .0409449 0.76 0.445 .9535912 1.114254
cond_ocu2 | 1.017959 .0318207 0.57 0.569 .9574633 1.082276
cond_ocu3 | .999806 .1409987 -0.00 0.999 .7583577 1.318127
cond_ocu4 | 1.106508 .0400035 2.80 0.005 1.030816 1.187758
cond_ocu5 | 1.161074 .0889711 1.95 0.051 .9991572 1.34923
cond_ocu6 | 1.13147 .0207277 6.74 0.000 1.091565 1.172833
policonsumo | 1.024753 .0223734 1.12 0.263 .9818272 1.069556
num_hij2 | 1.16554 .0227588 7.84 0.000 1.121776 1.211011
tenviv1 | 1.14912 .0752322 2.12 0.034 1.010736 1.306451
tenviv2 | 1.125703 .0493246 2.70 0.007 1.033063 1.22665
tenviv4 | 1.037002 .0237315 1.59 0.112 .9915165 1.084574
tenviv5 | 1.003128 .0179845 0.17 0.862 .9684915 1.039004
mzone2 | 1.30203 .0273634 12.56 0.000 1.249488 1.356781
mzone3 | 1.46557 .0421438 13.29 0.000 1.385254 1.550543
n_off_vio | 1.355247 .0258784 15.92 0.000 1.305464 1.406929
n_off_acq | 1.814517 .0324671 33.30 0.000 1.751986 1.879281
n_off_sud | 1.257634 .0233324 12.36 0.000 1.212724 1.304206
n_off_oth | 1.360542 .0257593 16.26 0.000 1.31098 1.411978
psy_com2 | 1.070087 .0256839 2.82 0.005 1.020913 1.121629
psy_com3 | 1.058207 .0187976 3.18 0.001 1.021998 1.095698
dep2 | 1.019814 .0195438 1.02 0.306 .9822191 1.058847
rural2 | 1.028759 .0287106 1.02 0.310 .9739987 1.086598
rural3 | 1.05462 .0324398 1.73 0.084 .9929176 1.120156
porc_pobr | 1.213751 .1436329 1.64 0.102 .9624983 1.530591
susini2 | 1.095699 .0455071 2.20 0.028 1.010041 1.188622
susini3 | 1.122574 .0372585 3.48 0.000 1.051873 1.198027
susini4 | 1.08256 .0193461 4.44 0.000 1.045299 1.121149
susini5 | 1.128407 .0561144 2.43 0.015 1.023615 1.243927
ano_nac_corr | .8768918 .0037468 -30.75 0.000 .869579 .8842662
cohab2 | .9703846 .0310543 -0.94 0.348 .9113889 1.033199
cohab3 | .9920769 .0390432 -0.20 0.840 .9184304 1.071629
cohab4 | .9525586 .0296268 -1.56 0.118 .8962257 1.012432
fis_com2 | 1.028402 .0166983 1.72 0.085 .9961892 1.061656
fis_com3 | .9026248 .033699 -2.74 0.006 .8389347 .9711501
rc_x1 | .8535792 .0048141 -28.07 0.000 .8441957 .863067
rc_x2 | 1.028655 .0186417 1.56 0.119 .9927591 1.065849
rc_x3 | .8957363 .0414753 -2.38 0.017 .8180258 .9808291
_rcs1 | 2.649643 .0477792 54.04 0.000 2.557633 2.744963
_rcs2 | 1.144131 .0165755 9.29 0.000 1.112101 1.177084
_rcs_mot_egr_early1 | .898752 .0190777 -5.03 0.000 .8621275 .9369324
_rcs_mot_egr_early2 | .968543 .0166296 -1.86 0.063 .936492 1.001691
_rcs_mot_egr_early3 | 1.033343 .0061409 5.52 0.000 1.021377 1.04545
_rcs_mot_egr_late1 | .9364913 .0187026 -3.29 0.001 .900543 .9738746
_rcs_mot_egr_late2 | .9671557 .0156632 -2.06 0.039 .9369386 .9983474
_rcs_mot_egr_late3 | 1.035581 .0046093 7.86 0.000 1.026586 1.044655
_cons | 3.5e+113 3.0e+114 30.38 0.000 1.7e+106 7.4e+120
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54493.591
Iteration 1: log likelihood = -54465.33
Iteration 2: log likelihood = -54465.15
Iteration 3: log likelihood = -54465.15
Log likelihood = -54465.15 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729904 .0501339 18.91 0.000 1.634382 1.831009
mot_egr_late | 1.580226 .0372516 19.41 0.000 1.508875 1.65495
tr_mod2 | 1.217743 .026205 9.15 0.000 1.16745 1.270202
sex_dum2 | .7595878 .0163185 -12.80 0.000 .7282681 .7922544
edad_ini_cons | .9868784 .0019514 -6.68 0.000 .9830611 .9907104
esc1 | 1.129296 .0298279 4.60 0.000 1.072322 1.189298
esc2 | 1.088987 .0259537 3.58 0.000 1.039289 1.141063
sus_prin2 | 1.065453 .0297032 2.27 0.023 1.008797 1.12529
sus_prin3 | 1.391987 .0326219 14.11 0.000 1.329495 1.457416
sus_prin4 | 1.075462 .0378224 2.07 0.039 1.003829 1.152207
sus_prin5 | 1.138445 .0823002 1.79 0.073 .9880456 1.311737
fr_cons_sus_prin2 | .9206918 .0450462 -1.69 0.091 .8365039 1.013353
fr_cons_sus_prin3 | .9968633 .0395657 -0.08 0.937 .9222554 1.077507
fr_cons_sus_prin4 | 1.008726 .0420378 0.21 0.835 .9296082 1.094577
fr_cons_sus_prin5 | 1.030899 .040949 0.77 0.444 .9536856 1.114365
cond_ocu2 | 1.017999 .0318218 0.57 0.568 .9575016 1.082319
cond_ocu3 | 1.002146 .1413298 0.02 0.988 .7601313 1.321215
cond_ocu4 | 1.105998 .0399851 2.79 0.005 1.03034 1.18721
cond_ocu5 | 1.160744 .0889477 1.95 0.052 .9988704 1.348851
cond_ocu6 | 1.131579 .0207296 6.75 0.000 1.09167 1.172946
policonsumo | 1.025386 .0223882 1.15 0.251 .9824318 1.070219
num_hij2 | 1.165492 .0227577 7.84 0.000 1.121731 1.210961
tenviv1 | 1.149559 .0752598 2.13 0.033 1.011124 1.306947
tenviv2 | 1.12594 .0493364 2.71 0.007 1.033278 1.226911
tenviv4 | 1.03721 .0237367 1.60 0.110 .9917154 1.084793
tenviv5 | 1.003339 .0179884 0.19 0.853 .9686944 1.039222
mzone2 | 1.302351 .0273708 12.57 0.000 1.249795 1.357117
mzone3 | 1.465701 .042153 13.29 0.000 1.385368 1.550692
n_off_vio | 1.355331 .0258771 15.92 0.000 1.30555 1.40701
n_off_acq | 1.81447 .0324627 33.30 0.000 1.751947 1.879225
n_off_sud | 1.257395 .0233269 12.35 0.000 1.212496 1.303956
n_off_oth | 1.360528 .025756 16.26 0.000 1.310973 1.411957
psy_com2 | 1.070431 .0256926 2.84 0.005 1.021241 1.121991
psy_com3 | 1.058245 .0187981 3.19 0.001 1.022035 1.095737
dep2 | 1.019886 .0195453 1.03 0.304 .9822888 1.058923
rural2 | 1.028851 .0287139 1.02 0.308 .9740845 1.086697
rural3 | 1.05451 .0324383 1.73 0.084 .9928109 1.120044
porc_pobr | 1.21931 .1442844 1.68 0.094 .9669169 1.537586
susini2 | 1.095833 .0455127 2.20 0.028 1.010164 1.188767
susini3 | 1.122377 .0372511 3.48 0.001 1.05169 1.197814
susini4 | 1.082558 .0193462 4.44 0.000 1.045296 1.121148
susini5 | 1.128746 .0561325 2.44 0.015 1.02392 1.244304
ano_nac_corr | .8762037 .003747 -30.90 0.000 .8688904 .8835785
cohab2 | .9705565 .0310594 -0.93 0.350 .911551 1.033382
cohab3 | .9920616 .0390421 -0.20 0.840 .9184173 1.071611
cohab4 | .9526515 .0296292 -1.56 0.119 .8963139 1.01253
fis_com2 | 1.028074 .0166931 1.71 0.088 .9958714 1.061318
fis_com3 | .9023944 .0336904 -2.75 0.006 .8387205 .9709024
rc_x1 | .8529052 .0048125 -28.20 0.000 .8435248 .86239
rc_x2 | 1.028739 .0186431 1.56 0.118 .9928401 1.065935
rc_x3 | .8955566 .0414664 -2.38 0.017 .8178628 .9806311
_rcs1 | 2.65385 .048018 53.94 0.000 2.561386 2.749652
_rcs2 | 1.1473 .0166922 9.44 0.000 1.115046 1.180487
_rcs_mot_egr_early1 | .8975947 .0191058 -5.08 0.000 .8609184 .9358334
_rcs_mot_egr_early2 | .9662035 .0167334 -1.99 0.047 .933957 .9995634
_rcs_mot_egr_early3 | 1.027012 .0066513 4.12 0.000 1.014058 1.040131
_rcs_mot_egr_early4 | 1.015468 .0041264 3.78 0.000 1.007413 1.023588
_rcs_mot_egr_late1 | .9352978 .0187374 -3.34 0.001 .8992849 .972753
_rcs_mot_egr_late2 | .9661558 .0157727 -2.11 0.035 .9357312 .9975697
_rcs_mot_egr_late3 | 1.026955 .0051701 5.28 0.000 1.016872 1.037138
_rcs_mot_egr_late4 | 1.016852 .0030456 5.58 0.000 1.0109 1.022839
_cons | 1.7e+114 1.5e+115 30.54 0.000 7.9e+106 3.6e+121
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54480.127
Iteration 1: log likelihood = -54458.77
Iteration 2: log likelihood = -54458.641
Iteration 3: log likelihood = -54458.641
Log likelihood = -54458.641 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730886 .0501654 18.93 0.000 1.635304 1.832055
mot_egr_late | 1.580461 .0372601 19.41 0.000 1.509094 1.655203
tr_mod2 | 1.217854 .0262073 9.16 0.000 1.167557 1.270318
sex_dum2 | .7598009 .0163233 -12.79 0.000 .7284721 .7924771
edad_ini_cons | .9868854 .0019513 -6.68 0.000 .9830682 .9907174
esc1 | 1.12914 .0298238 4.60 0.000 1.072173 1.189133
esc2 | 1.088832 .0259501 3.57 0.000 1.039141 1.1409
sus_prin2 | 1.065798 .0297137 2.29 0.022 1.009122 1.125656
sus_prin3 | 1.392229 .0326294 14.12 0.000 1.329723 1.457673
sus_prin4 | 1.075822 .0378359 2.08 0.038 1.004163 1.152594
sus_prin5 | 1.139031 .0823459 1.80 0.072 .9885486 1.31242
fr_cons_sus_prin2 | .9205595 .0450398 -1.69 0.091 .8363837 1.013207
fr_cons_sus_prin3 | .9969142 .0395677 -0.08 0.938 .9223025 1.077562
fr_cons_sus_prin4 | 1.008761 .0420392 0.21 0.834 .9296406 1.094614
fr_cons_sus_prin5 | 1.030869 .0409477 0.77 0.444 .9536574 1.114332
cond_ocu2 | 1.018008 .031822 0.57 0.568 .9575104 1.082329
cond_ocu3 | 1.00281 .1414233 0.02 0.984 .760635 1.32209
cond_ocu4 | 1.105498 .0399674 2.77 0.006 1.029874 1.186674
cond_ocu5 | 1.160697 .0889452 1.94 0.052 .9988275 1.348799
cond_ocu6 | 1.131593 .0207298 6.75 0.000 1.091684 1.172961
policonsumo | 1.025647 .0223944 1.16 0.246 .9826806 1.070492
num_hij2 | 1.165502 .0227579 7.84 0.000 1.12174 1.210971
tenviv1 | 1.150492 .0753211 2.14 0.032 1.011944 1.308009
tenviv2 | 1.12608 .0493436 2.71 0.007 1.033405 1.227066
tenviv4 | 1.037423 .0237417 1.61 0.108 .9919185 1.085015
tenviv5 | 1.003505 .0179913 0.20 0.845 .9688555 1.039395
mzone2 | 1.302521 .0273749 12.58 0.000 1.249957 1.357295
mzone3 | 1.46566 .0421552 13.29 0.000 1.385323 1.550656
n_off_vio | 1.355314 .0258752 15.92 0.000 1.305537 1.40699
n_off_acq | 1.814428 .0324594 33.30 0.000 1.751912 1.879176
n_off_sud | 1.257378 .0233257 12.35 0.000 1.212482 1.303937
n_off_oth | 1.360512 .0257535 16.26 0.000 1.310961 1.411936
psy_com2 | 1.070424 .0256929 2.84 0.005 1.021233 1.121985
psy_com3 | 1.058232 .0187978 3.19 0.001 1.022023 1.095724
dep2 | 1.019912 .0195458 1.03 0.304 .982313 1.058949
rural2 | 1.028918 .0287163 1.02 0.307 .9741465 1.086768
rural3 | 1.054552 .032441 1.73 0.084 .9928477 1.120091
porc_pobr | 1.222378 .1446428 1.70 0.090 .969357 1.541443
susini2 | 1.095993 .0455194 2.21 0.027 1.010311 1.188941
susini3 | 1.122341 .0372498 3.48 0.001 1.051657 1.197777
susini4 | 1.082537 .0193461 4.44 0.000 1.045276 1.121127
susini5 | 1.12905 .0561494 2.44 0.015 1.024193 1.244643
ano_nac_corr | .8758984 .0037467 -30.98 0.000 .8685857 .8832727
cohab2 | .9706332 .0310614 -0.93 0.352 .9116239 1.033462
cohab3 | .9918884 .039035 -0.21 0.836 .9182574 1.071424
cohab4 | .9525985 .0296272 -1.56 0.118 .8962648 1.012473
fis_com2 | 1.027892 .0166902 1.69 0.090 .9956948 1.06113
fis_com3 | .9022929 .0336866 -2.75 0.006 .8386261 .9707931
rc_x1 | .8526088 .0048116 -28.26 0.000 .8432303 .8620917
rc_x2 | 1.028756 .0186436 1.56 0.118 .9928567 1.065953
rc_x3 | .8955198 .0414649 -2.38 0.017 .8178287 .9805913
_rcs1 | 2.65404 .0480349 53.93 0.000 2.561543 2.749876
_rcs2 | 1.147614 .0167009 9.46 0.000 1.115343 1.180818
_rcs_mot_egr_early1 | .8975591 .0191076 -5.08 0.000 .8608793 .9358016
_rcs_mot_egr_early2 | .9635975 .0166483 -2.15 0.032 .9315138 .9967863
_rcs_mot_egr_early3 | 1.027081 .0069382 3.96 0.000 1.013572 1.04077
_rcs_mot_egr_early4 | 1.013599 .0043297 3.16 0.002 1.005148 1.02212
_rcs_mot_egr_early5 | 1.011571 .0030551 3.81 0.000 1.005601 1.017577
_rcs_mot_egr_late1 | .9351298 .0187382 -3.35 0.001 .8991154 .9725867
_rcs_mot_egr_late2 | .9646176 .0157553 -2.21 0.027 .9342267 .995997
_rcs_mot_egr_late3 | 1.024646 .0055248 4.52 0.000 1.013874 1.035532
_rcs_mot_egr_late4 | 1.017988 .0032565 5.57 0.000 1.011625 1.02439
_rcs_mot_egr_late5 | 1.009585 .0021951 4.39 0.000 1.005292 1.013896
_cons | 3.4e+114 2.9e+115 30.61 0.000 1.6e+107 7.4e+121
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54477.048
Iteration 1: log likelihood = -54455.313
Iteration 2: log likelihood = -54455.192
Iteration 3: log likelihood = -54455.192
Log likelihood = -54455.192 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.731045 .0501717 18.93 0.000 1.635451 1.832226
mot_egr_late | 1.580482 .0372616 19.41 0.000 1.509112 1.655227
tr_mod2 | 1.217892 .0262082 9.16 0.000 1.167593 1.270358
sex_dum2 | .7599407 .0163263 -12.78 0.000 .728606 .792623
edad_ini_cons | .9868837 .0019513 -6.68 0.000 .9830666 .9907156
esc1 | 1.129006 .0298204 4.59 0.000 1.072047 1.188993
esc2 | 1.088731 .0259477 3.57 0.000 1.039044 1.140794
sus_prin2 | 1.066039 .0297207 2.29 0.022 1.00935 1.125911
sus_prin3 | 1.392417 .0326346 14.12 0.000 1.329901 1.457871
sus_prin4 | 1.076066 .037845 2.08 0.037 1.00439 1.152857
sus_prin5 | 1.139554 .0823859 1.81 0.071 .9889997 1.313028
fr_cons_sus_prin2 | .9205043 .045037 -1.69 0.090 .8363336 1.013146
fr_cons_sus_prin3 | .9970374 .0395726 -0.07 0.940 .9224165 1.077695
fr_cons_sus_prin4 | 1.008818 .0420415 0.21 0.833 .9296934 1.094676
fr_cons_sus_prin5 | 1.030916 .0409497 0.77 0.443 .953701 1.114383
cond_ocu2 | 1.017956 .0318202 0.57 0.569 .9574611 1.082272
cond_ocu3 | 1.003189 .1414767 0.02 0.982 .7609227 1.32259
cond_ocu4 | 1.105288 .0399598 2.77 0.006 1.029679 1.18645
cond_ocu5 | 1.160853 .0889571 1.95 0.052 .9989616 1.348979
cond_ocu6 | 1.131569 .0207295 6.75 0.000 1.091661 1.172936
policonsumo | 1.025782 .0223974 1.17 0.244 .9828097 1.070633
num_hij2 | 1.165458 .0227571 7.84 0.000 1.121697 1.210925
tenviv1 | 1.150688 .075334 2.14 0.032 1.012116 1.308231
tenviv2 | 1.12631 .0493542 2.71 0.007 1.033615 1.227318
tenviv4 | 1.037544 .0237447 1.61 0.107 .9920341 1.085143
tenviv5 | 1.003655 .017994 0.20 0.839 .9689994 1.039549
mzone2 | 1.302642 .0273776 12.58 0.000 1.250073 1.357421
mzone3 | 1.465705 .0421581 13.29 0.000 1.385363 1.550707
n_off_vio | 1.355316 .0258743 15.93 0.000 1.30554 1.406989
n_off_acq | 1.814368 .0324575 33.30 0.000 1.751855 1.879112
n_off_sud | 1.257375 .0233252 12.35 0.000 1.21248 1.303933
n_off_oth | 1.360429 .0257509 16.26 0.000 1.310883 1.411848
psy_com2 | 1.070533 .0256957 2.84 0.005 1.021337 1.122099
psy_com3 | 1.058262 .0187984 3.19 0.001 1.022052 1.095756
dep2 | 1.01993 .0195462 1.03 0.303 .9823304 1.058968
rural2 | 1.028928 .0287167 1.02 0.307 .974156 1.08678
rural3 | 1.054518 .0324408 1.73 0.084 .9928143 1.120057
porc_pobr | 1.223955 .144828 1.71 0.088 .9706093 1.543428
susini2 | 1.095996 .0455194 2.21 0.027 1.010315 1.188944
susini3 | 1.122477 .0372544 3.48 0.000 1.051784 1.197921
susini4 | 1.082462 .0193449 4.43 0.000 1.045203 1.121049
susini5 | 1.129055 .0561508 2.44 0.015 1.024195 1.244651
ano_nac_corr | .8757545 .0037465 -31.01 0.000 .8684421 .8831284
cohab2 | .970649 .031062 -0.93 0.352 .9116384 1.033479
cohab3 | .9918605 .0390341 -0.21 0.835 .9182312 1.071394
cohab4 | .9525828 .0296268 -1.56 0.118 .8962498 1.012457
fis_com2 | 1.027775 .0166882 1.69 0.092 .9955821 1.06101
fis_com3 | .9023165 .0336877 -2.75 0.006 .8386477 .9708189
rc_x1 | .8524744 .0048111 -28.28 0.000 .8430967 .8619564
rc_x2 | 1.028743 .0186433 1.56 0.118 .9928444 1.06594
rc_x3 | .8955407 .041466 -2.38 0.017 .8178476 .9806144
_rcs1 | 2.653587 .0480172 53.93 0.000 2.561124 2.749388
_rcs2 | 1.147454 .0166959 9.45 0.000 1.115193 1.180648
_rcs_mot_egr_early1 | .8977199 .0191088 -5.07 0.000 .8610378 .9359647
_rcs_mot_egr_early2 | .9633046 .0166385 -2.16 0.030 .9312395 .9964737
_rcs_mot_egr_early3 | 1.025424 .0071765 3.59 0.000 1.011454 1.039587
_rcs_mot_egr_early4 | 1.013365 .0045154 2.98 0.003 1.004553 1.022254
_rcs_mot_egr_early5 | 1.013157 .0031667 4.18 0.000 1.00697 1.019383
_rcs_mot_egr_early6 | 1.005393 .0024626 2.20 0.028 1.000578 1.010231
_rcs_mot_egr_late1 | .9353837 .0187412 -3.33 0.001 .8993635 .9728466
_rcs_mot_egr_late2 | .9649693 .0157863 -2.18 0.029 .9345195 .9964113
_rcs_mot_egr_late3 | 1.020959 .0058019 3.65 0.000 1.009651 1.032394
_rcs_mot_egr_late4 | 1.019388 .0033928 5.77 0.000 1.01276 1.02606
_rcs_mot_egr_late5 | 1.01071 .0023084 4.66 0.000 1.006196 1.015245
_rcs_mot_egr_late6 | 1.00783 .0017535 4.48 0.000 1.004399 1.011272
_cons | 4.8e+114 4.1e+115 30.64 0.000 2.2e+107 1.0e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54474.786
Iteration 1: log likelihood = -54453.507
Iteration 2: log likelihood = -54453.372
Iteration 3: log likelihood = -54453.372
Log likelihood = -54453.372 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.731174 .050176 18.93 0.000 1.635572 1.832364
mot_egr_late | 1.580535 .0372634 19.42 0.000 1.509162 1.655284
tr_mod2 | 1.217951 .0262095 9.16 0.000 1.16765 1.270419
sex_dum2 | .7600275 .0163282 -12.77 0.000 .7286892 .7927136
edad_ini_cons | .9868824 .0019513 -6.68 0.000 .9830653 .9907143
esc1 | 1.128949 .029819 4.59 0.000 1.071992 1.188932
esc2 | 1.08868 .0259466 3.57 0.000 1.038996 1.140741
sus_prin2 | 1.06621 .0297256 2.30 0.021 1.009512 1.126092
sus_prin3 | 1.39254 .0326381 14.13 0.000 1.330018 1.458002
sus_prin4 | 1.076212 .0378505 2.09 0.037 1.004525 1.153014
sus_prin5 | 1.139822 .0824064 1.81 0.070 .98923 1.313339
fr_cons_sus_prin2 | .9205316 .0450384 -1.69 0.091 .8363584 1.013176
fr_cons_sus_prin3 | .9971484 .0395771 -0.07 0.943 .9225191 1.077815
fr_cons_sus_prin4 | 1.008875 .042044 0.21 0.832 .9297459 1.094738
fr_cons_sus_prin5 | 1.030951 .0409512 0.77 0.443 .9537326 1.114421
cond_ocu2 | 1.017909 .0318187 0.57 0.570 .9574177 1.082223
cond_ocu3 | 1.003305 .1414929 0.02 0.981 .7610102 1.322742
cond_ocu4 | 1.105125 .0399541 2.76 0.006 1.029527 1.186275
cond_ocu5 | 1.16072 .0889469 1.94 0.052 .9988476 1.348825
cond_ocu6 | 1.131543 .0207291 6.75 0.000 1.091635 1.172909
policonsumo | 1.025796 .0223978 1.17 0.243 .9828228 1.070647
num_hij2 | 1.165439 .0227568 7.84 0.000 1.12168 1.210906
tenviv1 | 1.150805 .0753415 2.15 0.032 1.012219 1.308364
tenviv2 | 1.126467 .0493617 2.72 0.007 1.033758 1.22749
tenviv4 | 1.037618 .0237465 1.61 0.107 .9921044 1.08522
tenviv5 | 1.003753 .0179958 0.21 0.834 .9690944 1.039651
mzone2 | 1.302726 .0273795 12.58 0.000 1.250154 1.35751
mzone3 | 1.465752 .0421606 13.29 0.000 1.385404 1.550759
n_off_vio | 1.355281 .0258731 15.92 0.000 1.305508 1.406952
n_off_acq | 1.814372 .0324569 33.30 0.000 1.75186 1.879115
n_off_sud | 1.257348 .0233245 12.34 0.000 1.212454 1.303905
n_off_oth | 1.360398 .0257497 16.26 0.000 1.310854 1.411814
psy_com2 | 1.070613 .0256976 2.84 0.004 1.021413 1.122183
psy_com3 | 1.058287 .0187988 3.19 0.001 1.022076 1.095781
dep2 | 1.019947 .0195466 1.03 0.303 .9823465 1.058986
rural2 | 1.028929 .0287168 1.02 0.307 .9741572 1.086781
rural3 | 1.054472 .0324399 1.72 0.085 .9927694 1.120009
porc_pobr | 1.224689 .1449144 1.71 0.087 .9711928 1.544353
susini2 | 1.096072 .0455225 2.21 0.027 1.010384 1.189027
susini3 | 1.122574 .0372577 3.48 0.000 1.051875 1.198026
susini4 | 1.082414 .0193442 4.43 0.000 1.045157 1.121
susini5 | 1.129016 .0561495 2.44 0.015 1.024159 1.24461
ano_nac_corr | .8756786 .0037466 -31.03 0.000 .8683662 .8830526
cohab2 | .9706062 .0310608 -0.93 0.351 .9115981 1.033434
cohab3 | .9918189 .0390325 -0.21 0.835 .9181926 1.071349
cohab4 | .9525518 .0296258 -1.56 0.118 .8962207 1.012424
fis_com2 | 1.027714 .0166872 1.68 0.092 .9955225 1.060946
fis_com3 | .9022968 .0336871 -2.75 0.006 .8386292 .9707979
rc_x1 | .8524045 .004811 -28.29 0.000 .843027 .8618862
rc_x2 | 1.02872 .0186428 1.56 0.118 .9928217 1.065915
rc_x3 | .895594 .0414682 -2.38 0.017 .8178966 .9806723
_rcs1 | 2.65354 .0480169 53.93 0.000 2.561078 2.74934
_rcs2 | 1.147501 .0166967 9.46 0.000 1.115238 1.180697
_rcs_mot_egr_early1 | .8976988 .0191087 -5.07 0.000 .8610169 .9359434
_rcs_mot_egr_early2 | .9632303 .016654 -2.17 0.030 .9311359 .9964309
_rcs_mot_egr_early3 | 1.022707 .0073909 3.11 0.002 1.008323 1.037296
_rcs_mot_egr_early4 | 1.014705 .0046747 3.17 0.002 1.005584 1.023908
_rcs_mot_egr_early5 | 1.011729 .0032428 3.64 0.000 1.005393 1.018105
_rcs_mot_egr_early6 | 1.009649 .0025677 3.78 0.000 1.004629 1.014694
_rcs_mot_egr_early7 | 1.00233 .0021193 1.10 0.271 .9981847 1.006492
_rcs_mot_egr_late1 | .9353092 .0187395 -3.34 0.001 .8992923 .9727685
_rcs_mot_egr_late2 | .9641974 .0157643 -2.23 0.026 .9337897 .9955952
_rcs_mot_egr_late3 | 1.019536 .006001 3.29 0.001 1.007842 1.031366
_rcs_mot_egr_late4 | 1.018607 .0035274 5.32 0.000 1.011717 1.025545
_rcs_mot_egr_late5 | 1.011925 .0023489 5.11 0.000 1.007331 1.016539
_rcs_mot_egr_late6 | 1.008523 .0018422 4.65 0.000 1.004919 1.012141
_rcs_mot_egr_late7 | 1.006281 .0015109 4.17 0.000 1.003324 1.009246
_cons | 5.7e+114 4.9e+115 30.66 0.000 2.6e+107 1.2e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54485.474
Iteration 1: log likelihood = -54463.46
Iteration 2: log likelihood = -54463.4
Iteration 3: log likelihood = -54463.4
Log likelihood = -54463.4 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.727298 .0499453 18.90 0.000 1.632129 1.828016
mot_egr_late | 1.578104 .0370717 19.42 0.000 1.507093 1.652462
tr_mod2 | 1.218277 .026215 9.18 0.000 1.167965 1.270757
sex_dum2 | .7593825 .0163133 -12.81 0.000 .7280728 .7920386
edad_ini_cons | .9868829 .0019514 -6.68 0.000 .9830657 .990715
esc1 | 1.129409 .0298306 4.61 0.000 1.07243 1.189416
esc2 | 1.089184 .0259582 3.58 0.000 1.039477 1.141268
sus_prin2 | 1.065224 .0296966 2.27 0.023 1.008582 1.125048
sus_prin3 | 1.391725 .0326154 14.10 0.000 1.329246 1.457141
sus_prin4 | 1.075004 .0378064 2.06 0.040 1.0034 1.151716
sus_prin5 | 1.139348 .0823601 1.80 0.071 .9888385 1.312765
fr_cons_sus_prin2 | .9206623 .0450447 -1.69 0.091 .8364773 1.01332
fr_cons_sus_prin3 | .996745 .0395611 -0.08 0.935 .9221458 1.077379
fr_cons_sus_prin4 | 1.008496 .0420281 0.20 0.839 .9293974 1.094328
fr_cons_sus_prin5 | 1.030716 .0409414 0.76 0.446 .953516 1.114165
cond_ocu2 | 1.017986 .0318198 0.57 0.568 .957492 1.082302
cond_ocu3 | 1.002066 .1413163 0.01 0.988 .7600732 1.321103
cond_ocu4 | 1.105903 .0399819 2.78 0.005 1.030252 1.187109
cond_ocu5 | 1.161287 .0889877 1.95 0.051 .9993404 1.349479
cond_ocu6 | 1.131315 .0207251 6.73 0.000 1.091416 1.172674
policonsumo | 1.025457 .0223906 1.15 0.250 .9824981 1.070295
num_hij2 | 1.165277 .0227535 7.83 0.000 1.121524 1.210738
tenviv1 | 1.149915 .0752825 2.13 0.033 1.011438 1.307351
tenviv2 | 1.126384 .0493547 2.72 0.007 1.033688 1.227392
tenviv4 | 1.037068 .023733 1.59 0.112 .9915796 1.084643
tenviv5 | 1.003148 .0179845 0.18 0.861 .9685115 1.039024
mzone2 | 1.302019 .0273626 12.56 0.000 1.249479 1.356768
mzone3 | 1.464882 .0421235 13.28 0.000 1.384605 1.549814
n_off_vio | 1.355191 .0258752 15.92 0.000 1.305414 1.406867
n_off_acq | 1.814523 .032463 33.30 0.000 1.752 1.879278
n_off_sud | 1.257247 .0233243 12.34 0.000 1.212353 1.303803
n_off_oth | 1.360506 .0257566 16.26 0.000 1.310949 1.411936
psy_com2 | 1.07031 .0256895 2.83 0.005 1.021125 1.121864
psy_com3 | 1.058302 .0187991 3.19 0.001 1.02209 1.095796
dep2 | 1.019825 .019544 1.02 0.306 .9822295 1.058859
rural2 | 1.0286 .0287059 1.01 0.312 .9738484 1.086429
rural3 | 1.054497 .0324355 1.73 0.085 .9928034 1.120025
porc_pobr | 1.217432 .1440745 1.66 0.096 .9654084 1.535248
susini2 | 1.095616 .0455022 2.20 0.028 1.009966 1.188528
susini3 | 1.122498 .0372559 3.48 0.000 1.051802 1.197945
susini4 | 1.082496 .0193454 4.44 0.000 1.045236 1.121084
susini5 | 1.128703 .0561294 2.43 0.015 1.023883 1.244255
ano_nac_corr | .8761407 .003746 -30.93 0.000 .8688293 .8835137
cohab2 | .970453 .0310548 -0.94 0.349 .9114562 1.033269
cohab3 | .9919098 .0390355 -0.21 0.836 .9182778 1.071446
cohab4 | .9524798 .0296238 -1.57 0.117 .8961526 1.012348
fis_com2 | 1.028044 .0166923 1.70 0.088 .9958425 1.061286
fis_com3 | .9025781 .0336969 -2.75 0.006 .8388918 .9710992
rc_x1 | .8528728 .0048118 -28.21 0.000 .8434938 .8623561
rc_x2 | 1.028702 .0186425 1.56 0.118 .9928048 1.065898
rc_x3 | .8954972 .0414638 -2.38 0.017 .8178081 .9805665
_rcs1 | 2.631419 .0397459 64.06 0.000 2.55466 2.710484
_rcs2 | 1.107345 .0059881 18.86 0.000 1.095671 1.119144
_rcs3 | 1.043332 .0034056 13.00 0.000 1.036678 1.050028
_rcs_mot_egr_early1 | .9051236 .0161402 -5.59 0.000 .8740358 .9373171
_rcs_mot_egr_late1 | .9430706 .0155021 -3.57 0.000 .9131713 .973949
_cons | 2.0e+114 1.7e+115 30.56 0.000 9.2e+106 4.2e+121
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54485.683
Iteration 1: log likelihood = -54463.402
Iteration 2: log likelihood = -54463.324
Iteration 3: log likelihood = -54463.324
Log likelihood = -54463.324 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728552 .0500908 18.89 0.000 1.633112 1.82957
mot_egr_late | 1.579286 .0372265 19.39 0.000 1.507983 1.65396
tr_mod2 | 1.218348 .0262172 9.18 0.000 1.168031 1.270831
sex_dum2 | .7593736 .0163132 -12.81 0.000 .7280641 .7920294
edad_ini_cons | .9868842 .0019514 -6.68 0.000 .983067 .9907163
esc1 | 1.129393 .0298302 4.61 0.000 1.072415 1.189399
esc2 | 1.08917 .0259579 3.58 0.000 1.039464 1.141254
sus_prin2 | 1.065249 .0296977 2.27 0.023 1.008605 1.125075
sus_prin3 | 1.391757 .0326164 14.11 0.000 1.329276 1.457175
sus_prin4 | 1.075016 .037807 2.06 0.040 1.003412 1.15173
sus_prin5 | 1.139509 .0823728 1.81 0.071 .9889765 1.312953
fr_cons_sus_prin2 | .9206538 .0450443 -1.69 0.091 .8364695 1.013311
fr_cons_sus_prin3 | .9967418 .039561 -0.08 0.934 .9221429 1.077376
fr_cons_sus_prin4 | 1.008474 .0420272 0.20 0.840 .929377 1.094304
fr_cons_sus_prin5 | 1.030705 .040941 0.76 0.446 .9535063 1.114154
cond_ocu2 | 1.017991 .0318201 0.57 0.568 .9574969 1.082307
cond_ocu3 | 1.002252 .1413432 0.02 0.987 .7602133 1.32135
cond_ocu4 | 1.105884 .0399813 2.78 0.005 1.030234 1.187089
cond_ocu5 | 1.161245 .0889847 1.95 0.051 .9993033 1.34943
cond_ocu6 | 1.131302 .0207249 6.73 0.000 1.091402 1.17266
policonsumo | 1.025492 .0223919 1.15 0.249 .9825301 1.070332
num_hij2 | 1.165266 .0227534 7.83 0.000 1.121513 1.210726
tenviv1 | 1.149995 .0752879 2.13 0.033 1.011509 1.307442
tenviv2 | 1.126405 .0493557 2.72 0.007 1.033707 1.227415
tenviv4 | 1.037054 .0237328 1.59 0.112 .9915668 1.084629
tenviv5 | 1.003141 .0179843 0.17 0.861 .9685045 1.039016
mzone2 | 1.302023 .0273628 12.56 0.000 1.249482 1.356773
mzone3 | 1.464826 .0421223 13.28 0.000 1.384552 1.549755
n_off_vio | 1.355203 .0258754 15.92 0.000 1.305425 1.406878
n_off_acq | 1.814541 .0324633 33.30 0.000 1.752017 1.879297
n_off_sud | 1.257237 .0233241 12.34 0.000 1.212344 1.303793
n_off_oth | 1.360517 .0257568 16.26 0.000 1.310959 1.411947
psy_com2 | 1.070308 .0256896 2.83 0.005 1.021123 1.121861
psy_com3 | 1.058311 .0187993 3.19 0.001 1.022099 1.095806
dep2 | 1.01983 .0195442 1.02 0.306 .982235 1.058865
rural2 | 1.028588 .0287057 1.01 0.312 .9738367 1.086417
rural3 | 1.054488 .0324353 1.72 0.085 .9927948 1.120016
porc_pobr | 1.217536 .1440879 1.66 0.096 .9654886 1.535381
susini2 | 1.095588 .0455014 2.20 0.028 1.00994 1.188499
susini3 | 1.122483 .0372556 3.48 0.000 1.051788 1.19793
susini4 | 1.082494 .0193454 4.44 0.000 1.045234 1.121083
susini5 | 1.128707 .0561293 2.43 0.015 1.023887 1.244258
ano_nac_corr | .8761266 .0037462 -30.93 0.000 .8688149 .8834998
cohab2 | .9704367 .0310543 -0.94 0.348 .9114409 1.033251
cohab3 | .9918807 .0390343 -0.21 0.836 .9182509 1.071414
cohab4 | .9524619 .0296232 -1.57 0.117 .8961356 1.012329
fis_com2 | 1.028041 .0166922 1.70 0.089 .9958398 1.061283
fis_com3 | .9025933 .0336975 -2.75 0.006 .8389059 .9711157
rc_x1 | .85286 .0048118 -28.21 0.000 .843481 .8623433
rc_x2 | 1.028705 .0186426 1.56 0.118 .9928071 1.0659
rc_x3 | .8954833 .0414633 -2.38 0.017 .8177952 .9805514
_rcs1 | 2.641116 .0471899 54.36 0.000 2.550226 2.735245
_rcs2 | 1.112897 .0155416 7.66 0.000 1.082849 1.143778
_rcs3 | 1.043556 .0034549 12.88 0.000 1.036806 1.050349
_rcs_mot_egr_early1 | .9014791 .019002 -4.92 0.000 .8649948 .9395023
_rcs_mot_egr_early2 | .994428 .0160676 -0.35 0.729 .9634294 1.026424
_rcs_mot_egr_late1 | .9391144 .0186139 -3.17 0.002 .9033313 .9763149
_rcs_mot_egr_late2 | .9942627 .0150877 -0.38 0.705 .9651267 1.024278
_cons | 2.0e+114 1.7e+115 30.56 0.000 9.4e+106 4.3e+121
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54485.265
Iteration 1: log likelihood = -54463.259
Iteration 2: log likelihood = -54463.18
Iteration 3: log likelihood = -54463.18
Log likelihood = -54463.18 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728582 .0500987 18.88 0.000 1.633127 1.829616
mot_egr_late | 1.579256 .0372355 19.38 0.000 1.507936 1.653948
tr_mod2 | 1.218351 .0262176 9.18 0.000 1.168034 1.270836
sex_dum2 | .7593803 .0163132 -12.81 0.000 .7280707 .7920363
edad_ini_cons | .9868853 .0019514 -6.68 0.000 .9830681 .9907174
esc1 | 1.129397 .0298303 4.61 0.000 1.072419 1.189403
esc2 | 1.089189 .0259583 3.58 0.000 1.039482 1.141273
sus_prin2 | 1.065283 .0296988 2.27 0.023 1.008636 1.125112
sus_prin3 | 1.391785 .0326174 14.11 0.000 1.329302 1.457205
sus_prin4 | 1.075003 .0378067 2.06 0.040 1.003399 1.151716
sus_prin5 | 1.139693 .0823868 1.81 0.070 .9891355 1.313167
fr_cons_sus_prin2 | .920616 .0450425 -1.69 0.091 .8364349 1.013269
fr_cons_sus_prin3 | .9967388 .0395609 -0.08 0.934 .92214 1.077372
fr_cons_sus_prin4 | 1.008472 .0420271 0.20 0.840 .9293748 1.094301
fr_cons_sus_prin5 | 1.030685 .0409402 0.76 0.447 .9534879 1.114133
cond_ocu2 | 1.017967 .0318192 0.57 0.569 .9574748 1.082282
cond_ocu3 | 1.002324 .1413534 0.02 0.987 .7602679 1.321445
cond_ocu4 | 1.105851 .0399801 2.78 0.005 1.030203 1.187053
cond_ocu5 | 1.161418 .0889985 1.95 0.051 .9994509 1.349632
cond_ocu6 | 1.131287 .0207248 6.73 0.000 1.091387 1.172644
policonsumo | 1.025549 .0223936 1.16 0.248 .9825841 1.070392
num_hij2 | 1.165273 .0227536 7.83 0.000 1.121519 1.210733
tenviv1 | 1.150065 .0752926 2.14 0.033 1.01157 1.307522
tenviv2 | 1.126433 .049357 2.72 0.007 1.033732 1.227446
tenviv4 | 1.037048 .0237326 1.59 0.112 .9915611 1.084622
tenviv5 | 1.003135 .0179842 0.17 0.861 .9684987 1.03901
mzone2 | 1.30202 .0273626 12.56 0.000 1.249479 1.356769
mzone3 | 1.464814 .0421221 13.27 0.000 1.384539 1.549742
n_off_vio | 1.355206 .0258755 15.92 0.000 1.305429 1.406882
n_off_acq | 1.814537 .0324632 33.30 0.000 1.752013 1.879292
n_off_sud | 1.257206 .0233236 12.34 0.000 1.212313 1.30376
n_off_oth | 1.360502 .0257565 16.26 0.000 1.310945 1.411932
psy_com2 | 1.070325 .0256903 2.83 0.005 1.021139 1.12188
psy_com3 | 1.058317 .0187994 3.19 0.001 1.022104 1.095812
dep2 | 1.019815 .0195439 1.02 0.306 .9822202 1.058849
rural2 | 1.028565 .0287051 1.01 0.313 .9738146 1.086393
rural3 | 1.054484 .0324351 1.72 0.085 .9927903 1.120011
porc_pobr | 1.217294 .1440605 1.66 0.097 .9652951 1.535079
susini2 | 1.095508 .0454985 2.20 0.028 1.009866 1.188414
susini3 | 1.12251 .0372567 3.48 0.000 1.051813 1.197959
susini4 | 1.082495 .0193456 4.44 0.000 1.045235 1.121084
susini5 | 1.12871 .0561294 2.43 0.015 1.02389 1.244262
ano_nac_corr | .8761274 .0037462 -30.93 0.000 .8688157 .8835007
cohab2 | .9704008 .0310533 -0.94 0.348 .9114068 1.033213
cohab3 | .9918277 .0390324 -0.21 0.835 .9182016 1.071358
cohab4 | .9524153 .0296219 -1.57 0.117 .8960915 1.012279
fis_com2 | 1.028023 .0166919 1.70 0.089 .9958224 1.061264
fis_com3 | .9026003 .0336978 -2.74 0.006 .8389124 .9711232
rc_x1 | .8528602 .0048118 -28.21 0.000 .8434813 .8623435
rc_x2 | 1.028712 .0186428 1.56 0.118 .9928139 1.065908
rc_x3 | .8954575 .0414621 -2.38 0.017 .8177716 .9805233
_rcs1 | 2.637027 .0468976 54.52 0.000 2.546693 2.730565
_rcs2 | 1.106506 .0170444 6.57 0.000 1.073599 1.140422
_rcs3 | 1.048021 .008974 5.48 0.000 1.030579 1.065758
_rcs_mot_egr_early1 | .9028189 .0189738 -4.86 0.000 .8663864 .9407835
_rcs_mot_egr_early2 | 1.001629 .018003 0.09 0.928 .9669581 1.037543
_rcs_mot_egr_early3 | .9934326 .0103037 -0.64 0.525 .9734416 1.013834
_rcs_mot_egr_late1 | .9407461 .0185774 -3.09 0.002 .9050307 .9778708
_rcs_mot_egr_late2 | 1.000255 .0170372 0.01 0.988 .967414 1.034211
_rcs_mot_egr_late3 | .9954904 .0095555 -0.47 0.638 .976937 1.014396
_cons | 2.0e+114 1.7e+115 30.56 0.000 9.4e+106 4.3e+121
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54483.507
Iteration 1: log likelihood = -54457.249
Iteration 2: log likelihood = -54457.112
Iteration 3: log likelihood = -54457.112
Log likelihood = -54457.112 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728869 .0501015 18.89 0.000 1.633409 1.829909
mot_egr_late | 1.578982 .0372231 19.38 0.000 1.507686 1.65365
tr_mod2 | 1.21822 .0262148 9.17 0.000 1.167908 1.270698
sex_dum2 | .7595819 .0163178 -12.80 0.000 .7282636 .7922471
edad_ini_cons | .9868904 .0019514 -6.67 0.000 .9830731 .9907225
esc1 | 1.129226 .0298259 4.60 0.000 1.072256 1.189223
esc2 | 1.088977 .0259535 3.58 0.000 1.039279 1.141052
sus_prin2 | 1.065782 .0297138 2.29 0.022 1.009107 1.125641
sus_prin3 | 1.39222 .0326297 14.12 0.000 1.329713 1.457665
sus_prin4 | 1.075636 .0378302 2.07 0.038 1.003987 1.152397
sus_prin5 | 1.139972 .0824098 1.81 0.070 .9893728 1.313495
fr_cons_sus_prin2 | .9205685 .0450402 -1.69 0.091 .8363918 1.013217
fr_cons_sus_prin3 | .9968563 .0395655 -0.08 0.937 .9222489 1.077499
fr_cons_sus_prin4 | 1.008664 .0420351 0.21 0.836 .9295516 1.09451
fr_cons_sus_prin5 | 1.03081 .0409452 0.76 0.445 .9536034 1.114268
cond_ocu2 | 1.017998 .0318203 0.57 0.568 .9575028 1.082314
cond_ocu3 | 1.003955 .1415847 0.03 0.978 .7615038 1.3236
cond_ocu4 | 1.105561 .0399696 2.78 0.006 1.029933 1.186743
cond_ocu5 | 1.16108 .088974 1.95 0.051 .9991586 1.349243
cond_ocu6 | 1.131428 .0207272 6.74 0.000 1.091524 1.172791
policonsumo | 1.025947 .0224027 1.17 0.241 .9829651 1.070809
num_hij2 | 1.165279 .0227535 7.83 0.000 1.121525 1.210739
tenviv1 | 1.150147 .0752972 2.14 0.033 1.011643 1.307613
tenviv2 | 1.126531 .0493624 2.72 0.007 1.033821 1.227556
tenviv4 | 1.037201 .0237364 1.60 0.110 .9917066 1.084783
tenviv5 | 1.003309 .0179875 0.18 0.854 .9686662 1.03919
mzone2 | 1.302311 .0273694 12.57 0.000 1.249758 1.357075
mzone3 | 1.465098 .0421348 13.28 0.000 1.3848 1.550053
n_off_vio | 1.355304 .0258753 15.92 0.000 1.305526 1.406979
n_off_acq | 1.814545 .0324612 33.31 0.000 1.752024 1.879296
n_off_sud | 1.257069 .0233204 12.33 0.000 1.212183 1.303617
n_off_oth | 1.360509 .0257545 16.26 0.000 1.310956 1.411935
psy_com2 | 1.070609 .0256973 2.84 0.004 1.02141 1.122179
psy_com3 | 1.058341 .0187997 3.19 0.001 1.022128 1.095836
dep2 | 1.019876 .0195451 1.03 0.304 .9822784 1.058912
rural2 | 1.028671 .0287087 1.01 0.311 .9739146 1.086507
rural3 | 1.054396 .0324341 1.72 0.085 .9927049 1.119921
porc_pobr | 1.221368 .1445366 1.69 0.091 .9685354 1.540202
susini2 | 1.095649 .0455046 2.20 0.028 1.009996 1.188567
susini3 | 1.12235 .0372506 3.48 0.001 1.051664 1.197787
susini4 | 1.082512 .0193458 4.44 0.000 1.045252 1.121101
susini5 | 1.128955 .0561426 2.44 0.015 1.02411 1.244534
ano_nac_corr | .8756925 .0037467 -31.02 0.000 .8683798 .8830667
cohab2 | .9705393 .0310578 -0.93 0.350 .9115368 1.033361
cohab3 | .9918875 .0390344 -0.21 0.836 .9182575 1.071421
cohab4 | .9525358 .0296254 -1.56 0.118 .8962055 1.012407
fis_com2 | 1.027809 .0166886 1.69 0.091 .995615 1.061044
fis_com3 | .9024008 .0336905 -2.75 0.006 .8387267 .9709088
rc_x1 | .8524267 .0048111 -28.29 0.000 .8430492 .8619086
rc_x2 | 1.028779 .0186438 1.57 0.117 .9928792 1.065977
rc_x3 | .8953429 .0414563 -2.39 0.017 .8176679 .9803967
_rcs1 | 2.636697 .0470501 54.33 0.000 2.546075 2.730545
_rcs2 | 1.11234 .0173334 6.83 0.000 1.078881 1.146837
_rcs3 | 1.041682 .0087673 4.85 0.000 1.02464 1.059008
_rcs_mot_egr_early1 | .9034275 .0190381 -4.82 0.000 .8668737 .9415227
_rcs_mot_egr_early2 | .9977855 .0183592 -0.12 0.904 .9624432 1.034426
_rcs_mot_egr_early3 | .9955726 .0102597 -0.43 0.667 .9756656 1.015886
_rcs_mot_egr_early4 | 1.007568 .0044402 1.71 0.087 .9989026 1.016308
_rcs_mot_egr_late1 | .9413795 .0186463 -3.05 0.002 .9055338 .9786442
_rcs_mot_egr_late2 | .9978389 .0174399 -0.12 0.901 .9642362 1.032613
_rcs_mot_egr_late3 | .9953192 .0094626 -0.49 0.622 .9769446 1.014039
_rcs_mot_egr_late4 | 1.009043 .0034644 2.62 0.009 1.002275 1.015856
_cons | 5.5e+114 4.7e+115 30.66 0.000 2.5e+107 1.2e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54466.092
Iteration 1: log likelihood = -54448.275
Iteration 2: log likelihood = -54448.209
Iteration 3: log likelihood = -54448.209
Log likelihood = -54448.209 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730729 .0501647 18.93 0.000 1.635149 1.831897
mot_egr_late | 1.579768 .0372503 19.39 0.000 1.50842 1.65449
tr_mod2 | 1.218409 .0262186 9.18 0.000 1.16809 1.270895
sex_dum2 | .7598336 .0163233 -12.78 0.000 .7285047 .7925097
edad_ini_cons | .9869009 .0019513 -6.67 0.000 .9830837 .9907329
esc1 | 1.129038 .029821 4.59 0.000 1.072078 1.189026
esc2 | 1.088803 .0259494 3.57 0.000 1.039113 1.14087
sus_prin2 | 1.066234 .0297276 2.30 0.021 1.009533 1.126121
sus_prin3 | 1.392531 .0326394 14.13 0.000 1.330006 1.457995
sus_prin4 | 1.076087 .0378473 2.08 0.037 1.004407 1.152883
sus_prin5 | 1.140913 .0824816 1.82 0.068 .9901834 1.314588
fr_cons_sus_prin2 | .9203896 .0450315 -1.70 0.090 .8362293 1.01302
fr_cons_sus_prin3 | .996908 .0395675 -0.08 0.938 .9222968 1.077555
fr_cons_sus_prin4 | 1.008692 .0420362 0.21 0.835 .9295775 1.094539
fr_cons_sus_prin5 | 1.030752 .0409428 0.76 0.446 .9535496 1.114205
cond_ocu2 | 1.018016 .0318207 0.57 0.568 .9575211 1.082334
cond_ocu3 | 1.004986 .1417297 0.04 0.972 .7622861 1.324958
cond_ocu4 | 1.104904 .0399464 2.76 0.006 1.02932 1.186038
cond_ocu5 | 1.161085 .0889758 1.95 0.051 .9991599 1.349251
cond_ocu6 | 1.131412 .020727 6.74 0.000 1.091508 1.172774
policonsumo | 1.026343 .0224122 1.19 0.234 .983343 1.071224
num_hij2 | 1.165261 .0227532 7.83 0.000 1.121509 1.210721
tenviv1 | 1.151374 .0753776 2.15 0.031 1.012722 1.309008
tenviv2 | 1.126754 .0493734 2.72 0.006 1.034023 1.227801
tenviv4 | 1.037449 .0237423 1.61 0.108 .9919432 1.085042
tenviv5 | 1.003485 .0179905 0.19 0.846 .9688365 1.039373
mzone2 | 1.302483 .0273736 12.57 0.000 1.249922 1.357255
mzone3 | 1.464949 .0421343 13.28 0.000 1.384652 1.549903
n_off_vio | 1.355272 .0258727 15.92 0.000 1.305499 1.406942
n_off_acq | 1.814483 .0324567 33.31 0.000 1.751971 1.879226
n_off_sud | 1.256992 .0233178 12.33 0.000 1.212111 1.303535
n_off_oth | 1.360494 .0257515 16.26 0.000 1.310947 1.411914
psy_com2 | 1.070622 .0256982 2.84 0.004 1.02142 1.122193
psy_com3 | 1.058335 .0187996 3.19 0.001 1.022123 1.095831
dep2 | 1.019902 .0195457 1.03 0.304 .982304 1.05894
rural2 | 1.02873 .0287109 1.01 0.310 .9739686 1.086569
rural3 | 1.054442 .032437 1.72 0.085 .9927457 1.119973
porc_pobr | 1.225179 .144983 1.72 0.086 .9715644 1.544996
susini2 | 1.0958 .0455108 2.20 0.028 1.010134 1.18873
susini3 | 1.122311 .0372492 3.48 0.001 1.051628 1.197745
susini4 | 1.082481 .0193456 4.43 0.000 1.04522 1.121069
susini5 | 1.129333 .0561633 2.45 0.014 1.02445 1.244955
ano_nac_corr | .8752535 .0037462 -31.13 0.000 .8679418 .8826268
cohab2 | .9706384 .0310602 -0.93 0.352 .9116313 1.033465
cohab3 | .9916585 .039025 -0.21 0.831 .9180462 1.071173
cohab4 | .9524517 .0296223 -1.57 0.117 .8961272 1.012316
fis_com2 | 1.027554 .0166845 1.67 0.094 .9953684 1.060781
fis_com3 | .9022765 .0336858 -2.75 0.006 .8386113 .970775
rc_x1 | .852004 .0048096 -28.37 0.000 .8426293 .8614831
rc_x2 | 1.028805 .0186445 1.57 0.117 .992904 1.066004
rc_x3 | .895266 .041453 -2.39 0.017 .8175972 .9803131
_rcs1 | 2.635947 .0468825 54.50 0.000 2.545642 2.729456
_rcs2 | 1.107067 .0170633 6.60 0.000 1.074123 1.141021
_rcs3 | 1.04752 .0089477 5.44 0.000 1.030129 1.065205
_rcs_mot_egr_early1 | .9035226 .0189928 -4.83 0.000 .8670538 .9415252
_rcs_mot_egr_early2 | 1.000795 .0182669 0.04 0.965 .9656254 1.037246
_rcs_mot_egr_early3 | .9934107 .010015 -0.66 0.512 .9739742 1.013235
_rcs_mot_egr_early4 | .999003 .0052091 -0.19 0.848 .9888454 1.009265
_rcs_mot_egr_early5 | 1.010355 .0030537 3.41 0.001 1.004388 1.016358
_rcs_mot_egr_late1 | .9414092 .0185956 -3.06 0.002 .905659 .9785705
_rcs_mot_egr_late2 | 1.00185 .0173977 0.11 0.915 .9683249 1.036536
_rcs_mot_egr_late3 | .9910115 .0091505 -0.98 0.328 .9732382 1.009109
_rcs_mot_egr_late4 | 1.003345 .0043906 0.76 0.445 .9947765 1.011988
_rcs_mot_egr_late5 | 1.008398 .0021973 3.84 0.000 1.0041 1.012713
_cons | 1.5e+115 1.3e+116 30.76 0.000 6.9e+107 3.3e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54462.558
Iteration 1: log likelihood = -54444.262
Iteration 2: log likelihood = -54444.201
Iteration 3: log likelihood = -54444.2
Log likelihood = -54444.2 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.731103 .0501788 18.93 0.000 1.635496 1.832299
mot_egr_late | 1.579975 .0372579 19.40 0.000 1.508613 1.654713
tr_mod2 | 1.218466 .0262199 9.18 0.000 1.168144 1.270955
sex_dum2 | .7599884 .0163266 -12.78 0.000 .7286531 .7926713
edad_ini_cons | .9868997 .0019513 -6.67 0.000 .9830826 .9907316
esc1 | 1.128892 .0298171 4.59 0.000 1.071938 1.188871
esc2 | 1.088689 .0259467 3.57 0.000 1.039004 1.14075
sus_prin2 | 1.066503 .0297355 2.31 0.021 1.009786 1.126405
sus_prin3 | 1.392736 .0326452 14.13 0.000 1.3302 1.458212
sus_prin4 | 1.076351 .0378572 2.09 0.036 1.004652 1.153167
sus_prin5 | 1.141509 .0825268 1.83 0.067 .9906964 1.315279
fr_cons_sus_prin2 | .9203293 .0450285 -1.70 0.090 .8361746 1.012954
fr_cons_sus_prin3 | .9970406 .0395727 -0.07 0.940 .9224195 1.077698
fr_cons_sus_prin4 | 1.008752 .0420386 0.21 0.834 .9296327 1.094604
fr_cons_sus_prin5 | 1.030799 .0409447 0.76 0.445 .9535932 1.114256
cond_ocu2 | 1.017958 .0318186 0.57 0.569 .9574663 1.082271
cond_ocu3 | 1.005433 .1417927 0.04 0.969 .7626253 1.325547
cond_ocu4 | 1.104671 .0399379 2.75 0.006 1.029103 1.185788
cond_ocu5 | 1.161254 .0889888 1.95 0.051 .9993056 1.349448
cond_ocu6 | 1.131384 .0207266 6.74 0.000 1.091481 1.172745
policonsumo | 1.026502 .0224158 1.20 0.231 .9834952 1.07139
num_hij2 | 1.165205 .0227521 7.83 0.000 1.121454 1.210662
tenviv1 | 1.1516 .0753925 2.16 0.031 1.012921 1.309266
tenviv2 | 1.127022 .0493858 2.73 0.006 1.034268 1.228095
tenviv4 | 1.037581 .0237455 1.61 0.107 .992069 1.085181
tenviv5 | 1.003648 .0179935 0.20 0.839 .9689941 1.039542
mzone2 | 1.302614 .0273764 12.58 0.000 1.250048 1.357392
mzone3 | 1.464977 .0421368 13.28 0.000 1.384675 1.549936
n_off_vio | 1.355272 .0258717 15.92 0.000 1.305502 1.40694
n_off_acq | 1.814414 .0324545 33.31 0.000 1.751906 1.879152
n_off_sud | 1.256982 .0233171 12.33 0.000 1.212102 1.303523
n_off_oth | 1.3604 .0257486 16.26 0.000 1.310858 1.411814
psy_com2 | 1.070736 .0257012 2.85 0.004 1.021529 1.122313
psy_com3 | 1.058367 .0188002 3.19 0.001 1.022154 1.095864
dep2 | 1.019923 .0195461 1.03 0.303 .9823241 1.058962
rural2 | 1.028735 .0287113 1.02 0.310 .9739735 1.086576
rural3 | 1.054404 .0324367 1.72 0.085 .9927074 1.119934
porc_pobr | 1.226967 .1451932 1.73 0.084 .9729843 1.547247
susini2 | 1.095807 .0455109 2.20 0.028 1.010141 1.188738
susini3 | 1.122447 .0372538 3.48 0.001 1.051755 1.19789
susini4 | 1.0824 .0193444 4.43 0.000 1.045142 1.120986
susini5 | 1.129348 .0561653 2.45 0.014 1.024461 1.244974
ano_nac_corr | .8750798 .003746 -31.17 0.000 .8677685 .8824527
cohab2 | .9706571 .0310609 -0.93 0.352 .9116487 1.033485
cohab3 | .9916186 .0390237 -0.21 0.831 .918009 1.071131
cohab4 | .9524347 .0296219 -1.57 0.117 .896111 1.012299
fis_com2 | 1.027424 .0166822 1.67 0.096 .995242 1.060646
fis_com3 | .902306 .0336871 -2.75 0.006 .8386384 .9708071
rc_x1 | .8518418 .0048091 -28.40 0.000 .8424681 .8613198
rc_x2 | 1.028792 .0186442 1.57 0.117 .9928909 1.06599
rc_x3 | .8952855 .041454 -2.39 0.017 .8176148 .9803346
_rcs1 | 2.636161 .0468817 54.51 0.000 2.545857 2.729667
_rcs2 | 1.106696 .0170453 6.58 0.000 1.073787 1.140614
_rcs3 | 1.04821 .0089714 5.50 0.000 1.030773 1.065942
_rcs_mot_egr_early1 | .9034555 .0189905 -4.83 0.000 .8669911 .9414534
_rcs_mot_egr_early2 | 1.001224 .0183102 0.07 0.947 .9659721 1.037762
_rcs_mot_egr_early3 | .9935885 .0098101 -0.65 0.515 .9745459 1.013003
_rcs_mot_egr_early4 | .9955556 .0057738 -0.77 0.442 .9843032 1.006937
_rcs_mot_egr_early5 | 1.008785 .0032495 2.72 0.007 1.002436 1.015174
_rcs_mot_egr_early6 | 1.005403 .0024612 2.20 0.028 1.000591 1.010238
_rcs_mot_egr_late1 | .9413817 .0185951 -3.06 0.002 .9056324 .9785421
_rcs_mot_egr_late2 | 1.002948 .0174777 0.17 0.866 .9692707 1.037795
_rcs_mot_egr_late3 | .9892547 .0088936 -1.20 0.229 .9719764 1.00684
_rcs_mot_egr_late4 | 1.00147 .0049941 0.29 0.768 .991729 1.011306
_rcs_mot_egr_late5 | 1.006353 .0024293 2.62 0.009 1.001603 1.011126
_rcs_mot_egr_late6 | 1.007845 .0017526 4.49 0.000 1.004416 1.011286
_cons | 2.2e+115 1.9e+116 30.81 0.000 1.0e+108 4.9e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54460.282
Iteration 1: log likelihood = -54442.429
Iteration 2: log likelihood = -54442.355
Iteration 3: log likelihood = -54442.355
Log likelihood = -54442.355 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.731231 .0501831 18.93 0.000 1.635616 1.832436
mot_egr_late | 1.58003 .0372597 19.40 0.000 1.508664 1.654772
tr_mod2 | 1.218525 .0262212 9.18 0.000 1.168201 1.271017
sex_dum2 | .7600783 .0163286 -12.77 0.000 .7287393 .7927651
edad_ini_cons | .9868983 .0019513 -6.67 0.000 .9830812 .9907302
esc1 | 1.128833 .0298157 4.59 0.000 1.071882 1.18881
esc2 | 1.088637 .0259455 3.56 0.000 1.038954 1.140696
sus_prin2 | 1.066678 .0297405 2.32 0.021 1.009952 1.12659
sus_prin3 | 1.392863 .0326487 14.14 0.000 1.330321 1.458346
sus_prin4 | 1.0765 .0378629 2.10 0.036 1.00479 1.153328
sus_prin5 | 1.141789 .0825482 1.83 0.067 .9909371 1.315604
fr_cons_sus_prin2 | .9203557 .0450298 -1.70 0.090 .8361985 1.012983
fr_cons_sus_prin3 | .9971514 .0395772 -0.07 0.943 .922522 1.077818
fr_cons_sus_prin4 | 1.008808 .042041 0.21 0.833 .9296844 1.094666
fr_cons_sus_prin5 | 1.030832 .0409462 0.76 0.445 .9536233 1.114292
cond_ocu2 | 1.01791 .031817 0.57 0.570 .9574213 1.08222
cond_ocu3 | 1.005557 .14181 0.04 0.969 .762719 1.32571
cond_ocu4 | 1.104501 .039932 2.75 0.006 1.028945 1.185606
cond_ocu5 | 1.161122 .0889787 1.95 0.051 .9991919 1.349295
cond_ocu6 | 1.131357 .0207262 6.74 0.000 1.091455 1.172718
policonsumo | 1.026518 .0224162 1.20 0.231 .98351 1.071407
num_hij2 | 1.165186 .0227518 7.83 0.000 1.121436 1.210643
tenviv1 | 1.151719 .0754001 2.16 0.031 1.013026 1.3094
tenviv2 | 1.127186 .0493936 2.73 0.006 1.034418 1.228275
tenviv4 | 1.037657 .0237474 1.62 0.106 .9921411 1.08526
tenviv5 | 1.00375 .0179953 0.21 0.835 .9690924 1.039647
mzone2 | 1.3027 .0273784 12.58 0.000 1.250129 1.357481
mzone3 | 1.465021 .0421393 13.28 0.000 1.384715 1.549985
n_off_vio | 1.355235 .0258704 15.92 0.000 1.305467 1.406901
n_off_acq | 1.814417 .0324539 33.31 0.000 1.751911 1.879154
n_off_sud | 1.256953 .0233164 12.33 0.000 1.212075 1.303494
n_off_oth | 1.360366 .0257473 16.26 0.000 1.310827 1.411778
psy_com2 | 1.070819 .0257032 2.85 0.004 1.021608 1.1224
psy_com3 | 1.058391 .0188006 3.19 0.001 1.022177 1.095888
dep2 | 1.019939 .0195466 1.03 0.303 .9823395 1.058979
rural2 | 1.028737 .0287114 1.02 0.310 .9739756 1.086578
rural3 | 1.054355 .0324358 1.72 0.085 .9926611 1.119884
porc_pobr | 1.227736 .1452837 1.73 0.083 .9735956 1.548217
susini2 | 1.095884 .0455141 2.20 0.027 1.010212 1.188821
susini3 | 1.122548 .0372572 3.48 0.000 1.05185 1.197999
susini4 | 1.082351 .0193436 4.43 0.000 1.045094 1.120935
susini5 | 1.129304 .0561638 2.45 0.014 1.024419 1.244926
ano_nac_corr | .8750015 .003746 -31.19 0.000 .8676902 .8823745
cohab2 | .970614 .0310596 -0.93 0.351 .911608 1.033439
cohab3 | .9915745 .039022 -0.22 0.830 .917968 1.071083
cohab4 | .9524028 .0296209 -1.57 0.117 .896081 1.012265
fis_com2 | 1.027361 .0166812 1.66 0.096 .9951817 1.060582
fis_com3 | .9022852 .0336864 -2.75 0.006 .8386189 .970785
rc_x1 | .8517697 .004809 -28.42 0.000 .8423963 .8612475
rc_x2 | 1.028767 .0186437 1.56 0.118 .9928672 1.065964
rc_x3 | .8953406 .0414564 -2.39 0.017 .8176655 .9803946
_rcs1 | 2.63609 .0468794 54.50 0.000 2.545791 2.729592
_rcs2 | 1.106687 .0170421 6.58 0.000 1.073784 1.140598
_rcs3 | 1.048258 .0089685 5.51 0.000 1.030827 1.065984
_rcs_mot_egr_early1 | .9034473 .0189902 -4.83 0.000 .8669834 .9414448
_rcs_mot_egr_early2 | 1.001711 .0183867 0.09 0.926 .9663144 1.038405
_rcs_mot_egr_early3 | .9930374 .0096477 -0.72 0.472 .9743071 1.012128
_rcs_mot_egr_early4 | .9952592 .0061389 -0.77 0.441 .9832995 1.007364
_rcs_mot_egr_early5 | 1.004814 .0034814 1.39 0.166 .998014 1.011661
_rcs_mot_egr_early6 | 1.008409 .0025705 3.29 0.001 1.003384 1.01346
_rcs_mot_egr_early7 | 1.002446 .0021187 1.16 0.248 .9983019 1.006607
_rcs_mot_egr_late1 | .94131 .0185928 -3.06 0.002 .9055652 .9784657
_rcs_mot_egr_late2 | 1.002702 .0175166 0.15 0.877 .9689515 1.037629
_rcs_mot_egr_late3 | .9899516 .0086772 -1.15 0.249 .9730899 1.007105
_rcs_mot_egr_late4 | .9990788 .0053642 -0.17 0.864 .9886203 1.009648
_rcs_mot_egr_late5 | 1.00501 .0026822 1.87 0.061 .9997668 1.010281
_rcs_mot_egr_late6 | 1.007291 .0018502 3.95 0.000 1.003671 1.010924
_rcs_mot_egr_late7 | 1.006403 .0015107 4.25 0.000 1.003446 1.009368
_cons | 2.7e+115 2.3e+116 30.82 0.000 1.2e+108 5.9e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54478.199
Iteration 1: log likelihood = -54453.061
Iteration 2: log likelihood = -54452.974
Iteration 3: log likelihood = -54452.974
Log likelihood = -54452.974 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728163 .0499693 18.92 0.000 1.632948 1.828929
mot_egr_late | 1.577998 .0370677 19.42 0.000 1.506994 1.652348
tr_mod2 | 1.218352 .0262163 9.18 0.000 1.168038 1.270834
sex_dum2 | .7596556 .016319 -12.80 0.000 .7283349 .7923232
edad_ini_cons | .9868945 .0019514 -6.67 0.000 .9830772 .9907265
esc1 | 1.129228 .0298258 4.60 0.000 1.072258 1.189225
esc2 | 1.08898 .0259535 3.58 0.000 1.039282 1.141054
sus_prin2 | 1.066001 .0297204 2.29 0.022 1.009313 1.125873
sus_prin3 | 1.392372 .0326345 14.12 0.000 1.329856 1.457826
sus_prin4 | 1.075867 .037839 2.08 0.038 1.004203 1.152646
sus_prin5 | 1.140486 .0824468 1.82 0.069 .9898195 1.314087
fr_cons_sus_prin2 | .9204567 .0450346 -1.69 0.090 .8362905 1.013094
fr_cons_sus_prin3 | .9968614 .0395656 -0.08 0.937 .9222538 1.077504
fr_cons_sus_prin4 | 1.008683 .0420357 0.21 0.836 .9295694 1.09453
fr_cons_sus_prin5 | 1.030727 .0409418 0.76 0.446 .9535266 1.114178
cond_ocu2 | 1.017991 .0318194 0.57 0.568 .9574978 1.082306
cond_ocu3 | 1.00445 .1416533 0.03 0.975 .7618808 1.324249
cond_ocu4 | 1.105238 .0399581 2.77 0.006 1.029631 1.186396
cond_ocu5 | 1.161483 .0890039 1.95 0.051 .999507 1.349709
cond_ocu6 | 1.131393 .0207266 6.74 0.000 1.09149 1.172754
policonsumo | 1.026192 .0224079 1.18 0.236 .9831993 1.071064
num_hij2 | 1.16523 .0227525 7.83 0.000 1.121478 1.210688
tenviv1 | 1.150669 .0753304 2.14 0.032 1.012104 1.308204
tenviv2 | 1.126888 .0493779 2.73 0.006 1.034148 1.227944
tenviv4 | 1.037294 .0237386 1.60 0.110 .9917955 1.08488
tenviv5 | 1.003341 .0179879 0.19 0.852 .9686971 1.039223
mzone2 | 1.302367 .0273705 12.57 0.000 1.249812 1.357132
mzone3 | 1.464819 .0421262 13.27 0.000 1.384537 1.549756
n_off_vio | 1.355269 .0258736 15.92 0.000 1.305495 1.406941
n_off_acq | 1.814445 .0324578 33.31 0.000 1.751931 1.87919
n_off_sud | 1.256929 .0233171 12.33 0.000 1.212049 1.303471
n_off_oth | 1.360485 .0257529 16.26 0.000 1.310935 1.411908
psy_com2 | 1.070658 .0256984 2.84 0.004 1.021456 1.122229
psy_com3 | 1.058338 .0187996 3.19 0.001 1.022125 1.095834
dep2 | 1.019898 .0195456 1.03 0.304 .9822999 1.058935
rural2 | 1.028685 .0287088 1.01 0.311 .9739276 1.08652
rural3 | 1.054425 .0324351 1.72 0.085 .992732 1.119952
porc_pobr | 1.223037 .1447318 1.70 0.089 .9698623 1.542301
susini2 | 1.095734 .0455071 2.20 0.028 1.010075 1.188657
susini3 | 1.122426 .0372528 3.48 0.001 1.051736 1.197868
susini4 | 1.082476 .0193453 4.43 0.000 1.045216 1.121064
susini5 | 1.129285 .0561603 2.44 0.014 1.024407 1.244901
ano_nac_corr | .8754581 .0037464 -31.08 0.000 .8681461 .8828318
cohab2 | .9706356 .03106 -0.93 0.352 .9116289 1.033462
cohab3 | .9917896 .03903 -0.21 0.834 .9181679 1.071315
cohab4 | .9525018 .0296239 -1.56 0.118 .8961743 1.01237
fis_com2 | 1.027612 .0166853 1.68 0.093 .9954244 1.060841
fis_com3 | .9023266 .0336875 -2.75 0.006 .8386581 .9708287
rc_x1 | .8522085 .0048103 -28.33 0.000 .8428325 .8616888
rc_x2 | 1.02877 .0186436 1.57 0.118 .9928706 1.065968
rc_x3 | .895324 .0414552 -2.39 0.017 .8176509 .9803756
_rcs1 | 2.631989 .0397344 64.10 0.000 2.555252 2.711031
_rcs2 | 1.108034 .0062104 18.30 0.000 1.095929 1.120274
_rcs3 | 1.040838 .0037398 11.14 0.000 1.033534 1.048194
_rcs4 | 1.016541 .0022599 7.38 0.000 1.012121 1.02098
_rcs_mot_egr_early1 | .9053008 .0161294 -5.58 0.000 .8742333 .9374723
_rcs_mot_egr_late1 | .9430969 .0154906 -3.57 0.000 .9132193 .973952
_cons | 9.4e+114 8.1e+115 30.71 0.000 4.3e+107 2.0e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54478.363
Iteration 1: log likelihood = -54453.042
Iteration 2: log likelihood = -54452.941
Iteration 3: log likelihood = -54452.941
Log likelihood = -54452.941 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728984 .0501003 18.90 0.000 1.633526 1.830021
mot_egr_late | 1.578756 .0372106 19.37 0.000 1.507484 1.653399
tr_mod2 | 1.218397 .0262179 9.18 0.000 1.168079 1.270882
sex_dum2 | .7596501 .016319 -12.80 0.000 .7283295 .7923177
edad_ini_cons | .9868953 .0019514 -6.67 0.000 .983078 .9907273
esc1 | 1.129218 .0298256 4.60 0.000 1.072248 1.189215
esc2 | 1.088972 .0259534 3.58 0.000 1.039274 1.141046
sus_prin2 | 1.066018 .0297211 2.29 0.022 1.009328 1.125891
sus_prin3 | 1.392391 .0326352 14.12 0.000 1.329875 1.457847
sus_prin4 | 1.075874 .0378393 2.08 0.038 1.004209 1.152654
sus_prin5 | 1.140594 .0824557 1.82 0.069 .9899105 1.314213
fr_cons_sus_prin2 | .9204508 .0450344 -1.69 0.090 .8362851 1.013087
fr_cons_sus_prin3 | .9968587 .0395655 -0.08 0.937 .9222513 1.077502
fr_cons_sus_prin4 | 1.008669 .0420352 0.21 0.836 .929556 1.094514
fr_cons_sus_prin5 | 1.03072 .0409416 0.76 0.446 .9535201 1.11417
cond_ocu2 | 1.017993 .0318196 0.57 0.568 .9574996 1.082308
cond_ocu3 | 1.004566 .1416704 0.03 0.974 .7619679 1.324404
cond_ocu4 | 1.105227 .0399578 2.77 0.006 1.029621 1.186384
cond_ocu5 | 1.161457 .0890021 1.95 0.051 .9994839 1.349679
cond_ocu6 | 1.131384 .0207265 6.74 0.000 1.091481 1.172745
policonsumo | 1.026214 .0224089 1.18 0.236 .9832198 1.071088
num_hij2 | 1.165223 .0227524 7.83 0.000 1.121471 1.210681
tenviv1 | 1.15072 .0753339 2.14 0.032 1.012148 1.308263
tenviv2 | 1.1269 .0493785 2.73 0.006 1.034159 1.227957
tenviv4 | 1.037286 .0237384 1.60 0.110 .9917874 1.084871
tenviv5 | 1.003336 .0179878 0.19 0.853 .9686926 1.039218
mzone2 | 1.302371 .0273707 12.57 0.000 1.249815 1.357136
mzone3 | 1.464784 .0421256 13.27 0.000 1.384503 1.54972
n_off_vio | 1.355277 .0258737 15.92 0.000 1.305502 1.406949
n_off_acq | 1.814458 .032458 33.31 0.000 1.751944 1.879203
n_off_sud | 1.256923 .023317 12.33 0.000 1.212043 1.303464
n_off_oth | 1.360491 .0257531 16.26 0.000 1.310941 1.411914
psy_com2 | 1.070657 .0256985 2.84 0.004 1.021455 1.122229
psy_com3 | 1.058343 .0187997 3.19 0.001 1.022131 1.095839
dep2 | 1.019901 .0195457 1.03 0.304 .9823031 1.058939
rural2 | 1.028675 .0287087 1.01 0.311 .9739182 1.08651
rural3 | 1.054418 .032435 1.72 0.085 .9927251 1.119945
porc_pobr | 1.223099 .14474 1.70 0.089 .9699101 1.542381
susini2 | 1.095713 .0455065 2.20 0.028 1.010055 1.188635
susini3 | 1.122419 .0372528 3.48 0.001 1.051729 1.19786
susini4 | 1.082476 .0193454 4.43 0.000 1.045216 1.121064
susini5 | 1.129286 .0561602 2.44 0.014 1.024409 1.244902
ano_nac_corr | .8754503 .0037465 -31.08 0.000 .868138 .8828242
cohab2 | .9706234 .0310597 -0.93 0.351 .9116172 1.033449
cohab3 | .9917705 .0390293 -0.21 0.834 .9181502 1.071294
cohab4 | .9524893 .0296235 -1.57 0.118 .8961624 1.012356
fis_com2 | 1.027611 .0166853 1.68 0.093 .995423 1.060839
fis_com3 | .9023361 .033688 -2.75 0.006 .8386668 .970839
rc_x1 | .8522013 .0048103 -28.33 0.000 .8428252 .8616817
rc_x2 | 1.028772 .0186437 1.57 0.118 .9928725 1.06597
rc_x3 | .8953141 .0414549 -2.39 0.017 .8176417 .980365
_rcs1 | 2.638301 .0470202 54.43 0.000 2.547734 2.732087
_rcs2 | 1.11164 .0155104 7.59 0.000 1.081652 1.142459
_rcs3 | 1.041107 .0038894 10.78 0.000 1.033512 1.048758
_rcs4 | 1.016539 .00226 7.38 0.000 1.01212 1.020979
_rcs_mot_egr_early1 | .9028721 .0189944 -4.86 0.000 .8664009 .9408785
_rcs_mot_egr_early2 | .9962581 .0160654 -0.23 0.816 .9652629 1.028249
_rcs_mot_egr_late1 | .9405552 .0186017 -3.10 0.002 .9047941 .9777297
_rcs_mot_egr_late2 | .9963162 .0150806 -0.24 0.807 .967193 1.026316
_cons | 9.6e+114 8.2e+115 30.72 0.000 4.4e+107 2.1e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54478.464
Iteration 1: log likelihood = -54452.548
Iteration 2: log likelihood = -54452.439
Iteration 3: log likelihood = -54452.439
Log likelihood = -54452.439 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730147 .0501514 18.91 0.000 1.634593 1.831288
mot_egr_late | 1.579771 .0372539 19.39 0.000 1.508416 1.654501
tr_mod2 | 1.218479 .0262199 9.18 0.000 1.168158 1.270968
sex_dum2 | .7596562 .016319 -12.80 0.000 .7283356 .7923238
edad_ini_cons | .9868978 .0019514 -6.67 0.000 .9830806 .9907298
esc1 | 1.129219 .0298256 4.60 0.000 1.072249 1.189216
esc2 | 1.08899 .0259538 3.58 0.000 1.039292 1.141066
sus_prin2 | 1.066083 .0297234 2.30 0.022 1.009389 1.12596
sus_prin3 | 1.392451 .0326372 14.12 0.000 1.32993 1.457911
sus_prin4 | 1.075874 .0378397 2.08 0.038 1.004208 1.152654
sus_prin5 | 1.140987 .0824845 1.82 0.068 .9902513 1.314668
fr_cons_sus_prin2 | .920395 .0450317 -1.70 0.090 .8362343 1.013026
fr_cons_sus_prin3 | .9968489 .0395651 -0.08 0.937 .9222422 1.077491
fr_cons_sus_prin4 | 1.008637 .0420339 0.21 0.837 .9295267 1.09448
fr_cons_sus_prin5 | 1.030671 .0409396 0.76 0.447 .9534748 1.114117
cond_ocu2 | 1.017974 .0318188 0.57 0.569 .9574824 1.082288
cond_ocu3 | 1.004787 .1417013 0.03 0.973 .7621353 1.324694
cond_ocu4 | 1.105126 .0399544 2.76 0.006 1.029527 1.186276
cond_ocu5 | 1.161655 .089018 1.96 0.051 .9996526 1.34991
cond_ocu6 | 1.131342 .0207259 6.74 0.000 1.09144 1.172702
policonsumo | 1.026319 .0224118 1.19 0.234 .9833194 1.071199
num_hij2 | 1.165211 .0227523 7.83 0.000 1.12146 1.210669
tenviv1 | 1.150938 .0753482 2.15 0.032 1.01234 1.308511
tenviv2 | 1.126989 .0493826 2.73 0.006 1.03424 1.228054
tenviv4 | 1.037274 .0237381 1.60 0.110 .9917765 1.084859
tenviv5 | 1.003321 .0179874 0.18 0.853 .9686786 1.039203
mzone2 | 1.302357 .0273703 12.57 0.000 1.249802 1.357122
mzone3 | 1.464673 .0421225 13.27 0.000 1.384398 1.549603
n_off_vio | 1.35527 .0258736 15.92 0.000 1.305496 1.406942
n_off_acq | 1.814432 .0324574 33.30 0.000 1.751919 1.879176
n_off_sud | 1.256861 .0233159 12.32 0.000 1.211983 1.3034
n_off_oth | 1.36048 .0257528 16.26 0.000 1.310931 1.411903
psy_com2 | 1.07066 .0256989 2.84 0.004 1.021458 1.122233
psy_com3 | 1.058354 .0188 3.19 0.001 1.022141 1.095851
dep2 | 1.019892 .0195455 1.03 0.304 .9822943 1.058929
rural2 | 1.02865 .028708 1.01 0.311 .9738944 1.086484
rural3 | 1.054429 .0324352 1.72 0.085 .9927352 1.119956
porc_pobr | 1.22293 .1447221 1.70 0.089 .9697727 1.542173
susini2 | 1.095626 .0455033 2.20 0.028 1.009975 1.188542
susini3 | 1.122451 .0372541 3.48 0.001 1.051759 1.197895
susini4 | 1.082471 .0193455 4.43 0.000 1.045211 1.121059
susini5 | 1.129309 .0561612 2.45 0.014 1.024429 1.244926
ano_nac_corr | .8754334 .0037465 -31.09 0.000 .8681211 .8828072
cohab2 | .9705938 .0310587 -0.93 0.351 .9115896 1.033417
cohab3 | .9916706 .0390254 -0.21 0.832 .9180577 1.071186
cohab4 | .9524199 .0296215 -1.57 0.117 .896097 1.012283
fis_com2 | 1.027575 .0166847 1.68 0.094 .9953886 1.060802
fis_com3 | .9023624 .0336889 -2.75 0.006 .8386913 .9708672
rc_x1 | .8521873 .0048102 -28.34 0.000 .8428114 .8616675
rc_x2 | 1.028777 .0186438 1.57 0.117 .9928769 1.065975
rc_x3 | .8952776 .0414533 -2.39 0.017 .8176083 .9803252
_rcs1 | 2.637986 .046855 54.61 0.000 2.547732 2.731438
_rcs2 | 1.103725 .0172376 6.32 0.000 1.070452 1.138033
_rcs3 | 1.048781 .0087592 5.70 0.000 1.031753 1.066089
_rcs4 | 1.018033 .0027225 6.68 0.000 1.012711 1.023383
_rcs_mot_egr_early1 | .902748 .0189527 -4.87 0.000 .8663553 .9406694
_rcs_mot_egr_early2 | 1.004408 .018127 0.24 0.807 .9695005 1.040572
_rcs_mot_egr_early3 | .990108 .0100629 -0.98 0.328 .9705802 1.010029
_rcs_mot_egr_late1 | .9405712 .0185545 -3.11 0.002 .9048991 .9776495
_rcs_mot_egr_late2 | 1.003609 .0171344 0.21 0.833 .9705818 1.03776
_rcs_mot_egr_late3 | .9915474 .0092802 -0.91 0.364 .9735243 1.009904
_cons | 9.9e+114 8.6e+115 30.72 0.000 4.6e+107 2.2e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54478.256
Iteration 1: log likelihood = -54452.952
Iteration 2: log likelihood = -54452.825
Iteration 3: log likelihood = -54452.825
Log likelihood = -54452.825 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729331 .0501216 18.90 0.000 1.633833 1.830411
mot_egr_late | 1.57909 .0372321 19.38 0.000 1.507777 1.653776
tr_mod2 | 1.218411 .0262186 9.18 0.000 1.168092 1.270898
sex_dum2 | .759652 .016319 -12.80 0.000 .7283314 .7923195
edad_ini_cons | .9868961 .0019514 -6.67 0.000 .9830788 .9907281
esc1 | 1.129216 .0298255 4.60 0.000 1.072246 1.189213
esc2 | 1.088985 .0259537 3.58 0.000 1.039287 1.141061
sus_prin2 | 1.066052 .0297224 2.29 0.022 1.009361 1.125928
sus_prin3 | 1.392426 .0326363 14.12 0.000 1.329907 1.457884
sus_prin4 | 1.075862 .0378391 2.08 0.038 1.004197 1.152641
sus_prin5 | 1.140766 .0824688 1.82 0.068 .9900592 1.314414
fr_cons_sus_prin2 | .9204239 .0450331 -1.69 0.090 .8362604 1.013058
fr_cons_sus_prin3 | .9968652 .0395658 -0.08 0.937 .9222573 1.077509
fr_cons_sus_prin4 | 1.008667 .0420352 0.21 0.836 .9295542 1.094512
fr_cons_sus_prin5 | 1.030709 .0409412 0.76 0.446 .9535097 1.114158
cond_ocu2 | 1.017977 .031819 0.57 0.569 .9574847 1.082291
cond_ocu3 | 1.004708 .1416906 0.03 0.973 .7620752 1.324591
cond_ocu4 | 1.105203 .0399571 2.77 0.006 1.029599 1.186359
cond_ocu5 | 1.1616 .0890145 1.95 0.051 .9996048 1.349849
cond_ocu6 | 1.131364 .0207263 6.74 0.000 1.091462 1.172725
policonsumo | 1.026265 .0224105 1.19 0.235 .9832681 1.071142
num_hij2 | 1.165221 .0227525 7.83 0.000 1.121469 1.210679
tenviv1 | 1.150776 .075338 2.15 0.032 1.012197 1.308328
tenviv2 | 1.126933 .0493803 2.73 0.006 1.034189 1.227995
tenviv4 | 1.037269 .023738 1.60 0.110 .991771 1.084854
tenviv5 | 1.003328 .0179876 0.19 0.853 .9686852 1.03921
mzone2 | 1.302353 .0273703 12.57 0.000 1.249798 1.357118
mzone3 | 1.464779 .0421261 13.27 0.000 1.384498 1.549716
n_off_vio | 1.35528 .0258738 15.92 0.000 1.305505 1.406952
n_off_acq | 1.814458 .0324581 33.31 0.000 1.751944 1.879203
n_off_sud | 1.256902 .0233167 12.33 0.000 1.212023 1.303442
n_off_oth | 1.360489 .025753 16.26 0.000 1.310939 1.411912
psy_com2 | 1.070673 .0256992 2.84 0.004 1.02147 1.122246
psy_com3 | 1.058351 .0187999 3.19 0.001 1.022138 1.095847
dep2 | 1.019889 .0195455 1.03 0.304 .9822916 1.058926
rural2 | 1.028661 .0287084 1.01 0.311 .9739048 1.086496
rural3 | 1.054411 .0324347 1.72 0.085 .9927182 1.119937
porc_pobr | 1.222878 .1447157 1.70 0.089 .9697319 1.542107
susini2 | 1.095642 .0455041 2.20 0.028 1.009989 1.188559
susini3 | 1.122447 .037254 3.48 0.001 1.051754 1.19789
susini4 | 1.082476 .0193455 4.43 0.000 1.045216 1.121064
susini5 | 1.129295 .0561606 2.45 0.014 1.024417 1.244911
ano_nac_corr | .8754436 .0037466 -31.08 0.000 .8681312 .8828176
cohab2 | .9706034 .0310593 -0.93 0.351 .911598 1.033428
cohab3 | .9917416 .0390284 -0.21 0.833 .918123 1.071263
cohab4 | .9524626 .0296229 -1.57 0.117 .8961369 1.012329
fis_com2 | 1.027592 .0166851 1.68 0.094 .9954044 1.06082
fis_com3 | .9023419 .0336882 -2.75 0.006 .8386721 .9708453
rc_x1 | .8521947 .0048104 -28.33 0.000 .8428186 .8616752
rc_x2 | 1.028778 .0186438 1.57 0.117 .9928777 1.065975
rc_x3 | .895293 .0414539 -2.39 0.017 .8176225 .9803419
_rcs1 | 2.637767 .0470088 54.43 0.000 2.547222 2.731531
_rcs2 | 1.108105 .017909 6.35 0.000 1.073554 1.143768
_rcs3 | 1.044451 .0102745 4.42 0.000 1.024506 1.064784
_rcs4 | 1.017 .0058129 2.95 0.003 1.005671 1.028458
_rcs_mot_egr_early1 | .9028543 .0190068 -4.85 0.000 .8663597 .9408862
_rcs_mot_egr_early2 | .9999152 .0187558 -0.00 0.996 .963822 1.03736
_rcs_mot_egr_early3 | .995986 .0116427 -0.34 0.731 .9734261 1.019069
_rcs_mot_egr_early4 | .9985514 .0069947 -0.21 0.836 .9849357 1.012355
_rcs_mot_egr_late1 | .9407959 .0186143 -3.08 0.002 .9050109 .9779959
_rcs_mot_egr_late2 | .9998632 .0178401 -0.01 0.994 .9655016 1.035448
_rcs_mot_egr_late3 | .995922 .010914 -0.37 0.709 .974759 1.017545
_rcs_mot_egr_late4 | .9999143 .0064449 -0.01 0.989 .9873619 1.012626
_cons | 9.7e+114 8.4e+115 30.72 0.000 4.5e+107 2.1e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54465.812
Iteration 1: log likelihood = -54447.689
Iteration 2: log likelihood = -54447.623
Iteration 3: log likelihood = -54447.623
Log likelihood = -54447.623 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730296 .050154 18.92 0.000 1.634736 1.831442
mot_egr_late | 1.579341 .0372428 19.38 0.000 1.508008 1.654049
tr_mod2 | 1.218461 .0262197 9.18 0.000 1.16814 1.27095
sex_dum2 | .7598326 .0163232 -12.79 0.000 .728504 .7925085
edad_ini_cons | .9869021 .0019513 -6.67 0.000 .983085 .9907341
esc1 | 1.129062 .0298216 4.60 0.000 1.0721 1.18905
esc2 | 1.088828 .02595 3.57 0.000 1.039136 1.140896
sus_prin2 | 1.066296 .0297297 2.30 0.021 1.00959 1.126187
sus_prin3 | 1.392587 .0326413 14.13 0.000 1.330059 1.458055
sus_prin4 | 1.076137 .0378493 2.09 0.037 1.004453 1.152937
sus_prin5 | 1.141093 .0824949 1.83 0.068 .9903391 1.314796
fr_cons_sus_prin2 | .9203457 .0450293 -1.70 0.090 .8361894 1.012972
fr_cons_sus_prin3 | .9969042 .0395673 -0.08 0.938 .9222933 1.077551
fr_cons_sus_prin4 | 1.008689 .042036 0.21 0.836 .9295749 1.094536
fr_cons_sus_prin5 | 1.030712 .0409413 0.76 0.446 .9535131 1.114162
cond_ocu2 | 1.018007 .0318202 0.57 0.568 .9575123 1.082323
cond_ocu3 | 1.005158 .141754 0.04 0.971 .7624166 1.325185
cond_ocu4 | 1.104834 .039944 2.76 0.006 1.029255 1.185963
cond_ocu5 | 1.161325 .0889946 1.95 0.051 .9993663 1.349532
cond_ocu6 | 1.131396 .0207268 6.74 0.000 1.091492 1.172758
policonsumo | 1.02642 .0224141 1.19 0.232 .9834158 1.071304
num_hij2 | 1.165253 .022753 7.83 0.000 1.121501 1.210713
tenviv1 | 1.151522 .0753871 2.16 0.031 1.012853 1.309177
tenviv2 | 1.126882 .049379 2.73 0.006 1.034141 1.227941
tenviv4 | 1.037444 .0237422 1.61 0.108 .9919382 1.085037
tenviv5 | 1.003467 .0179902 0.19 0.847 .9688195 1.039354
mzone2 | 1.30249 .0273737 12.57 0.000 1.249929 1.357262
mzone3 | 1.464831 .0421309 13.27 0.000 1.384541 1.549778
n_off_vio | 1.355269 .0258724 15.92 0.000 1.305497 1.406939
n_off_acq | 1.814459 .0324561 33.31 0.000 1.751948 1.8792
n_off_sud | 1.25694 .0233168 12.33 0.000 1.212061 1.303481
n_off_oth | 1.36049 .0257513 16.26 0.000 1.310943 1.411909
psy_com2 | 1.07064 .0256988 2.84 0.004 1.021438 1.122212
psy_com3 | 1.058337 .0187996 3.19 0.001 1.022124 1.095832
dep2 | 1.019911 .0195459 1.03 0.304 .9823121 1.058949
rural2 | 1.028726 .0287107 1.01 0.310 .9739649 1.086565
rural3 | 1.054452 .0324372 1.72 0.085 .9927545 1.119983
porc_pobr | 1.225355 .1450043 1.72 0.086 .9717029 1.545219
susini2 | 1.095786 .0455102 2.20 0.028 1.010122 1.188715
susini3 | 1.122351 .0372506 3.48 0.001 1.051665 1.197788
susini4 | 1.08247 .0193455 4.43 0.000 1.04521 1.121059
susini5 | 1.129458 .0561699 2.45 0.014 1.024563 1.245093
ano_nac_corr | .8752104 .0037463 -31.14 0.000 .8678985 .8825839
cohab2 | .9706518 .0310605 -0.93 0.352 .9116441 1.033479
cohab3 | .991619 .0390234 -0.21 0.831 .9180098 1.07113
cohab4 | .9524269 .0296215 -1.57 0.117 .8961039 1.01229
fis_com2 | 1.027494 .0166835 1.67 0.095 .9953099 1.060719
fis_com3 | .9022565 .033685 -2.76 0.006 .8385928 .9707534
rc_x1 | .8519647 .0048096 -28.38 0.000 .8425901 .8614437
rc_x2 | 1.028801 .0186444 1.57 0.117 .9929001 1.066
rc_x3 | .8952598 .0414526 -2.39 0.017 .8175917 .9803062
_rcs1 | 2.636285 .0469188 54.47 0.000 2.545911 2.729867
_rcs2 | 1.10617 .0176048 6.34 0.000 1.072197 1.141218
_rcs3 | 1.04824 .0100614 4.91 0.000 1.028704 1.068147
_rcs4 | 1.01227 .0053448 2.31 0.021 1.001848 1.0228
_rcs_mot_egr_early1 | .9033946 .0189985 -4.83 0.000 .8669152 .941409
_rcs_mot_egr_early2 | .9999874 .0186297 -0.00 0.999 .9641325 1.037176
_rcs_mot_egr_early3 | .995269 .011587 -0.41 0.684 .9728161 1.01824
_rcs_mot_egr_early4 | .9973673 .0064849 -0.41 0.685 .9847378 1.010159
_rcs_mot_egr_early5 | 1.007699 .0036382 2.12 0.034 1.000593 1.014855
_rcs_mot_egr_late1 | .9412962 .0186029 -3.06 0.002 .9055324 .9784725
_rcs_mot_egr_late2 | 1.000998 .0177961 0.06 0.955 .9667194 1.036493
_rcs_mot_egr_late3 | .9928962 .0108612 -0.65 0.515 .9718353 1.014414
_rcs_mot_egr_late4 | 1.001687 .00587 0.29 0.774 .9902477 1.013258
_rcs_mot_egr_late5 | 1.005757 .0029395 1.96 0.049 1.000013 1.011535
_cons | 1.7e+115 1.4e+116 30.77 0.000 7.6e+107 3.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54462.315
Iteration 1: log likelihood = -54443.323
Iteration 2: log likelihood = -54443.259
Iteration 3: log likelihood = -54443.259
Log likelihood = -54443.259 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730363 .0501557 18.92 0.000 1.6348 1.831512
mot_egr_late | 1.579255 .0372392 19.38 0.000 1.507928 1.653955
tr_mod2 | 1.218545 .0262215 9.19 0.000 1.168221 1.271038
sex_dum2 | .7599892 .0163265 -12.78 0.000 .7286542 .7926717
edad_ini_cons | .9869014 .0019513 -6.67 0.000 .9830843 .9907334
esc1 | 1.128933 .0298182 4.59 0.000 1.071977 1.188914
esc2 | 1.088735 .0259479 3.57 0.000 1.039048 1.140799
sus_prin2 | 1.066602 .0297388 2.31 0.021 1.009879 1.126511
sus_prin3 | 1.392827 .0326482 14.14 0.000 1.330285 1.458309
sus_prin4 | 1.076439 .0378606 2.09 0.036 1.004733 1.153262
sus_prin5 | 1.14179 .0825479 1.83 0.067 .9909393 1.315605
fr_cons_sus_prin2 | .9202467 .0450245 -1.70 0.089 .8360995 1.012863
fr_cons_sus_prin3 | .997034 .0395725 -0.07 0.940 .9224134 1.077691
fr_cons_sus_prin4 | 1.008759 .0420389 0.21 0.834 .9296396 1.094612
fr_cons_sus_prin5 | 1.030734 .0409422 0.76 0.446 .9535333 1.114186
cond_ocu2 | 1.017945 .0318179 0.57 0.569 .9574551 1.082257
cond_ocu3 | 1.005695 .1418296 0.04 0.968 .7628244 1.325893
cond_ocu4 | 1.10454 .0399334 2.75 0.006 1.028981 1.185648
cond_ocu5 | 1.161673 .0890213 1.96 0.051 .9996657 1.349936
cond_ocu6 | 1.131361 .0207264 6.74 0.000 1.091459 1.172722
policonsumo | 1.026632 .022419 1.20 0.229 .9836183 1.071526
num_hij2 | 1.165194 .022752 7.83 0.000 1.121443 1.210651
tenviv1 | 1.151889 .0754111 2.16 0.031 1.013175 1.309593
tenviv2 | 1.127238 .0493953 2.73 0.006 1.034466 1.22833
tenviv4 | 1.037588 .0237457 1.61 0.107 .9920758 1.085188
tenviv5 | 1.003625 .017993 0.20 0.840 .9689722 1.039518
mzone2 | 1.302627 .0273767 12.58 0.000 1.25006 1.357405
mzone3 | 1.464807 .0421318 13.27 0.000 1.384514 1.549755
n_off_vio | 1.35527 .0258713 15.93 0.000 1.3055 1.406937
n_off_acq | 1.814368 .0324533 33.31 0.000 1.751863 1.879104
n_off_sud | 1.256897 .0233154 12.33 0.000 1.212021 1.303436
n_off_oth | 1.360405 .0257484 16.26 0.000 1.310864 1.411819
psy_com2 | 1.070763 .025702 2.85 0.004 1.021555 1.122342
psy_com3 | 1.058369 .0188002 3.19 0.001 1.022155 1.095866
dep2 | 1.019932 .0195464 1.03 0.303 .9823323 1.058971
rural2 | 1.02874 .0287113 1.02 0.310 .9739782 1.08658
rural3 | 1.054426 .0324373 1.72 0.085 .9927288 1.119958
porc_pobr | 1.227266 .145229 1.73 0.084 .973221 1.547626
susini2 | 1.0958 .0455106 2.20 0.028 1.010135 1.18873
susini3 | 1.122522 .0372565 3.48 0.000 1.051825 1.197971
susini4 | 1.082387 .0193443 4.43 0.000 1.04513 1.120973
susini5 | 1.129602 .0561787 2.45 0.014 1.02469 1.245256
ano_nac_corr | .87502 .0037461 -31.19 0.000 .8677085 .8823931
cohab2 | .9706921 .0310618 -0.93 0.353 .911682 1.033522
cohab3 | .9915579 .0390211 -0.22 0.829 .917953 1.071065
cohab4 | .9523971 .0296206 -1.57 0.117 .8960757 1.012258
fis_com2 | 1.027309 .0166805 1.66 0.097 .9951304 1.060528
fis_com3 | .9022658 .0336855 -2.75 0.006 .8386011 .9707638
rc_x1 | .8517875 .004809 -28.41 0.000 .842414 .8612654
rc_x2 | 1.028785 .0186441 1.57 0.117 .9928847 1.065984
rc_x3 | .8952751 .0414534 -2.39 0.017 .8176055 .9803231
_rcs1 | 2.637188 .0469966 54.41 0.000 2.546666 2.730927
_rcs2 | 1.108174 .0178746 6.37 0.000 1.073688 1.143767
_rcs3 | 1.044772 .0102258 4.47 0.000 1.02492 1.065007
_rcs4 | 1.016163 .0057465 2.84 0.005 1.004962 1.027488
_rcs_mot_egr_early1 | .9030887 .0190097 -4.84 0.000 .8665884 .9411263
_rcs_mot_egr_early2 | .9980675 .0188534 -0.10 0.918 .9617912 1.035712
_rcs_mot_egr_early3 | .9997703 .0117022 -0.02 0.984 .9770956 1.022971
_rcs_mot_egr_early4 | .9931403 .0062441 -1.09 0.274 .9809771 1.005454
_rcs_mot_egr_early5 | 1.003628 .0046606 0.78 0.436 .9945344 1.012804
_rcs_mot_egr_early6 | 1.003992 .0025001 1.60 0.110 .999104 1.008904
_rcs_mot_egr_late1 | .9410328 .0186198 -3.07 0.002 .9052373 .9782437
_rcs_mot_egr_late2 | .9997887 .0180673 -0.01 0.991 .9649971 1.035835
_rcs_mot_egr_late3 | .9954031 .0109238 -0.42 0.675 .9742215 1.017045
_rcs_mot_egr_late4 | .9990474 .0055386 -0.17 0.864 .9882507 1.009962
_rcs_mot_egr_late5 | 1.001205 .004128 0.29 0.770 .9931473 1.009329
_rcs_mot_egr_late6 | 1.006433 .0018106 3.56 0.000 1.002891 1.009988
_cons | 2.6e+115 2.2e+116 30.82 0.000 1.2e+108 5.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54459.806
Iteration 1: log likelihood = -54441.332
Iteration 2: log likelihood = -54441.254
Iteration 3: log likelihood = -54441.254
Log likelihood = -54441.254 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730549 .0501621 18.92 0.000 1.634974 1.831712
mot_egr_late | 1.579366 .0372429 19.38 0.000 1.508032 1.654074
tr_mod2 | 1.218616 .0262231 9.19 0.000 1.168289 1.271112
sex_dum2 | .7600852 .0163286 -12.77 0.000 .7287462 .7927719
edad_ini_cons | .9869002 .0019513 -6.67 0.000 .9830831 .9907321
esc1 | 1.128867 .0298166 4.59 0.000 1.071915 1.188846
esc2 | 1.088677 .0259465 3.56 0.000 1.038992 1.140738
sus_prin2 | 1.066794 .0297443 2.32 0.020 1.01006 1.126714
sus_prin3 | 1.392967 .0326521 14.14 0.000 1.330418 1.458456
sus_prin4 | 1.076599 .0378667 2.10 0.036 1.004882 1.153434
sus_prin5 | 1.142111 .0825724 1.84 0.066 .9912152 1.315977
fr_cons_sus_prin2 | .9202699 .0450256 -1.70 0.089 .8361206 1.012888
fr_cons_sus_prin3 | .99715 .0395771 -0.07 0.943 .9225207 1.077817
fr_cons_sus_prin4 | 1.008817 .0420414 0.21 0.833 .9296923 1.094675
fr_cons_sus_prin5 | 1.030766 .0409437 0.76 0.446 .9535624 1.114221
cond_ocu2 | 1.017892 .0318161 0.57 0.570 .9574053 1.0822
cond_ocu3 | 1.005835 .1418492 0.04 0.967 .7629302 1.326076
cond_ocu4 | 1.104356 .039927 2.75 0.006 1.028809 1.185451
cond_ocu5 | 1.161546 .0890115 1.95 0.051 .999556 1.349788
cond_ocu6 | 1.13133 .0207259 6.74 0.000 1.091429 1.17269
policonsumo | 1.02666 .0224197 1.20 0.228 .9836453 1.071556
num_hij2 | 1.16517 .0227516 7.83 0.000 1.121421 1.210627
tenviv1 | 1.152008 .0754188 2.16 0.031 1.013281 1.309729
tenviv2 | 1.127432 .0494044 2.74 0.006 1.034643 1.228543
tenviv4 | 1.03767 .0237477 1.62 0.106 .9921541 1.085275
tenviv5 | 1.003736 .017995 0.21 0.835 .9690789 1.039632
mzone2 | 1.302725 .0273789 12.58 0.000 1.250154 1.357507
mzone3 | 1.464841 .042134 13.27 0.000 1.384545 1.549794
n_off_vio | 1.355235 .02587 15.92 0.000 1.305467 1.4069
n_off_acq | 1.81437 .0324526 33.31 0.000 1.751866 1.879104
n_off_sud | 1.256864 .0233146 12.32 0.000 1.211989 1.3034
n_off_oth | 1.360367 .025747 16.26 0.000 1.310828 1.411777
psy_com2 | 1.070852 .0257041 2.85 0.004 1.02164 1.122435
psy_com3 | 1.058392 .0188006 3.19 0.001 1.022178 1.09589
dep2 | 1.019952 .0195468 1.03 0.303 .9823511 1.058991
rural2 | 1.028739 .0287113 1.02 0.310 .9739776 1.08658
rural3 | 1.054376 .0324364 1.72 0.085 .9926809 1.119906
porc_pobr | 1.228161 .1453343 1.74 0.082 .9739317 1.548753
susini2 | 1.095877 .0455138 2.20 0.027 1.010206 1.188814
susini3 | 1.122629 .0372601 3.49 0.000 1.051926 1.198085
susini4 | 1.082336 .0193435 4.43 0.000 1.045079 1.12092
susini5 | 1.129556 .056177 2.45 0.014 1.024647 1.245206
ano_nac_corr | .8749286 .0037461 -31.21 0.000 .867617 .8823018
cohab2 | .97065 .0310605 -0.93 0.352 .9116423 1.033477
cohab3 | .991508 .0390192 -0.22 0.828 .9179067 1.071011
cohab4 | .9523654 .0296196 -1.57 0.117 .8960459 1.012225
fis_com2 | 1.02724 .0166793 1.66 0.098 .9950641 1.060457
fis_com3 | .9022472 .0336849 -2.76 0.006 .8385836 .970744
rc_x1 | .8517031 .0048088 -28.43 0.000 .8423299 .8611806
rc_x2 | 1.02876 .0186435 1.56 0.118 .9928608 1.065957
rc_x3 | .8953295 .0414557 -2.39 0.017 .8176556 .9803822
_rcs1 | 2.637156 .0469904 54.42 0.000 2.546647 2.730883
_rcs2 | 1.107948 .0178732 6.35 0.000 1.073466 1.143539
_rcs3 | 1.044987 .0102621 4.48 0.000 1.025066 1.065295
_rcs4 | 1.016354 .0057839 2.85 0.004 1.005081 1.027754
_rcs_mot_egr_early1 | .9030736 .0190081 -4.84 0.000 .8665764 .941108
_rcs_mot_egr_early2 | .9986307 .0189578 -0.07 0.942 .9621569 1.036487
_rcs_mot_egr_early3 | .9990949 .0115808 -0.08 0.938 .9766528 1.022053
_rcs_mot_egr_early4 | .9939861 .0061838 -0.97 0.332 .9819396 1.00618
_rcs_mot_egr_early5 | .9998583 .0050114 -0.03 0.977 .9900841 1.009729
_rcs_mot_egr_early6 | 1.005383 .002975 1.81 0.070 .9995691 1.011231
_rcs_mot_egr_early7 | 1.00198 .0021191 0.94 0.350 .9978351 1.006142
_rcs_mot_egr_late1 | .9409303 .0186151 -3.08 0.002 .9051436 .9781319
_rcs_mot_egr_late2 | .9996359 .0181284 -0.02 0.984 .9647289 1.035806
_rcs_mot_egr_late3 | .9959931 .0107628 -0.37 0.710 .9751203 1.017313
_rcs_mot_egr_late4 | .9978058 .0054181 -0.40 0.686 .9872427 1.008482
_rcs_mot_egr_late5 | 1.000054 .004496 0.01 0.990 .9912808 1.008905
_rcs_mot_egr_late6 | 1.004258 .0023847 1.79 0.074 .9995949 1.008943
_rcs_mot_egr_late7 | 1.005928 .0015132 3.93 0.000 1.002966 1.008898
_cons | 3.2e+115 2.7e+116 30.84 0.000 1.4e+108 7.0e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54464.554
Iteration 1: log likelihood = -54446.428
Iteration 2: log likelihood = -54446.376
Iteration 3: log likelihood = -54446.376
Log likelihood = -54446.376 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728856 .0499894 18.93 0.000 1.633603 1.829663
mot_egr_late | 1.57792 .0370654 19.42 0.000 1.50692 1.652265
tr_mod2 | 1.218511 .0262195 9.18 0.000 1.16819 1.271
sex_dum2 | .759886 .016324 -12.78 0.000 .7285558 .7925634
edad_ini_cons | .9869006 .0019513 -6.67 0.000 .9830834 .9907325
esc1 | 1.129094 .0298222 4.60 0.000 1.072131 1.189084
esc2 | 1.08885 .0259505 3.57 0.000 1.039158 1.140919
sus_prin2 | 1.066464 .0297345 2.31 0.021 1.009749 1.126364
sus_prin3 | 1.392725 .0326452 14.13 0.000 1.330189 1.458201
sus_prin4 | 1.076344 .0378569 2.09 0.036 1.004646 1.153159
sus_prin5 | 1.14136 .0825135 1.83 0.067 .990572 1.315102
fr_cons_sus_prin2 | .9202751 .0450257 -1.70 0.089 .8361255 1.012894
fr_cons_sus_prin3 | .9969198 .0395678 -0.08 0.938 .922308 1.077567
fr_cons_sus_prin4 | 1.008712 .0420369 0.21 0.835 .9295959 1.094561
fr_cons_sus_prin5 | 1.030656 .0409391 0.76 0.447 .9534607 1.114101
cond_ocu2 | 1.017963 .0318182 0.57 0.569 .9574722 1.082275
cond_ocu3 | 1.005258 .1417669 0.04 0.970 .7624944 1.325313
cond_ocu4 | 1.104656 .0399376 2.75 0.006 1.029089 1.185772
cond_ocu5 | 1.161791 .0890286 1.96 0.050 .9997695 1.350069
cond_ocu6 | 1.131368 .0207264 6.74 0.000 1.091466 1.172729
policonsumo | 1.026522 .0224156 1.20 0.231 .9835152 1.07141
num_hij2 | 1.165209 .0227521 7.83 0.000 1.121458 1.210667
tenviv1 | 1.15171 .0753987 2.16 0.031 1.013019 1.309388
tenviv2 | 1.127225 .0493937 2.73 0.006 1.034456 1.228314
tenviv4 | 1.03749 .0237432 1.61 0.108 .9919829 1.085086
tenviv5 | 1.00349 .0179905 0.19 0.846 .9688417 1.039378
mzone2 | 1.302521 .0273742 12.58 0.000 1.249958 1.357293
mzone3 | 1.46461 .0421237 13.27 0.000 1.384333 1.549542
n_off_vio | 1.355264 .0258716 15.92 0.000 1.305493 1.406932
n_off_acq | 1.814353 .0324535 33.30 0.000 1.751847 1.879089
n_off_sud | 1.256854 .0233145 12.32 0.000 1.211979 1.303391
n_off_oth | 1.360441 .0257498 16.26 0.000 1.310897 1.411858
psy_com2 | 1.070712 .0257 2.85 0.004 1.021507 1.122286
psy_com3 | 1.058327 .0187994 3.19 0.001 1.022115 1.095822
dep2 | 1.019953 .0195468 1.03 0.303 .9823523 1.058992
rural2 | 1.028763 .0287114 1.02 0.310 .9740007 1.086603
rural3 | 1.054508 .0324391 1.73 0.084 .9928075 1.120043
porc_pobr | 1.226318 .1451159 1.72 0.085 .9724706 1.546428
susini2 | 1.095821 .0455106 2.20 0.028 1.010156 1.188751
susini3 | 1.122529 .0372561 3.48 0.000 1.051833 1.197977
susini4 | 1.082415 .0193445 4.43 0.000 1.045157 1.121002
susini5 | 1.129668 .0561813 2.45 0.014 1.024752 1.245327
ano_nac_corr | .8751106 .0037461 -31.16 0.000 .8677991 .8824837
cohab2 | .970753 .0310633 -0.93 0.354 .91174 1.033586
cohab3 | .9915941 .0390221 -0.21 0.830 .9179873 1.071103
cohab4 | .9524458 .0296219 -1.57 0.117 .896122 1.01231
fis_com2 | 1.027342 .016681 1.66 0.097 .995163 1.060562
fis_com3 | .9022089 .0336832 -2.76 0.006 .8385486 .970702
rc_x1 | .8518768 .0048093 -28.40 0.000 .8425028 .8613551
rc_x2 | 1.02877 .0186436 1.57 0.118 .99287 1.065967
rc_x3 | .8953093 .0414545 -2.39 0.017 .8176376 .9803594
_rcs1 | 2.631973 .0397185 64.13 0.000 2.555267 2.710983
_rcs2 | 1.105576 .0062291 17.81 0.000 1.093435 1.117853
_rcs3 | 1.042978 .0039288 11.17 0.000 1.035306 1.050707
_rcs4 | 1.018067 .0024089 7.57 0.000 1.013356 1.022799
_rcs5 | 1.009995 .0016409 6.12 0.000 1.006784 1.013216
_rcs_mot_egr_early1 | .9052637 .0161217 -5.59 0.000 .8742108 .9374196
_rcs_mot_egr_late1 | .9429507 .0154826 -3.58 0.000 .9130884 .9737896
_cons | 2.1e+115 1.8e+116 30.80 0.000 9.6e+107 4.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54464.665
Iteration 1: log likelihood = -54446.408
Iteration 2: log likelihood = -54446.348
Iteration 3: log likelihood = -54446.348
Log likelihood = -54446.348 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729616 .0501196 18.91 0.000 1.634121 1.830692
mot_egr_late | 1.578607 .0372075 19.37 0.000 1.50734 1.653243
tr_mod2 | 1.21855 .026221 9.19 0.000 1.168227 1.271042
sex_dum2 | .7598816 .0163239 -12.78 0.000 .7285514 .792559
edad_ini_cons | .9869013 .0019513 -6.67 0.000 .9830842 .9907333
esc1 | 1.129085 .029822 4.60 0.000 1.072122 1.189074
esc2 | 1.088843 .0259503 3.57 0.000 1.039151 1.140911
sus_prin2 | 1.06648 .0297353 2.31 0.021 1.009764 1.126382
sus_prin3 | 1.392743 .0326458 14.13 0.000 1.330206 1.458221
sus_prin4 | 1.07635 .0378572 2.09 0.036 1.004651 1.153166
sus_prin5 | 1.141461 .0825219 1.83 0.067 .9906573 1.315221
fr_cons_sus_prin2 | .9202691 .0450255 -1.70 0.089 .83612 1.012887
fr_cons_sus_prin3 | .996917 .0395677 -0.08 0.938 .9223053 1.077564
fr_cons_sus_prin4 | 1.008699 .0420364 0.21 0.835 .9295839 1.094547
fr_cons_sus_prin5 | 1.030649 .0409388 0.76 0.447 .9534546 1.114094
cond_ocu2 | 1.017964 .0318183 0.57 0.569 .9574728 1.082276
cond_ocu3 | 1.005363 .1417824 0.04 0.970 .7625732 1.325454
cond_ocu4 | 1.104647 .0399373 2.75 0.006 1.02908 1.185763
cond_ocu5 | 1.161768 .0890271 1.96 0.050 .9997498 1.350043
cond_ocu6 | 1.131361 .0207263 6.74 0.000 1.091458 1.172721
policonsumo | 1.026543 .0224165 1.20 0.230 .9835347 1.071433
num_hij2 | 1.165203 .022752 7.83 0.000 1.121452 1.21066
tenviv1 | 1.151756 .0754019 2.16 0.031 1.013059 1.309441
tenviv2 | 1.127236 .0493943 2.73 0.006 1.034466 1.228326
tenviv4 | 1.037483 .0237431 1.61 0.108 .991976 1.085078
tenviv5 | 1.003486 .0179904 0.19 0.846 .9688377 1.039373
mzone2 | 1.302525 .0273745 12.58 0.000 1.249962 1.357299
mzone3 | 1.464579 .0421232 13.27 0.000 1.384303 1.54951
n_off_vio | 1.355271 .0258717 15.92 0.000 1.305501 1.40694
n_off_acq | 1.814365 .0324537 33.31 0.000 1.751859 1.879102
n_off_sud | 1.256848 .0233144 12.32 0.000 1.211973 1.303384
n_off_oth | 1.360446 .02575 16.26 0.000 1.310902 1.411863
psy_com2 | 1.070712 .0257001 2.85 0.004 1.021507 1.122287
psy_com3 | 1.058332 .0187995 3.19 0.001 1.022119 1.095827
dep2 | 1.019956 .0195468 1.03 0.303 .9823551 1.058995
rural2 | 1.028752 .0287113 1.02 0.310 .9739907 1.086593
rural3 | 1.0545 .0324389 1.73 0.085 .9928 1.120035
porc_pobr | 1.226377 .1451237 1.72 0.085 .9725159 1.546504
susini2 | 1.0958 .04551 2.20 0.028 1.010136 1.188729
susini3 | 1.122524 .0372562 3.48 0.000 1.051827 1.197972
susini4 | 1.082416 .0193446 4.43 0.000 1.045158 1.121002
susini5 | 1.129669 .0561812 2.45 0.014 1.024752 1.245327
ano_nac_corr | .8751037 .0037462 -31.16 0.000 .8677919 .882477
cohab2 | .9707407 .031063 -0.93 0.353 .9117283 1.033573
cohab3 | .9915764 .0390214 -0.21 0.830 .9179709 1.071084
cohab4 | .9524337 .0296216 -1.57 0.117 .8961106 1.012297
fis_com2 | 1.027341 .0166809 1.66 0.097 .9951615 1.060561
fis_com3 | .9022167 .0336835 -2.76 0.006 .8385558 .9707106
rc_x1 | .8518704 .0048093 -28.40 0.000 .8424963 .8613488
rc_x2 | 1.028772 .0186437 1.57 0.118 .9928722 1.06597
rc_x3 | .8952992 .0414541 -2.39 0.017 .8176282 .9803485
_rcs1 | 2.63773 .0469866 54.45 0.000 2.547227 2.731448
_rcs2 | 1.108846 .0154341 7.42 0.000 1.079005 1.139513
_rcs3 | 1.043296 .004165 10.62 0.000 1.035164 1.051491
_rcs4 | 1.018092 .0024115 7.57 0.000 1.013376 1.022829
_rcs5 | 1.009991 .0016411 6.12 0.000 1.00678 1.013213
_rcs_mot_egr_early1 | .9030018 .0189895 -4.85 0.000 .8665396 .9409982
_rcs_mot_egr_early2 | .9965008 .0160331 -0.22 0.828 .9655667 1.028426
_rcs_mot_egr_late1 | .940666 .0185957 -3.09 0.002 .9049161 .9778283
_rcs_mot_egr_late2 | .9966995 .0150571 -0.22 0.827 .9676208 1.026652
_cons | 2.1e+115 1.8e+116 30.80 0.000 9.7e+107 4.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54464.513
Iteration 1: log likelihood = -54446.24
Iteration 2: log likelihood = -54446.18
Iteration 3: log likelihood = -54446.18
Log likelihood = -54446.18 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730223 .0501492 18.92 0.000 1.634672 1.831359
mot_egr_late | 1.579113 .0372343 19.38 0.000 1.507796 1.653804
tr_mod2 | 1.218581 .026222 9.19 0.000 1.168256 1.271075
sex_dum2 | .7598844 .0163239 -12.78 0.000 .7285542 .7925618
edad_ini_cons | .9869028 .0019513 -6.67 0.000 .9830857 .9907347
esc1 | 1.129084 .029822 4.60 0.000 1.072121 1.189073
esc2 | 1.088855 .0259506 3.57 0.000 1.039162 1.140924
sus_prin2 | 1.066517 .0297365 2.31 0.021 1.009798 1.126421
sus_prin3 | 1.392778 .032647 14.13 0.000 1.330239 1.458257
sus_prin4 | 1.076341 .0378571 2.09 0.036 1.004643 1.153157
sus_prin5 | 1.141682 .0825383 1.83 0.067 .9908482 1.315476
fr_cons_sus_prin2 | .9202338 .0450238 -1.70 0.089 .8360878 1.012848
fr_cons_sus_prin3 | .9969135 .0395676 -0.08 0.938 .9223021 1.077561
fr_cons_sus_prin4 | 1.008687 .0420359 0.21 0.836 .9295732 1.094534
fr_cons_sus_prin5 | 1.030627 .0409379 0.76 0.448 .9534337 1.11407
cond_ocu2 | 1.017946 .0318177 0.57 0.569 .9574562 1.082257
cond_ocu3 | 1.005489 .1418001 0.04 0.969 .7626687 1.32562
cond_ocu4 | 1.104608 .039936 2.75 0.006 1.029044 1.185721
cond_ocu5 | 1.161906 .0890383 1.96 0.050 .9998673 1.350205
cond_ocu6 | 1.13134 .020726 6.74 0.000 1.091439 1.172701
policonsumo | 1.026605 .0224183 1.20 0.229 .9835929 1.071498
num_hij2 | 1.165202 .0227521 7.83 0.000 1.121452 1.21066
tenviv1 | 1.151847 .0754079 2.16 0.031 1.01314 1.309545
tenviv2 | 1.127272 .049396 2.73 0.006 1.034498 1.228365
tenviv4 | 1.037473 .0237428 1.61 0.108 .991966 1.085067
tenviv5 | 1.003478 .0179902 0.19 0.846 .96883 1.039365
mzone2 | 1.30252 .0273743 12.58 0.000 1.249958 1.357293
mzone3 | 1.464541 .0421222 13.27 0.000 1.384266 1.54947
n_off_vio | 1.355276 .0258719 15.93 0.000 1.305505 1.406944
n_off_acq | 1.814362 .0324537 33.31 0.000 1.751856 1.879098
n_off_sud | 1.256816 .0233139 12.32 0.000 1.211942 1.303351
n_off_oth | 1.360436 .0257497 16.26 0.000 1.310892 1.411852
psy_com2 | 1.070723 .0257007 2.85 0.004 1.021517 1.122299
psy_com3 | 1.05834 .0187997 3.19 0.001 1.022127 1.095836
dep2 | 1.019945 .0195467 1.03 0.303 .9823446 1.058984
rural2 | 1.028729 .0287107 1.01 0.310 .9739685 1.086568
rural3 | 1.054495 .0324387 1.72 0.085 .9927954 1.12003
porc_pobr | 1.226199 .1451043 1.72 0.085 .9723726 1.546284
susini2 | 1.095725 .0455073 2.20 0.028 1.010066 1.188648
susini3 | 1.122541 .0372569 3.48 0.000 1.051843 1.197991
susini4 | 1.082414 .0193447 4.43 0.000 1.045156 1.121001
susini5 | 1.129671 .0561812 2.45 0.014 1.024755 1.24533
ano_nac_corr | .8750985 .0037462 -31.17 0.000 .8677867 .8824718
cohab2 | .9707058 .031062 -0.93 0.353 .9116953 1.033536
cohab3 | .9915181 .0390192 -0.22 0.829 .9179168 1.071021
cohab4 | .9523869 .0296202 -1.57 0.117 .8960663 1.012247
fis_com2 | 1.027325 .0166806 1.66 0.097 .9951458 1.060544
fis_com3 | .902231 .033684 -2.76 0.006 .8385691 .970726
rc_x1 | .8518656 .0048093 -28.40 0.000 .8424916 .861344
rc_x2 | 1.028778 .0186438 1.57 0.117 .9928777 1.065976
rc_x3 | .8952737 .041453 -2.39 0.017 .8176049 .9803208
_rcs1 | 2.637795 .0469291 54.52 0.000 2.547401 2.731397
_rcs2 | 1.104789 .017466 6.30 0.000 1.071081 1.139558
_rcs3 | 1.04707 .0083534 5.77 0.000 1.030825 1.063571
_rcs4 | 1.01949 .0036255 5.43 0.000 1.012409 1.026621
_rcs5 | 1.010081 .0016483 6.15 0.000 1.006855 1.013316
_rcs_mot_egr_early1 | .9028125 .0189741 -4.86 0.000 .8663795 .9407776
_rcs_mot_egr_early2 | 1.001012 .0181673 0.06 0.956 .9660307 1.03726
_rcs_mot_egr_early3 | .9940569 .0100881 -0.59 0.557 .9744799 1.014027
_rcs_mot_egr_late1 | .9405926 .0185776 -3.10 0.002 .9048769 .977718
_rcs_mot_egr_late2 | 1.000091 .0172002 0.01 0.996 .9669415 1.034378
_rcs_mot_egr_late3 | .9957648 .0093524 -0.45 0.651 .9776021 1.014265
_cons | 2.1e+115 1.9e+116 30.80 0.000 9.8e+107 4.7e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54464.604
Iteration 1: log likelihood = -54446.072
Iteration 2: log likelihood = -54446.005
Iteration 3: log likelihood = -54446.005
Log likelihood = -54446.005 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730208 .0501499 18.91 0.000 1.634655 1.831345
mot_egr_late | 1.579163 .0372359 19.38 0.000 1.507843 1.653857
tr_mod2 | 1.218607 .0262226 9.19 0.000 1.168281 1.271102
sex_dum2 | .7598872 .0163239 -12.78 0.000 .728557 .7925646
edad_ini_cons | .9869022 .0019513 -6.67 0.000 .9830851 .9907341
esc1 | 1.129098 .0298223 4.60 0.000 1.072135 1.189088
esc2 | 1.088878 .0259512 3.57 0.000 1.039184 1.140948
sus_prin2 | 1.066539 .0297373 2.31 0.021 1.009819 1.126445
sus_prin3 | 1.392798 .0326477 14.13 0.000 1.330257 1.458279
sus_prin4 | 1.076349 .0378574 2.09 0.036 1.00465 1.153165
sus_prin5 | 1.141765 .0825448 1.83 0.067 .9909193 1.315573
fr_cons_sus_prin2 | .9202086 .0450226 -1.70 0.089 .8360648 1.012821
fr_cons_sus_prin3 | .9969181 .0395678 -0.08 0.938 .9223063 1.077566
fr_cons_sus_prin4 | 1.00869 .0420361 0.21 0.836 .9295761 1.094538
fr_cons_sus_prin5 | 1.030605 .0409371 0.76 0.448 .9534139 1.114047
cond_ocu2 | 1.017932 .0318171 0.57 0.570 .9574437 1.082243
cond_ocu3 | 1.005494 .1418009 0.04 0.969 .7626719 1.325626
cond_ocu4 | 1.104553 .0399341 2.75 0.006 1.028992 1.185662
cond_ocu5 | 1.162084 .0890525 1.96 0.050 1.000019 1.350413
cond_ocu6 | 1.131321 .0207258 6.74 0.000 1.091419 1.172681
policonsumo | 1.026627 .0224189 1.20 0.229 .9836143 1.071522
num_hij2 | 1.165191 .0227519 7.83 0.000 1.121441 1.210648
tenviv1 | 1.151931 .0754135 2.16 0.031 1.013213 1.30964
tenviv2 | 1.127374 .0494006 2.74 0.006 1.034591 1.228476
tenviv4 | 1.037478 .023743 1.61 0.108 .9919711 1.085073
tenviv5 | 1.00348 .0179902 0.19 0.846 .968832 1.039367
mzone2 | 1.302504 .027374 12.58 0.000 1.249942 1.357277
mzone3 | 1.46449 .0421209 13.26 0.000 1.384219 1.549417
n_off_vio | 1.355264 .0258715 15.92 0.000 1.305494 1.406932
n_off_acq | 1.814334 .0324531 33.30 0.000 1.751829 1.879069
n_off_sud | 1.256799 .0233135 12.32 0.000 1.211926 1.303333
n_off_oth | 1.360433 .0257496 16.26 0.000 1.310889 1.411849
psy_com2 | 1.070727 .0257008 2.85 0.004 1.021521 1.122303
psy_com3 | 1.05834 .0187997 3.19 0.001 1.022127 1.095835
dep2 | 1.019942 .0195466 1.03 0.303 .9823421 1.058982
rural2 | 1.028735 .0287108 1.02 0.310 .9739744 1.086575
rural3 | 1.054512 .0324392 1.73 0.084 .9928108 1.120047
porc_pobr | 1.226176 .1451021 1.72 0.085 .9723532 1.546256
susini2 | 1.095728 .0455075 2.20 0.028 1.010069 1.188652
susini3 | 1.122602 .0372592 3.48 0.000 1.0519 1.198057
susini4 | 1.082405 .0193445 4.43 0.000 1.045146 1.120991
susini5 | 1.129756 .0561858 2.45 0.014 1.02483 1.245424
ano_nac_corr | .8750932 .0037463 -31.17 0.000 .8677813 .8824667
cohab2 | .9707261 .0310626 -0.93 0.353 .9117144 1.033557
cohab3 | .9915062 .0390188 -0.22 0.828 .9179056 1.071008
cohab4 | .9523829 .0296202 -1.57 0.117 .8960624 1.012243
fis_com2 | 1.027283 .0166801 1.66 0.097 .9951058 1.060502
fis_com3 | .902226 .0336839 -2.76 0.006 .8385644 .9707206
rc_x1 | .8518627 .0048093 -28.40 0.000 .8424886 .8613412
rc_x2 | 1.02877 .0186437 1.57 0.118 .9928702 1.065967
rc_x3 | .8952818 .0414533 -2.39 0.017 .8176123 .9803296
_rcs1 | 2.638742 .0470282 54.44 0.000 2.54816 2.732545
_rcs2 | 1.106262 .0180013 6.21 0.000 1.071537 1.142113
_rcs3 | 1.043946 .0101651 4.42 0.000 1.024212 1.06406
_rcs4 | 1.021716 .0052128 4.21 0.000 1.01155 1.031984
_rcs5 | 1.011273 .0024529 4.62 0.000 1.006477 1.016092
_rcs_mot_egr_early1 | .9024066 .0189942 -4.88 0.000 .8659362 .9404131
_rcs_mot_egr_early2 | .9995195 .018678 -0.03 0.979 .9635736 1.036806
_rcs_mot_egr_early3 | .9978441 .011389 -0.19 0.850 .9757699 1.020418
_rcs_mot_egr_early4 | .9951246 .0065615 -0.74 0.459 .9823469 1.008068
_rcs_mot_egr_late1 | .9402426 .0186018 -3.11 0.002 .9044816 .9774175
_rcs_mot_egr_late2 | .9992253 .0177482 -0.04 0.965 .9650381 1.034624
_rcs_mot_egr_late3 | .9983661 .0106237 -0.15 0.878 .9777598 1.019407
_rcs_mot_egr_late4 | .9961541 .0059762 -0.64 0.521 .9845095 1.007937
_cons | 2.2e+115 1.9e+116 30.80 0.000 9.9e+107 4.8e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54464.469
Iteration 1: log likelihood = -54445.779
Iteration 2: log likelihood = -54445.711
Iteration 3: log likelihood = -54445.711
Log likelihood = -54445.711 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730022 .0501437 18.91 0.000 1.634481 1.831147
mot_egr_late | 1.57905 .037233 19.37 0.000 1.507735 1.653737
tr_mod2 | 1.21858 .0262221 9.19 0.000 1.168255 1.271074
sex_dum2 | .759878 .0163239 -12.78 0.000 .7285479 .7925553
edad_ini_cons | .9869034 .0019513 -6.67 0.000 .9830862 .9907353
esc1 | 1.129072 .0298217 4.60 0.000 1.072109 1.189061
esc2 | 1.088844 .0259504 3.57 0.000 1.039151 1.140912
sus_prin2 | 1.06648 .0297355 2.31 0.021 1.009763 1.126382
sus_prin3 | 1.392748 .0326463 14.13 0.000 1.330209 1.458226
sus_prin4 | 1.076299 .0378555 2.09 0.037 1.004603 1.153112
sus_prin5 | 1.141528 .0825275 1.83 0.067 .9907145 1.3153
fr_cons_sus_prin2 | .9202448 .0450244 -1.70 0.089 .8360977 1.012861
fr_cons_sus_prin3 | .9969148 .0395677 -0.08 0.938 .9223032 1.077562
fr_cons_sus_prin4 | 1.008691 .0420361 0.21 0.836 .9295767 1.094539
fr_cons_sus_prin5 | 1.030642 .0409386 0.76 0.447 .9534476 1.114086
cond_ocu2 | 1.017962 .0318183 0.57 0.569 .9574707 1.082274
cond_ocu3 | 1.005477 .1417988 0.04 0.969 .7626588 1.325605
cond_ocu4 | 1.104587 .0399354 2.75 0.006 1.029024 1.185698
cond_ocu5 | 1.1618 .0890314 1.96 0.050 .9997739 1.350084
cond_ocu6 | 1.131363 .0207265 6.74 0.000 1.09146 1.172724
policonsumo | 1.026574 .0224178 1.20 0.230 .9835633 1.071466
num_hij2 | 1.165228 .0227526 7.83 0.000 1.121477 1.210687
tenviv1 | 1.1519 .0754116 2.16 0.031 1.013186 1.309606
tenviv2 | 1.127205 .0493934 2.73 0.006 1.034437 1.228293
tenviv4 | 1.037476 .0237429 1.61 0.108 .9919691 1.085071
tenviv5 | 1.00349 .0179905 0.19 0.846 .9688418 1.039378
mzone2 | 1.30253 .0273746 12.58 0.000 1.249967 1.357304
mzone3 | 1.464592 .0421244 13.27 0.000 1.384314 1.549526
n_off_vio | 1.355263 .0258716 15.92 0.000 1.305492 1.406931
n_off_acq | 1.814404 .0324543 33.31 0.000 1.751897 1.879142
n_off_sud | 1.256852 .0233147 12.32 0.000 1.211977 1.303389
n_off_oth | 1.36045 .0257499 16.26 0.000 1.310905 1.411867
psy_com2 | 1.070697 .0257003 2.85 0.004 1.021491 1.122272
psy_com3 | 1.058342 .0187997 3.19 0.001 1.02213 1.095838
dep2 | 1.019933 .0195464 1.03 0.303 .9823329 1.058972
rural2 | 1.028735 .0287109 1.02 0.310 .9739738 1.086575
rural3 | 1.054493 .0324387 1.72 0.085 .9927934 1.120028
porc_pobr | 1.226215 .1451065 1.72 0.085 .9723849 1.546305
susini2 | 1.095783 .04551 2.20 0.028 1.010119 1.188711
susini3 | 1.122472 .0372549 3.48 0.000 1.051778 1.197918
susini4 | 1.082433 .019345 4.43 0.000 1.045174 1.121021
susini5 | 1.129727 .0561841 2.45 0.014 1.024805 1.245392
ano_nac_corr | .8751021 .0037463 -31.16 0.000 .8677901 .8824757
cohab2 | .970683 .0310612 -0.93 0.352 .911674 1.033512
cohab3 | .9915123 .0390191 -0.22 0.829 .9179112 1.071015
cohab4 | .95238 .02962 -1.57 0.117 .8960599 1.01224
fis_com2 | 1.027342 .0166811 1.66 0.097 .9951624 1.060562
fis_com3 | .9022155 .0336835 -2.76 0.006 .8385546 .9707092
rc_x1 | .8518651 .0048094 -28.40 0.000 .8424909 .8613437
rc_x2 | 1.028789 .0186441 1.57 0.117 .992889 1.065988
rc_x3 | .8952587 .0414524 -2.39 0.017 .8175911 .9803044
_rcs1 | 2.637207 .0469672 54.45 0.000 2.546741 2.730886
_rcs2 | 1.105274 .0180574 6.13 0.000 1.070443 1.141239
_rcs3 | 1.04665 .0109672 4.35 0.000 1.025374 1.068367
_rcs4 | 1.019408 .0063093 3.11 0.002 1.007117 1.03185
_rcs5 | 1.008959 .0041896 2.15 0.032 1.000781 1.017204
_rcs_mot_egr_early1 | .903076 .0190007 -4.85 0.000 .8665926 .9410953
_rcs_mot_egr_early2 | .9996785 .0188903 -0.02 0.986 .9633315 1.037397
_rcs_mot_egr_early3 | .9972857 .0123083 -0.22 0.826 .9734512 1.021704
_rcs_mot_egr_early4 | .9956923 .0074818 -0.57 0.566 .9811358 1.010465
_rcs_mot_egr_early5 | 1.002481 .0051383 0.48 0.629 .9924602 1.012602
_rcs_mot_egr_late1 | .9408957 .0186054 -3.08 0.002 .9051273 .9780775
_rcs_mot_egr_late2 | 1.000721 .0180501 0.04 0.968 .9659612 1.036731
_rcs_mot_egr_late3 | .9949253 .0115939 -0.44 0.662 .9724593 1.01791
_rcs_mot_egr_late4 | 1.000002 .0069608 0.00 1.000 .9864513 1.013738
_rcs_mot_egr_late5 | 1.00052 .0046833 0.11 0.912 .9913828 1.009741
_cons | 2.1e+115 1.8e+116 30.80 0.000 9.7e+107 4.7e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54462.271
Iteration 1: log likelihood = -54442.777
Iteration 2: log likelihood = -54442.714
Iteration 3: log likelihood = -54442.713
Log likelihood = -54442.713 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730235 .0501526 18.91 0.000 1.634678 1.831378
mot_egr_late | 1.57912 .0372371 19.37 0.000 1.507798 1.653816
tr_mod2 | 1.218587 .0262223 9.19 0.000 1.168261 1.271081
sex_dum2 | .7599984 .0163266 -12.78 0.000 .7286631 .7926812
edad_ini_cons | .9869018 .0019513 -6.67 0.000 .9830847 .9907337
esc1 | 1.128932 .0298181 4.59 0.000 1.071977 1.188914
esc2 | 1.088738 .0259479 3.57 0.000 1.039051 1.140802
sus_prin2 | 1.066654 .0297405 2.31 0.021 1.009928 1.126567
sus_prin3 | 1.392878 .0326498 14.14 0.000 1.330333 1.458363
sus_prin4 | 1.076489 .0378626 2.10 0.036 1.00478 1.153316
sus_prin5 | 1.141922 .0825576 1.84 0.066 .9910532 1.315757
fr_cons_sus_prin2 | .9202162 .045023 -1.70 0.089 .8360717 1.012829
fr_cons_sus_prin3 | .9970313 .0395724 -0.07 0.940 .922411 1.077688
fr_cons_sus_prin4 | 1.008751 .0420386 0.21 0.834 .9296317 1.094603
fr_cons_sus_prin5 | 1.030711 .0409413 0.76 0.446 .9535118 1.114161
cond_ocu2 | 1.017931 .0318173 0.57 0.570 .9574422 1.082242
cond_ocu3 | 1.00579 .1418429 0.04 0.967 .7628962 1.326017
cond_ocu4 | 1.104459 .0399306 2.75 0.006 1.028905 1.185561
cond_ocu5 | 1.161808 .0890318 1.96 0.050 .9997808 1.350093
cond_ocu6 | 1.131348 .0207262 6.74 0.000 1.091446 1.172709
policonsumo | 1.02667 .0224199 1.21 0.228 .9836553 1.071567
num_hij2 | 1.165187 .0227519 7.83 0.000 1.121437 1.210645
tenviv1 | 1.152009 .075419 2.16 0.031 1.013281 1.30973
tenviv2 | 1.127317 .0493988 2.73 0.006 1.034539 1.228417
tenviv4 | 1.037587 .0237457 1.61 0.107 .9920744 1.085187
tenviv5 | 1.003626 .017993 0.20 0.840 .968973 1.039519
mzone2 | 1.302629 .0273767 12.58 0.000 1.250061 1.357407
mzone3 | 1.464714 .0421293 13.27 0.000 1.384427 1.549658
n_off_vio | 1.355269 .0258711 15.93 0.000 1.3055 1.406936
n_off_acq | 1.814365 .032453 33.31 0.000 1.75186 1.879099
n_off_sud | 1.256875 .0233148 12.33 0.000 1.212 1.303412
n_off_oth | 1.360392 .025748 16.26 0.000 1.310851 1.411805
psy_com2 | 1.070784 .0257025 2.85 0.004 1.021574 1.122363
psy_com3 | 1.058373 .0188003 3.19 0.001 1.022159 1.09587
dep2 | 1.019942 .0195466 1.03 0.303 .9823424 1.058982
rural2 | 1.028742 .0287113 1.02 0.310 .9739806 1.086583
rural3 | 1.05445 .0324381 1.72 0.085 .9927508 1.119983
porc_pobr | 1.227455 .1452518 1.73 0.083 .9733699 1.547865
susini2 | 1.095788 .0455101 2.20 0.028 1.010124 1.188718
susini3 | 1.122554 .0372576 3.48 0.000 1.051855 1.198005
susini4 | 1.082374 .0193441 4.43 0.000 1.045117 1.12096
susini5 | 1.129675 .0561825 2.45 0.014 1.024756 1.245336
ano_nac_corr | .8749883 .0037461 -31.19 0.000 .8676768 .8823615
cohab2 | .9706912 .0310617 -0.93 0.353 .9116813 1.033521
cohab3 | .9915196 .0390196 -0.22 0.829 .9179177 1.071023
cohab4 | .9523778 .02962 -1.57 0.117 .8960576 1.012238
fis_com2 | 1.027265 .0166798 1.66 0.098 .9950884 1.060483
fis_com3 | .9022541 .0336851 -2.76 0.006 .8385902 .9707511
rc_x1 | .8517578 .004809 -28.42 0.000 .8423844 .8612355
rc_x2 | 1.028785 .0186441 1.57 0.117 .9928848 1.065984
rc_x3 | .8952676 .041453 -2.39 0.017 .8175989 .9803147
_rcs1 | 2.636941 .0469744 54.43 0.000 2.546462 2.730636
_rcs2 | 1.105909 .0180684 6.16 0.000 1.071056 1.141895
_rcs3 | 1.046064 .0107954 4.36 0.000 1.025118 1.067438
_rcs4 | 1.020316 .0060201 3.41 0.001 1.008584 1.032183
_rcs5 | 1.006724 .0036965 1.83 0.068 .999505 1.013995
_rcs_mot_egr_early1 | .9031681 .0190059 -4.84 0.000 .866675 .9411978
_rcs_mot_egr_early2 | .9991387 .0189821 -0.05 0.964 .9626187 1.037044
_rcs_mot_egr_early3 | .9985976 .0123567 -0.11 0.910 .9746703 1.023112
_rcs_mot_egr_early4 | .9936624 .007128 -0.89 0.375 .9797895 1.007732
_rcs_mot_egr_early5 | 1.002862 .0048024 0.60 0.551 .9934936 1.012319
_rcs_mot_egr_early6 | 1.002542 .0031186 0.82 0.414 .9964487 1.008673
_rcs_mot_egr_late1 | .9411172 .0186164 -3.07 0.002 .9053279 .9783213
_rcs_mot_egr_late2 | 1.00087 .0182061 0.05 0.962 .965815 1.037197
_rcs_mot_egr_late3 | .9942351 .0116269 -0.49 0.621 .971706 1.017287
_rcs_mot_egr_late4 | .999572 .0065398 -0.07 0.948 .9868361 1.012472
_rcs_mot_egr_late5 | 1.000439 .0042937 0.10 0.919 .9920587 1.00889
_rcs_mot_egr_late6 | 1.004979 .002594 1.92 0.054 .9999075 1.010076
_cons | 2.8e+115 2.4e+116 30.83 0.000 1.3e+108 6.1e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54459.803
Iteration 1: log likelihood = -54440.914
Iteration 2: log likelihood = -54440.837
Iteration 3: log likelihood = -54440.837
Log likelihood = -54440.837 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730313 .0501551 18.92 0.000 1.634751 1.831461
mot_egr_late | 1.579144 .0372374 19.38 0.000 1.507821 1.653841
tr_mod2 | 1.218662 .026224 9.19 0.000 1.168332 1.271159
sex_dum2 | .7600856 .0163285 -12.77 0.000 .7287468 .7927721
edad_ini_cons | .9869008 .0019513 -6.67 0.000 .9830838 .9907327
esc1 | 1.128874 .0298167 4.59 0.000 1.071921 1.188853
esc2 | 1.088685 .0259467 3.57 0.000 1.039 1.140747
sus_prin2 | 1.066839 .0297458 2.32 0.020 1.010103 1.126762
sus_prin3 | 1.393012 .0326536 14.14 0.000 1.33046 1.458505
sus_prin4 | 1.076647 .0378686 2.10 0.036 1.004927 1.153486
sus_prin5 | 1.142215 .08258 1.84 0.066 .9913052 1.316097
fr_cons_sus_prin2 | .9202334 .0450238 -1.70 0.089 .8360874 1.012848
fr_cons_sus_prin3 | .9971451 .0395769 -0.07 0.943 .9225161 1.077811
fr_cons_sus_prin4 | 1.008805 .0420409 0.21 0.833 .9296819 1.094663
fr_cons_sus_prin5 | 1.030741 .0409427 0.76 0.446 .9535392 1.114194
cond_ocu2 | 1.017881 .0318156 0.57 0.571 .9573949 1.082188
cond_ocu3 | 1.005896 .1418578 0.04 0.967 .7629769 1.326157
cond_ocu4 | 1.104289 .0399247 2.74 0.006 1.028747 1.18538
cond_ocu5 | 1.161727 .0890258 1.96 0.050 .9997111 1.349999
cond_ocu6 | 1.131319 .0207258 6.73 0.000 1.091418 1.172679
policonsumo | 1.026692 .0224205 1.21 0.228 .9836761 1.07159
num_hij2 | 1.165166 .0227515 7.83 0.000 1.121416 1.210622
tenviv1 | 1.152161 .0754287 2.16 0.031 1.013415 1.309902
tenviv2 | 1.127506 .0494077 2.74 0.006 1.03471 1.228623
tenviv4 | 1.03766 .0237475 1.62 0.106 .9921438 1.085263
tenviv5 | 1.003723 .0179947 0.21 0.836 .9690668 1.039619
mzone2 | 1.302723 .0273789 12.58 0.000 1.250152 1.357505
mzone3 | 1.464729 .042131 13.27 0.000 1.384439 1.549677
n_off_vio | 1.355241 .02587 15.92 0.000 1.305474 1.406906
n_off_acq | 1.814371 .0324524 33.31 0.000 1.751868 1.879105
n_off_sud | 1.256848 .0233141 12.32 0.000 1.211974 1.303384
n_off_oth | 1.360356 .0257466 16.26 0.000 1.310818 1.411766
psy_com2 | 1.070866 .0257046 2.85 0.004 1.021653 1.12245
psy_com3 | 1.058394 .0188007 3.19 0.001 1.02218 1.095892
dep2 | 1.019968 .0195472 1.03 0.302 .982367 1.059009
rural2 | 1.028745 .0287115 1.02 0.310 .9739831 1.086586
rural3 | 1.054412 .0324375 1.72 0.085 .9927143 1.119944
porc_pobr | 1.228241 .1453444 1.74 0.082 .9739945 1.548856
susini2 | 1.095856 .0455129 2.20 0.028 1.010187 1.188791
susini3 | 1.122656 .0372611 3.49 0.000 1.05195 1.198114
susini4 | 1.082323 .0193433 4.43 0.000 1.045067 1.120907
susini5 | 1.12966 .0561824 2.45 0.014 1.024741 1.245321
ano_nac_corr | .8749067 .0037462 -31.21 0.000 .867595 .8822799
cohab2 | .970651 .0310605 -0.93 0.352 .9116434 1.033478
cohab3 | .9914709 .0390177 -0.22 0.828 .9178725 1.070971
cohab4 | .9523447 .029619 -1.57 0.116 .8960265 1.012203
fis_com2 | 1.02719 .0166785 1.65 0.098 .9950153 1.060405
fis_com3 | .9022319 .0336844 -2.76 0.006 .8385694 .9707275
rc_x1 | .8516827 .0048088 -28.43 0.000 .8423095 .8611602
rc_x2 | 1.028759 .0186435 1.56 0.118 .9928601 1.065957
rc_x3 | .8953242 .0414553 -2.39 0.017 .817651 .9803761
_rcs1 | 2.636644 .0469483 54.45 0.000 2.546214 2.730285
_rcs2 | 1.105237 .0179981 6.14 0.000 1.070518 1.141082
_rcs3 | 1.047112 .0108428 4.45 0.000 1.026074 1.06858
_rcs4 | 1.019187 .0061853 3.13 0.002 1.007136 1.031383
_rcs5 | 1.007675 .0040614 1.90 0.058 .9997461 1.015667
_rcs_mot_egr_early1 | .9032496 .0190018 -4.84 0.000 .866764 .941271
_rcs_mot_egr_early2 | 1.000174 .0190603 0.01 0.993 .9635055 1.038238
_rcs_mot_egr_early3 | .9968173 .0123623 -0.26 0.797 .9728798 1.021344
_rcs_mot_egr_early4 | .9956521 .0070282 -0.62 0.537 .981972 1.009523
_rcs_mot_egr_early5 | .9998499 .0047698 -0.03 0.975 .9905448 1.009242
_rcs_mot_egr_early6 | 1.003334 .0040673 0.82 0.412 .9953941 1.011338
_rcs_mot_egr_early7 | 1.000881 .0022584 0.39 0.696 .9964649 1.005318
_rcs_mot_egr_late1 | .9411279 .0186086 -3.07 0.002 .9053534 .9783161
_rcs_mot_egr_late2 | 1.001201 .0182434 0.07 0.947 .9660756 1.037604
_rcs_mot_egr_late3 | .9937257 .0116057 -0.54 0.590 .9712374 1.016735
_rcs_mot_egr_late4 | .999482 .006371 -0.08 0.935 .9870727 1.012047
_rcs_mot_egr_late5 | 1.00004 .0042275 0.01 0.993 .9917882 1.00836
_rcs_mot_egr_late6 | 1.002206 .0036611 0.60 0.546 .9950556 1.009407
_rcs_mot_egr_late7 | 1.004823 .0017076 2.83 0.005 1.001482 1.008176
_cons | 3.3e+115 2.9e+116 30.84 0.000 1.5e+108 7.3e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54461.03
Iteration 1: log likelihood = -54443.24
Iteration 2: log likelihood = -54443.187
Iteration 3: log likelihood = -54443.187
Log likelihood = -54443.187 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728953 .0499925 18.94 0.000 1.633694 1.829766
mot_egr_late | 1.577917 .0370653 19.42 0.000 1.506917 1.652262
tr_mod2 | 1.218636 .0262221 9.19 0.000 1.16831 1.271129
sex_dum2 | .7600293 .016327 -12.77 0.000 .7286932 .7927129
edad_ini_cons | .9868996 .0019513 -6.67 0.000 .9830825 .9907315
esc1 | 1.128982 .0298192 4.59 0.000 1.072025 1.188966
esc2 | 1.088746 .025948 3.57 0.000 1.039058 1.14081
sus_prin2 | 1.066729 .0297425 2.32 0.021 1.009999 1.126646
sus_prin3 | 1.392948 .0326517 14.14 0.000 1.3304 1.458437
sus_prin4 | 1.076603 .0378667 2.10 0.036 1.004886 1.153438
sus_prin5 | 1.141834 .0825502 1.83 0.067 .9909792 1.315654
fr_cons_sus_prin2 | .920203 .0450222 -1.70 0.089 .8360601 1.012814
fr_cons_sus_prin3 | .9969857 .0395705 -0.08 0.939 .9223689 1.077639
fr_cons_sus_prin4 | 1.008748 .0420384 0.21 0.834 .9296295 1.0946
fr_cons_sus_prin5 | 1.030657 .0409393 0.76 0.447 .9534613 1.114103
cond_ocu2 | 1.017891 .0318157 0.57 0.570 .9574048 1.082198
cond_ocu3 | 1.005554 .1418086 0.04 0.969 .7627188 1.325704
cond_ocu4 | 1.104285 .0399243 2.74 0.006 1.028743 1.185375
cond_ocu5 | 1.161881 .089036 1.96 0.050 .9998462 1.350175
cond_ocu6 | 1.131352 .0207262 6.74 0.000 1.09145 1.172713
policonsumo | 1.026642 .0224184 1.20 0.229 .9836297 1.071535
num_hij2 | 1.165174 .0227514 7.83 0.000 1.121424 1.21063
tenviv1 | 1.152096 .075424 2.16 0.031 1.013358 1.309827
tenviv2 | 1.127523 .0494075 2.74 0.006 1.034728 1.22864
tenviv4 | 1.037621 .0237463 1.61 0.107 .9921074 1.085222
tenviv5 | 1.003652 .0179934 0.20 0.839 .9689976 1.039545
mzone2 | 1.302629 .0273768 12.58 0.000 1.250061 1.357407
mzone3 | 1.464532 .0421233 13.27 0.000 1.384256 1.549464
n_off_vio | 1.355274 .0258706 15.93 0.000 1.305506 1.40694
n_off_acq | 1.814333 .0324517 33.31 0.000 1.751831 1.879065
n_off_sud | 1.256841 .0233136 12.32 0.000 1.211967 1.303375
n_off_oth | 1.360377 .0257473 16.26 0.000 1.310838 1.411788
psy_com2 | 1.07078 .0257019 2.85 0.004 1.021572 1.122359
psy_com3 | 1.05835 .0187998 3.19 0.001 1.022137 1.095846
dep2 | 1.019981 .0195475 1.03 0.302 .9823791 1.059022
rural2 | 1.028789 .0287124 1.02 0.309 .9740256 1.086632
rural3 | 1.054563 .0324416 1.73 0.084 .9928578 1.120104
porc_pobr | 1.228279 .1453468 1.74 0.082 .974027 1.548898
susini2 | 1.095891 .0455133 2.20 0.027 1.01022 1.188826
susini3 | 1.122648 .0372602 3.49 0.000 1.051944 1.198104
susini4 | 1.082362 .0193437 4.43 0.000 1.045105 1.120947
susini5 | 1.129855 .056192 2.45 0.014 1.024918 1.245535
ano_nac_corr | .874961 .003746 -31.20 0.000 .8676497 .8823339
cohab2 | .9707827 .0310641 -0.93 0.354 .9117682 1.033617
cohab3 | .9914812 .0390175 -0.22 0.828 .917883 1.070981
cohab4 | .9524348 .0296215 -1.57 0.117 .8961117 1.012298
fis_com2 | 1.027195 .0166785 1.65 0.098 .9950202 1.06041
fis_com3 | .9022046 .0336831 -2.76 0.006 .8385445 .9706976
rc_x1 | .8517336 .0048089 -28.42 0.000 .8423604 .8612112
rc_x2 | 1.028766 .0186435 1.56 0.118 .992867 1.065963
rc_x3 | .8953119 .0414545 -2.39 0.017 .8176403 .9803619
_rcs1 | 2.632098 .0397141 64.14 0.000 2.555399 2.711098
_rcs2 | 1.104931 .0062859 17.54 0.000 1.092679 1.11732
_rcs3 | 1.042542 .0040782 10.65 0.000 1.03458 1.050566
_rcs4 | 1.020136 .0025116 8.10 0.000 1.015225 1.025071
_rcs5 | 1.011801 .0017195 6.90 0.000 1.008437 1.015177
_rcs6 | 1.006751 .001313 5.16 0.000 1.004181 1.009328
_rcs_mot_egr_early1 | .90547 .016121 -5.58 0.000 .8744183 .9376243
_rcs_mot_egr_late1 | .9427967 .0154771 -3.59 0.000 .9129449 .9736246
_cons | 2.9e+115 2.5e+116 30.83 0.000 1.3e+108 6.4e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54461.137
Iteration 1: log likelihood = -54443.213
Iteration 2: log likelihood = -54443.155
Iteration 3: log likelihood = -54443.155
Log likelihood = -54443.155 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729782 .0501254 18.91 0.000 1.634275 1.830869
mot_egr_late | 1.578649 .0372093 19.37 0.000 1.507379 1.653289
tr_mod2 | 1.218677 .0262236 9.19 0.000 1.168348 1.271173
sex_dum2 | .7600254 .016327 -12.77 0.000 .7286893 .792709
edad_ini_cons | .9869004 .0019513 -6.67 0.000 .9830834 .9907323
esc1 | 1.128972 .029819 4.59 0.000 1.072015 1.188955
esc2 | 1.088739 .0259478 3.57 0.000 1.039051 1.140802
sus_prin2 | 1.066748 .0297433 2.32 0.020 1.010017 1.126666
sus_prin3 | 1.392968 .0326523 14.14 0.000 1.330418 1.458458
sus_prin4 | 1.076609 .037867 2.10 0.036 1.004892 1.153445
sus_prin5 | 1.141946 .0825594 1.84 0.066 .9910745 1.315786
fr_cons_sus_prin2 | .9201958 .0450219 -1.70 0.089 .8360535 1.012806
fr_cons_sus_prin3 | .9969823 .0395704 -0.08 0.939 .9223658 1.077635
fr_cons_sus_prin4 | 1.008735 .0420379 0.21 0.835 .9296169 1.094586
fr_cons_sus_prin5 | 1.03065 .0409391 0.76 0.447 .9534547 1.114095
cond_ocu2 | 1.01789 .0318158 0.57 0.571 .9574043 1.082198
cond_ocu3 | 1.005667 .1418252 0.04 0.968 .7628033 1.325854
cond_ocu4 | 1.104275 .039924 2.74 0.006 1.028734 1.185364
cond_ocu5 | 1.161858 .0890345 1.96 0.050 .9998264 1.350149
cond_ocu6 | 1.131345 .0207261 6.74 0.000 1.091443 1.172705
policonsumo | 1.026666 .0224193 1.21 0.228 .9836518 1.071561
num_hij2 | 1.165168 .0227513 7.83 0.000 1.121418 1.210624
tenviv1 | 1.152146 .0754274 2.16 0.031 1.013402 1.309884
tenviv2 | 1.127534 .0494081 2.74 0.006 1.034738 1.228652
tenviv4 | 1.037614 .0237462 1.61 0.107 .9921005 1.085215
tenviv5 | 1.003648 .0179933 0.20 0.839 .9689938 1.039541
mzone2 | 1.302635 .0273771 12.58 0.000 1.250067 1.357414
mzone3 | 1.4645 .0421228 13.26 0.000 1.384225 1.549431
n_off_vio | 1.355283 .0258708 15.93 0.000 1.305514 1.406949
n_off_acq | 1.814347 .0324519 33.31 0.000 1.751845 1.87908
n_off_sud | 1.256833 .0233135 12.32 0.000 1.21196 1.303367
n_off_oth | 1.360381 .0257474 16.26 0.000 1.310842 1.411793
psy_com2 | 1.070782 .0257021 2.85 0.004 1.021573 1.122361
psy_com3 | 1.058354 .0187999 3.19 0.001 1.022141 1.09585
dep2 | 1.019984 .0195475 1.03 0.302 .982382 1.059025
rural2 | 1.028777 .0287123 1.02 0.309 .9740135 1.08662
rural3 | 1.054554 .0324414 1.73 0.084 .9928486 1.120094
porc_pobr | 1.228347 .1453559 1.74 0.082 .9740795 1.548986
susini2 | 1.095865 .0455125 2.20 0.028 1.010197 1.188799
susini3 | 1.122644 .0372603 3.49 0.000 1.05194 1.1981
susini4 | 1.082363 .0193438 4.43 0.000 1.045106 1.120948
susini5 | 1.129855 .0561919 2.45 0.014 1.024919 1.245536
ano_nac_corr | .8749536 .0037461 -31.20 0.000 .867642 .8823268
cohab2 | .9707682 .0310637 -0.93 0.354 .9117544 1.033602
cohab3 | .9914617 .0390168 -0.22 0.828 .9178649 1.07096
cohab4 | .9524211 .0296212 -1.57 0.117 .8960987 1.012283
fis_com2 | 1.027193 .0166785 1.65 0.098 .9950181 1.060408
fis_com3 | .9022123 .0336834 -2.76 0.006 .8385515 .970706
rc_x1 | .8517266 .0048089 -28.43 0.000 .8423533 .8612043
rc_x2 | 1.028769 .0186436 1.57 0.118 .99287 1.065967
rc_x3 | .8952999 .041454 -2.39 0.017 .8176291 .980349
_rcs1 | 2.638293 .0470117 54.44 0.000 2.547742 2.732062
_rcs2 | 1.108441 .0154346 7.39 0.000 1.078598 1.139109
_rcs3 | 1.042934 .0043785 10.01 0.000 1.034387 1.051551
_rcs4 | 1.020194 .0025227 8.09 0.000 1.015261 1.02515
_rcs5 | 1.011799 .0017195 6.90 0.000 1.008434 1.015175
_rcs6 | 1.006752 .0013131 5.16 0.000 1.004182 1.009329
_rcs_mot_egr_early1 | .902987 .0189929 -4.85 0.000 .8665185 .9409903
_rcs_mot_egr_early2 | .9961428 .0160267 -0.24 0.810 .9652212 1.028055
_rcs_mot_egr_late1 | .9403742 .018595 -3.11 0.002 .9046259 .9775351
_rcs_mot_egr_late2 | .9965102 .0150536 -0.23 0.817 .9674383 1.026456
_cons | 3.0e+115 2.6e+116 30.83 0.000 1.4e+108 6.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54460.972
Iteration 1: log likelihood = -54443.076
Iteration 2: log likelihood = -54443.019
Iteration 3: log likelihood = -54443.019
Log likelihood = -54443.019 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730259 .0501503 18.92 0.000 1.634705 1.831397
mot_egr_late | 1.579031 .0372321 19.37 0.000 1.507718 1.653717
tr_mod2 | 1.218698 .0262243 9.19 0.000 1.168368 1.271196
sex_dum2 | .7600282 .016327 -12.77 0.000 .7286921 .7927119
edad_ini_cons | .9869017 .0019513 -6.67 0.000 .9830847 .9907336
esc1 | 1.128972 .0298189 4.59 0.000 1.072015 1.188955
esc2 | 1.088751 .0259481 3.57 0.000 1.039063 1.140815
sus_prin2 | 1.06678 .0297444 2.32 0.020 1.010046 1.1267
sus_prin3 | 1.392997 .0326533 14.14 0.000 1.330446 1.458489
sus_prin4 | 1.0766 .0378669 2.10 0.036 1.004883 1.153436
sus_prin5 | 1.142136 .0825735 1.84 0.066 .9912387 1.316005
fr_cons_sus_prin2 | .9201634 .0450203 -1.70 0.089 .8360238 1.012771
fr_cons_sus_prin3 | .9969793 .0395702 -0.08 0.939 .9223629 1.077632
fr_cons_sus_prin4 | 1.008726 .0420376 0.21 0.835 .9296091 1.094577
fr_cons_sus_prin5 | 1.03063 .0409383 0.76 0.448 .9534368 1.114074
cond_ocu2 | 1.017874 .0318152 0.57 0.571 .9573886 1.08218
cond_ocu3 | 1.005771 .1418398 0.04 0.967 .762882 1.325991
cond_ocu4 | 1.104243 .0399229 2.74 0.006 1.028704 1.18533
cond_ocu5 | 1.16199 .0890452 1.96 0.050 .9999384 1.350303
cond_ocu6 | 1.131327 .0207259 6.74 0.000 1.091426 1.172687
policonsumo | 1.02672 .022421 1.21 0.227 .983703 1.071618
num_hij2 | 1.165169 .0227515 7.83 0.000 1.12142 1.210625
tenviv1 | 1.152223 .0754325 2.16 0.030 1.01347 1.309972
tenviv2 | 1.127562 .0494094 2.74 0.006 1.034764 1.228683
tenviv4 | 1.037605 .023746 1.61 0.107 .9920919 1.085206
tenviv5 | 1.00364 .0179931 0.20 0.839 .9689867 1.039533
mzone2 | 1.30263 .0273769 12.58 0.000 1.250062 1.357408
mzone3 | 1.464472 .0421221 13.26 0.000 1.384198 1.549401
n_off_vio | 1.355287 .0258709 15.93 0.000 1.305518 1.406954
n_off_acq | 1.814344 .0324519 33.31 0.000 1.751842 1.879077
n_off_sud | 1.256805 .023313 12.32 0.000 1.211933 1.303339
n_off_oth | 1.360371 .0257471 16.26 0.000 1.310832 1.411782
psy_com2 | 1.070794 .0257026 2.85 0.004 1.021584 1.122374
psy_com3 | 1.058361 .0188001 3.19 0.001 1.022148 1.095858
dep2 | 1.019972 .0195473 1.03 0.302 .982371 1.059013
rural2 | 1.028756 .0287117 1.02 0.310 .9739933 1.086597
rural3 | 1.054549 .0324412 1.73 0.084 .992844 1.120088
porc_pobr | 1.228169 .1453362 1.74 0.082 .9739362 1.548765
susini2 | 1.095795 .04551 2.20 0.028 1.01013 1.188723
susini3 | 1.122661 .037261 3.49 0.000 1.051955 1.198118
susini4 | 1.082362 .0193439 4.43 0.000 1.045105 1.120947
susini5 | 1.129856 .0561918 2.45 0.014 1.02492 1.245537
ano_nac_corr | .8749501 .0037461 -31.20 0.000 .8676385 .8823233
cohab2 | .9707355 .0310628 -0.93 0.353 .9117234 1.033567
cohab3 | .9914106 .0390149 -0.22 0.826 .9178174 1.070905
cohab4 | .9523784 .02962 -1.57 0.117 .8960583 1.012238
fis_com2 | 1.027178 .0166782 1.65 0.099 .9950042 1.060393
fis_com3 | .9022234 .0336839 -2.76 0.006 .8385618 .970718
rc_x1 | .8517233 .0048089 -28.43 0.000 .8423501 .8612009
rc_x2 | 1.028775 .0186437 1.57 0.117 .9928751 1.065972
rc_x3 | .8952771 .041453 -2.39 0.017 .8176082 .9803241
_rcs1 | 2.6378 .0469337 54.51 0.000 2.547397 2.731411
_rcs2 | 1.104433 .0175418 6.25 0.000 1.070581 1.139355
_rcs3 | 1.046067 .0080284 5.87 0.000 1.030449 1.061921
_rcs4 | 1.021758 .0042271 5.20 0.000 1.013507 1.030077
_rcs5 | 1.012136 .0018556 6.58 0.000 1.008506 1.01578
_rcs6 | 1.006753 .0013132 5.16 0.000 1.004183 1.00933
_rcs_mot_egr_early1 | .9030182 .0189789 -4.85 0.000 .8665761 .9409929
_rcs_mot_egr_early2 | 1.000584 .0181839 0.03 0.974 .9655712 1.036866
_rcs_mot_egr_early3 | .9945398 .0100877 -0.54 0.589 .9749635 1.014509
_rcs_mot_egr_late1 | .9405337 .0185772 -3.10 0.002 .9048189 .9776582
_rcs_mot_egr_late2 | .9998335 .0172204 -0.01 0.992 .9666455 1.034161
_rcs_mot_egr_late3 | .9962357 .0093558 -0.40 0.688 .9780664 1.014742
_cons | 3.0e+115 2.6e+116 30.83 0.000 1.4e+108 6.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54461.051
Iteration 1: log likelihood = -54443.057
Iteration 2: log likelihood = -54442.998
Iteration 3: log likelihood = -54442.998
Log likelihood = -54442.998 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730275 .050152 18.92 0.000 1.634719 1.831417
mot_egr_late | 1.579095 .0372345 19.37 0.000 1.507778 1.653786
tr_mod2 | 1.218707 .0262246 9.19 0.000 1.168376 1.271205
sex_dum2 | .7600266 .016327 -12.77 0.000 .7286906 .7927102
edad_ini_cons | .9869015 .0019513 -6.67 0.000 .9830844 .9907333
esc1 | 1.128975 .0298191 4.59 0.000 1.072018 1.188959
esc2 | 1.088758 .0259483 3.57 0.000 1.03907 1.140822
sus_prin2 | 1.066786 .0297447 2.32 0.020 1.010052 1.126707
sus_prin3 | 1.393006 .0326537 14.14 0.000 1.330454 1.458499
sus_prin4 | 1.076598 .0378668 2.10 0.036 1.004881 1.153434
sus_prin5 | 1.142164 .0825757 1.84 0.066 .9912623 1.316037
fr_cons_sus_prin2 | .920161 .0450202 -1.70 0.089 .8360216 1.012768
fr_cons_sus_prin3 | .9969842 .0395705 -0.08 0.939 .9223674 1.077637
fr_cons_sus_prin4 | 1.008727 .0420377 0.21 0.835 .9296095 1.094577
fr_cons_sus_prin5 | 1.030627 .0409382 0.76 0.448 .9534333 1.11407
cond_ocu2 | 1.017872 .0318151 0.57 0.571 .957387 1.082178
cond_ocu3 | 1.005804 .1418447 0.04 0.967 .7629073 1.326035
cond_ocu4 | 1.104233 .0399227 2.74 0.006 1.028694 1.185319
cond_ocu5 | 1.162037 .0890495 1.96 0.050 .9999781 1.35036
cond_ocu6 | 1.131319 .0207258 6.73 0.000 1.091418 1.172679
policonsumo | 1.026727 .0224212 1.21 0.227 .9837097 1.071626
num_hij2 | 1.165163 .0227513 7.83 0.000 1.121414 1.210619
tenviv1 | 1.152234 .0754334 2.16 0.030 1.013479 1.309985
tenviv2 | 1.127592 .0494109 2.74 0.006 1.034791 1.228716
tenviv4 | 1.037598 .0237458 1.61 0.107 .9920857 1.085199
tenviv5 | 1.003638 .017993 0.20 0.839 .9689843 1.03953
mzone2 | 1.302618 .0273767 12.58 0.000 1.25005 1.357395
mzone3 | 1.464462 .0421221 13.26 0.000 1.384188 1.549391
n_off_vio | 1.355282 .0258708 15.93 0.000 1.305513 1.406948
n_off_acq | 1.814336 .0324518 33.31 0.000 1.751834 1.879069
n_off_sud | 1.2568 .023313 12.32 0.000 1.211928 1.303334
n_off_oth | 1.360375 .0257472 16.26 0.000 1.310835 1.411786
psy_com2 | 1.070795 .0257027 2.85 0.004 1.021585 1.122375
psy_com3 | 1.058362 .0188001 3.19 0.001 1.022149 1.095859
dep2 | 1.019972 .0195473 1.03 0.302 .9823705 1.059013
rural2 | 1.02876 .0287118 1.02 0.310 .9739977 1.086602
rural3 | 1.054552 .0324413 1.73 0.084 .9928468 1.120091
porc_pobr | 1.22812 .1453311 1.74 0.082 .9738969 1.548706
susini2 | 1.095795 .0455101 2.20 0.028 1.01013 1.188724
susini3 | 1.122679 .0372617 3.49 0.000 1.051972 1.198138
susini4 | 1.08236 .0193439 4.43 0.000 1.045103 1.120945
susini5 | 1.129873 .0561928 2.46 0.014 1.024935 1.245556
ano_nac_corr | .8749473 .0037462 -31.20 0.000 .8676356 .8823206
cohab2 | .9707459 .0310631 -0.93 0.353 .9117332 1.033578
cohab3 | .9914147 .0390151 -0.22 0.827 .9178211 1.070909
cohab4 | .9523832 .0296202 -1.57 0.117 .8960628 1.012244
fis_com2 | 1.027168 .0166781 1.65 0.099 .9949943 1.060382
fis_com3 | .9022229 .0336838 -2.76 0.006 .8385614 .9707175
rc_x1 | .8517214 .0048089 -28.43 0.000 .842348 .8611991
rc_x2 | 1.028773 .0186437 1.57 0.118 .9928731 1.06597
rc_x3 | .895279 .0414531 -2.39 0.017 .81761 .9803261
_rcs1 | 2.637926 .0469682 54.48 0.000 2.547458 2.731607
_rcs2 | 1.104244 .0179527 6.10 0.000 1.069612 1.139998
_rcs3 | 1.04599 .0102229 4.60 0.000 1.026144 1.066219
_rcs4 | 1.021996 .0048044 4.63 0.000 1.012623 1.031456
_rcs5 | 1.012505 .0035924 3.50 0.000 1.005489 1.019571
_rcs6 | 1.006859 .0013801 4.99 0.000 1.004158 1.009568
_rcs_mot_egr_early1 | .9029199 .0189921 -4.86 0.000 .8664529 .9409218
_rcs_mot_egr_early2 | 1.000545 .0186383 0.03 0.977 .9646735 1.03775
_rcs_mot_egr_early3 | .9954134 .0115441 -0.40 0.692 .9730426 1.018299
_rcs_mot_egr_early4 | .9979331 .0068441 -0.30 0.763 .9846086 1.011438
_rcs_mot_egr_late1 | .9405071 .018591 -3.10 0.002 .9047662 .9776598
_rcs_mot_egr_late2 | 1.00045 .0177114 0.03 0.980 .9663315 1.035773
_rcs_mot_egr_late3 | .9958932 .0108188 -0.38 0.705 .9749129 1.017325
_rcs_mot_egr_late4 | .998917 .0063003 -0.17 0.864 .9866446 1.011342
_cons | 3.0e+115 2.6e+116 30.83 0.000 1.4e+108 6.7e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54461.183
Iteration 1: log likelihood = -54442.558
Iteration 2: log likelihood = -54442.492
Iteration 3: log likelihood = -54442.492
Log likelihood = -54442.492 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730221 .0501506 18.91 0.000 1.634668 1.83136
mot_egr_late | 1.579198 .0372373 19.38 0.000 1.507875 1.653894
tr_mod2 | 1.218731 .0262251 9.19 0.000 1.1684 1.271231
sex_dum2 | .7600202 .0163269 -12.77 0.000 .7286844 .7927035
edad_ini_cons | .9869021 .0019513 -6.67 0.000 .9830851 .990734
esc1 | 1.128977 .0298192 4.59 0.000 1.07202 1.188961
esc2 | 1.088758 .0259484 3.57 0.000 1.03907 1.140822
sus_prin2 | 1.066769 .0297443 2.32 0.020 1.010035 1.126689
sus_prin3 | 1.392992 .0326535 14.14 0.000 1.33044 1.458485
sus_prin4 | 1.076571 .0378659 2.10 0.036 1.004855 1.153405
sus_prin5 | 1.142064 .0825686 1.84 0.066 .991175 1.315922
fr_cons_sus_prin2 | .9201551 .04502 -1.70 0.089 .8360162 1.012762
fr_cons_sus_prin3 | .9969791 .0395703 -0.08 0.939 .9223626 1.077632
fr_cons_sus_prin4 | 1.008722 .0420375 0.21 0.835 .9296051 1.094572
fr_cons_sus_prin5 | 1.030623 .0409381 0.76 0.448 .95343 1.114067
cond_ocu2 | 1.01787 .0318151 0.57 0.571 .9573856 1.082177
cond_ocu3 | 1.005758 .1418384 0.04 0.968 .7628723 1.325975
cond_ocu4 | 1.104177 .0399208 2.74 0.006 1.028641 1.185259
cond_ocu5 | 1.162039 .0890501 1.96 0.050 .9999791 1.350363
cond_ocu6 | 1.131339 .0207262 6.74 0.000 1.091437 1.172699
policonsumo | 1.026706 .0224208 1.21 0.227 .9836888 1.071604
num_hij2 | 1.16519 .0227519 7.83 0.000 1.121439 1.210647
tenviv1 | 1.152335 .0754398 2.17 0.030 1.013568 1.310099
tenviv2 | 1.127589 .0494109 2.74 0.006 1.034787 1.228713
tenviv4 | 1.0376 .0237459 1.61 0.107 .992087 1.0852
tenviv5 | 1.003648 .0179932 0.20 0.839 .9689938 1.039541
mzone2 | 1.302641 .0273773 12.58 0.000 1.250073 1.35742
mzone3 | 1.464436 .0421215 13.26 0.000 1.384164 1.549364
n_off_vio | 1.355268 .0258705 15.93 0.000 1.305499 1.406933
n_off_acq | 1.814371 .0324522 33.31 0.000 1.751867 1.879104
n_off_sud | 1.25682 .0233133 12.32 0.000 1.211947 1.303354
n_off_oth | 1.360365 .025747 16.26 0.000 1.310826 1.411775
psy_com2 | 1.070779 .0257026 2.85 0.004 1.021569 1.122359
psy_com3 | 1.058365 .0188001 3.19 0.001 1.022152 1.095862
dep2 | 1.019966 .0195473 1.03 0.302 .9823646 1.059007
rural2 | 1.028758 .0287118 1.02 0.310 .9739957 1.0866
rural3 | 1.054562 .0324416 1.73 0.084 .9928566 1.120102
porc_pobr | 1.228181 .1453386 1.74 0.082 .9739448 1.548783
susini2 | 1.095843 .0455122 2.20 0.028 1.010175 1.188776
susini3 | 1.122632 .0372603 3.49 0.000 1.051927 1.198088
susini4 | 1.08237 .0193441 4.43 0.000 1.045112 1.120956
susini5 | 1.129973 .0561978 2.46 0.014 1.025025 1.245666
ano_nac_corr | .8749528 .0037463 -31.20 0.000 .8676409 .8823263
cohab2 | .9707089 .0310618 -0.93 0.353 .9116986 1.033539
cohab3 | .9913731 .0390135 -0.22 0.826 .9177827 1.070864
cohab4 | .9523542 .0296192 -1.57 0.116 .8960357 1.012213
fis_com2 | 1.027167 .0166781 1.65 0.099 .9949929 1.060381
fis_com3 | .902206 .0336832 -2.76 0.006 .8385457 .9706992
rc_x1 | .8517238 .004809 -28.42 0.000 .8423503 .8612016
rc_x2 | 1.028779 .0186438 1.57 0.117 .9928796 1.065977
rc_x3 | .8952697 .0414526 -2.39 0.017 .8176015 .9803159
_rcs1 | 2.637882 .0469674 54.48 0.000 2.547416 2.731562
_rcs2 | 1.103784 .0180865 6.03 0.000 1.068898 1.139808
_rcs3 | 1.0469 .0109541 4.38 0.000 1.025649 1.068591
_rcs4 | 1.020889 .0058166 3.63 0.000 1.009552 1.032353
_rcs5 | 1.012754 .0037877 3.39 0.001 1.005357 1.020204
_rcs6 | 1.007299 .0022052 3.32 0.001 1.002987 1.011631
_rcs_mot_egr_early1 | .9030083 .0189948 -4.85 0.000 .8665362 .9410154
_rcs_mot_egr_early2 | 1.000232 .0188565 0.01 0.990 .9639486 1.037882
_rcs_mot_egr_early3 | .9965383 .0121471 -0.28 0.776 .9730127 1.020633
_rcs_mot_egr_early4 | .9965284 .0071423 -0.49 0.628 .9826276 1.010626
_rcs_mot_egr_early5 | 1.000168 .0046717 0.04 0.971 .9910534 1.009366
_rcs_mot_egr_late1 | .940539 .018594 -3.10 0.002 .9047924 .9776979
_rcs_mot_egr_late2 | 1.001585 .0179999 0.09 0.930 .9669204 1.037493
_rcs_mot_egr_late3 | .9941571 .0113974 -0.51 0.609 .9720676 1.016749
_rcs_mot_egr_late4 | 1.000875 .006586 0.13 0.894 .9880496 1.013867
_rcs_mot_egr_late5 | .9980637 .0041816 -0.46 0.644 .9899015 1.006293
_cons | 3.0e+115 2.6e+116 30.83 0.000 1.4e+108 6.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54461.004
Iteration 1: log likelihood = -54441.67
Iteration 2: log likelihood = -54441.608
Iteration 3: log likelihood = -54441.608
Log likelihood = -54441.608 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730114 .0501481 18.91 0.000 1.634566 1.831248
mot_egr_late | 1.579002 .037233 19.37 0.000 1.507687 1.65369
tr_mod2 | 1.218658 .0262238 9.19 0.000 1.168329 1.271155
sex_dum2 | .7600324 .0163272 -12.77 0.000 .728696 .7927164
edad_ini_cons | .9869016 .0019513 -6.67 0.000 .9830846 .9907335
esc1 | 1.128932 .0298181 4.59 0.000 1.071976 1.188913
esc2 | 1.088739 .0259479 3.57 0.000 1.039051 1.140803
sus_prin2 | 1.066774 .0297443 2.32 0.020 1.01004 1.126694
sus_prin3 | 1.392992 .0326533 14.14 0.000 1.33044 1.458484
sus_prin4 | 1.076599 .0378668 2.10 0.036 1.004882 1.153435
sus_prin5 | 1.142173 .0825764 1.84 0.066 .9912697 1.316048
fr_cons_sus_prin2 | .9201666 .0450205 -1.70 0.089 .8360267 1.012775
fr_cons_sus_prin3 | .9970411 .0395727 -0.07 0.940 .92242 1.077699
fr_cons_sus_prin4 | 1.008748 .0420385 0.21 0.834 .9296291 1.0946
fr_cons_sus_prin5 | 1.030672 .0409399 0.76 0.447 .9534753 1.114119
cond_ocu2 | 1.017891 .0318159 0.57 0.570 .9574048 1.082199
cond_ocu3 | 1.005921 .1418613 0.04 0.967 .7629957 1.32619
cond_ocu4 | 1.104293 .0399249 2.74 0.006 1.028749 1.185383
cond_ocu5 | 1.162064 .0890518 1.96 0.050 1.000001 1.350391
cond_ocu6 | 1.131327 .020726 6.74 0.000 1.091426 1.172687
policonsumo | 1.026735 .0224214 1.21 0.227 .9837167 1.071634
num_hij2 | 1.165182 .0227518 7.83 0.000 1.121432 1.210639
tenviv1 | 1.152212 .075432 2.16 0.030 1.01346 1.30996
tenviv2 | 1.127503 .0494071 2.74 0.006 1.034708 1.228619
tenviv4 | 1.037595 .0237458 1.61 0.107 .9920828 1.085196
tenviv5 | 1.003648 .0179933 0.20 0.839 .9689938 1.039541
mzone2 | 1.30265 .0273773 12.58 0.000 1.250082 1.357429
mzone3 | 1.464553 .0421252 13.27 0.000 1.384273 1.549489
n_off_vio | 1.35526 .0258705 15.93 0.000 1.305492 1.406926
n_off_acq | 1.814339 .0324521 33.31 0.000 1.751836 1.879072
n_off_sud | 1.256835 .0233138 12.32 0.000 1.211961 1.30337
n_off_oth | 1.360355 .0257469 16.26 0.000 1.310817 1.411766
psy_com2 | 1.070827 .0257037 2.85 0.004 1.021616 1.122409
psy_com3 | 1.058381 .0188004 3.19 0.001 1.022167 1.095878
dep2 | 1.019962 .0195471 1.03 0.302 .9823606 1.059002
rural2 | 1.028755 .0287117 1.02 0.310 .9739931 1.086597
rural3 | 1.054493 .0324396 1.72 0.085 .9927911 1.120029
porc_pobr | 1.227963 .1453123 1.74 0.083 .9737728 1.548507
susini2 | 1.095789 .04551 2.20 0.028 1.010125 1.188718
susini3 | 1.122641 .0372607 3.49 0.000 1.051936 1.198099
susini4 | 1.082344 .0193437 4.43 0.000 1.045087 1.120929
susini5 | 1.129814 .0561899 2.45 0.014 1.024881 1.24549
ano_nac_corr | .8749312 .0037462 -31.21 0.000 .8676196 .8823045
cohab2 | .9706913 .0310616 -0.93 0.353 .9116816 1.03352
cohab3 | .9914412 .0390164 -0.22 0.827 .9178452 1.070938
cohab4 | .952344 .029619 -1.57 0.116 .8960258 1.012202
fis_com2 | 1.027183 .0166784 1.65 0.099 .9950085 1.060398
fis_com3 | .902232 .0336842 -2.76 0.006 .8385697 .9707274
rc_x1 | .8517049 .0048089 -28.43 0.000 .8423316 .8611824
rc_x2 | 1.028778 .0186438 1.57 0.117 .9928777 1.065976
rc_x3 | .8952729 .041453 -2.39 0.017 .817604 .98032
_rcs1 | 2.636969 .0469443 54.47 0.000 2.546547 2.730602
_rcs2 | 1.103484 .0181298 5.99 0.000 1.068517 1.139596
_rcs3 | 1.047909 .0114285 4.29 0.000 1.025747 1.07055
_rcs4 | 1.020369 .0066446 3.10 0.002 1.007429 1.033476
_rcs5 | 1.012602 .0044205 2.87 0.004 1.003975 1.021304
_rcs6 | 1.005265 .0033223 1.59 0.112 .9987746 1.011798
_rcs_mot_egr_early1 | .9031662 .0189975 -4.84 0.000 .8666888 .9411788
_rcs_mot_egr_early2 | 1.00056 .0190088 0.03 0.976 .9639891 1.038519
_rcs_mot_egr_early3 | .9966756 .0127632 -0.26 0.795 .9719714 1.022008
_rcs_mot_egr_early4 | .9959489 .0078472 -0.52 0.606 .9806868 1.011448
_rcs_mot_egr_early5 | 1.00059 .0053664 0.11 0.912 .9901275 1.011164
_rcs_mot_egr_early6 | 1.000157 .0041053 0.04 0.970 .992143 1.008235
_rcs_mot_egr_late1 | .9410678 .018604 -3.07 0.002 .9053019 .9782466
_rcs_mot_egr_late2 | 1.00229 .018212 0.13 0.900 .9672232 1.038628
_rcs_mot_egr_late3 | .9923331 .0120453 -0.63 0.526 .9690034 1.016225
_rcs_mot_egr_late4 | 1.001866 .0073156 0.26 0.798 .9876297 1.016307
_rcs_mot_egr_late5 | .9981733 .0049132 -0.37 0.710 .9885898 1.00785
_rcs_mot_egr_late6 | 1.002579 .003739 0.69 0.490 .9952775 1.009934
_cons | 3.2e+115 2.7e+116 30.84 0.000 1.4e+108 6.9e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54459.423
Iteration 1: log likelihood = -54440.338
Iteration 2: log likelihood = -54440.262
Iteration 3: log likelihood = -54440.262
Log likelihood = -54440.262 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.73024 .0501528 18.91 0.000 1.634682 1.831383
mot_egr_late | 1.579053 .037235 19.37 0.000 1.507734 1.653744
tr_mod2 | 1.218696 .0262247 9.19 0.000 1.168366 1.271195
sex_dum2 | .7600969 .0163287 -12.77 0.000 .7287577 .7927838
edad_ini_cons | .9869009 .0019513 -6.67 0.000 .9830838 .9907327
esc1 | 1.128873 .0298167 4.59 0.000 1.07192 1.188852
esc2 | 1.088687 .0259468 3.57 0.000 1.039001 1.140748
sus_prin2 | 1.066889 .0297474 2.32 0.020 1.010149 1.126815
sus_prin3 | 1.393063 .0326551 14.14 0.000 1.330508 1.458559
sus_prin4 | 1.076694 .0378704 2.10 0.036 1.00497 1.153537
sus_prin5 | 1.142334 .0825889 1.84 0.066 .9914086 1.316236
fr_cons_sus_prin2 | .9202127 .0450228 -1.70 0.089 .8360686 1.012825
fr_cons_sus_prin3 | .9971472 .039577 -0.07 0.943 .9225181 1.077814
fr_cons_sus_prin4 | 1.008804 .0420409 0.21 0.833 .9296806 1.094661
fr_cons_sus_prin5 | 1.030721 .040942 0.76 0.446 .9535206 1.114172
cond_ocu2 | 1.017865 .0318151 0.57 0.571 .9573803 1.082171
cond_ocu3 | 1.005955 .1418661 0.04 0.966 .7630219 1.326235
cond_ocu4 | 1.104217 .0399222 2.74 0.006 1.028679 1.185302
cond_ocu5 | 1.161822 .0890333 1.96 0.050 .9997928 1.35011
cond_ocu6 | 1.131306 .0207256 6.73 0.000 1.091405 1.172666
policonsumo | 1.026722 .0224212 1.21 0.227 .9837044 1.071621
num_hij2 | 1.165165 .0227515 7.83 0.000 1.121415 1.210621
tenviv1 | 1.152251 .0754345 2.16 0.030 1.013494 1.310004
tenviv2 | 1.127581 .049411 2.74 0.006 1.034779 1.228705
tenviv4 | 1.03766 .0237475 1.62 0.106 .9921445 1.085264
tenviv5 | 1.003729 .0179948 0.21 0.836 .9690726 1.039625
mzone2 | 1.302729 .027379 12.58 0.000 1.250157 1.357511
mzone3 | 1.464652 .042129 13.27 0.000 1.384366 1.549596
n_off_vio | 1.355236 .0258697 15.92 0.000 1.305469 1.4069
n_off_acq | 1.814361 .032452 33.31 0.000 1.751859 1.879094
n_off_sud | 1.256829 .0233136 12.32 0.000 1.211956 1.303363
n_off_oth | 1.360345 .0257463 16.26 0.000 1.310808 1.411754
psy_com2 | 1.070885 .025705 2.85 0.004 1.021671 1.12247
psy_com3 | 1.058398 .0188007 3.20 0.001 1.022184 1.095896
dep2 | 1.019976 .0195474 1.03 0.302 .9823747 1.059017
rural2 | 1.028753 .0287116 1.02 0.310 .9739902 1.086594
rural3 | 1.054431 .0324382 1.72 0.085 .9927322 1.119964
porc_pobr | 1.228435 .1453675 1.74 0.082 .9741475 1.5491
susini2 | 1.095856 .0455128 2.20 0.028 1.010186 1.18879
susini3 | 1.122694 .0372624 3.49 0.000 1.051986 1.198154
susini4 | 1.08231 .0193431 4.43 0.000 1.045054 1.120893
susini5 | 1.129723 .0561858 2.45 0.014 1.024798 1.245392
ano_nac_corr | .8748805 .0037462 -31.22 0.000 .8675689 .8822538
cohab2 | .9706473 .0310603 -0.93 0.352 .91164 1.033474
cohab3 | .9914345 .0390162 -0.22 0.827 .9178388 1.070931
cohab4 | .9523266 .0296184 -1.57 0.116 .8960095 1.012183
fis_com2 | 1.027155 .0166779 1.65 0.099 .9949813 1.060369
fis_com3 | .9022218 .033684 -2.76 0.006 .83856 .9707166
rc_x1 | .8516583 .0048088 -28.44 0.000 .8422853 .8611357
rc_x2 | 1.028757 .0186434 1.56 0.118 .9928583 1.065955
rc_x3 | .8953227 .0414552 -2.39 0.017 .8176497 .9803742
_rcs1 | 2.636572 .0469325 54.46 0.000 2.546172 2.730181
_rcs2 | 1.103517 .0180811 6.01 0.000 1.068642 1.139531
_rcs3 | 1.048155 .0112383 4.39 0.000 1.026358 1.070414
_rcs4 | 1.020463 .0063828 3.24 0.001 1.00803 1.03305
_rcs5 | 1.012063 .0041942 2.89 0.004 1.003876 1.020317
_rcs6 | 1.004162 .0029324 1.42 0.155 .9984315 1.009926
_rcs_mot_egr_early1 | .9032633 .0189981 -4.84 0.000 .8667848 .941277
_rcs_mot_egr_early2 | 1.001006 .0191055 0.05 0.958 .9642519 1.039161
_rcs_mot_egr_early3 | .9955821 .0127484 -0.35 0.730 .9709067 1.020885
_rcs_mot_egr_early4 | .996938 .0076269 -0.40 0.689 .9821011 1.011999
_rcs_mot_egr_early5 | .9989092 .0050871 -0.21 0.830 .9889882 1.00893
_rcs_mot_egr_early6 | 1.002488 .0039605 0.63 0.529 .9947551 1.01028
_rcs_mot_egr_early7 | 1.000134 .0028243 0.05 0.962 .9946137 1.005685
_rcs_mot_egr_late1 | .9411588 .0186044 -3.07 0.002 .9053922 .9783383
_rcs_mot_egr_late2 | 1.002055 .0182894 0.11 0.910 .9668418 1.03855
_rcs_mot_egr_late3 | .9924827 .0120192 -0.62 0.533 .969203 1.016322
_rcs_mot_egr_late4 | 1.000775 .0070327 0.11 0.912 .9870853 1.014654
_rcs_mot_egr_late5 | .9991004 .0045826 -0.20 0.844 .9901588 1.008123
_rcs_mot_egr_late6 | 1.001365 .0035447 0.39 0.700 .9944414 1.008337
_rcs_mot_egr_late7 | 1.00407 .0024126 1.69 0.091 .9993523 1.00881
_cons | 3.5e+115 3.1e+116 30.85 0.000 1.6e+108 7.8e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54458.088
Iteration 1: log likelihood = -54441.281
Iteration 2: log likelihood = -54441.231
Iteration 3: log likelihood = -54441.231
Log likelihood = -54441.231 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728827 .0499883 18.93 0.000 1.633576 1.829631
mot_egr_late | 1.577792 .0370619 19.41 0.000 1.506799 1.65213
tr_mod2 | 1.218701 .0262234 9.19 0.000 1.168373 1.271198
sex_dum2 | .7601291 .0163292 -12.77 0.000 .7287889 .792817
edad_ini_cons | .9868977 .0019513 -6.67 0.000 .9830806 .9907295
esc1 | 1.128921 .0298176 4.59 0.000 1.071967 1.188902
esc2 | 1.088681 .0259465 3.57 0.000 1.038996 1.140741
sus_prin2 | 1.066915 .029748 2.32 0.020 1.010174 1.126843
sus_prin3 | 1.393122 .0326565 14.14 0.000 1.330564 1.458621
sus_prin4 | 1.076758 .0378726 2.10 0.035 1.00503 1.153606
sus_prin5 | 1.14217 .0825759 1.84 0.066 .9912682 1.316044
fr_cons_sus_prin2 | .9201925 .0450216 -1.70 0.089 .8360506 1.012803
fr_cons_sus_prin3 | .9970364 .0395725 -0.07 0.940 .9224158 1.077694
fr_cons_sus_prin4 | 1.008772 .0420395 0.21 0.834 .9296517 1.094627
fr_cons_sus_prin5 | 1.030652 .0409393 0.76 0.447 .9534564 1.114098
cond_ocu2 | 1.017822 .0318135 0.57 0.572 .9573401 1.082125
cond_ocu3 | 1.005766 .1418385 0.04 0.967 .7628798 1.325983
cond_ocu4 | 1.104061 .0399164 2.74 0.006 1.028534 1.185134
cond_ocu5 | 1.161906 .0890382 1.96 0.050 .9998677 1.350205
cond_ocu6 | 1.131341 .020726 6.74 0.000 1.091439 1.172701
policonsumo | 1.026707 .0224198 1.21 0.227 .9836924 1.071603
num_hij2 | 1.165163 .0227512 7.83 0.000 1.121414 1.210618
tenviv1 | 1.152183 .0754299 2.16 0.030 1.013436 1.309927
tenviv2 | 1.127744 .0494177 2.74 0.006 1.03493 1.228882
tenviv4 | 1.037681 .0237477 1.62 0.106 .992165 1.085286
tenviv5 | 1.003765 .0179954 0.21 0.834 .9691067 1.039662
mzone2 | 1.302713 .0273788 12.58 0.000 1.250142 1.357496
mzone3 | 1.464486 .0421233 13.26 0.000 1.38421 1.549418
n_off_vio | 1.355252 .0258694 15.93 0.000 1.305486 1.406916
n_off_acq | 1.814308 .0324504 33.31 0.000 1.751808 1.879038
n_off_sud | 1.256809 .0233126 12.32 0.000 1.211938 1.303341
n_off_oth | 1.360318 .0257453 16.26 0.000 1.310783 1.411726
psy_com2 | 1.07085 .0257037 2.85 0.004 1.021638 1.122432
psy_com3 | 1.058371 .0188002 3.19 0.001 1.022158 1.095868
dep2 | 1.01999 .0195478 1.03 0.302 .9823879 1.059032
rural2 | 1.028805 .028713 1.02 0.309 .9740405 1.08665
rural3 | 1.054575 .0324426 1.73 0.084 .9928679 1.120118
porc_pobr | 1.229475 .1454878 1.75 0.081 .9749768 1.550405
susini2 | 1.095959 .045516 2.21 0.027 1.010284 1.1889
susini3 | 1.122754 .0372637 3.49 0.000 1.052044 1.198218
susini4 | 1.082324 .0193432 4.43 0.000 1.045069 1.120908
susini5 | 1.129882 .0561941 2.46 0.014 1.024941 1.245567
ano_nac_corr | .8748638 .003746 -31.22 0.000 .8675526 .8822367
cohab2 | .9707706 .0310637 -0.93 0.354 .9117568 1.033604
cohab3 | .991387 .0390138 -0.22 0.826 .9177959 1.070879
cohab4 | .952417 .029621 -1.57 0.117 .896095 1.012279
fis_com2 | 1.027136 .0166774 1.65 0.099 .9949634 1.060349
fis_com3 | .9022086 .0336833 -2.76 0.006 .8385481 .970702
rc_x1 | .8516407 .0048086 -28.44 0.000 .8422679 .8611177
rc_x2 | 1.028761 .0186433 1.56 0.118 .9928622 1.065958
rc_x3 | .8953223 .0414548 -2.39 0.017 .8176499 .9803731
_rcs1 | 2.631733 .0397028 64.14 0.000 2.555056 2.710711
_rcs2 | 1.10393 .0063179 17.28 0.000 1.091616 1.116383
_rcs3 | 1.043208 .0041757 10.57 0.000 1.035056 1.051425
_rcs4 | 1.020993 .0026053 8.14 0.000 1.015899 1.026112
_rcs5 | 1.012656 .0017549 7.26 0.000 1.009222 1.016101
_rcs6 | 1.008727 .0013772 6.36 0.000 1.006031 1.011429
_rcs7 | 1.005023 .0011276 4.47 0.000 1.002816 1.007236
_rcs_mot_egr_early1 | .9057439 .0161236 -5.56 0.000 .8746872 .9379032
_rcs_mot_egr_late1 | .9428507 .0154758 -3.59 0.000 .9130015 .9736758
_cons | 3.7e+115 3.2e+116 30.86 0.000 1.7e+108 8.1e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54458.179
Iteration 1: log likelihood = -54441.251
Iteration 2: log likelihood = -54441.198
Iteration 3: log likelihood = -54441.198
Log likelihood = -54441.198 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729668 .0501221 18.91 0.000 1.634168 1.830749
mot_egr_late | 1.578525 .0372061 19.37 0.000 1.507261 1.653158
tr_mod2 | 1.218742 .0262249 9.19 0.000 1.168411 1.271241
sex_dum2 | .7601257 .0163292 -12.77 0.000 .7287854 .7928136
edad_ini_cons | .9868986 .0019513 -6.67 0.000 .9830815 .9907304
esc1 | 1.12891 .0298173 4.59 0.000 1.071956 1.18889
esc2 | 1.088674 .0259463 3.56 0.000 1.038989 1.140734
sus_prin2 | 1.066935 .0297489 2.32 0.020 1.010193 1.126864
sus_prin3 | 1.393142 .0326572 14.14 0.000 1.330583 1.458642
sus_prin4 | 1.076765 .037873 2.10 0.035 1.005036 1.153613
sus_prin5 | 1.142285 .0825854 1.84 0.066 .9913664 1.316179
fr_cons_sus_prin2 | .9201848 .0450213 -1.70 0.089 .8360435 1.012794
fr_cons_sus_prin3 | .9970328 .0395724 -0.07 0.940 .9224124 1.07769
fr_cons_sus_prin4 | 1.008759 .042039 0.21 0.834 .9296392 1.094612
fr_cons_sus_prin5 | 1.030645 .040939 0.76 0.447 .9534496 1.11409
cond_ocu2 | 1.017821 .0318136 0.57 0.572 .9573389 1.082124
cond_ocu3 | 1.00588 .1418552 0.04 0.967 .7629646 1.326134
cond_ocu4 | 1.104051 .0399161 2.74 0.006 1.028524 1.185123
cond_ocu5 | 1.161884 .0890368 1.96 0.050 .9998485 1.35018
cond_ocu6 | 1.131333 .0207259 6.74 0.000 1.091432 1.172693
policonsumo | 1.026732 .0224207 1.21 0.227 .9837153 1.07163
num_hij2 | 1.165157 .0227511 7.83 0.000 1.121408 1.210612
tenviv1 | 1.152234 .0754333 2.16 0.030 1.01348 1.309985
tenviv2 | 1.127755 .0494182 2.74 0.006 1.03494 1.228894
tenviv4 | 1.037675 .0237476 1.62 0.106 .9921585 1.085279
tenviv5 | 1.003761 .0179953 0.21 0.834 .969103 1.039658
mzone2 | 1.302721 .0273791 12.58 0.000 1.250149 1.357504
mzone3 | 1.464455 .0421227 13.26 0.000 1.38418 1.549385
n_off_vio | 1.355261 .0258696 15.93 0.000 1.305494 1.406925
n_off_acq | 1.814323 .0324506 33.31 0.000 1.751823 1.879053
n_off_sud | 1.256801 .0233125 12.32 0.000 1.21193 1.303333
n_off_oth | 1.360322 .0257454 16.26 0.000 1.310787 1.41173
psy_com2 | 1.070852 .0257038 2.85 0.004 1.02164 1.122434
psy_com3 | 1.058376 .0188003 3.19 0.001 1.022162 1.095873
dep2 | 1.019993 .0195478 1.03 0.302 .9823906 1.059035
rural2 | 1.028792 .0287129 1.02 0.309 .9740275 1.086636
rural3 | 1.054565 .0324424 1.73 0.084 .9928579 1.120107
porc_pobr | 1.229547 .1454973 1.75 0.081 .9750319 1.550498
susini2 | 1.095932 .0455152 2.21 0.027 1.010258 1.188872
susini3 | 1.122752 .0372639 3.49 0.000 1.052041 1.198215
susini4 | 1.082326 .0193433 4.43 0.000 1.04507 1.12091
susini5 | 1.129882 .056194 2.46 0.014 1.024942 1.245567
ano_nac_corr | .8748565 .0037461 -31.22 0.000 .867545 .8822295
cohab2 | .9707551 .0310633 -0.93 0.354 .9117422 1.033588
cohab3 | .9913671 .039013 -0.22 0.826 .9177774 1.070857
cohab4 | .9524027 .0296206 -1.57 0.117 .8960815 1.012264
fis_com2 | 1.027133 .0166774 1.65 0.099 .994961 1.060346
fis_com3 | .9022158 .0336836 -2.76 0.006 .8385547 .9707099
rc_x1 | .8516336 .0048087 -28.44 0.000 .8422608 .8611108
rc_x2 | 1.028765 .0186434 1.56 0.118 .9928656 1.065962
rc_x3 | .8953094 .0414544 -2.39 0.017 .817638 .9803593
_rcs1 | 2.637969 .047004 54.44 0.000 2.547433 2.731723
_rcs2 | 1.107452 .0154046 7.34 0.000 1.077668 1.13806
_rcs3 | 1.043656 .0045526 9.80 0.000 1.034771 1.052618
_rcs4 | 1.021075 .0026266 8.11 0.000 1.01594 1.026236
_rcs5 | 1.01266 .001755 7.26 0.000 1.009226 1.016105
_rcs6 | 1.008726 .0013773 6.36 0.000 1.00603 1.011429
_rcs7 | 1.005025 .0011277 4.47 0.000 1.002817 1.007237
_rcs_mot_egr_early1 | .9032117 .0189976 -4.84 0.000 .8667341 .9412244
_rcs_mot_egr_early2 | .996057 .0160185 -0.25 0.806 .9651509 1.027953
_rcs_mot_egr_late1 | .9404343 .0185952 -3.11 0.002 .9046854 .9775957
_rcs_mot_egr_late2 | .9965251 .0150477 -0.23 0.818 .9674644 1.026459
_cons | 3.7e+115 3.2e+116 30.86 0.000 1.7e+108 8.2e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54458.058
Iteration 1: log likelihood = -54441.118
Iteration 2: log likelihood = -54441.066
Iteration 3: log likelihood = -54441.066
Log likelihood = -54441.066 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730137 .0501468 18.91 0.000 1.634591 1.831268
mot_egr_late | 1.578895 .0372287 19.37 0.000 1.507588 1.653574
tr_mod2 | 1.218761 .0262256 9.19 0.000 1.168429 1.271262
sex_dum2 | .7601287 .0163292 -12.77 0.000 .7287884 .7928167
edad_ini_cons | .9868998 .0019513 -6.67 0.000 .9830828 .9907317
esc1 | 1.12891 .0298173 4.59 0.000 1.071956 1.18889
esc2 | 1.088686 .0259466 3.57 0.000 1.039001 1.140746
sus_prin2 | 1.066966 .02975 2.32 0.020 1.010222 1.126898
sus_prin3 | 1.393171 .0326582 14.15 0.000 1.33061 1.458673
sus_prin4 | 1.076756 .0378728 2.10 0.036 1.005027 1.153604
sus_prin5 | 1.142471 .0825992 1.84 0.065 .9915268 1.316394
fr_cons_sus_prin2 | .9201525 .0450198 -1.70 0.089 .836014 1.012759
fr_cons_sus_prin3 | .9970302 .0395723 -0.07 0.940 .92241 1.077687
fr_cons_sus_prin4 | 1.008751 .0420387 0.21 0.834 .9296323 1.094604
fr_cons_sus_prin5 | 1.030626 .0409383 0.76 0.448 .9534325 1.11407
cond_ocu2 | 1.017804 .0318129 0.56 0.572 .9573231 1.082106
cond_ocu3 | 1.005981 .1418695 0.04 0.966 .7630415 1.326268
cond_ocu4 | 1.10402 .039915 2.74 0.006 1.028496 1.185091
cond_ocu5 | 1.162016 .0890475 1.96 0.050 .9999606 1.350334
cond_ocu6 | 1.131317 .0207257 6.73 0.000 1.091416 1.172676
policonsumo | 1.026786 .0224223 1.21 0.226 .9837659 1.071687
num_hij2 | 1.165159 .0227512 7.83 0.000 1.12141 1.210615
tenviv1 | 1.152309 .0754383 2.17 0.030 1.013545 1.31007
tenviv2 | 1.127782 .0494195 2.74 0.006 1.034964 1.228923
tenviv4 | 1.037666 .0237474 1.62 0.106 .9921501 1.085269
tenviv5 | 1.003754 .0179952 0.21 0.834 .9690962 1.03965
mzone2 | 1.302716 .0273789 12.58 0.000 1.250145 1.357499
mzone3 | 1.464429 .0421222 13.26 0.000 1.384155 1.549358
n_off_vio | 1.355266 .0258698 15.93 0.000 1.305499 1.40693
n_off_acq | 1.81432 .0324506 33.31 0.000 1.75182 1.87905
n_off_sud | 1.256774 .0233121 12.32 0.000 1.211904 1.303305
n_off_oth | 1.360312 .0257452 16.26 0.000 1.310777 1.411719
psy_com2 | 1.070864 .0257044 2.85 0.004 1.021651 1.122448
psy_com3 | 1.058383 .0188005 3.19 0.001 1.022169 1.09588
dep2 | 1.019982 .0195476 1.03 0.302 .9823795 1.059023
rural2 | 1.028771 .0287123 1.02 0.309 .9740071 1.086613
rural3 | 1.054559 .0324422 1.73 0.084 .9928529 1.120101
porc_pobr | 1.229369 .1454777 1.75 0.081 .9748893 1.550278
susini2 | 1.095861 .0455126 2.20 0.028 1.010192 1.188795
susini3 | 1.122768 .0372645 3.49 0.000 1.052056 1.198233
susini4 | 1.082325 .0193434 4.43 0.000 1.045069 1.120909
susini5 | 1.129884 .0561939 2.46 0.014 1.024943 1.245568
ano_nac_corr | .8748529 .0037461 -31.22 0.000 .8675414 .8822261
cohab2 | .9707219 .0310623 -0.93 0.353 .9117107 1.033553
cohab3 | .9913169 .0390112 -0.22 0.825 .9177307 1.070803
cohab4 | .9523603 .0296194 -1.57 0.117 .8960413 1.012219
fis_com2 | 1.027119 .0166771 1.65 0.099 .9949474 1.060331
fis_com3 | .9022263 .033684 -2.76 0.006 .8385645 .9707212
rc_x1 | .8516302 .0048086 -28.44 0.000 .8422573 .8611073
rc_x2 | 1.02877 .0186435 1.57 0.118 .9928708 1.065968
rc_x3 | .8952867 .0414534 -2.39 0.017 .8176172 .9803345
_rcs1 | 2.637492 .046929 54.51 0.000 2.547099 2.731094
_rcs2 | 1.103507 .0175746 6.18 0.000 1.069594 1.138496
_rcs3 | 1.046507 .0077579 6.13 0.000 1.031412 1.061824
_rcs4 | 1.022761 .0045864 5.02 0.000 1.013811 1.031789
_rcs5 | 1.013203 .0021139 6.29 0.000 1.009068 1.017354
_rcs6 | 1.008818 .0013902 6.37 0.000 1.006097 1.011546
_rcs7 | 1.005019 .0011279 4.46 0.000 1.002811 1.007232
_rcs_mot_egr_early1 | .9032352 .0189841 -4.84 0.000 .866783 .9412203
_rcs_mot_egr_early2 | 1.000413 .0181779 0.02 0.982 .9654119 1.036683
_rcs_mot_egr_early3 | .9946279 .0100677 -0.53 0.595 .9750899 1.014557
_rcs_mot_egr_late1 | .9405897 .0185783 -3.10 0.002 .9048727 .9777165
_rcs_mot_egr_late2 | .9997273 .0172168 -0.02 0.987 .9665462 1.034048
_rcs_mot_egr_late3 | .9963743 .0093386 -0.39 0.698 .978238 1.014847
_cons | 3.8e+115 3.3e+116 30.86 0.000 1.7e+108 8.3e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54458.108
Iteration 1: log likelihood = -54441.112
Iteration 2: log likelihood = -54441.058
Iteration 3: log likelihood = -54441.058
Log likelihood = -54441.058 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.73013 .0501475 18.91 0.000 1.634583 1.831263
mot_egr_late | 1.578934 .0372302 19.37 0.000 1.507624 1.653616
tr_mod2 | 1.218767 .0262258 9.19 0.000 1.168434 1.271268
sex_dum2 | .7601266 .0163292 -12.77 0.000 .7287865 .7928145
edad_ini_cons | .9868995 .0019513 -6.67 0.000 .9830825 .9907314
esc1 | 1.128914 .0298174 4.59 0.000 1.07196 1.188895
esc2 | 1.088693 .0259468 3.57 0.000 1.039007 1.140754
sus_prin2 | 1.06697 .0297501 2.32 0.020 1.010226 1.126902
sus_prin3 | 1.393177 .0326584 14.15 0.000 1.330616 1.45868
sus_prin4 | 1.076753 .0378727 2.10 0.036 1.005025 1.153601
sus_prin5 | 1.142488 .0826005 1.84 0.065 .9915411 1.316414
fr_cons_sus_prin2 | .9201511 .0450197 -1.70 0.089 .8360127 1.012757
fr_cons_sus_prin3 | .9970346 .0395725 -0.07 0.940 .922414 1.077692
fr_cons_sus_prin4 | 1.008752 .0420388 0.21 0.834 .929633 1.094605
fr_cons_sus_prin5 | 1.030624 .0409382 0.76 0.448 .95343 1.114067
cond_ocu2 | 1.017803 .0318129 0.56 0.572 .9573224 1.082105
cond_ocu3 | 1.006009 .1418735 0.04 0.966 .7630623 1.326305
cond_ocu4 | 1.104013 .0399148 2.74 0.006 1.028489 1.185083
cond_ocu5 | 1.162058 .0890514 1.96 0.050 .999996 1.350385
cond_ocu6 | 1.13131 .0207256 6.73 0.000 1.091409 1.172669
policonsumo | 1.02679 .0224225 1.21 0.226 .98377 1.071691
num_hij2 | 1.165153 .0227511 7.83 0.000 1.121404 1.210609
tenviv1 | 1.152316 .0754389 2.17 0.030 1.013551 1.310079
tenviv2 | 1.127807 .0494208 2.74 0.006 1.034987 1.228951
tenviv4 | 1.03766 .0237473 1.62 0.106 .9921442 1.085263
tenviv5 | 1.003751 .0179951 0.21 0.835 .9690934 1.039647
mzone2 | 1.302704 .0273787 12.58 0.000 1.250132 1.357485
mzone3 | 1.464422 .0421222 13.26 0.000 1.384148 1.549351
n_off_vio | 1.355261 .0258696 15.93 0.000 1.305494 1.406925
n_off_acq | 1.814313 .0324505 33.31 0.000 1.751813 1.879043
n_off_sud | 1.256771 .023312 12.32 0.000 1.211901 1.303302
n_off_oth | 1.360316 .0257453 16.26 0.000 1.310781 1.411724
psy_com2 | 1.070865 .0257044 2.85 0.004 1.021652 1.122449
psy_com3 | 1.058383 .0188005 3.19 0.001 1.022169 1.09588
dep2 | 1.019981 .0195476 1.03 0.302 .9823788 1.059022
rural2 | 1.028776 .0287124 1.02 0.309 .974012 1.086619
rural3 | 1.054562 .0324422 1.73 0.084 .9928554 1.120104
porc_pobr | 1.229319 .1454724 1.74 0.081 .9748486 1.550216
susini2 | 1.095862 .0455128 2.20 0.028 1.010193 1.188797
susini3 | 1.122784 .0372652 3.49 0.000 1.052071 1.198251
susini4 | 1.082323 .0193434 4.43 0.000 1.045067 1.120907
susini5 | 1.1299 .0561949 2.46 0.014 1.024958 1.245587
ano_nac_corr | .874851 .0037462 -31.22 0.000 .8675394 .8822242
cohab2 | .970733 .0310627 -0.93 0.353 .9117211 1.033564
cohab3 | .9913234 .0390115 -0.22 0.825 .9177366 1.07081
cohab4 | .9523663 .0296196 -1.57 0.117 .8960469 1.012226
fis_com2 | 1.02711 .016677 1.65 0.099 .9949386 1.060322
fis_com3 | .9022249 .033684 -2.76 0.006 .8385631 .9707197
rc_x1 | .851629 .0048087 -28.44 0.000 .8422561 .8611062
rc_x2 | 1.028768 .0186435 1.57 0.118 .9928687 1.065965
rc_x3 | .8952894 .0414534 -2.39 0.017 .8176197 .9803374
_rcs1 | 2.637628 .0469684 54.47 0.000 2.547159 2.731309
_rcs2 | 1.10352 .0180254 6.03 0.000 1.068751 1.139421
_rcs3 | 1.046259 .0100263 4.72 0.000 1.026791 1.066096
_rcs4 | 1.022863 .0046457 4.98 0.000 1.013799 1.032009
_rcs5 | 1.013522 .0039375 3.46 0.001 1.005834 1.021269
_rcs6 | 1.009026 .001946 4.66 0.000 1.00522 1.012848
_rcs7 | 1.005051 .0011335 4.47 0.000 1.002832 1.007275
_rcs_mot_egr_early1 | .9031387 .0189988 -4.84 0.000 .866659 .9411539
_rcs_mot_egr_early2 | 1.000192 .0186429 0.01 0.992 .9643121 1.037407
_rcs_mot_egr_early3 | .995679 .011562 -0.37 0.709 .9732738 1.0186
_rcs_mot_egr_early4 | .9980108 .0068353 -0.29 0.771 .9847034 1.011498
_rcs_mot_egr_late1 | .9405619 .0185937 -3.10 0.002 .9048159 .97772
_rcs_mot_egr_late2 | 1.000142 .0177204 0.01 0.994 .9660071 1.035484
_rcs_mot_egr_late3 | .9962337 .0108418 -0.35 0.729 .9752092 1.017711
_rcs_mot_egr_late4 | .9989744 .0062961 -0.16 0.871 .9867102 1.011391
_cons | 3.8e+115 3.3e+116 30.86 0.000 1.7e+108 8.3e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54458.139
Iteration 1: log likelihood = -54440.621
Iteration 2: log likelihood = -54440.561
Iteration 3: log likelihood = -54440.561
Log likelihood = -54440.561 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730103 .0501471 18.91 0.000 1.634556 1.831235
mot_egr_late | 1.579044 .0372335 19.37 0.000 1.507728 1.653733
tr_mod2 | 1.218793 .0262264 9.19 0.000 1.168459 1.271295
sex_dum2 | .7601182 .016329 -12.77 0.000 .7287784 .7928058
edad_ini_cons | .9869004 .0019513 -6.67 0.000 .9830833 .9907323
esc1 | 1.128916 .0298175 4.59 0.000 1.071962 1.188897
esc2 | 1.088695 .0259469 3.57 0.000 1.039009 1.140756
sus_prin2 | 1.066948 .0297496 2.32 0.020 1.010204 1.126879
sus_prin3 | 1.393158 .0326581 14.14 0.000 1.330598 1.45866
sus_prin4 | 1.076719 .0378716 2.10 0.036 1.004993 1.153564
sus_prin5 | 1.142399 .0825943 1.84 0.066 .9914639 1.316312
fr_cons_sus_prin2 | .9201438 .0450194 -1.70 0.089 .836006 1.012749
fr_cons_sus_prin3 | .9970258 .0395722 -0.08 0.940 .9224058 1.077682
fr_cons_sus_prin4 | 1.008745 .0420385 0.21 0.835 .9296262 1.094597
fr_cons_sus_prin5 | 1.030618 .0409381 0.76 0.448 .9534243 1.114061
cond_ocu2 | 1.017806 .031813 0.56 0.572 .9573248 1.082108
cond_ocu3 | 1.005969 .141868 0.04 0.966 .7630318 1.326253
cond_ocu4 | 1.103955 .039913 2.74 0.006 1.028435 1.185022
cond_ocu5 | 1.162045 .0890508 1.96 0.050 .9999836 1.35037
cond_ocu6 | 1.131327 .0207259 6.74 0.000 1.091425 1.172687
policonsumo | 1.026772 .0224222 1.21 0.226 .9837524 1.071673
num_hij2 | 1.165181 .0227517 7.83 0.000 1.121431 1.210638
tenviv1 | 1.152432 .0754464 2.17 0.030 1.013654 1.31021
tenviv2 | 1.12779 .0494202 2.74 0.006 1.034971 1.228933
tenviv4 | 1.037663 .0237474 1.62 0.106 .9921472 1.085266
tenviv5 | 1.003758 .0179952 0.21 0.834 .9691003 1.039655
mzone2 | 1.302723 .0273792 12.58 0.000 1.250151 1.357506
mzone3 | 1.464401 .0421218 13.26 0.000 1.384128 1.54933
n_off_vio | 1.355245 .0258694 15.93 0.000 1.305479 1.406909
n_off_acq | 1.814347 .0324509 33.31 0.000 1.751847 1.879078
n_off_sud | 1.256788 .0233123 12.32 0.000 1.211918 1.30332
n_off_oth | 1.360313 .0257452 16.26 0.000 1.310778 1.41172
psy_com2 | 1.070844 .0257042 2.85 0.004 1.021631 1.122427
psy_com3 | 1.058386 .0188005 3.19 0.001 1.022172 1.095884
dep2 | 1.019971 .0195475 1.03 0.302 .9823698 1.059012
rural2 | 1.028773 .0287123 1.02 0.309 .9740089 1.086615
rural3 | 1.054572 .0324425 1.73 0.084 .9928647 1.120114
porc_pobr | 1.229343 .1454754 1.74 0.081 .9748671 1.550247
susini2 | 1.095908 .0455148 2.21 0.027 1.010235 1.188847
susini3 | 1.122734 .0372638 3.49 0.000 1.052023 1.198198
susini4 | 1.082336 .0193437 4.43 0.000 1.045079 1.120921
susini5 | 1.129993 .0561996 2.46 0.014 1.025042 1.24569
ano_nac_corr | .8748584 .0037462 -31.22 0.000 .8675467 .8822318
cohab2 | .9706959 .0310614 -0.93 0.353 .9116865 1.033525
cohab3 | .9912807 .0390098 -0.22 0.824 .9176972 1.070764
cohab4 | .9523344 .0296185 -1.57 0.116 .896017 1.012191
fis_com2 | 1.027112 .0166771 1.65 0.099 .9949403 1.060324
fis_com3 | .9022089 .0336833 -2.76 0.006 .8385484 .9707025
rc_x1 | .8516332 .0048088 -28.44 0.000 .8422601 .8611105
rc_x2 | 1.028776 .0186437 1.57 0.117 .9928768 1.065974
rc_x3 | .8952753 .0414528 -2.39 0.017 .8176068 .980322
_rcs1 | 2.637651 .0469716 54.46 0.000 2.547177 2.731339
_rcs2 | 1.103237 .0182141 5.95 0.000 1.068109 1.13952
_rcs3 | 1.046598 .0109085 4.37 0.000 1.025435 1.068198
_rcs4 | 1.022713 .0056653 4.05 0.000 1.011669 1.033877
_rcs5 | 1.013489 .0036845 3.69 0.000 1.006293 1.020736
_rcs6 | 1.009198 .0032209 2.87 0.004 1.002905 1.015531
_rcs7 | 1.005142 .0013448 3.83 0.000 1.00251 1.007781
_rcs_mot_egr_early1 | .9031842 .0190018 -4.84 0.000 .8666988 .9412056
_rcs_mot_egr_early2 | .9997535 .0188858 -0.01 0.990 .9634148 1.037463
_rcs_mot_egr_early3 | .9972011 .0121727 -0.23 0.818 .9736263 1.021347
_rcs_mot_egr_early4 | .9956537 .0072847 -0.60 0.552 .9814779 1.010034
_rcs_mot_egr_early5 | 1.000725 .0049836 0.15 0.884 .9910048 1.010541
_rcs_mot_egr_late1 | .9405764 .0185964 -3.10 0.002 .9048252 .9777401
_rcs_mot_egr_late2 | 1.001128 .0180307 0.06 0.950 .9664049 1.037099
_rcs_mot_egr_late3 | .9949405 .0114435 -0.44 0.659 .9727626 1.017624
_rcs_mot_egr_late4 | 1.000017 .0067613 0.00 0.998 .9868528 1.013358
_rcs_mot_egr_late5 | .998603 .0045127 -0.31 0.757 .9897973 1.007487
_cons | 3.7e+115 3.2e+116 30.85 0.000 1.7e+108 8.2e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54458.175
Iteration 1: log likelihood = -54439.837
Iteration 2: log likelihood = -54439.779
Iteration 3: log likelihood = -54439.779
Log likelihood = -54439.779 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.730033 .0501457 18.91 0.000 1.634489 1.831162
mot_egr_late | 1.578952 .0372315 19.37 0.000 1.50764 1.653636
tr_mod2 | 1.218742 .0262254 9.19 0.000 1.16841 1.271242
sex_dum2 | .7601248 .0163291 -12.77 0.000 .7287847 .7928126
edad_ini_cons | .9868993 .0019513 -6.67 0.000 .9830823 .9907311
esc1 | 1.128892 .029817 4.59 0.000 1.071939 1.188872
esc2 | 1.088696 .0259469 3.57 0.000 1.03901 1.140758
sus_prin2 | 1.066973 .0297503 2.32 0.020 1.010228 1.126905
sus_prin3 | 1.39319 .0326589 14.15 0.000 1.330628 1.458694
sus_prin4 | 1.07677 .0378734 2.10 0.035 1.005041 1.153619
sus_prin5 | 1.14255 .0826052 1.84 0.065 .9915952 1.316486
fr_cons_sus_prin2 | .9201353 .045019 -1.70 0.089 .8359983 1.01274
fr_cons_sus_prin3 | .9970821 .0395744 -0.07 0.941 .9224579 1.077743
fr_cons_sus_prin4 | 1.008763 .0420392 0.21 0.834 .9296429 1.094617
fr_cons_sus_prin5 | 1.030645 .040939 0.76 0.447 .9534499 1.11409
cond_ocu2 | 1.017808 .0318131 0.56 0.572 .9573273 1.08211
cond_ocu3 | 1.00613 .1418907 0.04 0.965 .7631545 1.326465
cond_ocu4 | 1.104022 .0399154 2.74 0.006 1.028496 1.185093
cond_ocu5 | 1.162254 .0890665 1.96 0.050 1.000164 1.350612
cond_ocu6 | 1.131307 .0207257 6.73 0.000 1.091406 1.172666
policonsumo | 1.026807 .0224229 1.21 0.226 .983786 1.071709
num_hij2 | 1.165174 .0227516 7.83 0.000 1.121425 1.210631
tenviv1 | 1.152354 .0754412 2.17 0.030 1.013585 1.310122
tenviv2 | 1.127767 .0494192 2.74 0.006 1.03495 1.228908
tenviv4 | 1.037636 .0237468 1.61 0.106 .9921214 1.085238
tenviv5 | 1.003744 .017995 0.21 0.835 .9690871 1.039641
mzone2 | 1.302716 .0273789 12.58 0.000 1.250145 1.357498
mzone3 | 1.464425 .0421225 13.26 0.000 1.384151 1.549355
n_off_vio | 1.355234 .0258693 15.92 0.000 1.305468 1.406897
n_off_acq | 1.814306 .0324507 33.31 0.000 1.751806 1.879036
n_off_sud | 1.256785 .0233124 12.32 0.000 1.211915 1.303317
n_off_oth | 1.360286 .0257448 16.26 0.000 1.310751 1.411692
psy_com2 | 1.070909 .0257058 2.85 0.004 1.021694 1.122495
psy_com3 | 1.058407 .0188009 3.20 0.001 1.022192 1.095905
dep2 | 1.019977 .0195475 1.03 0.302 .9823752 1.059018
rural2 | 1.028774 .0287123 1.02 0.309 .9740107 1.086617
rural3 | 1.05454 .0324415 1.73 0.084 .9928348 1.12008
porc_pobr | 1.228996 .1454344 1.74 0.081 .9745913 1.549809
susini2 | 1.095846 .0455122 2.20 0.028 1.010177 1.188779
susini3 | 1.122777 .0372653 3.49 0.000 1.052063 1.198243
susini4 | 1.082298 .019343 4.43 0.000 1.045043 1.120881
susini5 | 1.129898 .0561949 2.46 0.014 1.024956 1.245585
ano_nac_corr | .8748386 .0037462 -31.23 0.000 .867527 .8822119
cohab2 | .9706736 .0310609 -0.93 0.352 .9116651 1.033502
cohab3 | .9913195 .0390116 -0.22 0.825 .9177326 1.070807
cohab4 | .9523087 .0296178 -1.57 0.116 .8959927 1.012164
fis_com2 | 1.027099 .0166769 1.65 0.100 .9949274 1.060311
fis_com3 | .9022304 .0336842 -2.76 0.006 .8385682 .9707258
rc_x1 | .8516165 .0048087 -28.45 0.000 .8422436 .8610937
rc_x2 | 1.028772 .0186436 1.57 0.118 .9928727 1.06597
rc_x3 | .8952792 .0414531 -2.39 0.017 .8176102 .9803263
_rcs1 | 2.637288 .0469476 54.48 0.000 2.546859 2.730928
_rcs2 | 1.102282 .0181879 5.90 0.000 1.067205 1.138512
_rcs3 | 1.048265 .0113626 4.35 0.000 1.02623 1.070774
_rcs4 | 1.021437 .0063324 3.42 0.001 1.009101 1.033924
_rcs5 | 1.013662 .0040547 3.39 0.001 1.005746 1.02164
_rcs6 | 1.00936 .003119 3.02 0.003 1.003266 1.015492
_rcs7 | 1.005097 .0020589 2.48 0.013 1.001069 1.00914
_rcs_mot_egr_early1 | .9031529 .0189976 -4.84 0.000 .8666754 .9411657
_rcs_mot_egr_early2 | 1.000685 .0189819 0.04 0.971 .9641647 1.038589
_rcs_mot_egr_early3 | .9970174 .0125813 -0.24 0.813 .9726609 1.021984
_rcs_mot_egr_early4 | .995911 .0075668 -0.54 0.590 .9811903 1.010853
_rcs_mot_egr_early5 | 1.000146 .0051158 0.03 0.977 .9901698 1.010224
_rcs_mot_egr_early6 | .9981553 .0037314 -0.49 0.621 .9908686 1.005496
_rcs_mot_egr_late1 | .9408404 .0185977 -3.09 0.002 .9050867 .9780065
_rcs_mot_egr_late2 | 1.002336 .0181605 0.13 0.898 .9673671 1.03857
_rcs_mot_egr_late3 | .9927418 .0118368 -0.61 0.541 .969811 1.016215
_rcs_mot_egr_late4 | 1.001799 .0070154 0.26 0.797 .9881428 1.015644
_rcs_mot_egr_late5 | .9977446 .0046444 -0.49 0.628 .9886831 1.006889
_rcs_mot_egr_late6 | 1.000548 .0033207 0.17 0.869 .9940611 1.007078
_cons | 3.9e+115 3.4e+116 30.86 0.000 1.8e+108 8.6e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54458.106
Iteration 1: log likelihood = -54438.853
Iteration 2: log likelihood = -54438.776
Iteration 3: log likelihood = -54438.776
Log likelihood = -54438.776 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.73011 .0501478 18.91 0.000 1.634562 1.831243
mot_egr_late | 1.578933 .0372311 19.37 0.000 1.507622 1.653617
tr_mod2 | 1.218767 .0262261 9.19 0.000 1.168434 1.271269
sex_dum2 | .7601398 .0163295 -12.77 0.000 .7287991 .7928283
edad_ini_cons | .9868997 .0019513 -6.67 0.000 .9830827 .9907315
esc1 | 1.128858 .0298162 4.59 0.000 1.071906 1.188835
esc2 | 1.088676 .0259465 3.56 0.000 1.038991 1.140737
sus_prin2 | 1.067006 .029751 2.33 0.020 1.01026 1.12694
sus_prin3 | 1.393207 .0326593 14.15 0.000 1.330644 1.458711
sus_prin4 | 1.076821 .0378752 2.10 0.035 1.005088 1.153673
sus_prin5 | 1.1426 .082609 1.84 0.065 .9916383 1.316544
fr_cons_sus_prin2 | .9201738 .0450209 -1.70 0.089 .8360333 1.012782
fr_cons_sus_prin3 | .9971556 .0395773 -0.07 0.943 .9225259 1.077823
fr_cons_sus_prin4 | 1.008815 .0420414 0.21 0.833 .9296907 1.094673
fr_cons_sus_prin5 | 1.030684 .0409406 0.76 0.447 .9534856 1.114132
cond_ocu2 | 1.017815 .0318133 0.56 0.572 .9573335 1.082117
cond_ocu3 | 1.006087 .1418846 0.04 0.966 .7631217 1.326408
cond_ocu4 | 1.104011 .039915 2.74 0.006 1.028487 1.185082
cond_ocu5 | 1.162027 .0890492 1.96 0.050 .9999691 1.350349
cond_ocu6 | 1.131283 .0207253 6.73 0.000 1.091382 1.172642
policonsumo | 1.026769 .0224222 1.21 0.226 .9837499 1.07167
num_hij2 | 1.165172 .0227516 7.83 0.000 1.121422 1.210629
tenviv1 | 1.152443 .0754467 2.17 0.030 1.013664 1.310222
tenviv2 | 1.127721 .0494173 2.74 0.006 1.034907 1.228858
tenviv4 | 1.037656 .0237474 1.62 0.106 .9921405 1.08526
tenviv5 | 1.003763 .0179953 0.21 0.834 .9691054 1.03966
mzone2 | 1.302726 .0273791 12.58 0.000 1.250154 1.357508
mzone3 | 1.464499 .0421251 13.26 0.000 1.384219 1.549434
n_off_vio | 1.355215 .0258689 15.92 0.000 1.30545 1.406877
n_off_acq | 1.814346 .0324513 33.31 0.000 1.751845 1.879078
n_off_sud | 1.256791 .0233125 12.32 0.000 1.21192 1.303323
n_off_oth | 1.360322 .0257454 16.26 0.000 1.310786 1.411729
psy_com2 | 1.070939 .0257064 2.86 0.004 1.021722 1.122527
psy_com3 | 1.058421 .0188011 3.20 0.001 1.022205 1.095919
dep2 | 1.019994 .0195479 1.03 0.302 .9823911 1.059035
rural2 | 1.028786 .0287126 1.02 0.309 .9740216 1.086629
rural3 | 1.054509 .0324407 1.73 0.084 .9928049 1.120047
porc_pobr | 1.228749 .1454051 1.74 0.082 .9743961 1.549498
susini2 | 1.095894 .0455143 2.20 0.027 1.010222 1.188832
susini3 | 1.122783 .0372655 3.49 0.000 1.052069 1.19825
susini4 | 1.082281 .0193427 4.42 0.000 1.045027 1.120864
susini5 | 1.129887 .0561945 2.46 0.014 1.024945 1.245573
ano_nac_corr | .8748287 .0037462 -31.23 0.000 .8675171 .8822021
cohab2 | .9706193 .0310593 -0.93 0.351 .9116139 1.033444
cohab3 | .9913279 .0390119 -0.22 0.825 .9177404 1.070816
cohab4 | .952278 .0296168 -1.57 0.116 .8959638 1.012132
fis_com2 | 1.027072 .0166765 1.65 0.100 .9949015 1.060283
fis_com3 | .902212 .0336836 -2.76 0.006 .838551 .970706
rc_x1 | .8516072 .0048087 -28.45 0.000 .8422344 .8610844
rc_x2 | 1.028764 .0186434 1.56 0.118 .9928645 1.065961
rc_x3 | .8953024 .0414541 -2.39 0.017 .8176315 .9803517
_rcs1 | 2.63681 .0469086 54.50 0.000 2.546455 2.730371
_rcs2 | 1.101236 .0180736 5.88 0.000 1.066376 1.137235
_rcs3 | 1.050507 .011667 4.44 0.000 1.027887 1.073624
_rcs4 | 1.01959 .0069211 2.86 0.004 1.006115 1.033246
_rcs5 | 1.015004 .0045324 3.34 0.001 1.00616 1.023927
_rcs6 | 1.007499 .0035121 2.14 0.032 1.000639 1.014406
_rcs7 | 1.005301 .0028139 1.89 0.059 .9998009 1.010831
_rcs_mot_egr_early1 | .9031883 .0189882 -4.84 0.000 .8667284 .941182
_rcs_mot_egr_early2 | 1.002213 .0190692 0.12 0.908 .9655265 1.040294
_rcs_mot_egr_early3 | .993856 .0129834 -0.47 0.637 .968732 1.019632
_rcs_mot_egr_early4 | .9991189 .0081795 -0.11 0.914 .9832154 1.01528
_rcs_mot_egr_early5 | .9971666 .0054765 -0.52 0.605 .9864904 1.007958
_rcs_mot_egr_early6 | 1.002129 .0043171 0.49 0.621 .9937036 1.010626
_rcs_mot_egr_early7 | .9970836 .0034903 -0.83 0.404 .9902661 1.003948
_rcs_mot_egr_late1 | .9410343 .018592 -3.08 0.002 .9052911 .9781887
_rcs_mot_egr_late2 | 1.003222 .0182338 0.18 0.860 .9681131 1.039603
_rcs_mot_egr_late3 | .9907725 .0122598 -0.75 0.454 .9670328 1.015095
_rcs_mot_egr_late4 | 1.002959 .0076262 0.39 0.698 .9881225 1.018018
_rcs_mot_egr_late5 | .9973588 .0050155 -0.53 0.599 .9875768 1.007238
_rcs_mot_egr_late6 | 1.001009 .0039348 0.26 0.797 .9933269 1.008751
_rcs_mot_egr_late7 | 1.00101 .0031752 0.32 0.750 .994806 1.007253
_cons | 4.0e+115 3.4e+116 30.86 0.000 1.8e+108 8.8e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54456.351
Iteration 1: log likelihood = -54439.913
Iteration 2: log likelihood = -54439.864
Iteration 3: log likelihood = -54439.864
Log likelihood = -54439.864 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.72866 .049983 18.93 0.000 1.633419 1.829454
mot_egr_late | 1.577625 .0370575 19.41 0.000 1.50664 1.651954
tr_mod2 | 1.218745 .0262243 9.19 0.000 1.168415 1.271243
sex_dum2 | .7602058 .0163309 -12.76 0.000 .7288624 .7928971
edad_ini_cons | .9868948 .0019513 -6.67 0.000 .9830778 .9907266
esc1 | 1.128887 .0298167 4.59 0.000 1.071934 1.188866
esc2 | 1.088643 .0259456 3.56 0.000 1.03896 1.140702
sus_prin2 | 1.067047 .0297519 2.33 0.020 1.010299 1.126983
sus_prin3 | 1.393249 .03266 14.15 0.000 1.330685 1.458755
sus_prin4 | 1.076861 .0378765 2.11 0.035 1.005125 1.153716
sus_prin5 | 1.142407 .0825942 1.84 0.066 .9914718 1.31632
fr_cons_sus_prin2 | .9201984 .0450219 -1.70 0.089 .836056 1.012809
fr_cons_sus_prin3 | .9970712 .0395739 -0.07 0.941 .922448 1.077731
fr_cons_sus_prin4 | 1.008785 .0420401 0.21 0.834 .9296637 1.094641
fr_cons_sus_prin5 | 1.030647 .0409393 0.76 0.447 .9534518 1.114093
cond_ocu2 | 1.017765 .0318117 0.56 0.573 .9572869 1.082064
cond_ocu3 | 1.005905 .1418581 0.04 0.967 .7629851 1.326166
cond_ocu4 | 1.103879 .03991 2.73 0.006 1.028364 1.18494
cond_ocu5 | 1.161901 .0890379 1.96 0.050 .9998628 1.350199
cond_ocu6 | 1.131335 .0207259 6.74 0.000 1.091434 1.172695
policonsumo | 1.026728 .0224201 1.21 0.227 .9837128 1.071625
num_hij2 | 1.165161 .0227511 7.83 0.000 1.121412 1.210617
tenviv1 | 1.152229 .0754329 2.16 0.030 1.013476 1.30998
tenviv2 | 1.127905 .049425 2.75 0.006 1.035077 1.229058
tenviv4 | 1.037725 .0237488 1.62 0.106 .9922072 1.085332
tenviv5 | 1.003853 .017997 0.21 0.830 .9691924 1.039754
mzone2 | 1.302765 .0273801 12.58 0.000 1.250191 1.357549
mzone3 | 1.464461 .0421235 13.26 0.000 1.384184 1.549393
n_off_vio | 1.355219 .0258683 15.92 0.000 1.305455 1.40688
n_off_acq | 1.814286 .0324494 33.31 0.000 1.751788 1.879014
n_off_sud | 1.256784 .0233118 12.32 0.000 1.211914 1.303315
n_off_oth | 1.360272 .0257439 16.26 0.000 1.310739 1.411677
psy_com2 | 1.070908 .0257051 2.85 0.004 1.021694 1.122493
psy_com3 | 1.058395 .0188006 3.19 0.001 1.022181 1.095893
dep2 | 1.019984 .0195477 1.03 0.302 .9823822 1.059026
rural2 | 1.02882 .0287135 1.02 0.309 .9740538 1.086665
rural3 | 1.054586 .0324434 1.73 0.084 .9928775 1.12013
porc_pobr | 1.23025 .1455789 1.75 0.080 .975592 1.551381
susini2 | 1.096017 .0455184 2.21 0.027 1.010337 1.188963
susini3 | 1.122855 .0372671 3.49 0.000 1.052138 1.198326
susini4 | 1.082294 .0193428 4.42 0.000 1.045039 1.120877
susini5 | 1.129886 .056195 2.46 0.014 1.024944 1.245573
ano_nac_corr | .8748071 .0037459 -31.24 0.000 .8674959 .8821799
cohab2 | .9707538 .0310632 -0.93 0.354 .9117411 1.033586
cohab3 | .9913129 .0390108 -0.22 0.825 .9177274 1.070799
cohab4 | .9523994 .0296204 -1.57 0.117 .8960785 1.01226
fis_com2 | 1.027105 .0166768 1.65 0.100 .9949335 1.060316
fis_com3 | .9022109 .0336834 -2.76 0.006 .8385502 .9707046
rc_x1 | .851587 .0048085 -28.45 0.000 .8422145 .8610639
rc_x2 | 1.028753 .0186431 1.56 0.118 .9928547 1.06595
rc_x3 | .8953399 .0414556 -2.39 0.017 .8176661 .9803922
_rcs1 | 2.631326 .0396927 64.14 0.000 2.554668 2.710283
_rcs2 | 1.10341 .0063375 17.13 0.000 1.091059 1.115902
_rcs3 | 1.042996 .0042487 10.33 0.000 1.034702 1.051357
_rcs4 | 1.021791 .0026674 8.26 0.000 1.016576 1.027033
_rcs5 | 1.013382 .0017953 7.50 0.000 1.009869 1.016907
_rcs6 | 1.009303 .0013967 6.69 0.000 1.006569 1.012044
_rcs7 | 1.007109 .0011977 5.96 0.000 1.004765 1.009459
_rcs8 | 1.003909 .0010145 3.86 0.000 1.001923 1.0059
_rcs_mot_egr_early1 | .9059748 .0161261 -5.55 0.000 .8749132 .9381391
_rcs_mot_egr_late1 | .9429687 .0154761 -3.58 0.000 .9131188 .9737943
_cons | 4.2e+115 3.6e+116 30.87 0.000 1.9e+108 9.2e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54456.436
Iteration 1: log likelihood = -54439.881
Iteration 2: log likelihood = -54439.829
Iteration 3: log likelihood = -54439.829
Log likelihood = -54439.829 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.72953 .0501179 18.91 0.000 1.634039 1.830603
mot_egr_late | 1.578377 .0372024 19.36 0.000 1.50712 1.653003
tr_mod2 | 1.218786 .0262258 9.19 0.000 1.168453 1.271287
sex_dum2 | .7602026 .0163309 -12.76 0.000 .7288592 .7928939
edad_ini_cons | .9868957 .0019513 -6.67 0.000 .9830787 .9907275
esc1 | 1.128876 .0298164 4.59 0.000 1.071924 1.188854
esc2 | 1.088636 .0259454 3.56 0.000 1.038953 1.140694
sus_prin2 | 1.067068 .0297528 2.33 0.020 1.010319 1.127006
sus_prin3 | 1.39327 .0326607 14.15 0.000 1.330705 1.458777
sus_prin4 | 1.076868 .0378769 2.11 0.035 1.005131 1.153724
sus_prin5 | 1.142527 .082604 1.84 0.065 .9915742 1.31646
fr_cons_sus_prin2 | .9201902 .0450215 -1.70 0.089 .8360485 1.0128
fr_cons_sus_prin3 | .9970674 .0395738 -0.07 0.941 .9224444 1.077727
fr_cons_sus_prin4 | 1.008772 .0420396 0.21 0.834 .9296509 1.094626
fr_cons_sus_prin5 | 1.03064 .040939 0.76 0.447 .9534447 1.114085
cond_ocu2 | 1.017764 .0318117 0.56 0.573 .9572852 1.082063
cond_ocu3 | 1.006022 .1418752 0.04 0.966 .7630724 1.326322
cond_ocu4 | 1.103869 .0399096 2.73 0.006 1.028355 1.184928
cond_ocu5 | 1.161879 .0890364 1.96 0.050 .9998433 1.350173
cond_ocu6 | 1.131327 .0207258 6.74 0.000 1.091426 1.172687
policonsumo | 1.026754 .0224211 1.21 0.227 .9837367 1.071653
num_hij2 | 1.165155 .0227511 7.83 0.000 1.121406 1.210611
tenviv1 | 1.152282 .0754365 2.17 0.030 1.013522 1.31004
tenviv2 | 1.127916 .0494256 2.75 0.006 1.035087 1.22907
tenviv4 | 1.037719 .0237487 1.62 0.106 .9922007 1.085325
tenviv5 | 1.00385 .0179969 0.21 0.830 .9691888 1.03975
mzone2 | 1.302773 .0273804 12.58 0.000 1.250199 1.357558
mzone3 | 1.464429 .0421229 13.26 0.000 1.384153 1.54936
n_off_vio | 1.355229 .0258685 15.92 0.000 1.305464 1.40689
n_off_acq | 1.814302 .0324497 33.31 0.000 1.751803 1.87903
n_off_sud | 1.256775 .0233117 12.32 0.000 1.211906 1.303306
n_off_oth | 1.360276 .025744 16.26 0.000 1.310743 1.411681
psy_com2 | 1.070911 .0257053 2.85 0.004 1.021696 1.122496
psy_com3 | 1.058399 .0188007 3.20 0.001 1.022185 1.095897
dep2 | 1.019987 .0195478 1.03 0.302 .982385 1.059029
rural2 | 1.028806 .0287133 1.02 0.309 .97404 1.08665
rural3 | 1.054575 .0324431 1.73 0.084 .9928669 1.120119
porc_pobr | 1.230325 .1455889 1.75 0.080 .9756503 1.551479
susini2 | 1.095989 .0455175 2.21 0.027 1.010311 1.188933
susini3 | 1.122853 .0372673 3.49 0.000 1.052136 1.198324
susini4 | 1.082295 .0193429 4.43 0.000 1.04504 1.120879
susini5 | 1.129886 .0561948 2.46 0.014 1.024944 1.245573
ano_nac_corr | .8747995 .0037461 -31.24 0.000 .8674881 .8821726
cohab2 | .9707375 .0310627 -0.93 0.353 .9117256 1.033569
cohab3 | .9912922 .03901 -0.22 0.824 .9177082 1.070776
cohab4 | .9523844 .02962 -1.57 0.117 .8960643 1.012244
fis_com2 | 1.027102 .0166768 1.65 0.100 .994931 1.060314
fis_com3 | .9022181 .0336837 -2.76 0.006 .8385568 .9707125
rc_x1 | .8515797 .0048085 -28.45 0.000 .8422071 .8610566
rc_x2 | 1.028757 .0186432 1.56 0.118 .9928584 1.065954
rc_x3 | .8953263 .0414551 -2.39 0.017 .8176535 .9803775
_rcs1 | 2.637749 .0470012 54.43 0.000 2.547218 2.731497
_rcs2 | 1.107032 .0153894 7.31 0.000 1.077276 1.137609
_rcs3 | 1.043484 .0046635 9.52 0.000 1.034384 1.052665
_rcs4 | 1.021901 .002702 8.19 0.000 1.016619 1.027211
_rcs5 | 1.013395 .0017964 7.51 0.000 1.009881 1.016923
_rcs6 | 1.009303 .0013968 6.69 0.000 1.006569 1.012044
_rcs7 | 1.00711 .0011978 5.96 0.000 1.004765 1.00946
_rcs8 | 1.003911 .0010146 3.86 0.000 1.001924 1.005902
_rcs_mot_egr_early1 | .9033492 .0190013 -4.83 0.000 .8668646 .9413695
_rcs_mot_egr_early2 | .9959077 .0160132 -0.26 0.799 .9650118 1.027793
_rcs_mot_egr_late1 | .9404915 .0185967 -3.10 0.002 .9047399 .9776559
_rcs_mot_egr_late2 | .9964415 .0150439 -0.24 0.813 .967388 1.026368
_cons | 4.3e+115 3.7e+116 30.87 0.000 1.9e+108 9.4e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54456.338
Iteration 1: log likelihood = -54439.744
Iteration 2: log likelihood = -54439.694
Iteration 3: log likelihood = -54439.694
Log likelihood = -54439.694 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.73 .0501427 18.91 0.000 1.634462 1.831123
mot_egr_late | 1.578743 .0372249 19.37 0.000 1.507443 1.653414
tr_mod2 | 1.218805 .0262265 9.20 0.000 1.168471 1.271307
sex_dum2 | .7602061 .0163309 -12.76 0.000 .7288626 .7928975
edad_ini_cons | .986897 .0019513 -6.67 0.000 .98308 .9907288
esc1 | 1.128875 .0298164 4.59 0.000 1.071923 1.188853
esc2 | 1.088648 .0259457 3.56 0.000 1.038965 1.140707
sus_prin2 | 1.067101 .0297539 2.33 0.020 1.010349 1.12704
sus_prin3 | 1.3933 .0326617 14.15 0.000 1.330733 1.458809
sus_prin4 | 1.076859 .0378768 2.11 0.035 1.005123 1.153715
sus_prin5 | 1.142715 .0826179 1.85 0.065 .9917363 1.316677
fr_cons_sus_prin2 | .9201572 .04502 -1.70 0.089 .8360183 1.012764
fr_cons_sus_prin3 | .9970651 .0395737 -0.07 0.941 .9224422 1.077725
fr_cons_sus_prin4 | 1.008765 .0420393 0.21 0.834 .9296444 1.094619
fr_cons_sus_prin5 | 1.030621 .0409382 0.76 0.448 .9534276 1.114065
cond_ocu2 | 1.017746 .0318111 0.56 0.574 .9572688 1.082044
cond_ocu3 | 1.006124 .1418896 0.04 0.965 .7631499 1.326456
cond_ocu4 | 1.103838 .0399086 2.73 0.006 1.028326 1.184896
cond_ocu5 | 1.162014 .0890474 1.96 0.050 .9999586 1.350332
cond_ocu6 | 1.131311 .0207256 6.73 0.000 1.09141 1.17267
policonsumo | 1.026809 .0224227 1.21 0.226 .9837881 1.07171
num_hij2 | 1.165158 .0227512 7.83 0.000 1.121409 1.210613
tenviv1 | 1.152356 .0754415 2.17 0.030 1.013587 1.310124
tenviv2 | 1.127942 .0494269 2.75 0.006 1.035111 1.229099
tenviv4 | 1.03771 .0237484 1.62 0.106 .9921923 1.085316
tenviv5 | 1.003843 .0179968 0.21 0.831 .9691822 1.039743
mzone2 | 1.302768 .0273802 12.58 0.000 1.250195 1.357553
mzone3 | 1.464404 .0421224 13.26 0.000 1.384129 1.549334
n_off_vio | 1.355234 .0258686 15.92 0.000 1.305469 1.406896
n_off_acq | 1.814299 .0324496 33.31 0.000 1.751801 1.879027
n_off_sud | 1.256748 .0233113 12.32 0.000 1.211879 1.303278
n_off_oth | 1.360265 .0257437 16.26 0.000 1.310733 1.411669
psy_com2 | 1.070924 .0257059 2.85 0.004 1.021708 1.12251
psy_com3 | 1.058407 .0188009 3.20 0.001 1.022192 1.095905
dep2 | 1.019976 .0195476 1.03 0.302 .9823736 1.059017
rural2 | 1.028784 .0287128 1.02 0.309 .9740192 1.086627
rural3 | 1.054569 .0324429 1.73 0.084 .9928614 1.120112
porc_pobr | 1.230146 .145569 1.75 0.080 .9755058 1.551255
susini2 | 1.095916 .0455149 2.21 0.027 1.010243 1.188855
susini3 | 1.12287 .037268 3.49 0.000 1.052151 1.198342
susini4 | 1.082294 .019343 4.42 0.000 1.045039 1.120878
susini5 | 1.129887 .0561947 2.46 0.014 1.024945 1.245574
ano_nac_corr | .8747958 .0037461 -31.24 0.000 .8674844 .8821689
cohab2 | .9707032 .0310617 -0.93 0.353 .9116931 1.033533
cohab3 | .9912411 .0390081 -0.22 0.823 .9176607 1.070721
cohab4 | .9523411 .0296187 -1.57 0.116 .8960233 1.012199
fis_com2 | 1.027088 .0166765 1.65 0.100 .9949171 1.060299
fis_com3 | .9022286 .0336841 -2.76 0.006 .8385665 .9707237
rc_x1 | .8515761 .0048085 -28.45 0.000 .8422035 .861053
rc_x2 | 1.028763 .0186434 1.56 0.118 .9928638 1.06596
rc_x3 | .8953033 .0414541 -2.39 0.017 .8176324 .9803525
_rcs1 | 2.637202 .0469217 54.50 0.000 2.546822 2.730789
_rcs2 | 1.102979 .0175887 6.15 0.000 1.069039 1.137996
_rcs3 | 1.046209 .0075527 6.26 0.000 1.03151 1.061117
_rcs4 | 1.023694 .0047931 5.00 0.000 1.014343 1.033132
_rcs5 | 1.014143 .002422 5.88 0.000 1.009407 1.018901
_rcs6 | 1.009518 .0014672 6.52 0.000 1.006646 1.012398
_rcs7 | 1.007135 .001199 5.97 0.000 1.004788 1.009488
_rcs8 | 1.003909 .0010148 3.86 0.000 1.001922 1.0059
_rcs_mot_egr_early1 | .9033937 .0189873 -4.83 0.000 .8669353 .9413854
_rcs_mot_egr_early2 | 1.000368 .0181748 0.02 0.984 .9653728 1.036632
_rcs_mot_egr_early3 | .9945409 .0100592 -0.54 0.588 .9750194 1.014453
_rcs_mot_egr_late1 | .9406762 .0185792 -3.10 0.002 .9049574 .9778048
_rcs_mot_egr_late2 | .9997058 .0172138 -0.02 0.986 .9665304 1.03402
_rcs_mot_egr_late3 | .9963539 .0093314 -0.39 0.697 .9782316 1.014812
_cons | 4.3e+115 3.7e+116 30.87 0.000 2.0e+108 9.5e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54456.365
Iteration 1: log likelihood = -54439.754
Iteration 2: log likelihood = -54439.704
Iteration 3: log likelihood = -54439.704
Log likelihood = -54439.704 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729963 .0501424 18.91 0.000 1.634425 1.831086
mot_egr_late | 1.578745 .0372255 19.37 0.000 1.507445 1.653418
tr_mod2 | 1.218801 .0262265 9.20 0.000 1.168467 1.271304
sex_dum2 | .7602054 .0163309 -12.76 0.000 .728862 .7928968
edad_ini_cons | .9868967 .0019513 -6.67 0.000 .9830796 .9907285
esc1 | 1.128873 .0298163 4.59 0.000 1.071921 1.188851
esc2 | 1.088647 .0259457 3.56 0.000 1.038964 1.140706
sus_prin2 | 1.067102 .029754 2.33 0.020 1.01035 1.127042
sus_prin3 | 1.393304 .0326618 14.15 0.000 1.330736 1.458813
sus_prin4 | 1.076858 .0378767 2.11 0.035 1.005122 1.153714
sus_prin5 | 1.142703 .0826171 1.85 0.065 .9917256 1.316664
fr_cons_sus_prin2 | .920163 .0450203 -1.70 0.089 .8360236 1.01277
fr_cons_sus_prin3 | .9970718 .039574 -0.07 0.941 .9224484 1.077732
fr_cons_sus_prin4 | 1.008768 .0420395 0.21 0.834 .9296473 1.094622
fr_cons_sus_prin5 | 1.030628 .0409385 0.76 0.448 .9534337 1.114072
cond_ocu2 | 1.017748 .0318112 0.56 0.574 .9572708 1.082046
cond_ocu3 | 1.006155 .1418943 0.04 0.965 .7631737 1.326499
cond_ocu4 | 1.103844 .0399088 2.73 0.006 1.028331 1.184901
cond_ocu5 | 1.162009 .0890478 1.96 0.050 .9999533 1.350328
cond_ocu6 | 1.131309 .0207256 6.73 0.000 1.091408 1.172668
policonsumo | 1.026806 .0224227 1.21 0.226 .9837852 1.071708
num_hij2 | 1.165154 .0227511 7.83 0.000 1.121405 1.21061
tenviv1 | 1.15234 .0754406 2.17 0.030 1.013572 1.310106
tenviv2 | 1.127943 .0494271 2.75 0.006 1.035111 1.229101
tenviv4 | 1.037704 .0237483 1.62 0.106 .9921864 1.085309
tenviv5 | 1.003843 .0179968 0.21 0.831 .9691821 1.039743
mzone2 | 1.302759 .0273801 12.58 0.000 1.250185 1.357544
mzone3 | 1.464418 .0421232 13.26 0.000 1.384143 1.54935
n_off_vio | 1.355233 .0258686 15.92 0.000 1.305468 1.406895
n_off_acq | 1.814301 .0324497 33.31 0.000 1.751802 1.879029
n_off_sud | 1.256753 .0233114 12.32 0.000 1.211884 1.303283
n_off_oth | 1.360271 .0257438 16.26 0.000 1.310738 1.411675
psy_com2 | 1.070926 .0257059 2.85 0.004 1.021711 1.122513
psy_com3 | 1.058407 .0188009 3.20 0.001 1.022192 1.095906
dep2 | 1.019976 .0195476 1.03 0.302 .9823739 1.059017
rural2 | 1.02879 .0287129 1.02 0.309 .9740249 1.086634
rural3 | 1.054568 .0324429 1.73 0.084 .9928607 1.120111
porc_pobr | 1.230128 .1455674 1.75 0.080 .975491 1.551234
susini2 | 1.095918 .045515 2.21 0.027 1.010244 1.188857
susini3 | 1.122873 .0372682 3.49 0.000 1.052154 1.198346
susini4 | 1.082294 .019343 4.42 0.000 1.045039 1.120878
susini5 | 1.129887 .0561948 2.46 0.014 1.024945 1.245573
ano_nac_corr | .8747928 .0037461 -31.24 0.000 .8674812 .882166
cohab2 | .9707128 .0310621 -0.93 0.353 .911702 1.033543
cohab3 | .9912566 .0390088 -0.22 0.823 .9176748 1.070738
cohab4 | .9523531 .0296192 -1.57 0.116 .8960345 1.012211
fis_com2 | 1.027086 .0166765 1.65 0.100 .9949155 1.060297
fis_com3 | .9022265 .0336841 -2.76 0.006 .8385645 .9707215
rc_x1 | .8515731 .0048086 -28.45 0.000 .8422004 .8610501
rc_x2 | 1.028763 .0186433 1.56 0.118 .9928636 1.06596
rc_x3 | .8953048 .0414541 -2.39 0.017 .8176339 .9803541
_rcs1 | 2.637085 .046949 54.47 0.000 2.546654 2.730728
_rcs2 | 1.102802 .0180146 5.99 0.000 1.068054 1.138682
_rcs3 | 1.046666 .0098593 4.84 0.000 1.027519 1.066169
_rcs4 | 1.023494 .0046714 5.09 0.000 1.014379 1.03269
_rcs5 | 1.013842 .0039228 3.55 0.000 1.006183 1.02156
_rcs6 | 1.009429 .0025751 3.68 0.000 1.004395 1.014489
_rcs7 | 1.007146 .0013368 5.36 0.000 1.004529 1.009769
_rcs8 | 1.003913 .0010148 3.86 0.000 1.001926 1.005904
_rcs_mot_egr_early1 | .9034 .0190013 -4.83 0.000 .8669153 .9414202
_rcs_mot_egr_early2 | 1.000271 .0186206 0.01 0.988 .9644331 1.037441
_rcs_mot_egr_early3 | .9950578 .0115701 -0.43 0.670 .9726373 1.017995
_rcs_mot_egr_early4 | .9989683 .0068371 -0.15 0.880 .9856574 1.012459
_rcs_mot_egr_late1 | .9407568 .0185944 -3.09 0.002 .9050093 .9779164
_rcs_mot_egr_late2 | 1.00026 .017701 0.01 0.988 .9661612 1.035562
_rcs_mot_egr_late3 | .995635 .0108499 -0.40 0.688 .9745952 1.017129
_rcs_mot_egr_late4 | .9999842 .0063033 -0.00 0.998 .987706 1.012415
_cons | 4.3e+115 3.7e+116 30.87 0.000 2.0e+108 9.5e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54456.342
Iteration 1: log likelihood = -54439.218
Iteration 2: log likelihood = -54439.159
Iteration 3: log likelihood = -54439.159
Log likelihood = -54439.159 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729926 .0501418 18.91 0.000 1.634389 1.831047
mot_egr_late | 1.578817 .037228 19.37 0.000 1.507512 1.653495
tr_mod2 | 1.218822 .026227 9.20 0.000 1.168487 1.271326
sex_dum2 | .7601987 .0163308 -12.76 0.000 .7288554 .7928898
edad_ini_cons | .9868978 .0019513 -6.67 0.000 .9830808 .9907297
esc1 | 1.128867 .0298162 4.59 0.000 1.071915 1.188845
esc2 | 1.088642 .0259456 3.56 0.000 1.038959 1.140701
sus_prin2 | 1.067071 .0297532 2.33 0.020 1.010321 1.127009
sus_prin3 | 1.393277 .0326612 14.15 0.000 1.33071 1.458785
sus_prin4 | 1.076815 .0378752 2.10 0.035 1.005082 1.153668
sus_prin5 | 1.142617 .0826111 1.84 0.065 .9916515 1.316566
fr_cons_sus_prin2 | .9201578 .0450201 -1.70 0.089 .8360188 1.012765
fr_cons_sus_prin3 | .9970617 .0395736 -0.07 0.941 .922439 1.077721
fr_cons_sus_prin4 | 1.008762 .0420392 0.21 0.834 .9296417 1.094616
fr_cons_sus_prin5 | 1.030624 .0409384 0.76 0.448 .9534303 1.114068
cond_ocu2 | 1.017758 .0318116 0.56 0.573 .9572799 1.082057
cond_ocu3 | 1.006129 .1418906 0.04 0.965 .7631532 1.326464
cond_ocu4 | 1.103795 .0399073 2.73 0.006 1.028285 1.18485
cond_ocu5 | 1.161947 .0890434 1.96 0.050 .9998988 1.350257
cond_ocu6 | 1.131326 .0207259 6.74 0.000 1.091425 1.172686
policonsumo | 1.026792 .0224225 1.21 0.226 .9837717 1.071693
num_hij2 | 1.165181 .0227517 7.83 0.000 1.121431 1.210638
tenviv1 | 1.152454 .075448 2.17 0.030 1.013672 1.310235
tenviv2 | 1.127899 .0494253 2.75 0.006 1.035071 1.229053
tenviv4 | 1.037713 .0237486 1.62 0.106 .9921953 1.085319
tenviv5 | 1.003853 .017997 0.21 0.830 .9691919 1.039753
mzone2 | 1.302779 .0273806 12.58 0.000 1.250204 1.357564
mzone3 | 1.464426 .0421236 13.26 0.000 1.38415 1.549359
n_off_vio | 1.355218 .0258684 15.92 0.000 1.305454 1.406879
n_off_acq | 1.814338 .0324501 33.31 0.000 1.751839 1.879067
n_off_sud | 1.256774 .0233118 12.32 0.000 1.211904 1.303305
n_off_oth | 1.360277 .0257439 16.26 0.000 1.310744 1.411682
psy_com2 | 1.070895 .0257055 2.85 0.004 1.02168 1.122481
psy_com3 | 1.058409 .0188009 3.20 0.001 1.022194 1.095907
dep2 | 1.019963 .0195474 1.03 0.302 .9823616 1.059004
rural2 | 1.028786 .0287129 1.02 0.309 .974021 1.086629
rural3 | 1.054571 .0324429 1.73 0.084 .9928627 1.120114
porc_pobr | 1.230149 .14557 1.75 0.080 .9755076 1.551261
susini2 | 1.095968 .0455173 2.21 0.027 1.010291 1.188912
susini3 | 1.122809 .0372662 3.49 0.000 1.052094 1.198278
susini4 | 1.082313 .0193434 4.43 0.000 1.045057 1.120897
susini5 | 1.12996 .0561986 2.46 0.014 1.025011 1.245654
ano_nac_corr | .8747995 .0037462 -31.24 0.000 .8674878 .8821729
cohab2 | .9706764 .0310608 -0.93 0.352 .9116682 1.033504
cohab3 | .9912172 .0390072 -0.22 0.823 .9176384 1.070696
cohab4 | .9523227 .0296181 -1.57 0.116 .8960061 1.012179
fis_com2 | 1.027097 .0166768 1.65 0.100 .9949261 1.060309
fis_com3 | .9022154 .0336836 -2.76 0.006 .8385543 .9707096
rc_x1 | .8515761 .0048086 -28.45 0.000 .8422033 .8610531
rc_x2 | 1.028775 .0186436 1.57 0.117 .9928751 1.065972
rc_x3 | .8952838 .0414532 -2.39 0.017 .8176145 .9803313
_rcs1 | 2.637195 .046973 54.44 0.000 2.546719 2.730887
_rcs2 | 1.103304 .0183148 5.92 0.000 1.067985 1.13979
_rcs3 | 1.045682 .0108596 4.30 0.000 1.024612 1.067184
_rcs4 | 1.024375 .0054036 4.57 0.000 1.013839 1.035021
_rcs5 | 1.014291 .0039373 3.66 0.000 1.006604 1.022038
_rcs6 | 1.008878 .0033046 2.70 0.007 1.002422 1.015376
_rcs7 | 1.006702 .0022124 3.04 0.002 1.002375 1.011048
_rcs8 | 1.003877 .0010381 3.74 0.000 1.001844 1.005914
_rcs_mot_egr_early1 | .9034095 .0190092 -4.83 0.000 .8669099 .9414459
_rcs_mot_egr_early2 | .9991531 .0189182 -0.04 0.964 .9627537 1.036929
_rcs_mot_egr_early3 | .9975441 .0122672 -0.20 0.842 .9737883 1.021879
_rcs_mot_egr_early4 | .994996 .0073832 -0.68 0.499 .98063 1.009573
_rcs_mot_egr_early5 | 1.002331 .0050628 0.46 0.645 .9924568 1.012303
_rcs_mot_egr_late1 | .9407456 .0186028 -3.09 0.002 .9049824 .9779221
_rcs_mot_egr_late2 | 1.000539 .018074 0.03 0.976 .9657339 1.036598
_rcs_mot_egr_late3 | .9953555 .0115473 -0.40 0.688 .9729784 1.018247
_rcs_mot_egr_late4 | .9994055 .006865 -0.09 0.931 .9860405 1.012952
_rcs_mot_egr_late5 | 1.000235 .0046063 0.05 0.959 .9912477 1.009304
_cons | 4.3e+115 3.7e+116 30.87 0.000 1.9e+108 9.4e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54456.475
Iteration 1: log likelihood = -54438.504
Iteration 2: log likelihood = -54438.447
Iteration 3: log likelihood = -54438.447
Log likelihood = -54438.447 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729901 .0501418 18.91 0.000 1.634364 1.831022
mot_egr_late | 1.578778 .0372273 19.37 0.000 1.507474 1.653454
tr_mod2 | 1.218783 .0262262 9.19 0.000 1.168449 1.271285
sex_dum2 | .7602036 .0163308 -12.76 0.000 .7288602 .7928948
edad_ini_cons | .9868964 .0019513 -6.67 0.000 .9830793 .9907282
esc1 | 1.128853 .0298159 4.59 0.000 1.071902 1.18883
esc2 | 1.088653 .0259459 3.56 0.000 1.038969 1.140713
sus_prin2 | 1.06711 .0297542 2.33 0.020 1.010357 1.12705
sus_prin3 | 1.393324 .0326625 14.15 0.000 1.330755 1.458834
sus_prin4 | 1.076878 .0378775 2.11 0.035 1.005141 1.153736
sus_prin5 | 1.142789 .0826235 1.85 0.065 .9918004 1.316763
fr_cons_sus_prin2 | .9201436 .0450193 -1.70 0.089 .8360059 1.012749
fr_cons_sus_prin3 | .9971168 .0395758 -0.07 0.942 .92249 1.077781
fr_cons_sus_prin4 | 1.008775 .0420398 0.21 0.834 .9296538 1.09463
fr_cons_sus_prin5 | 1.030642 .0409391 0.76 0.447 .9534472 1.114088
cond_ocu2 | 1.017751 .0318112 0.56 0.573 .9572736 1.082049
cond_ocu3 | 1.006279 .1419118 0.04 0.965 .7632675 1.326662
cond_ocu4 | 1.103835 .0399088 2.73 0.006 1.028322 1.184893
cond_ocu5 | 1.16222 .0890641 1.96 0.050 1.000134 1.350573
cond_ocu6 | 1.131301 .0207256 6.73 0.000 1.0914 1.17266
policonsumo | 1.026827 .0224232 1.21 0.225 .9838059 1.07173
num_hij2 | 1.165176 .0227516 7.83 0.000 1.121426 1.210632
tenviv1 | 1.15239 .0754437 2.17 0.030 1.013617 1.310163
tenviv2 | 1.127914 .0494259 2.75 0.006 1.035084 1.229068
tenviv4 | 1.03768 .0237478 1.62 0.106 .9921634 1.085284
tenviv5 | 1.003835 .0179966 0.21 0.831 .969175 1.039735
mzone2 | 1.302771 .0273802 12.58 0.000 1.250197 1.357556
mzone3 | 1.464408 .042123 13.26 0.000 1.384133 1.549339
n_off_vio | 1.355203 .0258681 15.92 0.000 1.305439 1.406864
n_off_acq | 1.81429 .0324498 33.31 0.000 1.751792 1.879019
n_off_sud | 1.256761 .0233116 12.32 0.000 1.211891 1.303291
n_off_oth | 1.360241 .0257434 16.26 0.000 1.310709 1.411644
psy_com2 | 1.070971 .0257072 2.86 0.004 1.021752 1.12256
psy_com3 | 1.058432 .0188013 3.20 0.001 1.022216 1.095931
dep2 | 1.019972 .0195475 1.03 0.302 .9823705 1.059013
rural2 | 1.028788 .0287128 1.02 0.309 .9740231 1.086631
rural3 | 1.054553 .0324423 1.73 0.084 .9928466 1.120095
porc_pobr | 1.229789 .1455277 1.75 0.080 .9752214 1.550808
susini2 | 1.095903 .0455144 2.21 0.027 1.01023 1.18884
susini3 | 1.122874 .0372685 3.49 0.000 1.052154 1.198347
susini4 | 1.082268 .0193426 4.42 0.000 1.045013 1.12085
susini5 | 1.129886 .056195 2.46 0.014 1.024944 1.245573
ano_nac_corr | .8747795 .0037462 -31.24 0.000 .8674679 .8821528
cohab2 | .9706529 .0310603 -0.93 0.352 .9116455 1.03348
cohab3 | .9912442 .0390086 -0.22 0.823 .917663 1.070725
cohab4 | .9522907 .0296173 -1.57 0.116 .8959758 1.012145
fis_com2 | 1.027073 .0166763 1.65 0.100 .9949025 1.060284
fis_com3 | .9022334 .0336844 -2.76 0.006 .8385709 .9707291
rc_x1 | .8515598 .0048086 -28.46 0.000 .8421871 .8610367
rc_x2 | 1.028768 .0186435 1.57 0.118 .9928684 1.065965
rc_x3 | .8952911 .0414536 -2.39 0.017 .8176211 .9803393
_rcs1 | 2.63704 .046949 54.46 0.000 2.546609 2.730683
_rcs2 | 1.102067 .0182585 5.87 0.000 1.066856 1.138441
_rcs3 | 1.047559 .0113566 4.29 0.000 1.025535 1.070055
_rcs4 | 1.023087 .0061208 3.82 0.000 1.011161 1.035154
_rcs5 | 1.013831 .0039257 3.55 0.000 1.006166 1.021555
_rcs6 | 1.009754 .0032497 3.02 0.003 1.003405 1.016144
_rcs7 | 1.007416 .0028504 2.61 0.009 1.001844 1.013018
_rcs8 | 1.003997 .0013303 3.01 0.003 1.001393 1.006608
_rcs_mot_egr_early1 | .903294 .0190028 -4.83 0.000 .8668066 .9413174
_rcs_mot_egr_early2 | 1.000359 .0189942 0.02 0.985 .9638152 1.038288
_rcs_mot_egr_early3 | .9973069 .0126228 -0.21 0.831 .9728711 1.022357
_rcs_mot_egr_early4 | .9954003 .007623 -0.60 0.547 .980571 1.010454
_rcs_mot_egr_early5 | 1.00089 .0052117 0.17 0.864 .9907272 1.011157
_rcs_mot_egr_early6 | .9980522 .0039306 -0.50 0.621 .990378 1.005786
_rcs_mot_egr_late1 | .9409191 .0186007 -3.08 0.002 .9051596 .9780913
_rcs_mot_egr_late2 | 1.002021 .0181778 0.11 0.911 .9670186 1.038289
_rcs_mot_egr_late3 | .9930656 .0118784 -0.58 0.561 .9700553 1.016622
_rcs_mot_egr_late4 | 1.001292 .0070686 0.18 0.855 .9875331 1.015243
_rcs_mot_egr_late5 | .9985027 .0047571 -0.31 0.753 .9892224 1.00787
_rcs_mot_egr_late6 | 1.00045 .0035429 0.13 0.899 .9935297 1.007418
_cons | 4.5e+115 3.9e+116 30.87 0.000 2.0e+108 9.8e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54456.199
Iteration 1: log likelihood = -54437.164
Iteration 2: log likelihood = -54437.091
Iteration 3: log likelihood = -54437.091
Log likelihood = -54437.091 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729925 .0501414 18.91 0.000 1.634389 1.831046
mot_egr_late | 1.578729 .0372251 19.37 0.000 1.507429 1.653401
tr_mod2 | 1.218815 .0262269 9.20 0.000 1.16848 1.271318
sex_dum2 | .7602325 .0163314 -12.76 0.000 .728888 .7929249
edad_ini_cons | .9868952 .0019513 -6.67 0.000 .9830782 .990727
esc1 | 1.12883 .0298154 4.59 0.000 1.07188 1.188806
esc2 | 1.088644 .0259457 3.56 0.000 1.038961 1.140704
sus_prin2 | 1.067172 .0297559 2.33 0.020 1.010416 1.127115
sus_prin3 | 1.393384 .0326642 14.15 0.000 1.330811 1.458898
sus_prin4 | 1.076962 .0378805 2.11 0.035 1.005219 1.153826
sus_prin5 | 1.142908 .0826325 1.85 0.065 .9919033 1.316902
fr_cons_sus_prin2 | .9201752 .0450209 -1.70 0.089 .8360347 1.012784
fr_cons_sus_prin3 | .9971856 .0395785 -0.07 0.943 .9225536 1.077855
fr_cons_sus_prin4 | 1.008824 .0420418 0.21 0.833 .929699 1.094683
fr_cons_sus_prin5 | 1.030665 .04094 0.76 0.447 .9534679 1.114112
cond_ocu2 | 1.017741 .0318109 0.56 0.574 .9572637 1.082038
cond_ocu3 | 1.006292 .1419135 0.04 0.965 .7632775 1.326679
cond_ocu4 | 1.103737 .0399054 2.73 0.006 1.028231 1.184788
cond_ocu5 | 1.1621 .0890548 1.96 0.050 1.000031 1.350434
cond_ocu6 | 1.13127 .0207251 6.73 0.000 1.091371 1.172629
policonsumo | 1.026789 .0224225 1.21 0.226 .9837691 1.07169
num_hij2 | 1.165183 .0227518 7.83 0.000 1.121433 1.21064
tenviv1 | 1.152559 .0754542 2.17 0.030 1.013766 1.310353
tenviv2 | 1.12789 .0494251 2.75 0.006 1.035062 1.229043
tenviv4 | 1.037688 .0237481 1.62 0.106 .9921708 1.085293
tenviv5 | 1.003856 .017997 0.21 0.830 .9691953 1.039757
mzone2 | 1.302749 .0273798 12.58 0.000 1.250176 1.357533
mzone3 | 1.464414 .0421238 13.26 0.000 1.384137 1.549346
n_off_vio | 1.355158 .0258672 15.92 0.000 1.305396 1.406817
n_off_acq | 1.814318 .0324502 33.31 0.000 1.751819 1.879047
n_off_sud | 1.25675 .0233114 12.32 0.000 1.211881 1.30328
n_off_oth | 1.360267 .0257437 16.26 0.000 1.310734 1.411671
psy_com2 | 1.071028 .0257086 2.86 0.004 1.021807 1.122619
psy_com3 | 1.058461 .0188019 3.20 0.001 1.022244 1.095961
dep2 | 1.019983 .0195478 1.03 0.302 .9823807 1.059025
rural2 | 1.02882 .0287137 1.02 0.309 .9740537 1.086665
rural3 | 1.054562 .0324427 1.73 0.084 .9928541 1.120104
porc_pobr | 1.229494 .1454926 1.75 0.081 .9749878 1.550435
susini2 | 1.095964 .045517 2.21 0.027 1.010287 1.188907
susini3 | 1.122917 .0372701 3.49 0.000 1.052194 1.198393
susini4 | 1.082241 .0193421 4.42 0.000 1.044988 1.120823
susini5 | 1.1299 .0561959 2.46 0.014 1.024956 1.245589
ano_nac_corr | .8747632 .0037462 -31.24 0.000 .8674515 .8821366
cohab2 | .9705877 .0310583 -0.93 0.351 .9115842 1.03341
cohab3 | .9912128 .0390073 -0.22 0.823 .917634 1.070691
cohab4 | .9522383 .0296155 -1.57 0.116 .8959266 1.012089
fis_com2 | 1.027024 .0166756 1.64 0.101 .9948548 1.060233
fis_com3 | .9022039 .0336833 -2.76 0.006 .8385435 .9706974
rc_x1 | .8515442 .0048086 -28.46 0.000 .8421716 .8610212
rc_x2 | 1.028758 .0186432 1.56 0.118 .9928596 1.065955
rc_x3 | .8953149 .0414545 -2.39 0.017 .8176432 .9803649
_rcs1 | 2.636777 .0469126 54.50 0.000 2.546414 2.730346
_rcs2 | 1.100955 .0181643 5.83 0.000 1.065923 1.137138
_rcs3 | 1.049552 .0116719 4.35 0.000 1.026923 1.07268
_rcs4 | 1.021397 .0066847 3.23 0.001 1.008379 1.034583
_rcs5 | 1.014897 .0041869 3.58 0.000 1.006724 1.023137
_rcs6 | 1.00866 .00326 2.67 0.008 1.002291 1.01507
_rcs7 | 1.007538 .0027896 2.71 0.007 1.002086 1.013021
_rcs8 | 1.005143 .0018783 2.75 0.006 1.001469 1.008832
_rcs_mot_egr_early1 | .9032596 .0189911 -4.84 0.000 .8667943 .9412591
_rcs_mot_egr_early2 | 1.001875 .0190838 0.10 0.922 .9651615 1.039986
_rcs_mot_egr_early3 | .99492 .0128896 -0.39 0.694 .9699749 1.020507
_rcs_mot_egr_early4 | .997932 .0079954 -0.26 0.796 .9823836 1.013726
_rcs_mot_egr_early5 | .9984137 .0052579 -0.30 0.763 .9881614 1.008772
_rcs_mot_egr_early6 | 1.001612 .0040969 0.39 0.694 .9936143 1.009674
_rcs_mot_egr_early7 | .9956454 .0031804 -1.37 0.172 .9894314 1.001898
_rcs_mot_egr_late1 | .9409963 .0185923 -3.08 0.002 .9052526 .9781514
_rcs_mot_egr_late2 | 1.002748 .0182395 0.15 0.880 .9676289 1.039141
_rcs_mot_egr_late3 | .9918364 .0121543 -0.67 0.504 .9682982 1.015947
_rcs_mot_egr_late4 | 1.001742 .0074151 0.24 0.814 .9873134 1.016381
_rcs_mot_egr_late5 | .9986262 .0047638 -0.29 0.773 .9893328 1.008007
_rcs_mot_egr_late6 | 1.000534 .0037029 0.14 0.885 .9933027 1.007818
_rcs_mot_egr_late7 | .9995852 .0028213 -0.15 0.883 .9940709 1.00513
_cons | 4.6e+115 4.0e+116 30.88 0.000 2.1e+108 1.0e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54455.117
Iteration 1: log likelihood = -54438.905
Iteration 2: log likelihood = -54438.856
Iteration 3: log likelihood = -54438.856
Log likelihood = -54438.856 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728483 .0499772 18.93 0.000 1.633253 1.829265
mot_egr_late | 1.577398 .0370519 19.40 0.000 1.506424 1.651716
tr_mod2 | 1.21877 .0262248 9.19 0.000 1.168439 1.271269
sex_dum2 | .7602726 .0163323 -12.76 0.000 .7289264 .7929668
edad_ini_cons | .9868911 .0019513 -6.67 0.000 .9830741 .990723
esc1 | 1.128881 .0298165 4.59 0.000 1.071929 1.188859
esc2 | 1.088633 .0259453 3.56 0.000 1.03895 1.140691
sus_prin2 | 1.067164 .0297552 2.33 0.020 1.01041 1.127106
sus_prin3 | 1.393362 .0326629 14.15 0.000 1.330793 1.458874
sus_prin4 | 1.076944 .0378797 2.11 0.035 1.005203 1.153806
sus_prin5 | 1.142598 .0826089 1.84 0.065 .9916364 1.316542
fr_cons_sus_prin2 | .9202234 .0450231 -1.70 0.089 .8360787 1.012837
fr_cons_sus_prin3 | .9970976 .0395749 -0.07 0.942 .9224724 1.07776
fr_cons_sus_prin4 | 1.008793 .0420405 0.21 0.834 .9296709 1.09465
fr_cons_sus_prin5 | 1.03064 .0409391 0.76 0.447 .9534453 1.114086
cond_ocu2 | 1.017717 .0318101 0.56 0.574 .9572417 1.082013
cond_ocu3 | 1.006052 .1418788 0.04 0.966 .7630965 1.32636
cond_ocu4 | 1.103712 .0399041 2.73 0.006 1.028208 1.18476
cond_ocu5 | 1.16185 .0890341 1.96 0.050 .9998192 1.35014
cond_ocu6 | 1.131334 .0207259 6.74 0.000 1.091433 1.172694
policonsumo | 1.026723 .0224199 1.21 0.227 .9837076 1.071619
num_hij2 | 1.165177 .0227515 7.83 0.000 1.121428 1.210634
tenviv1 | 1.152251 .0754345 2.16 0.030 1.013495 1.310005
tenviv2 | 1.128023 .0494305 2.75 0.006 1.035185 1.229187
tenviv4 | 1.037757 .0237495 1.62 0.105 .9922377 1.085365
tenviv5 | 1.003921 .0179982 0.22 0.827 .969258 1.039824
mzone2 | 1.302797 .0273809 12.59 0.000 1.250222 1.357583
mzone3 | 1.464452 .0421242 13.26 0.000 1.384174 1.549385
n_off_vio | 1.355164 .0258669 15.92 0.000 1.305403 1.406823
n_off_acq | 1.814256 .0324485 33.31 0.000 1.75176 1.878982
n_off_sud | 1.256742 .0233108 12.32 0.000 1.211874 1.303271
n_off_oth | 1.360238 .0257428 16.26 0.000 1.310707 1.41164
psy_com2 | 1.070976 .0257067 2.86 0.004 1.021758 1.122564
psy_com3 | 1.058424 .0188012 3.20 0.001 1.022208 1.095922
dep2 | 1.019963 .0195474 1.03 0.302 .9823612 1.059004
rural2 | 1.028834 .028714 1.02 0.308 .9740674 1.08668
rural3 | 1.054596 .032444 1.73 0.084 .9928856 1.120141
porc_pobr | 1.230784 .145641 1.75 0.079 .9760175 1.552051
susini2 | 1.096074 .0455207 2.21 0.027 1.01039 1.189025
susini3 | 1.122961 .0372707 3.49 0.000 1.052237 1.198439
susini4 | 1.082267 .0193424 4.42 0.000 1.045013 1.12085
susini5 | 1.129874 .0561949 2.46 0.014 1.024932 1.24556
ano_nac_corr | .874763 .0037459 -31.25 0.000 .8674518 .8821358
cohab2 | .9707328 .0310625 -0.93 0.353 .9117212 1.033564
cohab3 | .9912476 .0390082 -0.22 0.823 .917667 1.070728
cohab4 | .9523751 .0296196 -1.57 0.117 .8960557 1.012234
fis_com2 | 1.027095 .0166766 1.65 0.100 .9949241 1.060306
fis_com3 | .9021975 .0336829 -2.76 0.006 .8385377 .9706902
rc_x1 | .8515459 .0048084 -28.46 0.000 .8421735 .8610226
rc_x2 | 1.028743 .0186429 1.56 0.118 .9928449 1.065939
rc_x3 | .8953629 .0414566 -2.39 0.017 .8176872 .9804174
_rcs1 | 2.630824 .0396825 64.13 0.000 2.554187 2.709762
_rcs2 | 1.102939 .006361 16.99 0.000 1.090542 1.115477
_rcs3 | 1.042876 .0043105 10.16 0.000 1.034462 1.051359
_rcs4 | 1.022528 .0027211 8.37 0.000 1.017209 1.027875
_rcs5 | 1.013866 .0018347 7.61 0.000 1.010277 1.017468
_rcs6 | 1.009942 .0014162 7.05 0.000 1.00717 1.012721
_rcs7 | 1.007676 .0012048 6.40 0.000 1.005317 1.01004
_rcs8 | 1.005565 .0010806 5.16 0.000 1.003449 1.007685
_rcs9 | 1.003474 .0009354 3.72 0.000 1.001642 1.005309
_rcs_mot_egr_early1 | .9061661 .0161281 -5.54 0.000 .8751005 .9383345
_rcs_mot_egr_late1 | .9431765 .0154787 -3.56 0.000 .9133214 .9740074
_cons | 4.6e+115 4.0e+116 30.88 0.000 2.1e+108 1.0e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54455.199
Iteration 1: log likelihood = -54438.87
Iteration 2: log likelihood = -54438.819
Iteration 3: log likelihood = -54438.819
Log likelihood = -54438.819 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.72938 .0501129 18.90 0.000 1.633897 1.830442
mot_egr_late | 1.578166 .0371972 19.36 0.000 1.506919 1.652782
tr_mod2 | 1.218812 .0262263 9.20 0.000 1.168478 1.271314
sex_dum2 | .7602697 .0163323 -12.76 0.000 .7289235 .7929639
edad_ini_cons | .986892 .0019513 -6.67 0.000 .983075 .9907239
esc1 | 1.128869 .0298162 4.59 0.000 1.071918 1.188847
esc2 | 1.088625 .0259452 3.56 0.000 1.038943 1.140683
sus_prin2 | 1.067186 .0297561 2.33 0.020 1.01043 1.12713
sus_prin3 | 1.393384 .0326636 14.15 0.000 1.330813 1.458897
sus_prin4 | 1.076951 .03788 2.11 0.035 1.005209 1.153814
sus_prin5 | 1.142723 .082619 1.85 0.065 .9917429 1.316689
fr_cons_sus_prin2 | .9202148 .0450227 -1.70 0.089 .8360708 1.012827
fr_cons_sus_prin3 | .9970935 .0395748 -0.07 0.942 .9224686 1.077755
fr_cons_sus_prin4 | 1.008779 .0420399 0.21 0.834 .9296579 1.094635
fr_cons_sus_prin5 | 1.030633 .0409388 0.76 0.447 .9534379 1.114077
cond_ocu2 | 1.017715 .0318101 0.56 0.574 .9572395 1.082011
cond_ocu3 | 1.006171 .1418963 0.04 0.965 .7631859 1.326519
cond_ocu4 | 1.103701 .0399037 2.73 0.006 1.028198 1.184748
cond_ocu5 | 1.161828 .0890326 1.96 0.050 .9997997 1.350115
cond_ocu6 | 1.131326 .0207258 6.74 0.000 1.091425 1.172686
policonsumo | 1.02675 .0224209 1.21 0.227 .9837326 1.071648
num_hij2 | 1.165171 .0227514 7.83 0.000 1.121422 1.210627
tenviv1 | 1.152305 .0754381 2.17 0.030 1.013542 1.310067
tenviv2 | 1.128035 .049431 2.75 0.006 1.035196 1.2292
tenviv4 | 1.037751 .0237494 1.62 0.105 .9922313 1.085358
tenviv5 | 1.003918 .0179982 0.22 0.827 .9692546 1.039821
mzone2 | 1.302806 .0273812 12.59 0.000 1.25023 1.357593
mzone3 | 1.464419 .0421236 13.26 0.000 1.384143 1.549352
n_off_vio | 1.355174 .0258671 15.92 0.000 1.305412 1.406833
n_off_acq | 1.814273 .0324487 33.31 0.000 1.751776 1.878999
n_off_sud | 1.256733 .0233107 12.32 0.000 1.211866 1.303262
n_off_oth | 1.360241 .0257429 16.26 0.000 1.310711 1.411644
psy_com2 | 1.070979 .0257069 2.86 0.004 1.021761 1.122568
psy_com3 | 1.058428 .0188013 3.20 0.001 1.022212 1.095927
dep2 | 1.019966 .0195475 1.03 0.302 .982364 1.059007
rural2 | 1.028819 .0287138 1.02 0.309 .9740528 1.086665
rural3 | 1.054584 .0324437 1.73 0.084 .9928742 1.120128
porc_pobr | 1.230864 .1456515 1.76 0.079 .9760789 1.552155
susini2 | 1.096044 .0455197 2.21 0.027 1.010362 1.188993
susini3 | 1.12296 .0372709 3.49 0.000 1.052236 1.198438
susini4 | 1.082269 .0193425 4.42 0.000 1.045015 1.120852
susini5 | 1.129874 .0561947 2.46 0.014 1.024932 1.245561
ano_nac_corr | .8747552 .0037461 -31.25 0.000 .8674438 .8821283
cohab2 | .9707155 .031062 -0.93 0.353 .9117048 1.033546
cohab3 | .991226 .0390074 -0.22 0.823 .917647 1.070705
cohab4 | .9523594 .0296191 -1.57 0.117 .8960409 1.012218
fis_com2 | 1.027092 .0166765 1.65 0.100 .9949214 1.060303
fis_com3 | .9022045 .0336832 -2.76 0.006 .8385441 .9706979
rc_x1 | .8515384 .0048085 -28.46 0.000 .8421659 .8610151
rc_x2 | 1.028747 .018643 1.56 0.118 .9928489 1.065944
rc_x3 | .8953485 .0414561 -2.39 0.017 .8176739 .9804019
_rcs1 | 2.63741 .0469935 54.43 0.000 2.546894 2.731143
_rcs2 | 1.106646 .0153721 7.30 0.000 1.076924 1.137189
_rcs3 | 1.043408 .0047713 9.29 0.000 1.034099 1.052802
_rcs4 | 1.022662 .0027685 8.28 0.000 1.01725 1.028102
_rcs5 | 1.013892 .0018379 7.61 0.000 1.010296 1.0175
_rcs6 | 1.009944 .0014162 7.06 0.000 1.007172 1.012723
_rcs7 | 1.007676 .0012049 6.39 0.000 1.005317 1.01004
_rcs8 | 1.005567 .0010807 5.17 0.000 1.003451 1.007687
_rcs9 | 1.003476 .0009355 3.72 0.000 1.001644 1.005311
_rcs_mot_egr_early1 | .9034525 .0190031 -4.83 0.000 .8669645 .9414762
_rcs_mot_egr_early2 | .9957638 .0160079 -0.26 0.792 .964878 1.027638
_rcs_mot_egr_late1 | .9406506 .0185994 -3.09 0.002 .9048937 .9778204
_rcs_mot_egr_late2 | .9963753 .0150408 -0.24 0.810 .9673277 1.026295
_cons | 4.7e+115 4.1e+116 30.88 0.000 2.2e+108 1.0e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54455.122
Iteration 1: log likelihood = -54438.721
Iteration 2: log likelihood = -54438.672
Iteration 3: log likelihood = -54438.672
Log likelihood = -54438.672 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729883 .050139 18.91 0.000 1.634351 1.830998
mot_egr_late | 1.578556 .0372205 19.36 0.000 1.507265 1.653219
tr_mod2 | 1.218831 .026227 9.20 0.000 1.168495 1.271334
sex_dum2 | .7602738 .0163323 -12.76 0.000 .7289275 .7929681
edad_ini_cons | .9868934 .0019513 -6.67 0.000 .9830764 .9907252
esc1 | 1.128869 .0298162 4.59 0.000 1.071917 1.188846
esc2 | 1.088637 .0259455 3.56 0.000 1.038955 1.140696
sus_prin2 | 1.06722 .0297573 2.33 0.020 1.010462 1.127167
sus_prin3 | 1.393416 .0326647 14.15 0.000 1.330842 1.458931
sus_prin4 | 1.076942 .0378799 2.11 0.035 1.005201 1.153805
sus_prin5 | 1.14292 .0826337 1.85 0.065 .9919133 1.316916
fr_cons_sus_prin2 | .9201801 .0450211 -1.70 0.089 .8360392 1.012789
fr_cons_sus_prin3 | .9970913 .0395747 -0.07 0.941 .9224665 1.077753
fr_cons_sus_prin4 | 1.008772 .0420397 0.21 0.834 .9296512 1.094627
fr_cons_sus_prin5 | 1.030613 .040938 0.76 0.448 .9534202 1.114057
cond_ocu2 | 1.017696 .0318095 0.56 0.575 .957222 1.081991
cond_ocu3 | 1.006279 .1419115 0.04 0.965 .7632679 1.326661
cond_ocu4 | 1.103668 .0399026 2.73 0.006 1.028167 1.184714
cond_ocu5 | 1.161969 .089044 1.96 0.050 .9999203 1.35028
cond_ocu6 | 1.131309 .0207256 6.73 0.000 1.091409 1.172669
policonsumo | 1.026807 .0224226 1.21 0.226 .9837864 1.071708
num_hij2 | 1.165174 .0227515 7.83 0.000 1.121424 1.21063
tenviv1 | 1.152383 .0754433 2.17 0.030 1.01361 1.310155
tenviv2 | 1.128062 .0494324 2.75 0.006 1.03522 1.22923
tenviv4 | 1.037742 .0237492 1.62 0.105 .9922227 1.085349
tenviv5 | 1.003911 .017998 0.22 0.828 .9692481 1.039814
mzone2 | 1.302802 .027381 12.59 0.000 1.250226 1.357588
mzone3 | 1.464394 .042123 13.26 0.000 1.384118 1.549325
n_off_vio | 1.35518 .0258672 15.92 0.000 1.305418 1.406839
n_off_acq | 1.81427 .0324487 33.31 0.000 1.751774 1.878997
n_off_sud | 1.256705 .0233103 12.32 0.000 1.211838 1.303233
n_off_oth | 1.36023 .0257426 16.26 0.000 1.310699 1.411632
psy_com2 | 1.070994 .0257075 2.86 0.004 1.021775 1.122583
psy_com3 | 1.058436 .0188014 3.20 0.001 1.02222 1.095935
dep2 | 1.019953 .0195473 1.03 0.303 .9823517 1.058994
rural2 | 1.028796 .0287132 1.02 0.309 .974031 1.086641
rural3 | 1.054577 .0324435 1.73 0.084 .9928682 1.120121
porc_pobr | 1.23068 .1456311 1.75 0.079 .9759312 1.551926
susini2 | 1.095967 .0455169 2.21 0.027 1.01029 1.18891
susini3 | 1.122977 .0372716 3.49 0.000 1.052252 1.198457
susini4 | 1.082268 .0193426 4.42 0.000 1.045014 1.120851
susini5 | 1.129875 .0561946 2.46 0.014 1.024933 1.245561
ano_nac_corr | .874751 .0037461 -31.25 0.000 .8674396 .8821241
cohab2 | .9706791 .031061 -0.93 0.352 .9116704 1.033507
cohab3 | .9911724 .0390054 -0.23 0.822 .9175971 1.070647
cohab4 | .9523139 .0296179 -1.57 0.116 .8959978 1.01217
fis_com2 | 1.027077 .0166762 1.65 0.100 .9949068 1.060288
fis_com3 | .9022151 .0336836 -2.76 0.006 .838554 .9707093
rc_x1 | .8515342 .0048084 -28.46 0.000 .8421618 .8610109
rc_x2 | 1.028753 .0186432 1.56 0.118 .9928545 1.06595
rc_x3 | .8953246 .041455 -2.39 0.017 .8176519 .9803758
_rcs1 | 2.636955 .0469161 54.50 0.000 2.546585 2.73053
_rcs2 | 1.102494 .0176056 6.11 0.000 1.068522 1.137546
_rcs3 | 1.046082 .0073785 6.39 0.000 1.03172 1.060644
_rcs4 | 1.024561 .0049159 5.06 0.000 1.014971 1.034241
_rcs5 | 1.014828 .0027046 5.52 0.000 1.00954 1.020142
_rcs6 | 1.010294 .0015895 6.51 0.000 1.007183 1.013414
_rcs7 | 1.007767 .0012187 6.40 0.000 1.005381 1.010158
_rcs8 | 1.00557 .0010809 5.17 0.000 1.003454 1.007691
_rcs9 | 1.00348 .0009357 3.73 0.000 1.001647 1.005315
_rcs_mot_egr_early1 | .9034531 .0189885 -4.83 0.000 .8669925 .9414471
_rcs_mot_egr_early2 | 1.000326 .0181759 0.02 0.986 .9653285 1.036592
_rcs_mot_egr_early3 | .9943352 .010054 -0.56 0.574 .9748237 1.014237
_rcs_mot_egr_late1 | .940798 .0185813 -3.09 0.002 .9050752 .9779309
_rcs_mot_egr_late2 | .9996667 .0172129 -0.02 0.985 .966493 1.033979
_rcs_mot_egr_late3 | .9962592 .0093281 -0.40 0.689 .9781432 1.014711
_cons | 4.8e+115 4.1e+116 30.88 0.000 2.2e+108 1.0e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54455.15
Iteration 1: log likelihood = -54438.738
Iteration 2: log likelihood = -54438.689
Iteration 3: log likelihood = -54438.689
Log likelihood = -54438.689 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729815 .0501376 18.91 0.000 1.634286 1.830927
mot_egr_late | 1.57853 .0372203 19.36 0.000 1.507239 1.653192
tr_mod2 | 1.218825 .026227 9.20 0.000 1.16849 1.271328
sex_dum2 | .7602731 .0163323 -12.76 0.000 .7289268 .7929673
edad_ini_cons | .986893 .0019513 -6.67 0.000 .983076 .9907249
esc1 | 1.128866 .0298162 4.59 0.000 1.071915 1.188844
esc2 | 1.088637 .0259455 3.56 0.000 1.038954 1.140696
sus_prin2 | 1.067222 .0297574 2.33 0.020 1.010463 1.127168
sus_prin3 | 1.393419 .0326648 14.15 0.000 1.330846 1.458935
sus_prin4 | 1.076941 .0378798 2.11 0.035 1.005199 1.153803
sus_prin5 | 1.142904 .0826326 1.85 0.065 .991899 1.316898
fr_cons_sus_prin2 | .9201855 .0450213 -1.70 0.089 .8360441 1.012795
fr_cons_sus_prin3 | .9970987 .039575 -0.07 0.942 .9224733 1.077761
fr_cons_sus_prin4 | 1.008777 .0420399 0.21 0.834 .9296554 1.094632
fr_cons_sus_prin5 | 1.030621 .0409384 0.76 0.448 .9534268 1.114064
cond_ocu2 | 1.017698 .0318095 0.56 0.575 .9572236 1.081993
cond_ocu3 | 1.006309 .141916 0.04 0.964 .7632904 1.326701
cond_ocu4 | 1.103675 .0399029 2.73 0.006 1.028174 1.184721
cond_ocu5 | 1.161971 .0890449 1.96 0.050 .9999206 1.350284
cond_ocu6 | 1.131308 .0207256 6.73 0.000 1.091407 1.172667
policonsumo | 1.026803 .0224225 1.21 0.226 .9837833 1.071705
num_hij2 | 1.16517 .0227515 7.83 0.000 1.121421 1.210627
tenviv1 | 1.152364 .0754423 2.17 0.030 1.013593 1.310134
tenviv2 | 1.128063 .0494326 2.75 0.006 1.035221 1.229232
tenviv4 | 1.037735 .0237491 1.62 0.106 .9922166 1.085342
tenviv5 | 1.003911 .017998 0.22 0.828 .9692478 1.039813
mzone2 | 1.302792 .0273809 12.59 0.000 1.250216 1.357578
mzone3 | 1.464411 .0421239 13.26 0.000 1.384134 1.549344
n_off_vio | 1.355179 .0258672 15.92 0.000 1.305417 1.406838
n_off_acq | 1.814272 .0324488 33.31 0.000 1.751775 1.878998
n_off_sud | 1.256711 .0233104 12.32 0.000 1.211843 1.303239
n_off_oth | 1.360236 .0257427 16.26 0.000 1.310705 1.411638
psy_com2 | 1.070997 .0257076 2.86 0.004 1.021777 1.122587
psy_com3 | 1.058437 .0188014 3.20 0.001 1.022221 1.095936
dep2 | 1.019953 .0195473 1.03 0.303 .9823517 1.058994
rural2 | 1.028803 .0287134 1.02 0.309 .9740369 1.086647
rural3 | 1.054576 .0324435 1.73 0.084 .9928671 1.12012
porc_pobr | 1.230654 .1456284 1.75 0.079 .9759098 1.551894
susini2 | 1.095968 .0455171 2.21 0.027 1.010291 1.188912
susini3 | 1.122982 .0372719 3.49 0.000 1.052256 1.198462
susini4 | 1.082268 .0193426 4.42 0.000 1.045014 1.120851
susini5 | 1.129876 .0561948 2.46 0.014 1.024934 1.245563
ano_nac_corr | .874748 .0037461 -31.25 0.000 .8674364 .8821212
cohab2 | .9706891 .0310614 -0.93 0.353 .9116797 1.033518
cohab3 | .9911896 .0390061 -0.22 0.822 .9176129 1.070666
cohab4 | .9523267 .0296183 -1.57 0.116 .8960097 1.012183
fis_com2 | 1.027075 .0166763 1.65 0.100 .9949045 1.060285
fis_com3 | .9022121 .0336835 -2.76 0.006 .8385511 .970706
rc_x1 | .8515312 .0048085 -28.46 0.000 .8421587 .8610081
rc_x2 | 1.028753 .0186431 1.56 0.118 .9928543 1.065949
rc_x3 | .8953266 .0414551 -2.39 0.017 .8176538 .9803778
_rcs1 | 2.636779 .0469441 54.46 0.000 2.546357 2.730411
_rcs2 | 1.102441 .0180521 5.96 0.000 1.067621 1.138396
_rcs3 | 1.046353 .0096723 4.90 0.000 1.027566 1.065483
_rcs4 | 1.024372 .0047923 5.15 0.000 1.015022 1.033808
_rcs5 | 1.014538 .003773 3.88 0.000 1.00717 1.02196
_rcs6 | 1.010173 .0029656 3.45 0.001 1.004377 1.016002
_rcs7 | 1.007762 .0016981 4.59 0.000 1.004439 1.011095
_rcs8 | 1.005587 .0011091 5.05 0.000 1.003415 1.007763
_rcs9 | 1.003478 .0009361 3.72 0.000 1.001645 1.005314
_rcs_mot_egr_early1 | .9034774 .0190032 -4.83 0.000 .8669891 .9415013
_rcs_mot_egr_early2 | 1.000119 .0186217 0.01 0.995 .964279 1.037291
_rcs_mot_egr_early3 | .9951014 .0115689 -0.42 0.673 .9726832 1.018036
_rcs_mot_egr_early4 | .998813 .0068287 -0.17 0.862 .9855183 1.012287
_rcs_mot_egr_late1 | .9409079 .0185978 -3.08 0.002 .905154 .9780742
_rcs_mot_egr_late2 | 1.00012 .0177029 0.01 0.995 .966018 1.035426
_rcs_mot_egr_late3 | .9957401 .0108482 -0.39 0.695 .9747033 1.017231
_rcs_mot_egr_late4 | .9999236 .0062967 -0.01 0.990 .9876582 1.012341
_cons | 4.8e+115 4.1e+116 30.88 0.000 2.2e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54455.102
Iteration 1: log likelihood = -54438.201
Iteration 2: log likelihood = -54438.144
Iteration 3: log likelihood = -54438.144
Log likelihood = -54438.144 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729764 .0501366 18.91 0.000 1.634237 1.830875
mot_egr_late | 1.578587 .0372226 19.36 0.000 1.507293 1.653254
tr_mod2 | 1.218839 .0262273 9.20 0.000 1.168504 1.271343
sex_dum2 | .7602681 .0163323 -12.76 0.000 .7289219 .7929623
edad_ini_cons | .9868942 .0019513 -6.67 0.000 .9830771 .990726
esc1 | 1.128854 .0298159 4.59 0.000 1.071903 1.188832
esc2 | 1.088626 .0259453 3.56 0.000 1.038944 1.140684
sus_prin2 | 1.067191 .0297565 2.33 0.020 1.010434 1.127136
sus_prin3 | 1.393392 .0326642 14.15 0.000 1.330819 1.458906
sus_prin4 | 1.076901 .0378784 2.11 0.035 1.005161 1.15376
sus_prin5 | 1.142805 .0826256 1.85 0.065 .9918127 1.316784
fr_cons_sus_prin2 | .9201864 .0450214 -1.70 0.089 .8360449 1.012796
fr_cons_sus_prin3 | .9970918 .0395748 -0.07 0.942 .9224668 1.077754
fr_cons_sus_prin4 | 1.008773 .0420398 0.21 0.834 .929652 1.094628
fr_cons_sus_prin5 | 1.030625 .0409386 0.76 0.448 .9534307 1.114069
cond_ocu2 | 1.01771 .03181 0.56 0.574 .9572347 1.082006
cond_ocu3 | 1.006289 .1419133 0.04 0.965 .7632749 1.326675
cond_ocu4 | 1.103636 .0399017 2.73 0.006 1.028137 1.18468
cond_ocu5 | 1.161872 .0890378 1.96 0.050 .9998348 1.35017
cond_ocu6 | 1.131329 .020726 6.74 0.000 1.091427 1.172689
policonsumo | 1.026785 .0224222 1.21 0.226 .9837656 1.071686
num_hij2 | 1.165199 .022752 7.83 0.000 1.121448 1.210656
tenviv1 | 1.152458 .0754484 2.17 0.030 1.013676 1.31024
tenviv2 | 1.128003 .0494301 2.75 0.006 1.035166 1.229167
tenviv4 | 1.037745 .0237493 1.62 0.105 .9922255 1.085352
tenviv5 | 1.003924 .0179983 0.22 0.827 .9692604 1.039827
mzone2 | 1.302814 .0273814 12.59 0.000 1.250237 1.357601
mzone3 | 1.464437 .0421249 13.26 0.000 1.384158 1.549372
n_off_vio | 1.355167 .025867 15.92 0.000 1.305405 1.406826
n_off_acq | 1.814314 .0324493 33.31 0.000 1.751817 1.879042
n_off_sud | 1.256737 .0233109 12.32 0.000 1.211869 1.303266
n_off_oth | 1.360243 .0257428 16.26 0.000 1.310712 1.411646
psy_com2 | 1.070967 .0257072 2.86 0.004 1.021749 1.122556
psy_com3 | 1.058439 .0188015 3.20 0.001 1.022223 1.095938
dep2 | 1.019941 .0195471 1.03 0.303 .9823398 1.058981
rural2 | 1.028799 .0287133 1.02 0.309 .9740336 1.086644
rural3 | 1.054575 .0324434 1.73 0.084 .992866 1.120119
porc_pobr | 1.230702 .1456341 1.75 0.079 .9759482 1.551955
susini2 | 1.096019 .0455193 2.21 0.027 1.010337 1.188967
susini3 | 1.122911 .0372696 3.49 0.000 1.052189 1.198387
susini4 | 1.082287 .019343 4.42 0.000 1.045032 1.120871
susini5 | 1.129934 .0561978 2.46 0.014 1.024987 1.245627
ano_nac_corr | .874753 .0037462 -31.25 0.000 .8674413 .8821263
cohab2 | .9706524 .0310601 -0.93 0.352 .9116454 1.033479
cohab3 | .9911575 .0390049 -0.23 0.821 .9175833 1.070631
cohab4 | .9523013 .0296174 -1.57 0.116 .895986 1.012156
fis_com2 | 1.027091 .0166766 1.65 0.100 .9949203 1.060303
fis_com3 | .9022016 .0336831 -2.76 0.006 .8385414 .9706947
rc_x1 | .8515321 .0048085 -28.46 0.000 .8421595 .861009
rc_x2 | 1.028766 .0186434 1.56 0.118 .9928671 1.065963
rc_x3 | .8953048 .0414542 -2.39 0.017 .8176338 .9803542
_rcs1 | 2.636682 .0469555 54.44 0.000 2.546239 2.730338
_rcs2 | 1.102678 .0183246 5.88 0.000 1.067341 1.139185
_rcs3 | 1.046017 .0107474 4.38 0.000 1.025163 1.067295
_rcs4 | 1.024998 .0052156 4.85 0.000 1.014826 1.035271
_rcs5 | 1.014832 .0041793 3.58 0.000 1.006673 1.023056
_rcs6 | 1.009596 .0030512 3.16 0.002 1.003633 1.015594
_rcs7 | 1.007012 .0027793 2.53 0.011 1.001579 1.012474
_rcs8 | 1.005287 .0014829 3.57 0.000 1.002385 1.008198
_rcs9 | 1.00347 .000937 3.71 0.000 1.001635 1.005308
_rcs_mot_egr_early1 | .9035715 .0190099 -4.82 0.000 .8670705 .941609
_rcs_mot_egr_early2 | .9992248 .0189031 -0.04 0.967 .9628539 1.03697
_rcs_mot_egr_early3 | .9969638 .0122958 -0.25 0.805 .9731534 1.021357
_rcs_mot_egr_early4 | .9954884 .0074086 -0.61 0.543 .9810731 1.010115
_rcs_mot_egr_early5 | 1.002538 .0050377 0.50 0.614 .9927124 1.01246
_rcs_mot_egr_late1 | .9409804 .0186051 -3.08 0.002 .9052126 .9781614
_rcs_mot_egr_late2 | 1.000622 .0180623 0.03 0.973 .9658397 1.036657
_rcs_mot_egr_late3 | .9947971 .0115758 -0.45 0.654 .9723657 1.017746
_rcs_mot_egr_late4 | .9999863 .006893 -0.00 0.998 .9865671 1.013588
_rcs_mot_egr_late5 | 1.000544 .004594 0.12 0.906 .9915803 1.009589
_cons | 4.8e+115 4.1e+116 30.88 0.000 2.2e+108 1.0e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54455.128
Iteration 1: log likelihood = -54437.207
Iteration 2: log likelihood = -54437.147
Iteration 3: log likelihood = -54437.147
Log likelihood = -54437.147 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729777 .050138 18.91 0.000 1.634248 1.830891
mot_egr_late | 1.578547 .0372223 19.36 0.000 1.507253 1.653214
tr_mod2 | 1.218794 .0262265 9.20 0.000 1.16846 1.271296
sex_dum2 | .760274 .0163324 -12.76 0.000 .7289276 .7929684
edad_ini_cons | .9868935 .0019513 -6.67 0.000 .9830764 .9907253
esc1 | 1.128829 .0298153 4.59 0.000 1.071879 1.188805
esc2 | 1.088626 .0259453 3.56 0.000 1.038944 1.140684
sus_prin2 | 1.067207 .0297569 2.33 0.020 1.01045 1.127153
sus_prin3 | 1.393402 .0326644 14.15 0.000 1.330829 1.458917
sus_prin4 | 1.076935 .0378796 2.11 0.035 1.005194 1.153797
sus_prin5 | 1.142955 .0826364 1.85 0.065 .9919433 1.316957
fr_cons_sus_prin2 | .9201829 .0450212 -1.70 0.089 .8360417 1.012792
fr_cons_sus_prin3 | .9971523 .0395772 -0.07 0.943 .9225229 1.077819
fr_cons_sus_prin4 | 1.008791 .0420405 0.21 0.834 .9296689 1.094648
fr_cons_sus_prin5 | 1.030653 .0409395 0.76 0.447 .9534567 1.114099
cond_ocu2 | 1.017718 .0318103 0.56 0.574 .9572428 1.082015
cond_ocu3 | 1.006423 .141932 0.05 0.964 .7633764 1.326851
cond_ocu4 | 1.103725 .0399048 2.73 0.006 1.028219 1.184774
cond_ocu5 | 1.162036 .08905 1.96 0.050 .9999762 1.35036
cond_ocu6 | 1.131306 .0207256 6.73 0.000 1.091405 1.172665
policonsumo | 1.026822 .022423 1.21 0.225 .983801 1.071724
num_hij2 | 1.165181 .0227518 7.83 0.000 1.121431 1.210638
tenviv1 | 1.152379 .0754432 2.17 0.030 1.013607 1.310151
tenviv2 | 1.128011 .0494304 2.75 0.006 1.035173 1.229175
tenviv4 | 1.037733 .0237491 1.62 0.106 .9922142 1.08534
tenviv5 | 1.003917 .0179981 0.22 0.827 .9692539 1.03982
mzone2 | 1.302819 .0273814 12.59 0.000 1.250242 1.357606
mzone3 | 1.464469 .0421257 13.26 0.000 1.384188 1.549406
n_off_vio | 1.355154 .0258669 15.92 0.000 1.305392 1.406812
n_off_acq | 1.814261 .0324489 33.31 0.000 1.751764 1.878987
n_off_sud | 1.256738 .0233111 12.32 0.000 1.211869 1.303267
n_off_oth | 1.360215 .0257424 16.26 0.000 1.310685 1.411617
psy_com2 | 1.071021 .0257084 2.86 0.004 1.0218 1.122612
psy_com3 | 1.058453 .0188017 3.20 0.001 1.022236 1.095953
dep2 | 1.019943 .019547 1.03 0.303 .982342 1.058983
rural2 | 1.028799 .0287132 1.02 0.309 .9740333 1.086643
rural3 | 1.054515 .0324416 1.73 0.084 .9928096 1.120056
porc_pobr | 1.230475 .1456071 1.75 0.080 .9757681 1.551668
susini2 | 1.095962 .0455169 2.21 0.027 1.010285 1.188905
susini3 | 1.122958 .0372713 3.49 0.000 1.052233 1.198437
susini4 | 1.082249 .0193423 4.42 0.000 1.044995 1.120831
susini5 | 1.129832 .0561927 2.45 0.014 1.024894 1.245514
ano_nac_corr | .8747333 .0037461 -31.25 0.000 .8674217 .8821064
cohab2 | .9706399 .0310599 -0.93 0.352 .9116333 1.033466
cohab3 | .9912048 .039007 -0.22 0.822 .9176265 1.070683
cohab4 | .9522816 .0296169 -1.57 0.116 .8959673 1.012135
fis_com2 | 1.027081 .0166765 1.65 0.100 .9949105 1.060292
fis_com3 | .9022263 .0336841 -2.76 0.006 .8385643 .9707215
rc_x1 | .8515176 .0048084 -28.46 0.000 .8421452 .8609943
rc_x2 | 1.028753 .0186433 1.56 0.118 .9928546 1.06595
rc_x3 | .895324 .0414552 -2.39 0.017 .817651 .9803756
_rcs1 | 2.636199 .0469172 54.47 0.000 2.545828 2.729778
_rcs2 | 1.101242 .0182332 5.82 0.000 1.066079 1.137565
_rcs3 | 1.04866 .0113487 4.39 0.000 1.026651 1.071141
_rcs4 | 1.023018 .0058856 3.96 0.000 1.011547 1.034619
_rcs5 | 1.014737 .0040195 3.69 0.000 1.00689 1.022646
_rcs6 | 1.011183 .0034804 3.23 0.001 1.004385 1.018028
_rcs7 | 1.006887 .0027769 2.49 0.013 1.001459 1.012344
_rcs8 | 1.004361 .0022887 1.91 0.056 .9998852 1.008857
_rcs9 | 1.003283 .0009963 3.30 0.001 1.001332 1.005237
_rcs_mot_egr_early1 | .9035922 .0190033 -4.82 0.000 .8671036 .9416161
_rcs_mot_egr_early2 | 1.00053 .018957 0.03 0.978 .9640567 1.038384
_rcs_mot_egr_early3 | .9961384 .0126737 -0.30 0.761 .9716056 1.021291
_rcs_mot_egr_early4 | .9962925 .0077834 -0.48 0.634 .9811536 1.011665
_rcs_mot_egr_early5 | .9999062 .0052712 -0.02 0.986 .989628 1.010291
_rcs_mot_egr_early6 | 1.00047 .0040384 0.12 0.907 .9925865 1.008417
_rcs_mot_egr_late1 | .9412767 .0186031 -3.06 0.002 .9055125 .9784535
_rcs_mot_egr_late2 | 1.002277 .0181488 0.13 0.900 .96733 1.038487
_rcs_mot_egr_late3 | .9919318 .0119331 -0.67 0.501 .9688169 1.015598
_rcs_mot_egr_late4 | 1.002227 .0072342 0.31 0.758 .9881479 1.016506
_rcs_mot_egr_late5 | .9975287 .0048173 -0.51 0.608 .9881315 1.007015
_rcs_mot_egr_late6 | 1.002905 .0036686 0.79 0.428 .9957404 1.010121
_cons | 5.0e+115 4.3e+116 30.88 0.000 2.3e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54455.231
Iteration 1: log likelihood = -54436.483
Iteration 2: log likelihood = -54436.409
Iteration 3: log likelihood = -54436.409
Log likelihood = -54436.409 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729827 .0501387 18.91 0.000 1.634297 1.830942
mot_egr_late | 1.578551 .0372215 19.36 0.000 1.507259 1.653216
tr_mod2 | 1.218838 .0262274 9.20 0.000 1.168502 1.271342
sex_dum2 | .7602869 .0163326 -12.76 0.000 .7289401 .7929817
edad_ini_cons | .9868926 .0019513 -6.67 0.000 .9830756 .9907244
esc1 | 1.128819 .0298152 4.59 0.000 1.071869 1.188795
esc2 | 1.088629 .0259454 3.56 0.000 1.038946 1.140687
sus_prin2 | 1.06727 .0297587 2.33 0.020 1.010509 1.127219
sus_prin3 | 1.393468 .0326664 14.15 0.000 1.330892 1.458987
sus_prin4 | 1.077025 .0378829 2.11 0.035 1.005277 1.153893
sus_prin5 | 1.143049 .0826435 1.85 0.064 .9920245 1.317066
fr_cons_sus_prin2 | .920208 .0450224 -1.70 0.089 .8360646 1.01282
fr_cons_sus_prin3 | .9972206 .0395799 -0.07 0.944 .922586 1.077893
fr_cons_sus_prin4 | 1.008839 .0420425 0.21 0.833 .9297127 1.094699
fr_cons_sus_prin5 | 1.030673 .0409404 0.76 0.447 .9534751 1.114121
cond_ocu2 | 1.017703 .0318097 0.56 0.575 .9572284 1.081998
cond_ocu3 | 1.006385 .1419266 0.05 0.964 .763348 1.326801
cond_ocu4 | 1.103629 .0399016 2.73 0.006 1.02813 1.184672
cond_ocu5 | 1.161997 .089047 1.96 0.050 .9999424 1.350314
cond_ocu6 | 1.131275 .0207252 6.73 0.000 1.091375 1.172634
policonsumo | 1.026781 .0224222 1.21 0.226 .9837619 1.071682
num_hij2 | 1.165196 .0227521 7.83 0.000 1.121445 1.210653
tenviv1 | 1.152519 .0754518 2.17 0.030 1.013731 1.310308
tenviv2 | 1.128 .0494302 2.75 0.006 1.035163 1.229164
tenviv4 | 1.037722 .0237489 1.62 0.106 .9922032 1.085328
tenviv5 | 1.00392 .0179982 0.22 0.827 .9692566 1.039823
mzone2 | 1.302801 .027381 12.59 0.000 1.250226 1.357588
mzone3 | 1.464446 .0421255 13.26 0.000 1.384165 1.549382
n_off_vio | 1.355122 .0258662 15.92 0.000 1.305362 1.406779
n_off_acq | 1.814292 .0324493 33.31 0.000 1.751795 1.87902
n_off_sud | 1.256716 .0233106 12.32 0.000 1.211848 1.303244
n_off_oth | 1.36024 .0257428 16.26 0.000 1.31071 1.411643
psy_com2 | 1.07108 .0257098 2.86 0.004 1.021857 1.122675
psy_com3 | 1.058485 .0188023 3.20 0.001 1.022267 1.095986
dep2 | 1.019968 .0195476 1.03 0.302 .9823661 1.059009
rural2 | 1.028819 .0287137 1.02 0.309 .9740525 1.086664
rural3 | 1.054556 .0324429 1.73 0.084 .9928477 1.120099
porc_pobr | 1.229992 .1455502 1.75 0.080 .9753851 1.55106
susini2 | 1.096014 .0455191 2.21 0.027 1.010333 1.188962
susini3 | 1.123003 .0372729 3.50 0.000 1.052275 1.198486
susini4 | 1.082221 .0193418 4.42 0.000 1.044968 1.120802
susini5 | 1.129891 .0561959 2.46 0.014 1.024947 1.24558
ano_nac_corr | .8747241 .0037462 -31.25 0.000 .8674124 .8820974
cohab2 | .9705743 .0310579 -0.93 0.351 .9115716 1.033396
cohab3 | .9911738 .0390058 -0.23 0.822 .9175978 1.070649
cohab4 | .9522289 .0296152 -1.57 0.116 .8959178 1.012079
fis_com2 | 1.027024 .0166755 1.64 0.101 .9948554 1.060233
fis_com3 | .9022018 .0336832 -2.76 0.006 .8385414 .9706951
rc_x1 | .8515077 .0048085 -28.47 0.000 .8421352 .8609844
rc_x2 | 1.02875 .0186431 1.56 0.118 .9928514 1.065946
rc_x3 | .8953343 .0414554 -2.39 0.017 .8176609 .9803863
_rcs1 | 2.63634 .0469036 54.49 0.000 2.545995 2.729891
_rcs2 | 1.100469 .0181926 5.79 0.000 1.065383 1.13671
_rcs3 | 1.049731 .0116497 4.37 0.000 1.027144 1.072814
_rcs4 | 1.022045 .0064871 3.44 0.001 1.009409 1.034839
_rcs5 | 1.015306 .0041607 3.71 0.000 1.007184 1.023494
_rcs6 | 1.010383 .0032947 3.17 0.002 1.003946 1.016861
_rcs7 | 1.006788 .0029755 2.29 0.022 1.000973 1.012637
_rcs8 | 1.006241 .0025447 2.46 0.014 1.001266 1.011241
_rcs9 | 1.003848 .0013258 2.91 0.004 1.001252 1.00645
_rcs_mot_egr_early1 | .9033992 .0189939 -4.83 0.000 .8669285 .9414041
_rcs_mot_egr_early2 | 1.001797 .0190723 0.09 0.925 .9651045 1.039884
_rcs_mot_egr_early3 | .9945701 .012882 -0.42 0.674 .9696397 1.020142
_rcs_mot_egr_early4 | .9983101 .0080038 -0.21 0.833 .9827456 1.014121
_rcs_mot_egr_early5 | .9979367 .0053697 -0.38 0.701 .9874676 1.008517
_rcs_mot_egr_early6 | 1.001909 .0042318 0.45 0.652 .993649 1.010238
_rcs_mot_egr_early7 | .9963656 .0033249 -1.09 0.275 .9898702 1.002904
_rcs_mot_egr_late1 | .9411885 .0185961 -3.07 0.002 .9054375 .978351
_rcs_mot_egr_late2 | 1.002729 .0182284 0.15 0.881 .967631 1.0391
_rcs_mot_egr_late3 | .9914982 .0121339 -0.70 0.485 .9679991 1.015568
_rcs_mot_egr_late4 | 1.002112 .007422 0.28 0.776 .9876702 1.016765
_rcs_mot_egr_late5 | .9981627 .0048879 -0.38 0.707 .9886284 1.007789
_rcs_mot_egr_late6 | 1.000849 .0038511 0.22 0.825 .9933291 1.008425
_rcs_mot_egr_late7 | 1.000308 .0029917 0.10 0.918 .9944615 1.006189
_cons | 5.1e+115 4.4e+116 30.89 0.000 2.3e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54454.203
Iteration 1: log likelihood = -54438.127
Iteration 2: log likelihood = -54438.079
Iteration 3: log likelihood = -54438.079
Log likelihood = -54438.079 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728394 .0499742 18.93 0.000 1.63317 1.82917
mot_egr_late | 1.577274 .0370486 19.40 0.000 1.506306 1.651586
tr_mod2 | 1.218781 .026225 9.19 0.000 1.16845 1.27128
sex_dum2 | .7603186 .0163333 -12.76 0.000 .7289705 .7930147
edad_ini_cons | .9868884 .0019513 -6.68 0.000 .9830714 .9907202
esc1 | 1.128893 .0298169 4.59 0.000 1.07194 1.188872
esc2 | 1.088638 .0259455 3.56 0.000 1.038955 1.140697
sus_prin2 | 1.067232 .0297572 2.33 0.020 1.010474 1.127178
sus_prin3 | 1.393428 .0326648 14.15 0.000 1.330855 1.458944
sus_prin4 | 1.07699 .0378814 2.11 0.035 1.005245 1.153855
sus_prin5 | 1.142657 .0826138 1.84 0.065 .9916862 1.316611
fr_cons_sus_prin2 | .9202449 .0450241 -1.70 0.089 .8360983 1.01286
fr_cons_sus_prin3 | .9971219 .0395759 -0.07 0.942 .9224949 1.077786
fr_cons_sus_prin4 | 1.008809 .0420412 0.21 0.833 .9296854 1.094667
fr_cons_sus_prin5 | 1.030647 .0409394 0.76 0.447 .9534512 1.114093
cond_ocu2 | 1.017689 .0318092 0.56 0.575 .9572158 1.081984
cond_ocu3 | 1.006181 .141897 0.04 0.965 .7631943 1.32653
cond_ocu4 | 1.103625 .039901 2.73 0.006 1.028127 1.184667
cond_ocu5 | 1.161866 .0890353 1.96 0.050 .9998331 1.350159
cond_ocu6 | 1.131357 .0207263 6.74 0.000 1.091454 1.172717
policonsumo | 1.026702 .0224193 1.21 0.228 .983688 1.071597
num_hij2 | 1.165187 .0227517 7.83 0.000 1.121437 1.210644
tenviv1 | 1.152262 .0754352 2.16 0.030 1.013505 1.310017
tenviv2 | 1.128086 .0494334 2.75 0.006 1.035242 1.229256
tenviv4 | 1.037776 .02375 1.62 0.105 .9922556 1.085385
tenviv5 | 1.003963 .017999 0.22 0.825 .9692979 1.039867
mzone2 | 1.302813 .0273813 12.59 0.000 1.250237 1.3576
mzone3 | 1.464493 .042126 13.26 0.000 1.384212 1.54943
n_off_vio | 1.35513 .025866 15.92 0.000 1.30537 1.406787
n_off_acq | 1.814238 .0324478 33.31 0.000 1.751743 1.878962
n_off_sud | 1.256713 .0233102 12.32 0.000 1.211846 1.303241
n_off_oth | 1.360214 .0257421 16.26 0.000 1.310685 1.411615
psy_com2 | 1.071006 .0257075 2.86 0.004 1.021787 1.122596
psy_com3 | 1.058453 .0188017 3.20 0.001 1.022237 1.095953
dep2 | 1.019943 .0195472 1.03 0.303 .9823422 1.058984
rural2 | 1.028837 .0287142 1.02 0.308 .97407 1.086684
rural3 | 1.054608 .0324446 1.73 0.084 .9928971 1.120155
porc_pobr | 1.230904 .1456541 1.76 0.079 .9761146 1.552201
susini2 | 1.096132 .045523 2.21 0.027 1.010443 1.189087
susini3 | 1.123012 .0372724 3.50 0.000 1.052285 1.198493
susini4 | 1.082254 .0193422 4.42 0.000 1.045 1.120836
susini5 | 1.129953 .0561992 2.46 0.014 1.025003 1.245648
ano_nac_corr | .8747392 .0037459 -31.25 0.000 .867428 .882112
cohab2 | .970731 .0310625 -0.93 0.353 .9117195 1.033562
cohab3 | .9912159 .0390069 -0.22 0.823 .9176377 1.070694
cohab4 | .952366 .0296193 -1.57 0.117 .8960472 1.012225
fis_com2 | 1.027084 .0166764 1.65 0.100 .9949134 1.060295
fis_com3 | .9021971 .0336829 -2.76 0.006 .8385373 .9706898
rc_x1 | .851524 .0048084 -28.46 0.000 .8421517 .8610007
rc_x2 | 1.028735 .0186427 1.56 0.118 .9928373 1.065931
rc_x3 | .8953805 .0414574 -2.39 0.017 .8177033 .9804367
_rcs1 | 2.630556 .0396754 64.13 0.000 2.553932 2.709479
_rcs2 | 1.102593 .0063793 16.88 0.000 1.090161 1.115167
_rcs3 | 1.042625 .0043581 9.99 0.000 1.034118 1.051202
_rcs4 | 1.023182 .0027618 8.49 0.000 1.017783 1.028609
_rcs5 | 1.014184 .0018646 7.66 0.000 1.010536 1.017845
_rcs6 | 1.010322 .001424 7.29 0.000 1.007535 1.013117
_rcs7 | 1.007987 .0012173 6.59 0.000 1.005604 1.010376
_rcs8 | 1.006572 .0010787 6.11 0.000 1.00446 1.008688
_rcs9 | 1.004581 .0010061 4.56 0.000 1.002611 1.006555
_rcs10 | 1.003061 .000875 3.50 0.000 1.001348 1.004778
_rcs_mot_egr_early1 | .9062596 .0161281 -5.53 0.000 .8751941 .9384279
_rcs_mot_egr_late1 | .9432926 .0154796 -3.56 0.000 .9134358 .9741254
_cons | 4.9e+115 4.2e+116 30.88 0.000 2.2e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54454.291
Iteration 1: log likelihood = -54438.09
Iteration 2: log likelihood = -54438.04
Iteration 3: log likelihood = -54438.04
Log likelihood = -54438.04 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729311 .0501107 18.90 0.000 1.633833 1.830369
mot_egr_late | 1.578057 .0371944 19.36 0.000 1.506816 1.652667
tr_mod2 | 1.218824 .0262265 9.20 0.000 1.168489 1.271326
sex_dum2 | .7603158 .0163333 -12.76 0.000 .7289677 .7930119
edad_ini_cons | .9868893 .0019513 -6.67 0.000 .9830723 .9907212
esc1 | 1.128882 .0298166 4.59 0.000 1.071929 1.18886
esc2 | 1.088631 .0259453 3.56 0.000 1.038948 1.140689
sus_prin2 | 1.067255 .0297581 2.33 0.020 1.010495 1.127203
sus_prin3 | 1.393451 .0326655 14.15 0.000 1.330876 1.458968
sus_prin4 | 1.076997 .0378818 2.11 0.035 1.005252 1.153863
sus_prin5 | 1.142785 .0826242 1.85 0.065 .9917957 1.316762
fr_cons_sus_prin2 | .920236 .0450237 -1.70 0.089 .8360901 1.01285
fr_cons_sus_prin3 | .9971177 .0395757 -0.07 0.942 .922491 1.077782
fr_cons_sus_prin4 | 1.008795 .0420406 0.21 0.834 .9296722 1.094652
fr_cons_sus_prin5 | 1.030639 .0409391 0.76 0.447 .9534436 1.114084
cond_ocu2 | 1.017687 .0318092 0.56 0.575 .9572133 1.081981
cond_ocu3 | 1.006303 .1419149 0.04 0.964 .7632857 1.326693
cond_ocu4 | 1.103614 .0399006 2.73 0.006 1.028117 1.184655
cond_ocu5 | 1.161844 .0890339 1.96 0.050 .9998134 1.350133
cond_ocu6 | 1.131349 .0207262 6.74 0.000 1.091447 1.172709
policonsumo | 1.02673 .0224203 1.21 0.227 .9837137 1.071627
num_hij2 | 1.165181 .0227516 7.83 0.000 1.121431 1.210638
tenviv1 | 1.152317 .0754389 2.17 0.030 1.013553 1.31008
tenviv2 | 1.128098 .049434 2.75 0.006 1.035253 1.229269
tenviv4 | 1.037769 .0237498 1.62 0.105 .9922491 1.085378
tenviv5 | 1.003959 .0179989 0.22 0.826 .9692944 1.039863
mzone2 | 1.302822 .0273816 12.59 0.000 1.250246 1.35761
mzone3 | 1.46446 .0421254 13.26 0.000 1.38418 1.549396
n_off_vio | 1.35514 .0258662 15.92 0.000 1.30538 1.406797
n_off_acq | 1.814254 .0324481 33.31 0.000 1.751759 1.878979
n_off_sud | 1.256704 .02331 12.32 0.000 1.211837 1.303231
n_off_oth | 1.360218 .0257421 16.26 0.000 1.310688 1.411619
psy_com2 | 1.071009 .0257077 2.86 0.004 1.02179 1.1226
psy_com3 | 1.058458 .0188018 3.20 0.001 1.022241 1.095957
dep2 | 1.019946 .0195472 1.03 0.303 .982345 1.058987
rural2 | 1.028822 .028714 1.02 0.309 .9740548 1.086668
rural3 | 1.054596 .0324443 1.73 0.084 .9928853 1.120142
porc_pobr | 1.230986 .145665 1.76 0.079 .976178 1.552307
susini2 | 1.096101 .045522 2.21 0.027 1.010414 1.189054
susini3 | 1.12301 .0372726 3.50 0.000 1.052283 1.198492
susini4 | 1.082256 .0193423 4.42 0.000 1.045002 1.120838
susini5 | 1.129953 .0561991 2.46 0.014 1.025003 1.245649
ano_nac_corr | .8747312 .0037461 -31.25 0.000 .8674198 .8821043
cohab2 | .9707131 .031062 -0.93 0.353 .9117025 1.033543
cohab3 | .9911938 .0390061 -0.22 0.822 .9176173 1.07067
cohab4 | .9523499 .0296188 -1.57 0.116 .896032 1.012208
fis_com2 | 1.027081 .0166763 1.65 0.100 .9949106 1.060292
fis_com3 | .9022041 .0336832 -2.76 0.006 .8385437 .9706975
rc_x1 | .8515163 .0048084 -28.46 0.000 .842144 .860993
rc_x2 | 1.02874 .0186429 1.56 0.118 .9928415 1.065936
rc_x3 | .8953656 .0414569 -2.39 0.017 .8176895 .9804206
_rcs1 | 2.637279 .046992 54.42 0.000 2.546766 2.731008
_rcs2 | 1.106371 .0153588 7.28 0.000 1.076674 1.136887
_rcs3 | 1.043192 .0048533 9.09 0.000 1.033723 1.052748
_rcs4 | 1.023337 .0028213 8.37 0.000 1.017822 1.028881
_rcs5 | 1.014221 .0018707 7.66 0.000 1.010561 1.017894
_rcs6 | 1.010329 .0014242 7.29 0.000 1.007541 1.013124
_rcs7 | 1.007987 .0012174 6.59 0.000 1.005604 1.010376
_rcs8 | 1.006573 .0010788 6.11 0.000 1.00446 1.008689
_rcs9 | 1.004583 .0010063 4.57 0.000 1.002613 1.006558
_rcs10 | 1.003064 .0008752 3.51 0.000 1.00135 1.004781
_rcs_mot_egr_early1 | .90348 .0190035 -4.83 0.000 .8669911 .9415047
_rcs_mot_egr_early2 | .9956586 .0160047 -0.27 0.787 .964779 1.027527
_rcs_mot_egr_late1 | .9407203 .0186013 -3.09 0.002 .9049598 .977894
_rcs_mot_egr_late2 | .996311 .0150392 -0.24 0.807 .9672665 1.026228
_cons | 5.0e+115 4.3e+116 30.89 0.000 2.3e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54454.215
Iteration 1: log likelihood = -54437.939
Iteration 2: log likelihood = -54437.89
Iteration 3: log likelihood = -54437.89
Log likelihood = -54437.89 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729814 .0501368 18.91 0.000 1.634287 1.830925
mot_egr_late | 1.578444 .0372177 19.36 0.000 1.507158 1.653101
tr_mod2 | 1.218842 .0262272 9.20 0.000 1.168507 1.271346
sex_dum2 | .7603203 .0163333 -12.76 0.000 .7289721 .7930165
edad_ini_cons | .9868907 .0019513 -6.67 0.000 .9830737 .9907225
esc1 | 1.128881 .0298165 4.59 0.000 1.071928 1.188859
esc2 | 1.088643 .0259456 3.56 0.000 1.03896 1.140702
sus_prin2 | 1.06729 .0297593 2.34 0.020 1.010528 1.12724
sus_prin3 | 1.393483 .0326666 14.15 0.000 1.330906 1.459002
sus_prin4 | 1.076989 .0378816 2.11 0.035 1.005243 1.153854
sus_prin5 | 1.142984 .0826389 1.85 0.065 .9919672 1.316991
fr_cons_sus_prin2 | .920201 .0450221 -1.70 0.089 .8360583 1.012812
fr_cons_sus_prin3 | .9971157 .0395757 -0.07 0.942 .9224891 1.077779
fr_cons_sus_prin4 | 1.008788 .0420404 0.21 0.834 .9296659 1.094644
fr_cons_sus_prin5 | 1.03062 .0409383 0.76 0.448 .9534261 1.114064
cond_ocu2 | 1.017668 .0318085 0.56 0.575 .9571954 1.081961
cond_ocu3 | 1.006411 .1419301 0.05 0.964 .7633678 1.326835
cond_ocu4 | 1.103581 .0398995 2.73 0.006 1.028086 1.18462
cond_ocu5 | 1.161988 .0890455 1.96 0.050 .999936 1.350302
cond_ocu6 | 1.131332 .020726 6.74 0.000 1.09143 1.172692
policonsumo | 1.026787 .022422 1.21 0.226 .9837679 1.071687
num_hij2 | 1.165184 .0227518 7.83 0.000 1.121434 1.210641
tenviv1 | 1.152395 .0754441 2.17 0.030 1.013621 1.310168
tenviv2 | 1.128125 .0494353 2.75 0.006 1.035278 1.229299
tenviv4 | 1.03776 .0237496 1.62 0.105 .9922406 1.085369
tenviv5 | 1.003953 .0179988 0.22 0.826 .9692881 1.039857
mzone2 | 1.302818 .0273814 12.59 0.000 1.250242 1.357605
mzone3 | 1.464435 .0421249 13.26 0.000 1.384156 1.54937
n_off_vio | 1.355146 .0258663 15.92 0.000 1.305385 1.406803
n_off_acq | 1.814252 .032448 33.31 0.000 1.751757 1.878977
n_off_sud | 1.256675 .0233096 12.32 0.000 1.21181 1.303202
n_off_oth | 1.360206 .0257419 16.26 0.000 1.310677 1.411606
psy_com2 | 1.071024 .0257083 2.86 0.004 1.021804 1.122616
psy_com3 | 1.058466 .018802 3.20 0.001 1.022248 1.095966
dep2 | 1.019933 .019547 1.03 0.303 .9823324 1.058973
rural2 | 1.028798 .0287133 1.02 0.309 .9740327 1.086643
rural3 | 1.054589 .0324441 1.73 0.084 .9928792 1.120135
porc_pobr | 1.230801 .1456443 1.75 0.079 .9760286 1.552076
susini2 | 1.096023 .0455192 2.21 0.027 1.010342 1.188971
susini3 | 1.123028 .0372733 3.50 0.000 1.052299 1.198511
susini4 | 1.082255 .0193424 4.42 0.000 1.045001 1.120837
susini5 | 1.129954 .0561989 2.46 0.014 1.025004 1.245649
ano_nac_corr | .8747269 .0037461 -31.25 0.000 .8674154 .8821
cohab2 | .970676 .0310609 -0.93 0.352 .9116675 1.033504
cohab3 | .9911397 .0390041 -0.23 0.821 .9175669 1.070612
cohab4 | .9523038 .0296175 -1.57 0.116 .8959884 1.012159
fis_com2 | 1.027066 .016676 1.64 0.100 .9948958 1.060276
fis_com3 | .9022147 .0336836 -2.76 0.006 .8385535 .9707089
rc_x1 | .851512 .0048084 -28.47 0.000 .8421397 .8609887
rc_x2 | 1.028746 .018643 1.56 0.118 .9928472 1.065942
rc_x3 | .8953415 .0414558 -2.39 0.017 .8176674 .9803943
_rcs1 | 2.636763 .0469109 54.50 0.000 2.546403 2.730328
_rcs2 | 1.102128 .0176149 6.08 0.000 1.068139 1.137199
_rcs3 | 1.045763 .007235 6.47 0.000 1.031678 1.06004
_rcs4 | 1.025277 .0049918 5.13 0.000 1.01554 1.035108
_rcs5 | 1.015275 .0029054 5.30 0.000 1.009596 1.020985
_rcs6 | 1.010803 .0017297 6.28 0.000 1.007419 1.014199
_rcs7 | 1.008163 .0012676 6.47 0.000 1.005682 1.010651
_rcs8 | 1.006608 .0010811 6.13 0.000 1.004491 1.008729
_rcs9 | 1.004585 .0010065 4.57 0.000 1.002614 1.006559
_rcs10 | 1.003069 .0008754 3.51 0.000 1.001355 1.004786
_rcs_mot_egr_early1 | .9034994 .0189886 -4.83 0.000 .8670385 .9414936
_rcs_mot_egr_early2 | 1.000305 .0181757 0.02 0.987 .9653085 1.036571
_rcs_mot_egr_early3 | .9942684 .01005 -0.57 0.570 .9747646 1.014162
_rcs_mot_egr_late1 | .9408931 .0185829 -3.08 0.002 .9051672 .9780289
_rcs_mot_egr_late2 | .9996583 .0172126 -0.02 0.984 .966485 1.03397
_rcs_mot_egr_late3 | .9962387 .0093255 -0.40 0.687 .9781278 1.014685
_cons | 5.0e+115 4.4e+116 30.89 0.000 2.3e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54454.253
Iteration 1: log likelihood = -54437.951
Iteration 2: log likelihood = -54437.902
Iteration 3: log likelihood = -54437.902
Log likelihood = -54437.902 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729751 .0501356 18.91 0.000 1.634226 1.83086
mot_egr_late | 1.578422 .0372176 19.36 0.000 1.507136 1.653079
tr_mod2 | 1.218835 .0262271 9.20 0.000 1.1685 1.271338
sex_dum2 | .7603199 .0163333 -12.76 0.000 .7289718 .7930162
edad_ini_cons | .9868903 .0019513 -6.67 0.000 .9830733 .9907222
esc1 | 1.128877 .0298165 4.59 0.000 1.071925 1.188855
esc2 | 1.088642 .0259456 3.56 0.000 1.038959 1.140701
sus_prin2 | 1.067292 .0297594 2.34 0.020 1.01053 1.127242
sus_prin3 | 1.393487 .0326667 14.15 0.000 1.33091 1.459007
sus_prin4 | 1.076987 .0378816 2.11 0.035 1.005242 1.153853
sus_prin5 | 1.142967 .0826378 1.85 0.065 .9919524 1.316972
fr_cons_sus_prin2 | .920207 .0450224 -1.70 0.089 .8360637 1.012819
fr_cons_sus_prin3 | .9971241 .039576 -0.07 0.942 .9224968 1.077788
fr_cons_sus_prin4 | 1.008793 .0420406 0.21 0.834 .9296705 1.09465
fr_cons_sus_prin5 | 1.030629 .0409387 0.76 0.448 .9534341 1.114073
cond_ocu2 | 1.01767 .0318086 0.56 0.575 .9571971 1.081963
cond_ocu3 | 1.006448 .1419355 0.05 0.964 .7633952 1.326884
cond_ocu4 | 1.10359 .0398999 2.73 0.006 1.028094 1.184629
cond_ocu5 | 1.161986 .089046 1.96 0.050 .999933 1.350301
cond_ocu6 | 1.13133 .020726 6.74 0.000 1.091429 1.172691
policonsumo | 1.026784 .022422 1.21 0.226 .9837649 1.071684
num_hij2 | 1.165181 .0227517 7.83 0.000 1.121431 1.210638
tenviv1 | 1.152373 .0754428 2.17 0.030 1.013601 1.310144
tenviv2 | 1.128123 .0494354 2.75 0.006 1.035276 1.229298
tenviv4 | 1.037754 .0237495 1.62 0.105 .9922339 1.085361
tenviv5 | 1.003953 .0179988 0.22 0.826 .9692883 1.039857
mzone2 | 1.302808 .0273812 12.59 0.000 1.250232 1.357594
mzone3 | 1.464457 .0421259 13.26 0.000 1.384176 1.549394
n_off_vio | 1.355146 .0258663 15.92 0.000 1.305385 1.406803
n_off_acq | 1.814255 .0324482 33.31 0.000 1.75176 1.87898
n_off_sud | 1.256682 .0233097 12.32 0.000 1.211816 1.303209
n_off_oth | 1.360212 .025742 16.26 0.000 1.310683 1.411613
psy_com2 | 1.071028 .0257085 2.86 0.004 1.021807 1.12262
psy_com3 | 1.058466 .018802 3.20 0.001 1.022249 1.095967
dep2 | 1.019933 .019547 1.03 0.303 .9823321 1.058973
rural2 | 1.028805 .0287135 1.02 0.309 .9740387 1.08665
rural3 | 1.054587 .032444 1.73 0.084 .9928771 1.120132
porc_pobr | 1.230775 .1456417 1.75 0.079 .9760075 1.552044
susini2 | 1.096022 .0455192 2.21 0.027 1.010341 1.18897
susini3 | 1.123032 .0372736 3.50 0.000 1.052303 1.198516
susini4 | 1.082255 .0193424 4.42 0.000 1.045001 1.120837
susini5 | 1.129952 .0561989 2.46 0.014 1.025003 1.245648
ano_nac_corr | .8747232 .0037461 -31.25 0.000 .8674117 .8820964
cohab2 | .9706855 .0310613 -0.93 0.352 .9116763 1.033514
cohab3 | .9911581 .0390049 -0.23 0.821 .9175838 1.070632
cohab4 | .9523172 .029618 -1.57 0.116 .8960009 1.012173
fis_com2 | 1.027064 .0166761 1.64 0.100 .9948942 1.060274
fis_com3 | .9022114 .0336835 -2.76 0.006 .8385505 .9707054
rc_x1 | .8515083 .0048085 -28.47 0.000 .8421359 .8609851
rc_x2 | 1.028746 .018643 1.56 0.118 .9928474 1.065942
rc_x3 | .895343 .0414558 -2.39 0.017 .8176688 .9803957
_rcs1 | 2.636581 .0469374 54.46 0.000 2.546172 2.7302
_rcs2 | 1.102003 .0180654 5.92 0.000 1.067158 1.137985
_rcs3 | 1.046206 .0095176 4.97 0.000 1.027717 1.065028
_rcs4 | 1.025147 .0049358 5.16 0.000 1.015518 1.034867
_rcs5 | 1.014925 .0035976 4.18 0.000 1.007898 1.022
_rcs6 | 1.01056 .0031114 3.41 0.001 1.004481 1.016677
_rcs7 | 1.008072 .0021172 3.83 0.000 1.003931 1.01223
_rcs8 | 1.006603 .0012608 5.25 0.000 1.004135 1.009077
_rcs9 | 1.004591 .0010121 4.55 0.000 1.002609 1.006576
_rcs10 | 1.003068 .0008758 3.51 0.000 1.001353 1.004786
_rcs_mot_egr_early1 | .9035213 .019003 -4.82 0.000 .8670332 .9415449
_rcs_mot_egr_early2 | 1.000146 .0186182 0.01 0.994 .9643123 1.03731
_rcs_mot_egr_early3 | .9949064 .0115676 -0.44 0.661 .9724907 1.017839
_rcs_mot_egr_early4 | .9989194 .0068256 -0.16 0.874 .9856306 1.012387
_rcs_mot_egr_late1 | .941008 .0185989 -3.08 0.002 .9052518 .9781765
_rcs_mot_egr_late2 | 1.000172 .0176999 0.01 0.992 .9660754 1.035472
_rcs_mot_egr_late3 | .9955403 .0108505 -0.41 0.682 .9744992 1.017036
_rcs_mot_egr_late4 | 1.000114 .0062951 0.02 0.986 .9878511 1.012528
_cons | 5.1e+115 4.4e+116 30.89 0.000 2.3e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54454.206
Iteration 1: log likelihood = -54437.425
Iteration 2: log likelihood = -54437.369
Iteration 3: log likelihood = -54437.369
Log likelihood = -54437.369 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729685 .0501341 18.90 0.000 1.634163 1.83079
mot_egr_late | 1.578462 .0372194 19.36 0.000 1.507173 1.653122
tr_mod2 | 1.218848 .0262274 9.20 0.000 1.168512 1.271352
sex_dum2 | .7603156 .0163333 -12.76 0.000 .7289675 .7930118
edad_ini_cons | .9868914 .0019513 -6.67 0.000 .9830744 .9907233
esc1 | 1.128865 .0298162 4.59 0.000 1.071913 1.188843
esc2 | 1.088631 .0259454 3.56 0.000 1.038948 1.140689
sus_prin2 | 1.067261 .0297586 2.33 0.020 1.010501 1.12721
sus_prin3 | 1.39346 .0326661 14.15 0.000 1.330884 1.458978
sus_prin4 | 1.076947 .0378802 2.11 0.035 1.005204 1.15381
sus_prin5 | 1.142869 .0826309 1.85 0.065 .9918673 1.31686
fr_cons_sus_prin2 | .920208 .0450225 -1.70 0.089 .8360645 1.01282
fr_cons_sus_prin3 | .9971179 .0395758 -0.07 0.942 .922491 1.077782
fr_cons_sus_prin4 | 1.008791 .0420405 0.21 0.834 .9296683 1.094648
fr_cons_sus_prin5 | 1.030633 .0409389 0.76 0.447 .9534382 1.114078
cond_ocu2 | 1.017681 .0318091 0.56 0.575 .9572077 1.081975
cond_ocu3 | 1.006429 .141933 0.05 0.964 .7633809 1.32686
cond_ocu4 | 1.103553 .0398987 2.73 0.006 1.028059 1.18459
cond_ocu5 | 1.16189 .0890391 1.96 0.050 .9998498 1.350191
cond_ocu6 | 1.131351 .0207264 6.74 0.000 1.091448 1.172712
policonsumo | 1.026767 .0224217 1.21 0.226 .983748 1.071666
num_hij2 | 1.165209 .0227523 7.83 0.000 1.121458 1.210667
tenviv1 | 1.152463 .0754487 2.17 0.030 1.013681 1.310246
tenviv2 | 1.128064 .049433 2.75 0.006 1.035221 1.229233
tenviv4 | 1.037763 .0237497 1.62 0.105 .9922433 1.085372
tenviv5 | 1.003966 .017999 0.22 0.825 .9693009 1.03987
mzone2 | 1.302829 .0273818 12.59 0.000 1.250252 1.357617
mzone3 | 1.464486 .042127 13.26 0.000 1.384203 1.549425
n_off_vio | 1.355134 .0258661 15.92 0.000 1.305374 1.406791
n_off_acq | 1.814296 .0324487 33.31 0.000 1.7518 1.879022
n_off_sud | 1.256708 .0233102 12.32 0.000 1.211841 1.303236
n_off_oth | 1.36022 .0257421 16.26 0.000 1.310691 1.411621
psy_com2 | 1.070999 .025708 2.86 0.004 1.021779 1.12259
psy_com3 | 1.058469 .018802 3.20 0.001 1.022252 1.095969
dep2 | 1.01992 .0195468 1.03 0.303 .9823201 1.05896
rural2 | 1.028801 .0287135 1.02 0.309 .9740351 1.086646
rural3 | 1.054585 .032444 1.73 0.084 .9928752 1.12013
porc_pobr | 1.230818 .1456467 1.76 0.079 .9760418 1.552099
susini2 | 1.096072 .0455214 2.21 0.027 1.010386 1.189024
susini3 | 1.122963 .0372714 3.49 0.000 1.052238 1.198442
susini4 | 1.082274 .0193428 4.42 0.000 1.045019 1.120857
susini5 | 1.130009 .0562019 2.46 0.014 1.025054 1.24571
ano_nac_corr | .8747281 .0037462 -31.25 0.000 .8674165 .8821015
cohab2 | .9706496 .03106 -0.93 0.352 .9116428 1.033476
cohab3 | .9911275 .0390037 -0.23 0.821 .9175556 1.070599
cohab4 | .9522925 .0296171 -1.57 0.116 .8959778 1.012147
fis_com2 | 1.02708 .0166764 1.65 0.100 .9949096 1.060291
fis_com3 | .9022014 .0336831 -2.76 0.006 .8385412 .9706945
rc_x1 | .8515091 .0048085 -28.47 0.000 .8421366 .8609859
rc_x2 | 1.028759 .0186433 1.56 0.118 .9928601 1.065956
rc_x3 | .8953214 .0414549 -2.39 0.017 .817649 .9803724
_rcs1 | 2.636478 .0469522 54.44 0.000 2.546041 2.730128
_rcs2 | 1.102417 .0183639 5.85 0.000 1.067006 1.139004
_rcs3 | 1.04556 .0106208 4.39 0.000 1.02495 1.066585
_rcs4 | 1.025712 .0051615 5.05 0.000 1.015646 1.035879
_rcs5 | 1.015447 .0042552 3.66 0.000 1.007141 1.023821
_rcs6 | 1.010307 .0029358 3.53 0.000 1.004569 1.016077
_rcs7 | 1.007318 .0028855 2.55 0.011 1.001679 1.01299
_rcs8 | 1.006026 .0021052 2.87 0.004 1.001908 1.01016
_rcs9 | 1.004427 .0011457 3.87 0.000 1.002184 1.006675
_rcs10 | 1.003068 .0008756 3.51 0.000 1.001353 1.004786
_rcs_mot_egr_early1 | .903613 .0190105 -4.82 0.000 .8671108 .9416519
_rcs_mot_egr_early2 | .9991175 .0189102 -0.05 0.963 .9627333 1.036877
_rcs_mot_egr_early3 | .9970221 .0123042 -0.24 0.809 .9731956 1.021432
_rcs_mot_egr_early4 | .9954032 .0074161 -0.62 0.536 .9809735 1.010045
_rcs_mot_egr_early5 | 1.00259 .0050491 0.51 0.607 .9927431 1.012536
_rcs_mot_egr_late1 | .9410858 .0186075 -3.07 0.002 .9053134 .9782717
_rcs_mot_egr_late2 | 1.000517 .0180712 0.03 0.977 .9657174 1.03657
_rcs_mot_egr_late3 | .9948885 .0115905 -0.44 0.660 .9724289 1.017867
_rcs_mot_egr_late4 | .9999216 .0069052 -0.01 0.991 .9864789 1.013547
_rcs_mot_egr_late5 | 1.000693 .0046057 0.15 0.880 .9917065 1.009761
_cons | 5.0e+115 4.3e+116 30.88 0.000 2.3e+108 1.1e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54454.214
Iteration 1: log likelihood = -54436.491
Iteration 2: log likelihood = -54436.431
Iteration 3: log likelihood = -54436.431
Log likelihood = -54436.431 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729704 .0501357 18.90 0.000 1.634179 1.830813
mot_egr_late | 1.57841 .037219 19.36 0.000 1.507122 1.653069
tr_mod2 | 1.218803 .0262267 9.20 0.000 1.168469 1.271306
sex_dum2 | .7603232 .0163335 -12.76 0.000 .7289747 .7930197
edad_ini_cons | .9868907 .0019513 -6.67 0.000 .9830737 .9907225
esc1 | 1.128838 .0298156 4.59 0.000 1.071888 1.188815
esc2 | 1.088628 .0259453 3.56 0.000 1.038945 1.140686
sus_prin2 | 1.067281 .029759 2.34 0.020 1.010519 1.127231
sus_prin3 | 1.393475 .0326664 14.15 0.000 1.330899 1.458994
sus_prin4 | 1.076986 .0378815 2.11 0.035 1.005241 1.153852
sus_prin5 | 1.143014 .0826414 1.85 0.064 .9919935 1.317027
fr_cons_sus_prin2 | .9202074 .0450224 -1.70 0.089 .8360639 1.012819
fr_cons_sus_prin3 | .9971799 .0395782 -0.07 0.943 .9225485 1.077849
fr_cons_sus_prin4 | 1.00881 .0420413 0.21 0.833 .9296862 1.094668
fr_cons_sus_prin5 | 1.030665 .0409401 0.76 0.447 .9534679 1.114112
cond_ocu2 | 1.01769 .0318093 0.56 0.575 .9572157 1.081984
cond_ocu3 | 1.006567 .1419524 0.05 0.963 .7634857 1.327041
cond_ocu4 | 1.103637 .0399017 2.73 0.006 1.028138 1.18468
cond_ocu5 | 1.162036 .08905 1.96 0.050 .9999762 1.35036
cond_ocu6 | 1.13133 .0207261 6.74 0.000 1.091429 1.172691
policonsumo | 1.026801 .0224225 1.21 0.226 .9837814 1.071703
num_hij2 | 1.165195 .0227521 7.83 0.000 1.121445 1.210653
tenviv1 | 1.152377 .0754431 2.17 0.030 1.013605 1.310148
tenviv2 | 1.12806 .0494328 2.75 0.006 1.035217 1.229228
tenviv4 | 1.037752 .0237495 1.62 0.105 .9922324 1.08536
tenviv5 | 1.00396 .0179989 0.22 0.826 .9692957 1.039865
mzone2 | 1.302836 .0273818 12.59 0.000 1.250259 1.357624
mzone3 | 1.464524 .0421281 13.26 0.000 1.384239 1.549465
n_off_vio | 1.355121 .025866 15.92 0.000 1.305361 1.406777
n_off_acq | 1.814246 .0324483 33.31 0.000 1.75175 1.878971
n_off_sud | 1.256708 .0233104 12.32 0.000 1.211841 1.303236
n_off_oth | 1.360193 .0257417 16.26 0.000 1.310665 1.411593
psy_com2 | 1.071054 .0257093 2.86 0.004 1.021831 1.122647
psy_com3 | 1.058485 .0188023 3.20 0.001 1.022267 1.095986
dep2 | 1.019922 .0195467 1.03 0.303 .9823217 1.058961
rural2 | 1.028799 .0287133 1.02 0.309 .9740339 1.086644
rural3 | 1.05453 .0324423 1.73 0.084 .9928228 1.120072
porc_pobr | 1.230616 .1456226 1.75 0.079 .9758818 1.551843
susini2 | 1.096018 .0455192 2.21 0.027 1.010337 1.188966
susini3 | 1.123003 .0372728 3.50 0.000 1.052275 1.198485
susini4 | 1.082236 .0193421 4.42 0.000 1.044983 1.120818
susini5 | 1.129903 .0561966 2.46 0.014 1.024958 1.245594
ano_nac_corr | .8747068 .0037461 -31.26 0.000 .8673953 .8820799
cohab2 | .9706363 .0310598 -0.93 0.352 .9116299 1.033462
cohab3 | .991176 .0390058 -0.23 0.822 .9175999 1.070652
cohab4 | .9522736 .0296167 -1.57 0.116 .8959598 1.012127
fis_com2 | 1.027073 .0166763 1.65 0.100 .9949031 1.060284
fis_com3 | .9022257 .0336841 -2.76 0.006 .8385637 .9707209
rc_x1 | .8514927 .0048084 -28.47 0.000 .8421204 .8609693
rc_x2 | 1.028747 .0186431 1.56 0.118 .9928487 1.065944
rc_x3 | .8953398 .0414559 -2.39 0.017 .8176655 .9803928
_rcs1 | 2.635941 .0469118 54.46 0.000 2.54558 2.729508
_rcs2 | 1.100957 .018282 5.79 0.000 1.065701 1.137378
_rcs3 | 1.048228 .0113052 4.37 0.000 1.026303 1.070622
_rcs4 | 1.024325 .0056865 4.33 0.000 1.01324 1.035531
_rcs5 | 1.014716 .0041934 3.54 0.000 1.006531 1.022969
_rcs6 | 1.01133 .0034132 3.34 0.001 1.004662 1.018042
_rcs7 | 1.007832 .0027296 2.88 0.004 1.002496 1.013196
_rcs8 | 1.005334 .0026251 2.04 0.042 1.000202 1.010492
_rcs9 | 1.003803 .0016802 2.27 0.023 1.000515 1.007101
_rcs10 | 1.003009 .0008826 3.41 0.001 1.00128 1.00474
_rcs_mot_egr_early1 | .9036491 .0190037 -4.82 0.000 .8671598 .9416738
_rcs_mot_egr_early2 | 1.000431 .0189706 0.02 0.982 .9639318 1.038312
_rcs_mot_egr_early3 | .9961288 .0127314 -0.30 0.762 .9714856 1.021397
_rcs_mot_egr_early4 | .9960001 .0078341 -0.51 0.610 .9807632 1.011474
_rcs_mot_egr_early5 | 1.00052 .005297 0.10 0.922 .9901917 1.010956
_rcs_mot_egr_early6 | 1.000401 .0040327 0.10 0.921 .9925283 1.008336
_rcs_mot_egr_late1 | .9414144 .0186057 -3.05 0.002 .905645 .9785965
_rcs_mot_egr_late2 | 1.002192 .0181682 0.12 0.904 .9672079 1.038441
_rcs_mot_egr_late3 | .9919203 .0120014 -0.67 0.503 .9686746 1.015724
_rcs_mot_egr_late4 | 1.001993 .0072976 0.27 0.785 .9877919 1.016399
_rcs_mot_egr_late5 | .9981998 .0048519 -0.37 0.711 .9887354 1.007755
_rcs_mot_egr_late6 | 1.002857 .003673 0.78 0.436 .995684 1.010082
_cons | 5.3e+115 4.6e+116 30.89 0.000 2.4e+108 1.2e+123
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -54454.256
Iteration 1: log likelihood = -54435.735
Iteration 2: log likelihood = -54435.663
Iteration 3: log likelihood = -54435.663
Log likelihood = -54435.663 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.729791 .0501379 18.91 0.000 1.634262 1.830905
mot_egr_late | 1.578433 .037219 19.36 0.000 1.507145 1.653092
tr_mod2 | 1.218844 .0262276 9.20 0.000 1.168507 1.271348
sex_dum2 | .7603322 .0163336 -12.75 0.000 .7289835 .793029
edad_ini_cons | .9868904 .0019513 -6.67 0.000 .9830734 .9907222
esc1 | 1.128826 .0298154 4.59 0.000 1.071876 1.188802
esc2 | 1.088629 .0259454 3.56 0.000 1.038946 1.140687
sus_prin2 | 1.067325 .0297602 2.34 0.019 1.010561 1.127277
sus_prin3 | 1.393514 .0326676 14.15 0.000 1.330935 1.459035
sus_prin4 | 1.077053 .037884 2.11 0.035 1.005304 1.153924
sus_prin5 | 1.143087 .0826467 1.85 0.064 .9920561 1.317111
fr_cons_sus_prin2 | .9202279 .0450234 -1.70 0.089 .8360826 1.012842
fr_cons_sus_prin3 | .9972433 .0395808 -0.07 0.945 .922607 1.077917
fr_cons_sus_prin4 | 1.00885 .042043 0.21 0.833 .9297232 1.094712
fr_cons_sus_prin5 | 1.030681 .0409408 0.76 0.447 .953483 1.11413
cond_ocu2 | 1.017682 .031809 0.56 0.575 .9572091 1.081976
cond_ocu3 | 1.006532 .1419474 0.05 0.963 .7634592 1.326995
cond_ocu4 | 1.103581 .0398999 2.73 0.006 1.028085 1.184621
cond_ocu5 | 1.161966 .0890446 1.96 0.050 .9999157 1.350278
cond_ocu6 | 1.131298 .0207256 6.73 0.000 1.091397 1.172658
policonsumo | 1.026767 .0224218 1.21 0.226 .9837478 1.071666
num_hij2 | 1.165194 .0227521 7.83 0.000 1.121444 1.210652
tenviv1 | 1.152514 .0754516 2.17 0.030 1.013726 1.310303
tenviv2 | 1.128054 .0494327 2.75 0.006 1.035212 1.229222
tenviv4 | 1.037752 .0237496 1.62 0.105 .9922318 1.08536
tenviv5 | 1.003963 .017999 0.22 0.825 .9692978 1.039867
mzone2 | 1.302825 .0273815 12.59 0.000 1.250249 1.357612
mzone3 | 1.464514 .0421281 13.26 0.000 1.384229 1.549456
n_off_vio | 1.355095 .0258655 15.92 0.000 1.305336 1.40675
n_off_acq | 1.814277 .0324487 33.31 0.000 1.75178 1.879003
n_off_sud | 1.256696 .0233102 12.32 0.000 1.211829 1.303224
n_off_oth | 1.360216 .0257421 16.26 0.000 1.310687 1.411617
psy_com2 | 1.071098 .0257103 2.86 0.004 1.021874 1.122694
psy_com3 | 1.058504 .0188026 3.20 0.001 1.022285 1.096005
dep2 | 1.019947 .0195473 1.03 0.303 .9823454 1.058987
rural2 | 1.028815 .0287137 1.02 0.309 .974049 1.086661
rural3 | 1.054535 .0324425 1.73 0.084 .9928282 1.120078
porc_pobr | 1.230197 .1455733 1.75 0.080 .975549 1.551316
susini2 | 1.096062 .045521 2.21 0.027 1.010377 1.189013
susini3 | 1.123034 .0372739 3.50 0.000 1.052304 1.198518
susini4 | 1.08221 .0193417 4.42 0.000 1.044957 1.120791
susini5 | 1.129939 .0561986 2.46 0.014 1.02499 1.245633
ano_nac_corr | .8747008 .0037462 -31.26 0.000 .8673892 .8820741
cohab2 | .9705789 .0310581 -0.93 0.351 .9115758 1.033401
cohab3 | .9911575 .0390051 -0.23 0.821 .9175828 1.070632
cohab4 | .9522275 .0296152 -1.57 0.115 .8959165 1.012078
fis_com2 | 1.027024 .0166755 1.64 0.101 .994855 1.060233
fis_com3 | .9022034 .0336833 -2.76 0.006 .8385429 .9706969
rc_x1 | .8514874 .0048084 -28.47 0.000 .842115 .8609641
rc_x2 | 1.028738 .0186429 1.56 0.118 .9928401 1.065934
rc_x3 | .8953603 .0414567 -2.39 0.017 .8176845 .9804148
_rcs1 | 2.636035 .0468922 54.49 0.000 2.545712 2.729563
_rcs2 | 1.099958 .0181914 5.76 0.000 1.064875 1.136197
_rcs3 | 1.050106 .0116764 4.40 0.000 1.027468 1.073242
_rcs4 | 1.022632 .0063234 3.62 0.000 1.010313 1.035101
_rcs5 | 1.01508 .0040916 3.71 0.000 1.007092 1.023131
_rcs6 | 1.011743 .0034767 3.40 0.001 1.004952 1.01858
_rcs7 | 1.007289 .0029603 2.47 0.013 1.001503 1.013108
_rcs8 | 1.006 .0024939 2.41 0.016 1.001124 1.0109
_rcs9 | 1.004711 .0022697 2.08 0.037 1.000272 1.009169
_rcs10 | 1.003077 .0009922 3.11 0.002 1.001135 1.005024
_rcs_mot_egr_early1 | .9034703 .0189934 -4.83 0.000 .8670004 .9414743
_rcs_mot_egr_early2 | 1.001851 .0190544 0.10 0.923 .9651925 1.039902
_rcs_mot_egr_early3 | .993881 .0129413 -0.47 0.637 .9688374 1.019572
_rcs_mot_egr_early4 | .9988226 .008119 -0.14 0.885 .9830356 1.014863
_rcs_mot_egr_early5 | .997585 .0054216 -0.44 0.656 .9870153 1.008268
_rcs_mot_egr_early6 | 1.002101 .0042127 0.50 0.618 .9938783 1.010392
_rcs_mot_egr_early7 | .9972319 .0034204 -0.81 0.419 .9905505 1.003958
_rcs_mot_egr_late1 | .9413502 .0185975 -3.06 0.002 .9055964 .9785156
_rcs_mot_egr_late2 | 1.002868 .0182151 0.16 0.875 .9677951 1.039212
_rcs_mot_egr_late3 | .9908502 .0121916 -0.75 0.455 .967241 1.015036
_rcs_mot_egr_late4 | 1.002671 .0075521 0.35 0.723 .9879777 1.017582
_rcs_mot_egr_late5 | .9977674 .0049641 -0.45 0.653 .9880853 1.007544
_rcs_mot_egr_late6 | 1.001044 .0038365 0.27 0.785 .9935532 1.008592
_rcs_mot_egr_late7 | 1.00119 .0031039 0.38 0.701 .9951249 1.007292
_cons | 5.4e+115 4.6e+116 30.89 0.000 2.4e+108 1.2e+123
---------------------------------------------------------------------------------------
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 | 22,287 . -54836 55 109782 110222.7
m_nostag_r~2 | 22,287 . -54572.45 57 109258.9 109715.6
m_nostag_r~3 | 22,287 . -54524.23 59 109166.5 109639.2
m_nostag_r~4 | 22,287 . -54515.49 61 109153 109641.7
m_nostag_r~5 | 22,287 . -54509.16 63 109144.3 109649.1
m_nostag_r~6 | 22,287 . -54505.62 65 109141.2 109662
m_nostag_r~7 | 22,287 . -54503.83 67 109141.7 109678.5
m_nostag_r~1 | 22,287 . -54523.74 56 109159.5 109608.1
m_nostag_r~2 | 22,287 . -54523.41 58 109162.8 109627.5
m_nostag_r~3 | 22,287 . -54475.59 60 109071.2 109551.9
m_nostag_r~4 | 22,287 . -54465.15 62 109054.3 109551
m_nostag_r~5 | 22,287 . -54458.64 64 109045.3 109558
m_nostag_r~6 | 22,287 . -54455.19 66 109042.4 109571.2
m_nostag_r~7 | 22,287 . -54453.37 68 109042.7 109587.5
m_nostag_r~1 | 22,287 . -54463.4 57 109040.8 109497.5
m_nostag_r~2 | 22,287 . -54463.32 59 109044.6 109517.3
m_nostag_r~3 | 22,287 . -54463.18 61 109048.4 109537.1
m_nostag_r~4 | 22,287 . -54457.11 63 109040.2 109545
m_nostag_r~5 | 22,287 . -54448.21 65 109026.4 109547.2
m_nostag_r~6 | 22,287 . -54444.2 67 109022.4 109559.2
m_nostag_r~7 | 22,287 . -54442.36 69 109022.7 109575.5
m_nostag_r~1 | 22,287 . -54452.97 58 109021.9 109486.6
m_nostag_r~2 | 22,287 . -54452.94 60 109025.9 109506.6
m_nostag_r~3 | 22,287 . -54452.44 62 109028.9 109525.6
m_nostag_r~4 | 22,287 . -54452.82 64 109033.6 109546.4
m_nostag_r~5 | 22,287 . -54447.62 66 109027.2 109556
m_nostag_r~6 | 22,287 . -54443.26 68 109022.5 109567.3
m_nostag_r~7 | 22,287 . -54441.25 70 109022.5 109583.3
m_nostag_r~1 | 22,287 . -54446.38 59 109010.8 109483.4
m_nostag_r~2 | 22,287 . -54446.35 61 109014.7 109503.4
m_nostag_r~3 | 22,287 . -54446.18 63 109018.4 109523.1
m_nostag_r~4 | 22,287 . -54446.01 65 109022 109542.8
m_nostag_r~5 | 22,287 . -54445.71 67 109025.4 109562.2
m_nostag_r~6 | 22,287 . -54442.71 69 109023.4 109576.2
m_nostag_r~7 | 22,287 . -54440.84 71 109023.7 109592.5
m_nostag_r~1 | 22,287 . -54443.19 60 109006.4 109487.1
m_nostag_r~2 | 22,287 . -54443.16 62 109010.3 109507
m_nostag_r~3 | 22,287 . -54443.02 64 109014 109526.8
m_nostag_r~4 | 22,287 . -54443 66 109018 109546.8
m_nostag_r~5 | 22,287 . -54442.49 68 109021 109565.8
m_nostag_r~6 | 22,287 . -54441.61 70 109023.2 109584
m_nostag_r~7 | 22,287 . -54440.26 72 109024.5 109601.4
m_nostag_r~1 | 22,287 . -54441.23 61 109004.5 109493.2
m_nostag_r~2 | 22,287 . -54441.2 63 109008.4 109513.1
m_nostag_r~3 | 22,287 . -54441.07 65 109012.1 109532.9
m_nostag_r~4 | 22,287 . -54441.06 67 109016.1 109552.9
m_nostag_r~5 | 22,287 . -54440.56 69 109019.1 109571.9
m_nostag_r~6 | 22,287 . -54439.78 71 109021.6 109590.4
m_nostag_r~7 | 22,287 . -54438.78 73 109023.6 109608.4
m_nostag_r~1 | 22,287 . -54439.86 62 109003.7 109500.5
m_nostag_r~2 | 22,287 . -54439.83 64 109007.7 109520.4
m_nostag_r~3 | 22,287 . -54439.69 66 109011.4 109540.2
m_nostag_r~4 | 22,287 . -54439.7 68 109015.4 109560.2
m_nostag_r~5 | 22,287 . -54439.16 70 109018.3 109579.1
m_nostag_r~6 | 22,287 . -54438.45 72 109020.9 109597.7
m_nostag_r~7 | 22,287 . -54437.09 74 109022.2 109615.1
m_nostag_r~1 | 22,287 . -54438.86 63 109003.7 109508.5
m_nostag_r~2 | 22,287 . -54438.82 65 109007.6 109528.4
m_nostag_r~3 | 22,287 . -54438.67 67 109011.3 109548.1
m_nostag_r~4 | 22,287 . -54438.69 69 109015.4 109568.2
m_nostag_r~5 | 22,287 . -54438.14 71 109018.3 109587.1
m_nostag_r~6 | 22,287 . -54437.15 73 109020.3 109605.2
m_nostag_r~7 | 22,287 . -54436.41 75 109022.8 109623.7
m_nostag_r~1 | 22,287 . -54438.08 64 109004.2 109516.9
m_nostag_r~2 | 22,287 . -54438.04 66 109008.1 109536.9
m_nostag_r~3 | 22,287 . -54437.89 68 109011.8 109556.6
m_nostag_r~4 | 22,287 . -54437.9 70 109015.8 109576.6
m_nostag_r~5 | 22,287 . -54437.37 72 109018.7 109595.6
m_nostag_r~6 | 22,287 . -54436.43 74 109020.9 109613.7
m_nostag_r~7 | 22,287 . -54435.66 76 109023.3 109632.2
-----------------------------------------------------------------------------
. //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
. *di "$st_rownames"
. esttab matrix(stats_1) using "testreg_aic_bic_mrl_23_1.csv", replace
(output written to testreg_aic_bic_mrl_23_1.csv)
. esttab matrix(stats_1) using "testreg_aic_bic_mrl_23_1.html", replace
(output written to testreg_aic_bic_mrl_23_1.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://sta
> ts.stackexchange.com/q/232494
| stats_1 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m_nostag_rp9_tvc_1 | 22287 | . | -54438.86 | 63 | 109003.7 | 109508.5 |
| m_nostag_rp8_tvc_1 | 22287 | . | -54439.86 | 62 | 109003.7 | 109500.5 |
| m_nostag_rp10_tvc_1 | 22287 | . | -54438.08 | 64 | 109004.2 | 109516.9 |
| m_nostag_rp7_tvc_1 | 22287 | . | -54441.23 | 61 | 109004.5 | 109493.2 |
| m_nostag_rp6_tvc_1 | 22287 | . | -54443.19 | 60 | 109006.4 | 109487.1 |
| m_nostag_rp9_tvc_2 | 22287 | . | -54438.82 | 65 | 109007.6 | 109528.4 |
| m_nostag_rp8_tvc_2 | 22287 | . | -54439.83 | 64 | 109007.7 | 109520.4 |
| m_nostag_rp10_tvc_2 | 22287 | . | -54438.04 | 66 | 109008.1 | 109536.9 |
| m_nostag_rp7_tvc_2 | 22287 | . | -54441.2 | 63 | 109008.4 | 109513.1 |
| m_nostag_rp6_tvc_2 | 22287 | . | -54443.16 | 62 | 109010.3 | 109507 |
| m_nostag_rp5_tvc_1 | 22287 | . | -54446.38 | 59 | 109010.8 | 109483.4 |
| m_nostag_rp9_tvc_3 | 22287 | . | -54438.67 | 67 | 109011.3 | 109548.1 |
| m_nostag_rp8_tvc_3 | 22287 | . | -54439.69 | 66 | 109011.4 | 109540.2 |
| m_nostag_rp10_tvc_3 | 22287 | . | -54437.89 | 68 | 109011.8 | 109556.6 |
| m_nostag_rp7_tvc_3 | 22287 | . | -54441.07 | 65 | 109012.1 | 109532.9 |
| m_nostag_rp6_tvc_3 | 22287 | . | -54443.02 | 64 | 109014 | 109526.8 |
| m_nostag_rp5_tvc_2 | 22287 | . | -54446.35 | 61 | 109014.7 | 109503.4 |
| m_nostag_rp9_tvc_4 | 22287 | . | -54438.69 | 69 | 109015.4 | 109568.2 |
| m_nostag_rp8_tvc_4 | 22287 | . | -54439.7 | 68 | 109015.4 | 109560.2 |
| m_nostag_rp10_tvc_4 | 22287 | . | -54437.9 | 70 | 109015.8 | 109576.6 |
| m_nostag_rp7_tvc_4 | 22287 | . | -54441.06 | 67 | 109016.1 | 109552.9 |
| m_nostag_rp6_tvc_4 | 22287 | . | -54443 | 66 | 109018 | 109546.8 |
| m_nostag_rp9_tvc_5 | 22287 | . | -54438.14 | 71 | 109018.3 | 109587.1 |
| m_nostag_rp8_tvc_5 | 22287 | . | -54439.16 | 70 | 109018.3 | 109579.1 |
| m_nostag_rp5_tvc_3 | 22287 | . | -54446.18 | 63 | 109018.4 | 109523.1 |
| m_nostag_rp10_tvc_5 | 22287 | . | -54437.37 | 72 | 109018.7 | 109595.6 |
| m_nostag_rp7_tvc_5 | 22287 | . | -54440.56 | 69 | 109019.1 | 109571.9 |
| m_nostag_rp9_tvc_6 | 22287 | . | -54437.15 | 73 | 109020.3 | 109605.2 |
| m_nostag_rp10_tvc_6 | 22287 | . | -54436.43 | 74 | 109020.9 | 109613.7 |
| m_nostag_rp8_tvc_6 | 22287 | . | -54438.45 | 72 | 109020.9 | 109597.7 |
| m_nostag_rp6_tvc_5 | 22287 | . | -54442.49 | 68 | 109021 | 109565.8 |
| m_nostag_rp7_tvc_6 | 22287 | . | -54439.78 | 71 | 109021.6 | 109590.4 |
| m_nostag_rp4_tvc_1 | 22287 | . | -54452.97 | 58 | 109021.9 | 109486.6 |
| m_nostag_rp5_tvc_4 | 22287 | . | -54446.01 | 65 | 109022 | 109542.8 |
| m_nostag_rp8_tvc_7 | 22287 | . | -54437.09 | 74 | 109022.2 | 109615.1 |
| m_nostag_rp3_tvc_6 | 22287 | . | -54444.2 | 67 | 109022.4 | 109559.2 |
| m_nostag_rp4_tvc_7 | 22287 | . | -54441.25 | 70 | 109022.5 | 109583.3 |
| m_nostag_rp4_tvc_6 | 22287 | . | -54443.26 | 68 | 109022.5 | 109567.3 |
| m_nostag_rp3_tvc_7 | 22287 | . | -54442.36 | 69 | 109022.7 | 109575.5 |
| m_nostag_rp9_tvc_7 | 22287 | . | -54436.41 | 75 | 109022.8 | 109623.7 |
| m_nostag_rp6_tvc_6 | 22287 | . | -54441.61 | 70 | 109023.2 | 109584 |
| m_nostag_rp10_tvc_7 | 22287 | . | -54435.66 | 76 | 109023.3 | 109632.2 |
| m_nostag_rp5_tvc_6 | 22287 | . | -54442.71 | 69 | 109023.4 | 109576.2 |
| m_nostag_rp7_tvc_7 | 22287 | . | -54438.78 | 73 | 109023.6 | 109608.4 |
| m_nostag_rp5_tvc_7 | 22287 | . | -54440.84 | 71 | 109023.7 | 109592.5 |
| m_nostag_rp6_tvc_7 | 22287 | . | -54440.26 | 72 | 109024.5 | 109601.4 |
| m_nostag_rp5_tvc_5 | 22287 | . | -54445.71 | 67 | 109025.4 | 109562.2 |
| m_nostag_rp4_tvc_2 | 22287 | . | -54452.94 | 60 | 109025.9 | 109506.6 |
| m_nostag_rp3_tvc_5 | 22287 | . | -54448.21 | 65 | 109026.4 | 109547.2 |
| m_nostag_rp4_tvc_5 | 22287 | . | -54447.62 | 66 | 109027.2 | 109556 |
| m_nostag_rp4_tvc_3 | 22287 | . | -54452.44 | 62 | 109028.9 | 109525.6 |
| m_nostag_rp4_tvc_4 | 22287 | . | -54452.82 | 64 | 109033.6 | 109546.4 |
| m_nostag_rp3_tvc_4 | 22287 | . | -54457.11 | 63 | 109040.2 | 109545 |
| m_nostag_rp3_tvc_1 | 22287 | . | -54463.4 | 57 | 109040.8 | 109497.5 |
| m_nostag_rp2_tvc_6 | 22287 | . | -54455.19 | 66 | 109042.4 | 109571.2 |
| m_nostag_rp2_tvc_7 | 22287 | . | -54453.37 | 68 | 109042.7 | 109587.5 |
| m_nostag_rp3_tvc_2 | 22287 | . | -54463.32 | 59 | 109044.6 | 109517.3 |
| m_nostag_rp2_tvc_5 | 22287 | . | -54458.64 | 64 | 109045.3 | 109558 |
| m_nostag_rp3_tvc_3 | 22287 | . | -54463.18 | 61 | 109048.4 | 109537.1 |
| m_nostag_rp2_tvc_4 | 22287 | . | -54465.15 | 62 | 109054.3 | 109551 |
| m_nostag_rp2_tvc_3 | 22287 | . | -54475.59 | 60 | 109071.2 | 109551.9 |
| m_nostag_rp1_tvc_6 | 22287 | . | -54505.62 | 65 | 109141.2 | 109662 |
| m_nostag_rp1_tvc_7 | 22287 | . | -54503.83 | 67 | 109141.7 | 109678.5 |
| m_nostag_rp1_tvc_5 | 22287 | . | -54509.16 | 63 | 109144.3 | 109649.1 |
| m_nostag_rp1_tvc_4 | 22287 | . | -54515.49 | 61 | 109153 | 109641.7 |
| m_nostag_rp2_tvc_1 | 22287 | . | -54523.74 | 56 | 109159.5 | 109608.1 |
| m_nostag_rp2_tvc_2 | 22287 | . | -54523.41 | 58 | 109162.8 | 109627.5 |
| m_nostag_rp1_tvc_3 | 22287 | . | -54524.23 | 59 | 109166.5 | 109639.2 |
| m_nostag_rp1_tvc_2 | 22287 | . | -54572.45 | 57 | 109258.9 | 109715.6 |
| m_nostag_rp1_tvc_1 | 22287 | . | -54836 | 55 | 109782 | 110222.7 |
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 baseline hazard function was fitted using restricted cubic splines with 6 degrees of freedom, generating 5 interior knots placed at equally-spaced percentiles (17, 33, 50, 67 & 83). To allow for non-proportional hazards, the time-dependent effect of treatment outcome was fitted using restricted cubic splines with 1 degrees of freedom.
.
. *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_rp6_tvc_1, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp6_tvc_1
------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -54443.187 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728953 .0499925 18.94 0.000 1.633694 1.829766
mot_egr_late | 1.577917 .0370653 19.42 0.000 1.506917 1.652262
tr_mod2 | 1.218636 .0262221 9.19 0.000 1.16831 1.271129
sex_dum2 | .7600293 .016327 -12.77 0.000 .7286932 .7927129
edad_ini_cons | .9868996 .0019513 -6.67 0.000 .9830825 .9907315
esc1 | 1.128982 .0298192 4.59 0.000 1.072025 1.188966
esc2 | 1.088746 .025948 3.57 0.000 1.039058 1.14081
sus_prin2 | 1.066729 .0297425 2.32 0.021 1.009999 1.126646
sus_prin3 | 1.392948 .0326517 14.14 0.000 1.3304 1.458437
sus_prin4 | 1.076603 .0378667 2.10 0.036 1.004886 1.153438
sus_prin5 | 1.141834 .0825502 1.83 0.067 .9909792 1.315654
fr_cons_sus_prin2 | .920203 .0450222 -1.70 0.089 .8360601 1.012814
fr_cons_sus_prin3 | .9969857 .0395705 -0.08 0.939 .9223689 1.077639
fr_cons_sus_prin4 | 1.008748 .0420384 0.21 0.834 .9296295 1.0946
fr_cons_sus_prin5 | 1.030657 .0409393 0.76 0.447 .9534613 1.114103
cond_ocu2 | 1.017891 .0318157 0.57 0.570 .9574048 1.082198
cond_ocu3 | 1.005554 .1418086 0.04 0.969 .7627188 1.325704
cond_ocu4 | 1.104285 .0399243 2.74 0.006 1.028743 1.185375
cond_ocu5 | 1.161881 .089036 1.96 0.050 .9998462 1.350175
cond_ocu6 | 1.131352 .0207262 6.74 0.000 1.09145 1.172713
policonsumo | 1.026642 .0224184 1.20 0.229 .9836297 1.071535
num_hij2 | 1.165174 .0227514 7.83 0.000 1.121424 1.21063
tenviv1 | 1.152096 .075424 2.16 0.031 1.013358 1.309827
tenviv2 | 1.127523 .0494075 2.74 0.006 1.034728 1.22864
tenviv4 | 1.037621 .0237463 1.61 0.107 .9921074 1.085222
tenviv5 | 1.003652 .0179934 0.20 0.839 .9689976 1.039545
mzone2 | 1.302629 .0273768 12.58 0.000 1.250061 1.357407
mzone3 | 1.464532 .0421233 13.27 0.000 1.384256 1.549464
n_off_vio | 1.355274 .0258706 15.93 0.000 1.305506 1.40694
n_off_acq | 1.814333 .0324517 33.31 0.000 1.751831 1.879065
n_off_sud | 1.256841 .0233136 12.32 0.000 1.211967 1.303375
n_off_oth | 1.360377 .0257473 16.26 0.000 1.310838 1.411788
psy_com2 | 1.07078 .0257019 2.85 0.004 1.021572 1.122359
psy_com3 | 1.05835 .0187998 3.19 0.001 1.022137 1.095846
dep2 | 1.019981 .0195475 1.03 0.302 .9823791 1.059022
rural2 | 1.028789 .0287124 1.02 0.309 .9740256 1.086632
rural3 | 1.054563 .0324416 1.73 0.084 .9928578 1.120104
porc_pobr | 1.228279 .1453468 1.74 0.082 .974027 1.548898
susini2 | 1.095891 .0455133 2.20 0.027 1.01022 1.188826
susini3 | 1.122648 .0372602 3.49 0.000 1.051944 1.198104
susini4 | 1.082362 .0193437 4.43 0.000 1.045105 1.120947
susini5 | 1.129855 .056192 2.45 0.014 1.024918 1.245535
ano_nac_corr | .874961 .003746 -31.20 0.000 .8676497 .8823339
cohab2 | .9707827 .0310641 -0.93 0.354 .9117682 1.033617
cohab3 | .9914812 .0390175 -0.22 0.828 .917883 1.070981
cohab4 | .9524348 .0296215 -1.57 0.117 .8961117 1.012298
fis_com2 | 1.027195 .0166785 1.65 0.098 .9950202 1.06041
fis_com3 | .9022046 .0336831 -2.76 0.006 .8385445 .9706976
rc_x1 | .8517336 .0048089 -28.42 0.000 .8423604 .8612112
rc_x2 | 1.028766 .0186435 1.56 0.118 .992867 1.065963
rc_x3 | .8953119 .0414545 -2.39 0.017 .8176403 .9803619
_rcs1 | 2.632098 .0397141 64.14 0.000 2.555399 2.711098
_rcs2 | 1.104931 .0062859 17.54 0.000 1.092679 1.11732
_rcs3 | 1.042542 .0040782 10.65 0.000 1.03458 1.050566
_rcs4 | 1.020136 .0025116 8.10 0.000 1.015225 1.025071
_rcs5 | 1.011801 .0017195 6.90 0.000 1.008437 1.015177
_rcs6 | 1.006751 .001313 5.16 0.000 1.004181 1.009328
_rcs_mot_egr_early1 | .90547 .016121 -5.58 0.000 .8744183 .9376243
_rcs_mot_egr_late1 | .9427967 .0154771 -3.59 0.000 .9129449 .9736246
_cons | 2.9e+115 2.5e+116 30.83 0.000 1.3e+108 6.4e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_nostag_rp6_tvc_1
(results m_nostag_rp6_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 restore m_nostag_rp6_tvc_1
(results m_nostag_rp6_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%5
> 0)) ///
> (line km _t if motivodeegreso_mod_imp_rec==2 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs6%5
> 0)) ///
> (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)) no
> box) ///
> 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_rp6tvc2.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6tvc2.gph saved)
.
. *https://www.pauldickman.com/software/stata/sex-differences/
.
. estimates restore m_nostag_rp6_tvc_1
(results m_nostag_rp6_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) r
> egion(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_rp6_stddif_s.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_stddif_s.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 cond_ocu3 cond_ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 t
> enviv5 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_nac_
> corr cohab2 cohab3 cohab4 fis_com2 fis_com3 rc_x1 rc_x2 rc_x3"
.
.
. qui noi stpm2 $covs_3b_dum , scale(hazard) df(6) eform tvc(mot_egr_early mot_egr_late) dftvc(1)
Iteration 0: log likelihood = -54461.03
Iteration 1: log likelihood = -54443.24
Iteration 2: log likelihood = -54443.187
Iteration 3: log likelihood = -54443.187
Log likelihood = -54443.187 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728953 .0499925 18.94 0.000 1.633694 1.829766
mot_egr_late | 1.577917 .0370653 19.42 0.000 1.506917 1.652262
tr_mod2 | 1.218636 .0262221 9.19 0.000 1.16831 1.271129
sex_dum2 | .7600293 .016327 -12.77 0.000 .7286932 .7927129
edad_ini_cons | .9868996 .0019513 -6.67 0.000 .9830825 .9907315
esc1 | 1.128982 .0298192 4.59 0.000 1.072025 1.188966
esc2 | 1.088746 .025948 3.57 0.000 1.039058 1.14081
sus_prin2 | 1.066729 .0297425 2.32 0.021 1.009999 1.126646
sus_prin3 | 1.392948 .0326517 14.14 0.000 1.3304 1.458437
sus_prin4 | 1.076603 .0378667 2.10 0.036 1.004886 1.153438
sus_prin5 | 1.141834 .0825502 1.83 0.067 .9909792 1.315654
fr_cons_sus_prin2 | .920203 .0450222 -1.70 0.089 .8360601 1.012814
fr_cons_sus_prin3 | .9969857 .0395705 -0.08 0.939 .9223689 1.077639
fr_cons_sus_prin4 | 1.008748 .0420384 0.21 0.834 .9296295 1.0946
fr_cons_sus_prin5 | 1.030657 .0409393 0.76 0.447 .9534613 1.114103
cond_ocu2 | 1.017891 .0318157 0.57 0.570 .9574048 1.082198
cond_ocu3 | 1.005554 .1418086 0.04 0.969 .7627188 1.325704
cond_ocu4 | 1.104285 .0399243 2.74 0.006 1.028743 1.185375
cond_ocu5 | 1.161881 .089036 1.96 0.050 .9998462 1.350175
cond_ocu6 | 1.131352 .0207262 6.74 0.000 1.09145 1.172713
policonsumo | 1.026642 .0224184 1.20 0.229 .9836297 1.071535
num_hij2 | 1.165174 .0227514 7.83 0.000 1.121424 1.21063
tenviv1 | 1.152096 .075424 2.16 0.031 1.013358 1.309827
tenviv2 | 1.127523 .0494075 2.74 0.006 1.034728 1.22864
tenviv4 | 1.037621 .0237463 1.61 0.107 .9921074 1.085222
tenviv5 | 1.003652 .0179934 0.20 0.839 .9689976 1.039545
mzone2 | 1.302629 .0273768 12.58 0.000 1.250061 1.357407
mzone3 | 1.464532 .0421233 13.27 0.000 1.384256 1.549464
n_off_vio | 1.355274 .0258706 15.93 0.000 1.305506 1.40694
n_off_acq | 1.814333 .0324517 33.31 0.000 1.751831 1.879065
n_off_sud | 1.256841 .0233136 12.32 0.000 1.211967 1.303375
n_off_oth | 1.360377 .0257473 16.26 0.000 1.310838 1.411788
psy_com2 | 1.07078 .0257019 2.85 0.004 1.021572 1.122359
psy_com3 | 1.05835 .0187998 3.19 0.001 1.022137 1.095846
dep2 | 1.019981 .0195475 1.03 0.302 .9823791 1.059022
rural2 | 1.028789 .0287124 1.02 0.309 .9740256 1.086632
rural3 | 1.054563 .0324416 1.73 0.084 .9928578 1.120104
porc_pobr | 1.228279 .1453468 1.74 0.082 .974027 1.548898
susini2 | 1.095891 .0455133 2.20 0.027 1.01022 1.188826
susini3 | 1.122648 .0372602 3.49 0.000 1.051944 1.198104
susini4 | 1.082362 .0193437 4.43 0.000 1.045105 1.120947
susini5 | 1.129855 .056192 2.45 0.014 1.024918 1.245535
ano_nac_corr | .874961 .003746 -31.20 0.000 .8676497 .8823339
cohab2 | .9707827 .0310641 -0.93 0.354 .9117682 1.033617
cohab3 | .9914812 .0390175 -0.22 0.828 .917883 1.070981
cohab4 | .9524348 .0296215 -1.57 0.117 .8961117 1.012298
fis_com2 | 1.027195 .0166785 1.65 0.098 .9950202 1.06041
fis_com3 | .9022046 .0336831 -2.76 0.006 .8385445 .9706976
rc_x1 | .8517336 .0048089 -28.42 0.000 .8423604 .8612112
rc_x2 | 1.028766 .0186435 1.56 0.118 .992867 1.065963
rc_x3 | .8953119 .0414545 -2.39 0.017 .8176403 .9803619
_rcs1 | 2.632098 .0397141 64.14 0.000 2.555399 2.711098
_rcs2 | 1.104931 .0062859 17.54 0.000 1.092679 1.11732
_rcs3 | 1.042542 .0040782 10.65 0.000 1.03458 1.050566
_rcs4 | 1.020136 .0025116 8.10 0.000 1.015225 1.025071
_rcs5 | 1.011801 .0017195 6.90 0.000 1.008437 1.015177
_rcs6 | 1.006751 .001313 5.16 0.000 1.004181 1.009328
_rcs_mot_egr_early1 | .90547 .016121 -5.58 0.000 .8744183 .9376243
_rcs_mot_egr_late1 | .9427967 .0154771 -3.59 0.000 .9129449 .9736246
_cons | 2.9e+115 2.5e+116 30.83 0.000 1.3e+108 6.4e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates store m_nostag_rp6_tvc_1_dum
.
. estimates replay m_nostag_rp6_tvc_1_dum, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp6_tvc_1_dum
------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -54443.187 Number of obs = 60,253
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.728953 .0499925 18.94 0.000 1.633694 1.829766
mot_egr_late | 1.577917 .0370653 19.42 0.000 1.506917 1.652262
tr_mod2 | 1.218636 .0262221 9.19 0.000 1.16831 1.271129
sex_dum2 | .7600293 .016327 -12.77 0.000 .7286932 .7927129
edad_ini_cons | .9868996 .0019513 -6.67 0.000 .9830825 .9907315
esc1 | 1.128982 .0298192 4.59 0.000 1.072025 1.188966
esc2 | 1.088746 .025948 3.57 0.000 1.039058 1.14081
sus_prin2 | 1.066729 .0297425 2.32 0.021 1.009999 1.126646
sus_prin3 | 1.392948 .0326517 14.14 0.000 1.3304 1.458437
sus_prin4 | 1.076603 .0378667 2.10 0.036 1.004886 1.153438
sus_prin5 | 1.141834 .0825502 1.83 0.067 .9909792 1.315654
fr_cons_sus_prin2 | .920203 .0450222 -1.70 0.089 .8360601 1.012814
fr_cons_sus_prin3 | .9969857 .0395705 -0.08 0.939 .9223689 1.077639
fr_cons_sus_prin4 | 1.008748 .0420384 0.21 0.834 .9296295 1.0946
fr_cons_sus_prin5 | 1.030657 .0409393 0.76 0.447 .9534613 1.114103
cond_ocu2 | 1.017891 .0318157 0.57 0.570 .9574048 1.082198
cond_ocu3 | 1.005554 .1418086 0.04 0.969 .7627188 1.325704
cond_ocu4 | 1.104285 .0399243 2.74 0.006 1.028743 1.185375
cond_ocu5 | 1.161881 .089036 1.96 0.050 .9998462 1.350175
cond_ocu6 | 1.131352 .0207262 6.74 0.000 1.09145 1.172713
policonsumo | 1.026642 .0224184 1.20 0.229 .9836297 1.071535
num_hij2 | 1.165174 .0227514 7.83 0.000 1.121424 1.21063
tenviv1 | 1.152096 .075424 2.16 0.031 1.013358 1.309827
tenviv2 | 1.127523 .0494075 2.74 0.006 1.034728 1.22864
tenviv4 | 1.037621 .0237463 1.61 0.107 .9921074 1.085222
tenviv5 | 1.003652 .0179934 0.20 0.839 .9689976 1.039545
mzone2 | 1.302629 .0273768 12.58 0.000 1.250061 1.357407
mzone3 | 1.464532 .0421233 13.27 0.000 1.384256 1.549464
n_off_vio | 1.355274 .0258706 15.93 0.000 1.305506 1.40694
n_off_acq | 1.814333 .0324517 33.31 0.000 1.751831 1.879065
n_off_sud | 1.256841 .0233136 12.32 0.000 1.211967 1.303375
n_off_oth | 1.360377 .0257473 16.26 0.000 1.310838 1.411788
psy_com2 | 1.07078 .0257019 2.85 0.004 1.021572 1.122359
psy_com3 | 1.05835 .0187998 3.19 0.001 1.022137 1.095846
dep2 | 1.019981 .0195475 1.03 0.302 .9823791 1.059022
rural2 | 1.028789 .0287124 1.02 0.309 .9740256 1.086632
rural3 | 1.054563 .0324416 1.73 0.084 .9928578 1.120104
porc_pobr | 1.228279 .1453468 1.74 0.082 .974027 1.548898
susini2 | 1.095891 .0455133 2.20 0.027 1.01022 1.188826
susini3 | 1.122648 .0372602 3.49 0.000 1.051944 1.198104
susini4 | 1.082362 .0193437 4.43 0.000 1.045105 1.120947
susini5 | 1.129855 .056192 2.45 0.014 1.024918 1.245535
ano_nac_corr | .874961 .003746 -31.20 0.000 .8676497 .8823339
cohab2 | .9707827 .0310641 -0.93 0.354 .9117682 1.033617
cohab3 | .9914812 .0390175 -0.22 0.828 .917883 1.070981
cohab4 | .9524348 .0296215 -1.57 0.117 .8961117 1.012298
fis_com2 | 1.027195 .0166785 1.65 0.098 .9950202 1.06041
fis_com3 | .9022046 .0336831 -2.76 0.006 .8385445 .9706976
rc_x1 | .8517336 .0048089 -28.42 0.000 .8423604 .8612112
rc_x2 | 1.028766 .0186435 1.56 0.118 .992867 1.065963
rc_x3 | .8953119 .0414545 -2.39 0.017 .8176403 .9803619
_rcs1 | 2.632098 .0397141 64.14 0.000 2.555399 2.711098
_rcs2 | 1.104931 .0062859 17.54 0.000 1.092679 1.11732
_rcs3 | 1.042542 .0040782 10.65 0.000 1.03458 1.050566
_rcs4 | 1.020136 .0025116 8.10 0.000 1.015225 1.025071
_rcs5 | 1.011801 .0017195 6.90 0.000 1.008437 1.015177
_rcs6 | 1.006751 .001313 5.16 0.000 1.004181 1.009328
_rcs_mot_egr_early1 | .90547 .016121 -5.58 0.000 .8744183 .9376243
_rcs_mot_egr_late1 | .9427967 .0154771 -3.59 0.000 .9129449 .9736246
_cons | 2.9e+115 2.5e+116 30.83 0.000 1.3e+108 6.4e+122
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_nostag_rp6_tvc_1_dum
(results m_nostag_rp6_tvc_1_dum 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%3
> 5)) ///
> (line km _t if motivodeegreso_mod_imp_rec==2 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs6%3
> 5)) ///
> (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)) no
> box) ///
> 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_rp6_s.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_s.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(ls
> tyle(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_rp6_stdif_s2.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_stdif_s2.gph saved)
.
. estimates restore m_nostag_rp6_tvc_1_dum
(results m_nostag_rp6_tvc_1_dum 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(ls
> tyle(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_rp6_stdif_rmst.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_stdif_rmst.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 cond_ocu3 cond_ocu4 cond_ocu5 cond_ocu6 policonsumo num_hij2 tenviv1 tenviv2 tenviv4 t
> enviv5 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_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_pr
> in3 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 mzone
> 2 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_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(mest
> imation) 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 = -43783.79
Iteration 1: log pseudolikelihood = -43552.765
Iteration 2: log pseudolikelihood = -43550.548
Iteration 3: log pseudolikelihood = -43550.547
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43550.547 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.441268 .0371267 14.19 0.000 1.370307 1.515903
_rcs1 | 2.475474 .0398 56.38 0.000 2.398683 2.554722
_rcs_tr_outcome1 | .9311442 .0161838 -4.10 0.000 .8999586 .9634103
_cons | .1626052 .0038748 -76.23 0.000 .1551854 .1703797
------------------------------------------------------------------------------------
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 = -43474.974
Iteration 1: log pseudolikelihood = -43418.851
Iteration 2: log pseudolikelihood = -43418.681
Iteration 3: log pseudolikelihood = -43418.681
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43418.681 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.443514 .0372623 14.22 0.000 1.372298 1.518425
_rcs1 | 2.475474 .0398 56.38 0.000 2.398683 2.554722
_rcs_tr_outcome1 | .9729536 .0178274 -1.50 0.135 .9386325 1.00853
_rcs_tr_outcome2 | 1.113615 .0078731 15.22 0.000 1.098291 1.129154
_cons | .1626052 .0038748 -76.23 0.000 .1551854 .1703797
------------------------------------------------------------------------------------
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 = -43442.857
Iteration 1: log pseudolikelihood = -43410.534
Iteration 2: log pseudolikelihood = -43410.468
Iteration 3: log pseudolikelihood = -43410.468
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43410.468 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.442914 .0372468 14.20 0.000 1.371728 1.517795
_rcs1 | 2.475474 .0398 56.38 0.000 2.398683 2.554722
_rcs_tr_outcome1 | .9716591 .0176543 -1.58 0.114 .9376661 1.006884
_rcs_tr_outcome2 | 1.10116 .0076643 13.84 0.000 1.08624 1.116284
_rcs_tr_outcome3 | 1.023942 .0044722 5.42 0.000 1.015214 1.032745
_cons | .1626052 .0038748 -76.23 0.000 .1551854 .1703797
------------------------------------------------------------------------------------
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 = -43443.775
Iteration 1: log pseudolikelihood = -43409.309
Iteration 2: log pseudolikelihood = -43409.233
Iteration 3: log pseudolikelihood = -43409.233
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43409.233 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.442894 .0372465 14.20 0.000 1.371708 1.517774
_rcs1 | 2.475474 .0398 56.38 0.000 2.398683 2.554722
_rcs_tr_outcome1 | .9717655 .0176676 -1.58 0.115 .9377474 1.007018
_rcs_tr_outcome2 | 1.101266 .0079266 13.40 0.000 1.085839 1.116912
_rcs_tr_outcome3 | 1.025222 .0048692 5.24 0.000 1.015723 1.03481
_rcs_tr_outcome4 | 1.00805 .0030716 2.63 0.009 1.002048 1.014088
_cons | .1626052 .0038748 -76.23 0.000 .1551854 .1703797
------------------------------------------------------------------------------------
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 = -43440.064
Iteration 1: log pseudolikelihood = -43407.865
Iteration 2: log pseudolikelihood = -43407.798
Iteration 3: log pseudolikelihood = -43407.798
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43407.798 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.442801 .0372445 14.20 0.000 1.371619 1.517677
_rcs1 | 2.475474 .0398 56.38 0.000 2.398683 2.554722
_rcs_tr_outcome1 | .9717057 .0176578 -1.58 0.114 .9377062 1.006938
_rcs_tr_outcome2 | 1.100107 .007889 13.30 0.000 1.084753 1.115679
_rcs_tr_outcome3 | 1.027339 .0051166 5.42 0.000 1.017359 1.037416
_rcs_tr_outcome4 | 1.009862 .0032317 3.07 0.002 1.003548 1.016216
_rcs_tr_outcome5 | 1.00552 .0022345 2.48 0.013 1.00115 1.009909
_cons | .1626052 .0038748 -76.23 0.000 .1551854 .1703797
------------------------------------------------------------------------------------
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 = -43438.433
Iteration 1: log pseudolikelihood = -43405.529
Iteration 2: log pseudolikelihood = -43405.459
Iteration 3: log pseudolikelihood = -43405.459
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43405.459 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.44276 .0372436 14.20 0.000 1.37158 1.517634
_rcs1 | 2.475474 .0398 56.38 0.000 2.398683 2.554722
_rcs_tr_outcome1 | .971749 .0176672 -1.58 0.115 .9377317 1.007
_rcs_tr_outcome2 | 1.100376 .0080753 13.03 0.000 1.084662 1.116317
_rcs_tr_outcome3 | 1.026773 .0053085 5.11 0.000 1.016421 1.03723
_rcs_tr_outcome4 | 1.01257 .0033457 3.78 0.000 1.006033 1.019148
_rcs_tr_outcome5 | 1.005527 .0023314 2.38 0.017 1.000968 1.010107
_rcs_tr_outcome6 | 1.005555 .0017982 3.10 0.002 1.002037 1.009085
_cons | .1626052 .0038748 -76.23 0.000 .1551854 .1703797
------------------------------------------------------------------------------------
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 = -43437.319
Iteration 1: log pseudolikelihood = -43405.182
Iteration 2: log pseudolikelihood = -43405.114
Iteration 3: log pseudolikelihood = -43405.114
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43405.114 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.442747 .0372432 14.20 0.000 1.371568 1.517621
_rcs1 | 2.475474 .0398 56.38 0.000 2.398683 2.554722
_rcs_tr_outcome1 | .9716996 .0176628 -1.58 0.114 .9376906 1.006942
_rcs_tr_outcome2 | 1.09962 .0080658 12.95 0.000 1.083924 1.115542
_rcs_tr_outcome3 | 1.028045 .0054079 5.26 0.000 1.0175 1.038699
_rcs_tr_outcome4 | 1.013307 .0034525 3.88 0.000 1.006562 1.020096
_rcs_tr_outcome5 | 1.006173 .0023685 2.61 0.009 1.001542 1.010826
_rcs_tr_outcome6 | 1.005609 .0018933 2.97 0.003 1.001905 1.009326
_rcs_tr_outcome7 | 1.004392 .0015732 2.80 0.005 1.001314 1.00748
_cons | .1626052 .0038748 -76.23 0.000 .1551854 .1703797
------------------------------------------------------------------------------------
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 = -43412.512
Iteration 1: log pseudolikelihood = -43376.397
Iteration 2: log pseudolikelihood = -43376.297
Iteration 3: log pseudolikelihood = -43376.297
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43376.297 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.449047 .0378912 14.18 0.000 1.376652 1.525248
_rcs1 | 2.619601 .049796 50.66 0.000 2.523798 2.71904
_rcs2 | 1.114431 .0076932 15.69 0.000 1.099454 1.129611
_rcs_tr_outcome1 | .9197991 .0184682 -4.16 0.000 .8843051 .9567177
_cons | .1619756 .0039204 -75.21 0.000 .1544711 .1698447
------------------------------------------------------------------------------------
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 = -43413.059
Iteration 1: log pseudolikelihood = -43376.428
Iteration 2: log pseudolikelihood = -43376.274
Iteration 3: log pseudolikelihood = -43376.274
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43376.274 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.449518 .0379507 14.18 0.000 1.377012 1.525841
_rcs1 | 2.623679 .0574855 44.02 0.000 2.513394 2.738802
_rcs2 | 1.117106 .0204775 6.04 0.000 1.077683 1.157971
_rcs_tr_outcome1 | .9179939 .0216739 -3.62 0.000 .8764817 .9614722
_rcs_tr_outcome2 | .9968751 .0195836 -0.16 0.873 .9592215 1.036007
_cons | .1619317 .0039236 -75.14 0.000 .1544214 .1698073
------------------------------------------------------------------------------------
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 = -43381.356
Iteration 1: log pseudolikelihood = -43368.375
Iteration 2: log pseudolikelihood = -43368.325
Iteration 3: log pseudolikelihood = -43368.325
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43368.325 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448819 .0379266 14.16 0.000 1.376359 1.525094
_rcs1 | 2.62292 .0573612 44.09 0.000 2.51287 2.73779
_rcs2 | 1.11661 .0204211 6.03 0.000 1.077294 1.15736
_rcs_tr_outcome1 | .9170281 .0215015 -3.69 0.000 .8758395 .9601537
_rcs_tr_outcome2 | .9860535 .0192903 -0.72 0.473 .9489609 1.024596
_rcs_tr_outcome3 | 1.017823 .0045594 3.94 0.000 1.008925 1.026798
_cons | .1619399 .0039231 -75.15 0.000 .1544304 .1698146
------------------------------------------------------------------------------------
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 = -43381.86
Iteration 1: log pseudolikelihood = -43366.887
Iteration 2: log pseudolikelihood = -43366.826
Iteration 3: log pseudolikelihood = -43366.826
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43366.826 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448895 .0379346 14.16 0.000 1.37642 1.525187
_rcs1 | 2.623679 .0574855 44.02 0.000 2.513394 2.738802
_rcs2 | 1.117106 .0204775 6.04 0.000 1.077683 1.157971
_rcs_tr_outcome1 | .9168729 .0215463 -3.69 0.000 .8756007 .9600905
_rcs_tr_outcome2 | .9862943 .0193473 -0.70 0.482 .9490941 1.024953
_rcs_tr_outcome3 | 1.014709 .0051214 2.89 0.004 1.004721 1.024797
_rcs_tr_outcome4 | 1.00805 .0030716 2.63 0.009 1.002048 1.014088
_cons | .1619317 .0039236 -75.14 0.000 .1544214 .1698073
------------------------------------------------------------------------------------
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 = -43378.073
Iteration 1: log pseudolikelihood = -43365.345
Iteration 2: log pseudolikelihood = -43365.293
Iteration 3: log pseudolikelihood = -43365.293
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43365.293 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.44883 .037935 14.16 0.000 1.376354 1.525122
_rcs1 | 2.623917 .0575197 44.01 0.000 2.513568 2.739111
_rcs2 | 1.117262 .020489 6.05 0.000 1.077818 1.15815
_rcs_tr_outcome1 | .9167282 .0215468 -3.70 0.000 .8754552 .9599469
_rcs_tr_outcome2 | .9854129 .0192839 -0.75 0.453 .9483328 1.023943
_rcs_tr_outcome3 | 1.014127 .0054993 2.59 0.010 1.003406 1.024963
_rcs_tr_outcome4 | 1.008774 .0032324 2.73 0.006 1.002458 1.015129
_rcs_tr_outcome5 | 1.005639 .0022352 2.53 0.011 1.001267 1.010029
_cons | .1619291 .0039237 -75.14 0.000 .1544185 .1698049
------------------------------------------------------------------------------------
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 = -43376.518
Iteration 1: log pseudolikelihood = -43363.107
Iteration 2: log pseudolikelihood = -43363.053
Iteration 3: log pseudolikelihood = -43363.053
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43363.053 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448761 .0379316 14.16 0.000 1.376292 1.525046
_rcs1 | 2.623679 .0574855 44.02 0.000 2.513394 2.738802
_rcs2 | 1.117106 .0204775 6.04 0.000 1.077683 1.157971
_rcs_tr_outcome1 | .9168574 .0215458 -3.69 0.000 .8755861 .960074
_rcs_tr_outcome2 | .9859953 .0193161 -0.72 0.472 .948854 1.02459
_rcs_tr_outcome3 | 1.011911 .0057745 2.07 0.038 1.000657 1.023293
_rcs_tr_outcome4 | 1.010345 .0033585 3.10 0.002 1.003783 1.016949
_rcs_tr_outcome5 | 1.005527 .0023314 2.38 0.017 1.000968 1.010107
_rcs_tr_outcome6 | 1.005555 .0017982 3.10 0.002 1.002037 1.009085
_cons | .1619317 .0039236 -75.14 0.000 .1544214 .1698073
------------------------------------------------------------------------------------
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 = -43375.397
Iteration 1: log pseudolikelihood = -43362.747
Iteration 2: log pseudolikelihood = -43362.695
Iteration 3: log pseudolikelihood = -43362.695
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43362.695 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448751 .0379315 14.16 0.000 1.376282 1.525036
_rcs1 | 2.623708 .0574897 44.02 0.000 2.513416 2.738841
_rcs2 | 1.117125 .0204789 6.04 0.000 1.0777 1.157993
_rcs_tr_outcome1 | .9167994 .0215431 -3.70 0.000 .8755332 .9600105
_rcs_tr_outcome2 | .9855821 .0192639 -0.74 0.457 .9485396 1.024071
_rcs_tr_outcome3 | 1.011264 .0059914 1.89 0.059 .9995891 1.023075
_rcs_tr_outcome4 | 1.010168 .0034808 2.94 0.003 1.003368 1.017013
_rcs_tr_outcome5 | 1.005885 .0023681 2.49 0.013 1.001255 1.010538
_rcs_tr_outcome6 | 1.005642 .0018935 2.99 0.003 1.001937 1.00936
_rcs_tr_outcome7 | 1.004381 .0015731 2.79 0.005 1.001302 1.007469
_cons | .1619314 .0039236 -75.14 0.000 .154421 .169807
------------------------------------------------------------------------------------
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 = -43380.148
Iteration 1: log pseudolikelihood = -43364.529
Iteration 2: log pseudolikelihood = -43364.491
Iteration 3: log pseudolikelihood = -43364.491
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43364.491 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448837 .0379246 14.16 0.000 1.37638 1.525107
_rcs1 | 2.616118 .049482 50.84 0.000 2.52091 2.714921
_rcs2 | 1.100708 .0074067 14.26 0.000 1.086286 1.115321
_rcs3 | 1.024642 .0043325 5.76 0.000 1.016186 1.033169
_rcs_tr_outcome1 | .9194261 .0185412 -4.17 0.000 .8837948 .956494
_cons | .1619337 .0039232 -75.14 0.000 .154424 .1698086
------------------------------------------------------------------------------------
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 = -43379.757
Iteration 1: log pseudolikelihood = -43364.529
Iteration 2: log pseudolikelihood = -43364.491
Iteration 3: log pseudolikelihood = -43364.491
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43364.491 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448911 .0378819 14.18 0.000 1.376534 1.525093
_rcs1 | 2.616729 .0548419 45.90 0.000 2.511419 2.726456
_rcs2 | 1.101105 .018747 5.66 0.000 1.064968 1.138468
_rcs3 | 1.024656 .0044079 5.66 0.000 1.016053 1.033331
_rcs_tr_outcome1 | .9191556 .0207792 -3.73 0.000 .8793182 .9607978
_rcs_tr_outcome2 | .9995361 .0180419 -0.03 0.979 .9647929 1.03553
_cons | .1619268 .0039187 -75.23 0.000 .1544255 .1697925
------------------------------------------------------------------------------------
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 = -43379.915
Iteration 1: log pseudolikelihood = -43364.481
Iteration 2: log pseudolikelihood = -43364.443
Iteration 3: log pseudolikelihood = -43364.443
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43364.443 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.449025 .0379176 14.17 0.000 1.376582 1.525281
_rcs1 | 2.615718 .0544136 46.22 0.000 2.511214 2.724571
_rcs2 | 1.098925 .0197022 5.26 0.000 1.06098 1.138227
_rcs3 | 1.0268 .0110009 2.47 0.014 1.005464 1.04859
_rcs_tr_outcome1 | .9195625 .0206542 -3.73 0.000 .8799592 .9609483
_rcs_tr_outcome2 | 1.002034 .0192685 0.11 0.916 .9649711 1.04052
_rcs_tr_outcome3 | .9972157 .0115359 -0.24 0.810 .9748602 1.020084
_cons | .1619194 .0039209 -75.19 0.000 .1544141 .1697896
------------------------------------------------------------------------------------
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 = -43381.756
Iteration 1: log pseudolikelihood = -43363.862
Iteration 2: log pseudolikelihood = -43363.811
Iteration 3: log pseudolikelihood = -43363.811
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43363.811 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448869 .037907 14.17 0.000 1.376445 1.525103
_rcs1 | 2.615851 .0546516 46.03 0.000 2.5109 2.72519
_rcs2 | 1.100261 .0198959 5.28 0.000 1.061949 1.139956
_rcs3 | 1.025029 .0108161 2.34 0.019 1.004048 1.046449
_rcs_tr_outcome1 | .9196816 .020744 -3.71 0.000 .8799097 .9612511
_rcs_tr_outcome2 | 1.002014 .0196493 0.10 0.918 .9642327 1.041276
_rcs_tr_outcome3 | .9970832 .0113537 -0.26 0.798 .9750768 1.019586
_rcs_tr_outcome4 | 1.003317 .0037531 0.89 0.376 .9959883 1.0107
_cons | .1619334 .0039205 -75.20 0.000 .1544288 .1698027
------------------------------------------------------------------------------------
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 = -43377.277
Iteration 1: log pseudolikelihood = -43361.956
Iteration 2: log pseudolikelihood = -43361.917
Iteration 3: log pseudolikelihood = -43361.917
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43361.917 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448872 .0379122 14.17 0.000 1.376439 1.525117
_rcs1 | 2.61551 .0544012 46.23 0.000 2.51103 2.724338
_rcs2 | 1.098898 .0197004 5.26 0.000 1.060956 1.138196
_rcs3 | 1.026644 .0109788 2.46 0.014 1.00535 1.048389
_rcs_tr_outcome1 | .9196933 .0206561 -3.73 0.000 .8800862 .9610829
_rcs_tr_outcome2 | 1.002743 .0195099 0.14 0.888 .9652244 1.041721
_rcs_tr_outcome3 | .9967266 .0109227 -0.30 0.765 .9755468 1.018366
_rcs_tr_outcome4 | 1.000859 .0049555 0.17 0.862 .9911937 1.010619
_rcs_tr_outcome5 | 1.004887 .0022446 2.18 0.029 1.000498 1.009296
_cons | .1619231 .0039208 -75.19 0.000 .1544181 .169793
------------------------------------------------------------------------------------
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 = -43375.491
Iteration 1: log pseudolikelihood = -43359.476
Iteration 2: log pseudolikelihood = -43359.434
Iteration 3: log pseudolikelihood = -43359.434
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43359.434 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.44887 .0379143 14.17 0.000 1.376433 1.525119
_rcs1 | 2.615718 .0544136 46.22 0.000 2.511214 2.724571
_rcs2 | 1.098925 .0197022 5.26 0.000 1.06098 1.138227
_rcs3 | 1.0268 .0110009 2.47 0.014 1.005464 1.04859
_rcs_tr_outcome1 | .9196477 .0206646 -3.73 0.000 .8800247 .9610547
_rcs_tr_outcome2 | 1.003432 .0196023 0.18 0.861 .9657387 1.042597
_rcs_tr_outcome3 | .9955507 .0105472 -0.42 0.674 .9750918 1.016439
_rcs_tr_outcome4 | 1.000958 .0057271 0.17 0.867 .9897961 1.012247
_rcs_tr_outcome5 | 1.003089 .0025258 1.22 0.221 .9981505 1.008051
_rcs_tr_outcome6 | 1.005555 .0017982 3.10 0.002 1.002037 1.009085
_cons | .1619194 .0039209 -75.19 0.000 .1544141 .1697896
------------------------------------------------------------------------------------
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 = -43374.357
Iteration 1: log pseudolikelihood = -43359.113
Iteration 2: log pseudolikelihood = -43359.073
Iteration 3: log pseudolikelihood = -43359.073
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43359.073 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.44886 .0379141 14.17 0.000 1.376423 1.525109
_rcs1 | 2.615691 .0544017 46.23 0.000 2.511209 2.724519
_rcs2 | 1.098862 .0196906 5.26 0.000 1.060939 1.13814
_rcs3 | 1.026863 .0109979 2.48 0.013 1.005532 1.048646
_rcs_tr_outcome1 | .9196082 .0206572 -3.73 0.000 .8799991 .9610002
_rcs_tr_outcome2 | 1.003359 .0196113 0.17 0.864 .9656481 1.042542
_rcs_tr_outcome3 | .9959543 .010209 -0.40 0.692 .9761446 1.016166
_rcs_tr_outcome4 | .9999496 .0062113 -0.01 0.994 .9878494 1.012198
_rcs_tr_outcome5 | 1.002086 .0028879 0.72 0.470 .9964415 1.007762
_rcs_tr_outcome6 | 1.004912 .0019115 2.58 0.010 1.001172 1.008665
_rcs_tr_outcome7 | 1.004454 .0015737 2.84 0.005 1.001374 1.007543
_cons | .1619192 .0039209 -75.19 0.000 .1544138 .1697893
------------------------------------------------------------------------------------
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 = -43380.671
Iteration 1: log pseudolikelihood = -43362.922
Iteration 2: log pseudolikelihood = -43362.879
Iteration 3: log pseudolikelihood = -43362.879
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43362.879 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448677 .0379201 14.16 0.000 1.376229 1.524938
_rcs1 | 2.616854 .0495581 50.80 0.000 2.521502 2.715811
_rcs2 | 1.100584 .0075544 13.96 0.000 1.085877 1.115491
_rcs3 | 1.026096 .0045617 5.79 0.000 1.017195 1.035076
_rcs4 | 1.007958 .0030341 2.63 0.008 1.002029 1.013922
_rcs_tr_outcome1 | .9192227 .0185527 -4.17 0.000 .8835698 .9563142
_cons | .1619498 .0039233 -75.15 0.000 .15444 .1698248
------------------------------------------------------------------------------------
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 = -43380.219
Iteration 1: log pseudolikelihood = -43362.923
Iteration 2: log pseudolikelihood = -43362.879
Iteration 3: log pseudolikelihood = -43362.879
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43362.879 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448663 .037871 14.18 0.000 1.376306 1.524823
_rcs1 | 2.616741 .0548796 45.87 0.000 2.511359 2.726544
_rcs2 | 1.100511 .0187705 5.62 0.000 1.06433 1.137923
_rcs3 | 1.026091 .0047959 5.51 0.000 1.016735 1.035534
_rcs4 | 1.007958 .0030341 2.63 0.008 1.002029 1.013923
_rcs_tr_outcome1 | .9192727 .0208022 -3.72 0.000 .879392 .960962
_rcs_tr_outcome2 | 1.000086 .0181568 0.00 0.996 .9651248 1.036313
_cons | .1619511 .0039185 -75.24 0.000 .1544501 .1698163
------------------------------------------------------------------------------------
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 = -43380.881
Iteration 1: log pseudolikelihood = -43362.802
Iteration 2: log pseudolikelihood = -43362.751
Iteration 3: log pseudolikelihood = -43362.751
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43362.751 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448987 .0379102 14.18 0.000 1.376557 1.525227
_rcs1 | 2.616087 .0543466 46.29 0.000 2.511709 2.724802
_rcs2 | 1.097409 .0195603 5.21 0.000 1.059733 1.136424
_rcs3 | 1.029492 .0104909 2.85 0.004 1.009134 1.050261
_rcs4 | 1.008611 .0037477 2.31 0.021 1.001293 1.015984
_rcs_tr_outcome1 | .9194866 .0206421 -3.74 0.000 .879906 .9608476
_rcs_tr_outcome2 | 1.003408 .0191609 0.18 0.859 .9665474 1.041674
_rcs_tr_outcome3 | .9955459 .0111682 -0.40 0.691 .9738954 1.017678
_cons | .1619262 .0039205 -75.20 0.000 .1544216 .1697955
------------------------------------------------------------------------------------
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 = -43380.514
Iteration 1: log pseudolikelihood = -43362.867
Iteration 2: log pseudolikelihood = -43362.81
Iteration 3: log pseudolikelihood = -43362.81
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43362.81 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448864 .0378988 14.17 0.000 1.376456 1.525082
_rcs1 | 2.6158 .0543704 46.26 0.000 2.511377 2.724564
_rcs2 | 1.09787 .0194911 5.26 0.000 1.060325 1.136744
_rcs3 | 1.028984 .0110222 2.67 0.008 1.007606 1.050815
_rcs4 | 1.00759 .0076979 0.99 0.322 .9926142 1.022791
_rcs_tr_outcome1 | .9196346 .0206498 -3.73 0.000 .8800394 .9610112
_rcs_tr_outcome2 | 1.003093 .0192131 0.16 0.872 .9661346 1.041466
_rcs_tr_outcome3 | .9963439 .0116731 -0.31 0.755 .9737256 1.019487
_rcs_tr_outcome4 | 1.000457 .0082284 0.06 0.956 .984459 1.016715
_cons | .1619351 .0039195 -75.22 0.000 .1544324 .1698024
------------------------------------------------------------------------------------
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 = -43376.722
Iteration 1: log pseudolikelihood = -43361.637
Iteration 2: log pseudolikelihood = -43361.597
Iteration 3: log pseudolikelihood = -43361.597
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43361.597 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448897 .037902 14.17 0.000 1.376483 1.525121
_rcs1 | 2.615081 .05409 46.48 0.000 2.511187 2.723274
_rcs2 | 1.096495 .0190309 5.31 0.000 1.059822 1.134436
_rcs3 | 1.031066 .0109293 2.89 0.004 1.009866 1.052711
_rcs4 | 1.004744 .0072194 0.66 0.510 .9906934 1.018994
_rcs_tr_outcome1 | .9198426 .0205619 -3.74 0.000 .8804122 .961039
_rcs_tr_outcome2 | 1.004092 .0189054 0.22 0.828 .9677137 1.041838
_rcs_tr_outcome3 | .9949241 .0116435 -0.43 0.664 .9723629 1.018009
_rcs_tr_outcome4 | 1.001518 .0077003 0.20 0.844 .9865391 1.016725
_rcs_tr_outcome5 | 1.004265 .0034678 1.23 0.218 .9974912 1.011085
_cons | .1619209 .0039194 -75.22 0.000 .1544184 .1697879
------------------------------------------------------------------------------------
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 = -43375.382
Iteration 1: log pseudolikelihood = -43359.198
Iteration 2: log pseudolikelihood = -43359.148
Iteration 3: log pseudolikelihood = -43359.148
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43359.148 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448701 .0378942 14.17 0.000 1.376302 1.524909
_rcs1 | 2.615906 .0544393 46.21 0.000 2.511354 2.724811
_rcs2 | 1.09824 .0195628 5.26 0.000 1.06056 1.13726
_rcs3 | 1.028582 .0109969 2.64 0.008 1.007253 1.050363
_rcs4 | 1.007574 .007642 0.99 0.320 .9927064 1.022664
_rcs_tr_outcome1 | .9195939 .0206706 -3.73 0.000 .8799597 .9610132
_rcs_tr_outcome2 | 1.00309 .0194089 0.16 0.873 .9657611 1.041861
_rcs_tr_outcome3 | .9966641 .0115994 -0.29 0.774 .9741871 1.01966
_rcs_tr_outcome4 | .9999949 .0071778 -0.00 0.999 .9860251 1.014163
_rcs_tr_outcome5 | 1.001104 .0051138 0.22 0.829 .9911315 1.011178
_rcs_tr_outcome6 | 1.004874 .0019083 2.56 0.010 1.001141 1.008621
_cons | .1619378 .0039195 -75.22 0.000 .1544351 .169805
------------------------------------------------------------------------------------
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 = -43374.11
Iteration 1: log pseudolikelihood = -43358.782
Iteration 2: log pseudolikelihood = -43358.734
Iteration 3: log pseudolikelihood = -43358.734
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.734 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448705 .0378949 14.17 0.000 1.376304 1.524914
_rcs1 | 2.615899 .0544155 46.23 0.000 2.511392 2.724755
_rcs2 | 1.098105 .0195389 5.26 0.000 1.060469 1.137076
_rcs3 | 1.028746 .0110152 2.65 0.008 1.007382 1.050563
_rcs4 | 1.007616 .0076755 1.00 0.319 .992684 1.022772
_rcs_tr_outcome1 | .9195417 .020659 -3.73 0.000 .8799293 .9609373
_rcs_tr_outcome2 | 1.003058 .0194091 0.16 0.875 .9657293 1.04183
_rcs_tr_outcome3 | .9970212 .0114074 -0.26 0.794 .9749121 1.019632
_rcs_tr_outcome4 | .9993196 .0067962 -0.10 0.920 .9860878 1.012729
_rcs_tr_outcome5 | 1.000281 .0057051 0.05 0.961 .9891613 1.011525
_rcs_tr_outcome6 | 1.003628 .0027905 1.30 0.193 .9981732 1.009112
_rcs_tr_outcome7 | 1.004205 .0015813 2.66 0.008 1.00111 1.007309
_cons | .1619363 .0039195 -75.22 0.000 .1544335 .1698036
------------------------------------------------------------------------------------
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 = -43376.462
Iteration 1: log pseudolikelihood = -43360.714
Iteration 2: log pseudolikelihood = -43360.673
Iteration 3: log pseudolikelihood = -43360.673
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43360.673 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448434 .0379376 14.14 0.000 1.375954 1.524732
_rcs1 | 2.61702 .0495833 50.78 0.000 2.521621 2.716028
_rcs2 | 1.098726 .0073426 14.09 0.000 1.084428 1.113211
_rcs3 | 1.029212 .0048124 6.16 0.000 1.019823 1.038687
_rcs4 | 1.008922 .003255 2.75 0.006 1.002562 1.015321
_rcs5 | 1.005858 .0021725 2.70 0.007 1.001609 1.010125
_rcs_tr_outcome1 | .9190283 .0185765 -4.18 0.000 .8833309 .9561684
_cons | .1619635 .0039252 -75.11 0.000 .1544501 .1698424
------------------------------------------------------------------------------------
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 = -43375.9
Iteration 1: log pseudolikelihood = -43360.711
Iteration 2: log pseudolikelihood = -43360.672
Iteration 3: log pseudolikelihood = -43360.672
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43360.672 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448325 .0378777 14.16 0.000 1.375957 1.5245
_rcs1 | 2.616131 .054606 46.07 0.000 2.511265 2.725376
_rcs2 | 1.098152 .0183229 5.61 0.000 1.06282 1.134658
_rcs3 | 1.02916 .0052115 5.68 0.000 1.018997 1.039426
_rcs4 | 1.008917 .0032527 2.75 0.006 1.002562 1.015312
_rcs5 | 1.00586 .0021677 2.71 0.007 1.001621 1.010118
_rcs_tr_outcome1 | .9194216 .0207173 -3.73 0.000 .8797001 .9609367
_rcs_tr_outcome2 | 1.000675 .0179715 0.04 0.970 .9660644 1.036526
_cons | .1619735 .0039199 -75.22 0.000 .15447 .1698416
------------------------------------------------------------------------------------
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 = -43376.396
Iteration 1: log pseudolikelihood = -43360.675
Iteration 2: log pseudolikelihood = -43360.633
Iteration 3: log pseudolikelihood = -43360.633
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43360.633 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448524 .0379196 14.15 0.000 1.376077 1.524785
_rcs1 | 2.615933 .0543028 46.32 0.000 2.511637 2.724559
_rcs2 | 1.096531 .0193598 5.22 0.000 1.059235 1.135139
_rcs3 | 1.03092 .010008 3.14 0.002 1.01149 1.050723
_rcs4 | 1.009564 .0049271 1.95 0.051 .9999533 1.019267
_rcs5 | 1.005887 .0021823 2.71 0.007 1.001619 1.010174
_rcs_tr_outcome1 | .9194712 .0206189 -3.74 0.000 .8799342 .9607846
_rcs_tr_outcome2 | 1.002377 .0190644 0.12 0.901 .9656991 1.040448
_rcs_tr_outcome3 | .9975484 .0112914 -0.22 0.828 .9756613 1.019927
_cons | .1619581 .0039225 -75.16 0.000 .1544497 .1698315
------------------------------------------------------------------------------------
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 = -43376.292
Iteration 1: log pseudolikelihood = -43360.643
Iteration 2: log pseudolikelihood = -43360.599
Iteration 3: log pseudolikelihood = -43360.599
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43360.599 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.44855 .0379135 14.16 0.000 1.376114 1.524798
_rcs1 | 2.615653 .0541195 46.47 0.000 2.511703 2.723906
_rcs2 | 1.095935 .0189449 5.30 0.000 1.059426 1.133703
_rcs3 | 1.0312 .0109672 2.89 0.004 1.009927 1.052921
_rcs4 | 1.00999 .0073993 1.36 0.175 .9955914 1.024597
_rcs5 | 1.006117 .0032529 1.89 0.059 .9997617 1.012513
_rcs_tr_outcome1 | .9195862 .0205583 -3.75 0.000 .8801626 .9607756
_rcs_tr_outcome2 | 1.003148 .0187554 0.17 0.867 .967053 1.040589
_rcs_tr_outcome3 | .9972314 .0114452 -0.24 0.809 .9750496 1.019918
_rcs_tr_outcome4 | .9988749 .0077883 -0.14 0.885 .9837262 1.014257
_cons | .1619573 .0039214 -75.19 0.000 .1544511 .1698284
------------------------------------------------------------------------------------
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 = -43376.241
Iteration 1: log pseudolikelihood = -43360.28
Iteration 2: log pseudolikelihood = -43360.236
Iteration 3: log pseudolikelihood = -43360.236
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43360.236 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448682 .0378903 14.17 0.000 1.37629 1.524883
_rcs1 | 2.614867 .0536087 46.88 0.000 2.511879 2.722077
_rcs2 | 1.093363 .0177134 5.51 0.000 1.05919 1.128637
_rcs3 | 1.035442 .0116598 3.09 0.002 1.01284 1.058549
_rcs4 | 1.005977 .0085354 0.70 0.482 .9893866 1.022846
_rcs5 | 1.006898 .0054657 1.27 0.205 .9962418 1.017667
_rcs_tr_outcome1 | .9199062 .0204076 -3.76 0.000 .8807652 .9607866
_rcs_tr_outcome2 | 1.006169 .0178229 0.35 0.728 .9718357 1.041714
_rcs_tr_outcome3 | .9921741 .0122149 -0.64 0.523 .9685198 1.016406
_rcs_tr_outcome4 | 1.003861 .0091029 0.43 0.671 .9861777 1.021862
_rcs_tr_outcome5 | .9986322 .0058567 -0.23 0.815 .987219 1.010177
_cons | .161945 .0039193 -75.22 0.000 .1544427 .1698117
------------------------------------------------------------------------------------
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 = -43375.341
Iteration 1: log pseudolikelihood = -43358.522
Iteration 2: log pseudolikelihood = -43358.475
Iteration 3: log pseudolikelihood = -43358.475
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.475 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448586 .0379001 14.16 0.000 1.376176 1.524807
_rcs1 | 2.614741 .0537739 46.74 0.000 2.511442 2.722289
_rcs2 | 1.09431 .0180992 5.45 0.000 1.059405 1.130365
_rcs3 | 1.034086 .011499 3.01 0.003 1.011792 1.056871
_rcs4 | 1.007213 .0083004 0.87 0.383 .9910747 1.023613
_rcs5 | 1.005134 .004925 1.05 0.296 .9955271 1.014833
_rcs_tr_outcome1 | .9200573 .0204736 -3.74 0.000 .8807923 .9610726
_rcs_tr_outcome2 | 1.006276 .0182056 0.35 0.729 .9712193 1.042599
_rcs_tr_outcome3 | .991842 .0123425 -0.66 0.510 .9679438 1.01633
_rcs_tr_outcome4 | 1.003577 .0086377 0.41 0.678 .9867892 1.02065
_rcs_tr_outcome5 | .9997105 .0054749 -0.05 0.958 .9890374 1.010499
_rcs_tr_outcome6 | 1.00301 .0031385 0.96 0.337 .9968773 1.00918
_cons | .1619485 .0039204 -75.20 0.000 .154444 .1698176
------------------------------------------------------------------------------------
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 = -43373.76
Iteration 1: log pseudolikelihood = -43357.905
Iteration 2: log pseudolikelihood = -43357.862
Iteration 3: log pseudolikelihood = -43357.862
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.862 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.44857 .0378921 14.17 0.000 1.376175 1.524774
_rcs1 | 2.614872 .0537261 46.78 0.000 2.511663 2.722322
_rcs2 | 1.094051 .017946 5.48 0.000 1.059437 1.129796
_rcs3 | 1.034484 .0115703 3.03 0.002 1.012053 1.057411
_rcs4 | 1.006621 .0084432 0.79 0.431 .9902075 1.023306
_rcs5 | 1.006252 .0053329 1.18 0.240 .9958535 1.016759
_rcs_tr_outcome1 | .9199364 .0204503 -3.75 0.000 .8807151 .9609043
_rcs_tr_outcome2 | 1.006429 .018092 0.36 0.721 .9715872 1.042521
_rcs_tr_outcome3 | .9916818 .0124305 -0.67 0.505 .9676153 1.016347
_rcs_tr_outcome4 | 1.003168 .008191 0.39 0.698 .987242 1.019352
_rcs_tr_outcome5 | 1.000261 .0054403 0.05 0.962 .9896548 1.010981
_rcs_tr_outcome6 | 1.000512 .0046035 0.11 0.911 .9915295 1.009575
_rcs_tr_outcome7 | 1.003157 .0019074 1.66 0.097 .9994257 1.006903
_cons | .1619502 .0039198 -75.21 0.000 .1544468 .1698181
------------------------------------------------------------------------------------
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 = -43375.347
Iteration 1: log pseudolikelihood = -43359.568
Iteration 2: log pseudolikelihood = -43359.526
Iteration 3: log pseudolikelihood = -43359.526
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43359.526 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448509 .0379473 14.14 0.000 1.376011 1.524827
_rcs1 | 2.617764 .0496248 50.76 0.000 2.522285 2.716856
_rcs2 | 1.098241 .0073305 14.04 0.000 1.083967 1.112703
_rcs3 | 1.029909 .0049938 6.08 0.000 1.020168 1.039743
_rcs4 | 1.010459 .0033829 3.11 0.002 1.003851 1.017111
_rcs5 | 1.006619 .0022704 2.92 0.003 1.002178 1.011078
_rcs6 | 1.004283 .0017927 2.39 0.017 1.000776 1.007803
_rcs_tr_outcome1 | .918683 .0185825 -4.19 0.000 .8829745 .9558356
_cons | .1619569 .0039256 -75.10 0.000 .1544428 .1698366
------------------------------------------------------------------------------------
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 = -43374.771
Iteration 1: log pseudolikelihood = -43359.565
Iteration 2: log pseudolikelihood = -43359.525
Iteration 3: log pseudolikelihood = -43359.525
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43359.525 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448391 .0378874 14.16 0.000 1.376005 1.524586
_rcs1 | 2.616796 .0546138 46.09 0.000 2.511915 2.726057
_rcs2 | 1.097618 .0182335 5.61 0.000 1.062457 1.133943
_rcs3 | 1.029845 .0055116 5.49 0.000 1.019099 1.040704
_rcs4 | 1.010449 .0033774 3.11 0.002 1.003851 1.01709
_rcs5 | 1.00662 .0022665 2.93 0.003 1.002188 1.011072
_rcs6 | 1.004284 .0017919 2.40 0.017 1.000778 1.007802
_rcs_tr_outcome1 | .9191105 .0207105 -3.74 0.000 .8794021 .9606119
_rcs_tr_outcome2 | 1.000734 .0179808 0.04 0.967 .9661054 1.036603
_cons | .1619678 .0039204 -75.21 0.000 .1544633 .1698368
------------------------------------------------------------------------------------
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 = -43375.26
Iteration 1: log pseudolikelihood = -43359.535
Iteration 2: log pseudolikelihood = -43359.493
Iteration 3: log pseudolikelihood = -43359.493
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43359.493 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448552 .0379249 14.15 0.000 1.376095 1.524823
_rcs1 | 2.61646 .0543203 46.33 0.000 2.512131 2.725121
_rcs2 | 1.096032 .0193658 5.19 0.000 1.058725 1.134653
_rcs3 | 1.031328 .0096746 3.29 0.001 1.01254 1.050466
_rcs4 | 1.01118 .0056509 1.99 0.047 1.000165 1.022317
_rcs5 | 1.00676 .00247 2.75 0.006 1.00193 1.011613
_rcs6 | 1.004281 .0017919 2.39 0.017 1.000775 1.007799
_rcs_tr_outcome1 | .9192255 .0206168 -3.76 0.000 .8796926 .9605349
_rcs_tr_outcome2 | 1.002391 .0191347 0.13 0.900 .9655806 1.040605
_rcs_tr_outcome3 | .9977954 .0113509 -0.19 0.846 .9757943 1.020293
_cons | .1619555 .0039227 -75.16 0.000 .1544469 .1698292
------------------------------------------------------------------------------------
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 = -43375.082
Iteration 1: log pseudolikelihood = -43359.487
Iteration 2: log pseudolikelihood = -43359.444
Iteration 3: log pseudolikelihood = -43359.444
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43359.444 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448627 .0379199 14.16 0.000 1.37618 1.524888
_rcs1 | 2.615896 .0540086 46.58 0.000 2.512154 2.723922
_rcs2 | 1.094676 .0187619 5.28 0.000 1.058514 1.132073
_rcs3 | 1.032804 .0108688 3.07 0.002 1.01172 1.054328
_rcs4 | 1.011038 .0068945 1.61 0.107 .9976151 1.024642
_rcs5 | 1.00648 .0047064 1.38 0.167 .9972977 1.015747
_rcs6 | 1.004272 .0018891 2.27 0.023 1.000576 1.007981
_rcs_tr_outcome1 | .9194492 .0205206 -3.76 0.000 .8800965 .9605616
_rcs_tr_outcome2 | 1.003924 .0187041 0.21 0.833 .9679263 1.041261
_rcs_tr_outcome3 | .9961463 .0114919 -0.33 0.738 .9738754 1.018927
_rcs_tr_outcome4 | 1.000234 .0081019 0.03 0.977 .9844794 1.01624
_cons | .1619492 .0039217 -75.18 0.000 .1544423 .1698209
------------------------------------------------------------------------------------
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 = -43375.153
Iteration 1: log pseudolikelihood = -43358.963
Iteration 2: log pseudolikelihood = -43358.917
Iteration 3: log pseudolikelihood = -43358.917
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.917 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448802 .0378994 14.17 0.000 1.376393 1.525021
_rcs1 | 2.615574 .0535421 46.97 0.000 2.512711 2.722649
_rcs2 | 1.092239 .0174919 5.51 0.000 1.058488 1.127066
_rcs3 | 1.03682 .0116697 3.21 0.001 1.014198 1.059947
_rcs4 | 1.007657 .0081448 0.94 0.345 .9918191 1.023747
_rcs5 | 1.007173 .0051275 1.40 0.160 .9971736 1.017273
_rcs6 | 1.005272 .0030576 1.73 0.084 .9992972 1.011283
_rcs_tr_outcome1 | .919562 .0203744 -3.78 0.000 .8804835 .9603748
_rcs_tr_outcome2 | 1.006597 .0177448 0.37 0.709 .972412 1.041984
_rcs_tr_outcome3 | .9919237 .011897 -0.68 0.499 .968878 1.015518
_rcs_tr_outcome4 | 1.004002 .0087589 0.46 0.647 .9869802 1.021316
_rcs_tr_outcome5 | .9978662 .0053369 -0.40 0.690 .9874607 1.008381
_cons | .1619355 .0039193 -75.22 0.000 .154433 .1698023
------------------------------------------------------------------------------------
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 = -43374.825
Iteration 1: log pseudolikelihood = -43356.523
Iteration 2: log pseudolikelihood = -43356.442
Iteration 3: log pseudolikelihood = -43356.442
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43356.442 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448922 .0378515 14.19 0.000 1.376601 1.525041
_rcs1 | 2.615065 .0533251 47.14 0.000 2.512611 2.721697
_rcs2 | 1.091033 .0165925 5.73 0.000 1.058992 1.124043
_rcs3 | 1.040331 .0121836 3.38 0.001 1.016724 1.064487
_rcs4 | 1.003875 .0090055 0.43 0.666 .9863786 1.021681
_rcs5 | 1.009985 .0056763 1.77 0.077 .9989212 1.021172
_rcs6 | 1.000591 .0046049 0.13 0.898 .991606 1.009657
_rcs_tr_outcome1 | .9198774 .0203202 -3.78 0.000 .8809003 .9605791
_rcs_tr_outcome2 | 1.008563 .017027 0.51 0.614 .9757372 1.042494
_rcs_tr_outcome3 | .9869671 .0126334 -1.02 0.305 .9625142 1.012041
_rcs_tr_outcome4 | 1.008661 .0096427 0.90 0.367 .9899378 1.027739
_rcs_tr_outcome5 | .9955857 .0060524 -0.73 0.467 .9837935 1.007519
_rcs_tr_outcome6 | 1.004961 .0049616 1.00 0.316 .9952834 1.014733
_cons | .1619137 .003913 -75.34 0.000 .1544231 .1697676
------------------------------------------------------------------------------------
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 = -43373.725
Iteration 1: log pseudolikelihood = -43357.062
Iteration 2: log pseudolikelihood = -43357.008
Iteration 3: log pseudolikelihood = -43357.008
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.008 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448635 .0378791 14.17 0.000 1.376263 1.524812
_rcs1 | 2.614542 .0533744 47.08 0.000 2.511995 2.721275
_rcs2 | 1.091941 .0170416 5.64 0.000 1.059046 1.125858
_rcs3 | 1.038322 .0119779 3.26 0.001 1.015109 1.062066
_rcs4 | 1.005748 .0087587 0.66 0.510 .988727 1.023062
_rcs5 | 1.008529 .0054989 1.56 0.119 .9978083 1.019364
_rcs6 | 1.001453 .0041132 0.35 0.724 .9934236 1.009547
_rcs_tr_outcome1 | .9201138 .0203345 -3.77 0.000 .8811097 .9608445
_rcs_tr_outcome2 | 1.008067 .0173493 0.47 0.641 .9746302 1.042651
_rcs_tr_outcome3 | .9883534 .0128098 -0.90 0.366 .9635628 1.013782
_rcs_tr_outcome4 | 1.006453 .0091629 0.71 0.480 .9886535 1.024573
_rcs_tr_outcome5 | .9980279 .0057818 -0.34 0.733 .9867599 1.009425
_rcs_tr_outcome6 | 1.001301 .0046725 0.28 0.780 .9921852 1.010501
_rcs_tr_outcome7 | 1.003914 .0031366 1.25 0.211 .9977854 1.010081
_cons | .1619401 .0039172 -75.26 0.000 .1544417 .1698025
------------------------------------------------------------------------------------
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 = -43374.448
Iteration 1: log pseudolikelihood = -43358.735
Iteration 2: log pseudolikelihood = -43358.691
Iteration 3: log pseudolikelihood = -43358.691
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.691 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448477 .037939 14.15 0.000 1.375995 1.524778
_rcs1 | 2.617951 .0496233 50.77 0.000 2.522475 2.71704
_rcs2 | 1.097357 .0072602 14.04 0.000 1.083219 1.11168
_rcs3 | 1.031468 .0050782 6.29 0.000 1.021563 1.04147
_rcs4 | 1.011117 .0034637 3.23 0.001 1.004351 1.017929
_rcs5 | 1.007044 .0023312 3.03 0.002 1.002485 1.011624
_rcs6 | 1.005336 .0018524 2.89 0.004 1.001712 1.008973
_rcs7 | 1.003476 .0015445 2.25 0.024 1.000453 1.006508
_rcs_tr_outcome1 | .9185744 .0185837 -4.20 0.000 .8828637 .9557295
_cons | .1619581 .0039249 -75.12 0.000 .1544453 .1698364
------------------------------------------------------------------------------------
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 = -43373.849
Iteration 1: log pseudolikelihood = -43358.73
Iteration 2: log pseudolikelihood = -43358.689
Iteration 3: log pseudolikelihood = -43358.689
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.689 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448337 .0378741 14.17 0.000 1.375975 1.524504
_rcs1 | 2.616802 .0545456 46.15 0.000 2.512049 2.725923
_rcs2 | 1.09662 .0180931 5.59 0.000 1.061725 1.132661
_rcs3 | 1.03138 .0057352 5.56 0.000 1.0202 1.042682
_rcs4 | 1.0111 .0034611 3.22 0.001 1.004339 1.017906
_rcs5 | 1.007044 .0023311 3.03 0.002 1.002486 1.011623
_rcs6 | 1.005338 .0018502 2.89 0.004 1.001718 1.008971
_rcs7 | 1.003477 .0015434 2.26 0.024 1.000456 1.006506
_rcs_tr_outcome1 | .9190822 .0206864 -3.75 0.000 .8794189 .9605343
_rcs_tr_outcome2 | 1.000872 .0179517 0.05 0.961 .966298 1.036682
_cons | .1619711 .0039194 -75.23 0.000 .1544686 .169838
------------------------------------------------------------------------------------
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 = -43374.35
Iteration 1: log pseudolikelihood = -43358.708
Iteration 2: log pseudolikelihood = -43358.665
Iteration 3: log pseudolikelihood = -43358.665
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.665 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448481 .0379113 14.16 0.000 1.37605 1.524724
_rcs1 | 2.616522 .0542803 46.36 0.000 2.512268 2.725102
_rcs2 | 1.095211 .0192546 5.17 0.000 1.058115 1.133607
_rcs3 | 1.032612 .0093546 3.54 0.000 1.014439 1.05111
_rcs4 | 1.011821 .0060713 1.96 0.050 .9999909 1.02379
_rcs5 | 1.007262 .0028306 2.57 0.010 1.00173 1.012826
_rcs6 | 1.005367 .0018764 2.87 0.004 1.001696 1.009052
_rcs7 | 1.003472 .0015434 2.25 0.024 1.000451 1.006501
_rcs_tr_outcome1 | .9191758 .0206001 -3.76 0.000 .8796743 .9604511
_rcs_tr_outcome2 | 1.002318 .0190666 0.12 0.903 .9656364 1.040394
_rcs_tr_outcome3 | .9980684 .0113263 -0.17 0.865 .9761142 1.020516
_cons | .1619601 .0039217 -75.18 0.000 .1544533 .1698318
------------------------------------------------------------------------------------
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 = -43374.142
Iteration 1: log pseudolikelihood = -43358.665
Iteration 2: log pseudolikelihood = -43358.622
Iteration 3: log pseudolikelihood = -43358.622
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.622 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448548 .0379066 14.16 0.000 1.376126 1.524782
_rcs1 | 2.615969 .0539715 46.61 0.000 2.512297 2.723919
_rcs2 | 1.093895 .0186794 5.26 0.000 1.05789 1.131125
_rcs3 | 1.03399 .010697 3.23 0.001 1.013236 1.05517
_rcs4 | 1.011857 .006531 1.83 0.068 .9991373 1.024739
_rcs5 | 1.006941 .0052685 1.32 0.186 .9966678 1.01732
_rcs6 | 1.005266 .0026101 2.02 0.043 1.000163 1.010394
_rcs7 | 1.003471 .0015573 2.23 0.026 1.000423 1.006528
_rcs_tr_outcome1 | .9193969 .0205037 -3.77 0.000 .8800761 .9604746
_rcs_tr_outcome2 | 1.003778 .0186643 0.20 0.839 .9678549 1.041034
_rcs_tr_outcome3 | .9964884 .0115409 -0.30 0.761 .9741235 1.019367
_rcs_tr_outcome4 | 1.000294 .0080749 0.04 0.971 .9845922 1.016246
_cons | .1619544 .0039208 -75.20 0.000 .1544493 .1698241
------------------------------------------------------------------------------------
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 = -43374.182
Iteration 1: log pseudolikelihood = -43358.152
Iteration 2: log pseudolikelihood = -43358.104
Iteration 3: log pseudolikelihood = -43358.104
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.104 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448742 .0378901 14.17 0.000 1.37635 1.524942
_rcs1 | 2.615676 .053501 47.01 0.000 2.51289 2.722667
_rcs2 | 1.091281 .0173522 5.49 0.000 1.057796 1.125826
_rcs3 | 1.038345 .0116948 3.34 0.001 1.015674 1.061521
_rcs4 | 1.008941 .0077116 1.16 0.244 .9939391 1.024169
_rcs5 | 1.006419 .0050477 1.28 0.202 .9965747 1.016362
_rcs6 | 1.006721 .004266 1.58 0.114 .998394 1.015117
_rcs7 | 1.00381 .0018896 2.02 0.043 1.000113 1.007521
_rcs_tr_outcome1 | .9195014 .0203578 -3.79 0.000 .8804542 .9602804
_rcs_tr_outcome2 | 1.006596 .0176732 0.37 0.708 .9725465 1.041838
_rcs_tr_outcome3 | .991978 .0119336 -0.67 0.503 .9688621 1.015645
_rcs_tr_outcome4 | 1.004168 .0088418 0.47 0.637 .986987 1.021648
_rcs_tr_outcome5 | .9976672 .0056415 -0.41 0.680 .9866711 1.008786
_cons | .1619397 .0039188 -75.23 0.000 .1544382 .1698055
------------------------------------------------------------------------------------
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 = -43374.269
Iteration 1: log pseudolikelihood = -43356.266
Iteration 2: log pseudolikelihood = -43356.198
Iteration 3: log pseudolikelihood = -43356.198
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43356.198 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448809 .0378668 14.18 0.000 1.376461 1.52496
_rcs1 | 2.61547 .0532672 47.21 0.000 2.513124 2.721983
_rcs2 | 1.090026 .0164024 5.73 0.000 1.058347 1.122653
_rcs3 | 1.042208 .0120992 3.56 0.000 1.018762 1.066194
_rcs4 | 1.004764 .0086128 0.55 0.579 .9880243 1.021788
_rcs5 | 1.00935 .0054156 1.73 0.083 .9987911 1.02002
_rcs6 | 1.005218 .0043448 1.20 0.229 .9967386 1.01377
_rcs7 | 1.001778 .0029288 0.61 0.543 .9960545 1.007535
_rcs_tr_outcome1 | .9196605 .020281 -3.80 0.000 .8807572 .9602822
_rcs_tr_outcome2 | 1.008001 .0169401 0.47 0.635 .9753403 1.041756
_rcs_tr_outcome3 | .9879081 .0121679 -0.99 0.323 .964345 1.012047
_rcs_tr_outcome4 | 1.00797 .009295 0.86 0.389 .9899161 1.026354
_rcs_tr_outcome5 | .9959087 .0058539 -0.70 0.486 .9845011 1.007448
_rcs_tr_outcome6 | 1.002813 .0043576 0.65 0.518 .9943084 1.01139
_cons | .1619233 .0039151 -75.30 0.000 .1544288 .1697816
------------------------------------------------------------------------------------
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 = -43374.075
Iteration 1: log pseudolikelihood = -43356.225
Iteration 2: log pseudolikelihood = -43356.143
Iteration 3: log pseudolikelihood = -43356.143
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43356.143 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448932 .0378551 14.19 0.000 1.376605 1.525059
_rcs1 | 2.615584 .0533784 47.11 0.000 2.513029 2.722324
_rcs2 | 1.08992 .0161774 5.80 0.000 1.05867 1.122093
_rcs3 | 1.042846 .0124633 3.51 0.000 1.018702 1.067562
_rcs4 | 1.004073 .0092753 0.44 0.660 .9860572 1.022418
_rcs5 | 1.009847 .005959 1.66 0.097 .9982344 1.021594
_rcs6 | 1.00461 .0046486 0.99 0.320 .9955402 1.013763
_rcs7 | 1.000921 .0038438 0.24 0.810 .9934158 1.008483
_rcs_tr_outcome1 | .919648 .0203263 -3.79 0.000 .8806598 .9603623
_rcs_tr_outcome2 | 1.008899 .0166996 0.54 0.592 .9766936 1.042166
_rcs_tr_outcome3 | .9858067 .0128717 -1.09 0.274 .9608988 1.01136
_rcs_tr_outcome4 | 1.009196 .0099366 0.93 0.353 .9899075 1.028861
_rcs_tr_outcome5 | .9963623 .00633 -0.57 0.566 .9840326 1.008846
_rcs_tr_outcome6 | 1.000994 .0049999 0.20 0.842 .9912422 1.010842
_rcs_tr_outcome7 | 1.003468 .0041616 0.83 0.404 .9953445 1.011658
_cons | .1619111 .0039134 -75.33 0.000 .1544198 .1697657
------------------------------------------------------------------------------------
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 = -43373.955
Iteration 1: log pseudolikelihood = -43358.104
Iteration 2: log pseudolikelihood = -43358.059
Iteration 3: log pseudolikelihood = -43358.059
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.059 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448434 .0379444 14.14 0.000 1.375942 1.524746
_rcs1 | 2.617986 .0496265 50.77 0.000 2.522505 2.717082
_rcs2 | 1.096856 .0072089 14.07 0.000 1.082817 1.111076
_rcs3 | 1.032054 .0050951 6.39 0.000 1.022116 1.042089
_rcs4 | 1.011638 .0034849 3.36 0.001 1.00483 1.018491
_rcs5 | 1.007675 .0024153 3.19 0.001 1.002952 1.01242
_rcs6 | 1.005262 .0018651 2.83 0.005 1.001613 1.008924
_rcs7 | 1.004697 .0016844 2.80 0.005 1.001401 1.008004
_rcs8 | 1.00288 .0013931 2.07 0.038 1.000154 1.005615
_rcs_tr_outcome1 | .9185487 .0185895 -4.20 0.000 .8828271 .9557156
_cons | .1619608 .0039258 -75.10 0.000 .1544463 .1698408
------------------------------------------------------------------------------------
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 = -43373.354
Iteration 1: log pseudolikelihood = -43358.099
Iteration 2: log pseudolikelihood = -43358.057
Iteration 3: log pseudolikelihood = -43358.057
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.057 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448294 .0378771 14.16 0.000 1.375926 1.524467
_rcs1 | 2.616838 .0545108 46.18 0.000 2.51215 2.725888
_rcs2 | 1.09612 .0180318 5.58 0.000 1.061342 1.132038
_rcs3 | 1.03196 .0058155 5.58 0.000 1.020625 1.043422
_rcs4 | 1.011616 .0034956 3.34 0.001 1.004788 1.018491
_rcs5 | 1.007672 .002418 3.19 0.001 1.002944 1.012422
_rcs6 | 1.005263 .0018621 2.83 0.005 1.00162 1.008919
_rcs7 | 1.004698 .0016831 2.80 0.005 1.001405 1.008003
_rcs8 | 1.002881 .0013919 2.07 0.038 1.000157 1.005613
_rcs_tr_outcome1 | .9190564 .0206723 -3.75 0.000 .8794195 .9604798
_rcs_tr_outcome2 | 1.000871 .0179377 0.05 0.961 .9663243 1.036653
_cons | .1619737 .00392 -75.21 0.000 .15447 .169842
------------------------------------------------------------------------------------
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 = -43373.847
Iteration 1: log pseudolikelihood = -43358.077
Iteration 2: log pseudolikelihood = -43358.033
Iteration 3: log pseudolikelihood = -43358.033
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.033 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448435 .0379135 14.15 0.000 1.376 1.524683
_rcs1 | 2.616539 .0542435 46.40 0.000 2.512355 2.725044
_rcs2 | 1.094696 .0191828 5.16 0.000 1.057736 1.132946
_rcs3 | 1.033124 .0090843 3.71 0.000 1.015472 1.051083
_rcs4 | 1.01237 .0062517 1.99 0.047 1.00019 1.024697
_rcs5 | 1.007981 .0032704 2.45 0.014 1.001591 1.014411
_rcs6 | 1.005342 .0019838 2.70 0.007 1.001461 1.009237
_rcs7 | 1.004704 .0016856 2.80 0.005 1.001406 1.008013
_rcs8 | 1.002875 .0013898 2.07 0.038 1.000155 1.005603
_rcs_tr_outcome1 | .9191584 .0205856 -3.76 0.000 .879684 .9604041
_rcs_tr_outcome2 | 1.002325 .0190153 0.12 0.903 .9657397 1.040296
_rcs_tr_outcome3 | .9980826 .0113049 -0.17 0.865 .9761696 1.020488
_cons | .161963 .0039223 -75.17 0.000 .154455 .1698359
------------------------------------------------------------------------------------
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 = -43373.604
Iteration 1: log pseudolikelihood = -43358.033
Iteration 2: log pseudolikelihood = -43357.989
Iteration 3: log pseudolikelihood = -43357.989
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.989 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448485 .037907 14.16 0.000 1.376062 1.52472
_rcs1 | 2.615955 .0539452 46.63 0.000 2.512332 2.723851
_rcs2 | 1.09343 .0185742 5.26 0.000 1.057625 1.130448
_rcs3 | 1.034569 .0104932 3.35 0.001 1.014206 1.055341
_rcs4 | 1.012389 .006311 1.98 0.048 1.000095 1.024834
_rcs5 | 1.007467 .0054196 1.38 0.167 .996901 1.018146
_rcs6 | 1.005023 .003425 1.47 0.141 .9983327 1.011758
_rcs7 | 1.004632 .0018765 2.47 0.013 1.000961 1.008316
_rcs8 | 1.00288 .0013893 2.08 0.038 1.00016 1.005607
_rcs_tr_outcome1 | .9193948 .0204927 -3.77 0.000 .8800945 .96045
_rcs_tr_outcome2 | 1.003706 .0185836 0.20 0.842 .9679354 1.040798
_rcs_tr_outcome3 | .9965031 .0115254 -0.30 0.762 .9741679 1.01935
_rcs_tr_outcome4 | 1.000733 .0081014 0.09 0.928 .9849797 1.016738
_cons | .1619581 .0039212 -75.19 0.000 .1544522 .1698289
------------------------------------------------------------------------------------
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 = -43373.654
Iteration 1: log pseudolikelihood = -43357.729
Iteration 2: log pseudolikelihood = -43357.683
Iteration 3: log pseudolikelihood = -43357.683
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.683 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448622 .0378911 14.17 0.000 1.376228 1.524824
_rcs1 | 2.615504 .0535303 46.98 0.000 2.512663 2.722554
_rcs2 | 1.091225 .0174075 5.47 0.000 1.057635 1.125882
_rcs3 | 1.038174 .0116948 3.33 0.001 1.015504 1.06135
_rcs4 | 1.010794 .0072304 1.50 0.133 .9967215 1.025065
_rcs5 | 1.006236 .0054567 1.15 0.252 .9955976 1.016988
_rcs6 | 1.005514 .0044023 1.26 0.209 .9969229 1.01418
_rcs7 | 1.005084 .003059 1.67 0.096 .9991058 1.011097
_rcs8 | 1.002914 .0014349 2.03 0.042 1.000105 1.00573
_rcs_tr_outcome1 | .9195719 .0203664 -3.79 0.000 .8805084 .9603683
_rcs_tr_outcome2 | 1.006109 .0177012 0.35 0.729 .9720069 1.041408
_rcs_tr_outcome3 | .9926677 .0120841 -0.60 0.545 .9692636 1.016637
_rcs_tr_outcome4 | 1.003591 .0090275 0.40 0.690 .9860521 1.021441
_rcs_tr_outcome5 | .9990072 .0058019 -0.17 0.864 .9877001 1.010444
_cons | .161947 .0039196 -75.22 0.000 .1544442 .1698144
------------------------------------------------------------------------------------
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 = -43373.741
Iteration 1: log pseudolikelihood = -43355.46
Iteration 2: log pseudolikelihood = -43355.383
Iteration 3: log pseudolikelihood = -43355.383
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43355.383 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448789 .0378609 14.19 0.000 1.376452 1.524929
_rcs1 | 2.615578 .0533153 47.17 0.000 2.513142 2.72219
_rcs2 | 1.089634 .0163176 5.73 0.000 1.058117 1.12209
_rcs3 | 1.043163 .012216 3.61 0.000 1.019492 1.067382
_rcs4 | 1.00592 .0080069 0.74 0.458 .9903484 1.021736
_rcs5 | 1.008139 .0054622 1.50 0.135 .9974904 1.018902
_rcs6 | 1.006903 .0043466 1.59 0.111 .9984201 1.015459
_rcs7 | 1.002229 .0041332 0.54 0.589 .9941605 1.010363
_rcs8 | 1.00195 .0018726 1.04 0.297 .9982867 1.005627
_rcs_tr_outcome1 | .9196085 .0202969 -3.80 0.000 .8806754 .9602627
_rcs_tr_outcome2 | 1.00791 .0168985 0.47 0.638 .9753277 1.041581
_rcs_tr_outcome3 | .9876749 .0122766 -1.00 0.318 .963904 1.012032
_rcs_tr_outcome4 | 1.008235 .009285 0.89 0.373 .9901997 1.026598
_rcs_tr_outcome5 | .9958533 .0059175 -0.70 0.484 .9843225 1.007519
_rcs_tr_outcome6 | 1.003986 .0047979 0.83 0.405 .994626 1.013434
_cons | .1619228 .0039147 -75.31 0.000 .1544291 .1697802
------------------------------------------------------------------------------------
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 = -43373.743
Iteration 1: log pseudolikelihood = -43355.837
Iteration 2: log pseudolikelihood = -43355.761
Iteration 3: log pseudolikelihood = -43355.761
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43355.761 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448957 .0378732 14.19 0.000 1.376597 1.525122
_rcs1 | 2.616237 .0534368 47.09 0.000 2.513571 2.723095
_rcs2 | 1.089422 .016012 5.83 0.000 1.058487 1.121262
_rcs3 | 1.044252 .0124298 3.64 0.000 1.020172 1.0689
_rcs4 | 1.004626 .0087577 0.53 0.596 .9876074 1.021939
_rcs5 | 1.009267 .0058169 1.60 0.110 .9979297 1.020732
_rcs6 | 1.005818 .0043554 1.34 0.180 .9973173 1.014391
_rcs7 | 1.002938 .0040654 0.72 0.469 .9950016 1.010938
_rcs8 | 1.00194 .0026255 0.74 0.460 .9968071 1.007099
_rcs_tr_outcome1 | .9193721 .0203298 -3.80 0.000 .8803776 .9600938
_rcs_tr_outcome2 | 1.008553 .016611 0.52 0.605 .9765158 1.041641
_rcs_tr_outcome3 | .9858353 .0125939 -1.12 0.264 .9614582 1.010831
_rcs_tr_outcome4 | 1.009245 .0096034 0.97 0.333 .9905968 1.028244
_rcs_tr_outcome5 | .9962974 .0061011 -0.61 0.545 .984411 1.008327
_rcs_tr_outcome6 | 1.001581 .0048985 0.32 0.747 .9920263 1.011228
_rcs_tr_outcome7 | 1.002055 .0037607 0.55 0.584 .9947115 1.009453
_cons | .1619066 .0039153 -75.29 0.000 .1544118 .1697652
------------------------------------------------------------------------------------
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 = -43373.898
Iteration 1: log pseudolikelihood = -43357.654
Iteration 2: log pseudolikelihood = -43357.608
Iteration 3: log pseudolikelihood = -43357.608
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.608 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448319 .0379526 14.14 0.000 1.375811 1.524648
_rcs1 | 2.617773 .0496295 50.76 0.000 2.522286 2.716875
_rcs2 | 1.096426 .007187 14.04 0.000 1.08243 1.110603
_rcs3 | 1.032632 .0051116 6.49 0.000 1.022662 1.042699
_rcs4 | 1.012304 .0035064 3.53 0.000 1.005455 1.0192
_rcs5 | 1.007973 .0025279 3.17 0.002 1.003031 1.01294
_rcs6 | 1.005347 .0019001 2.82 0.005 1.00163 1.009078
_rcs7 | 1.005006 .0016265 3.09 0.002 1.001824 1.008199
_rcs8 | 1.003616 .0015288 2.37 0.018 1.000624 1.006617
_rcs9 | 1.002654 .0012994 2.05 0.041 1.000111 1.005204
_rcs_tr_outcome1 | .918652 .018598 -4.19 0.000 .8829143 .9558363
_cons | .1619705 .0039272 -75.08 0.000 .1544534 .1698534
------------------------------------------------------------------------------------
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 = -43373.304
Iteration 1: log pseudolikelihood = -43357.65
Iteration 2: log pseudolikelihood = -43357.606
Iteration 3: log pseudolikelihood = -43357.606
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.606 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448187 .0378854 14.16 0.000 1.375805 1.524378
_rcs1 | 2.616704 .0545076 46.18 0.000 2.512022 2.725747
_rcs2 | 1.095743 .0179835 5.57 0.000 1.061057 1.131563
_rcs3 | 1.032537 .005916 5.59 0.000 1.021007 1.044198
_rcs4 | 1.012281 .0035329 3.50 0.000 1.005381 1.019229
_rcs5 | 1.007968 .0025307 3.16 0.002 1.00302 1.01294
_rcs6 | 1.005348 .0018978 2.83 0.005 1.001635 1.009075
_rcs7 | 1.005008 .0016249 3.09 0.002 1.001828 1.008197
_rcs8 | 1.003617 .0015272 2.37 0.018 1.000628 1.006615
_rcs9 | 1.002654 .0012984 2.05 0.041 1.000113 1.005203
_rcs_tr_outcome1 | .919125 .0206742 -3.75 0.000 .8794845 .9605521
_rcs_tr_outcome2 | 1.000811 .0179378 0.05 0.964 .9662642 1.036594
_cons | .1619825 .0039214 -75.19 0.000 .1544762 .1698537
------------------------------------------------------------------------------------
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 = -43373.778
Iteration 1: log pseudolikelihood = -43357.628
Iteration 2: log pseudolikelihood = -43357.582
Iteration 3: log pseudolikelihood = -43357.582
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.582 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448332 .0379231 14.15 0.000 1.375879 1.524601
_rcs1 | 2.616433 .0542439 46.39 0.000 2.512248 2.724939
_rcs2 | 1.094328 .0191697 5.15 0.000 1.057394 1.132552
_rcs3 | 1.033641 .0088932 3.85 0.000 1.016356 1.051219
_rcs4 | 1.013053 .0063267 2.08 0.038 1.000728 1.025529
_rcs5 | 1.00834 .0036718 2.28 0.023 1.00117 1.015563
_rcs6 | 1.005479 .0021554 2.55 0.011 1.001263 1.009712
_rcs7 | 1.005036 .0016517 3.06 0.002 1.001804 1.008278
_rcs8 | 1.003615 .0015276 2.37 0.018 1.000625 1.006613
_rcs9 | 1.00265 .0012964 2.05 0.041 1.000112 1.005194
_rcs_tr_outcome1 | .9192145 .0205882 -3.76 0.000 .8797353 .9604655
_rcs_tr_outcome2 | 1.002239 .0190099 0.12 0.906 .9656644 1.040199
_rcs_tr_outcome3 | .9980908 .0113001 -0.17 0.866 .9761869 1.020486
_cons | .1619715 .0039238 -75.14 0.000 .1544607 .1698474
------------------------------------------------------------------------------------
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 = -43373.545
Iteration 1: log pseudolikelihood = -43357.584
Iteration 2: log pseudolikelihood = -43357.539
Iteration 3: log pseudolikelihood = -43357.539
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.539 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448374 .0379149 14.15 0.000 1.375936 1.524625
_rcs1 | 2.615816 .0539484 46.62 0.000 2.512188 2.723719
_rcs2 | 1.093055 .018544 5.24 0.000 1.057307 1.130011
_rcs3 | 1.035067 .0103188 3.46 0.001 1.015039 1.05549
_rcs4 | 1.01318 .0062191 2.13 0.033 1.001064 1.025443
_rcs5 | 1.007855 .0053547 1.47 0.141 .9974142 1.018405
_rcs6 | 1.005053 .0039468 1.28 0.199 .9973472 1.012819
_rcs7 | 1.004858 .0022914 2.13 0.034 1.000377 1.009359
_rcs8 | 1.003586 .0015735 2.28 0.022 1.000507 1.006675
_rcs9 | 1.002657 .001292 2.06 0.039 1.000128 1.005192
_rcs_tr_outcome1 | .9194659 .0204967 -3.77 0.000 .8801582 .9605291
_rcs_tr_outcome2 | 1.00361 .0185672 0.19 0.846 .9678705 1.040669
_rcs_tr_outcome3 | .9965265 .0115132 -0.30 0.763 .9742146 1.019349
_rcs_tr_outcome4 | 1.000822 .0080965 0.10 0.919 .9850783 1.016817
_cons | .1619673 .0039226 -75.16 0.000 .1544587 .1698408
------------------------------------------------------------------------------------
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 = -43373.583
Iteration 1: log pseudolikelihood = -43357.206
Iteration 2: log pseudolikelihood = -43357.157
Iteration 3: log pseudolikelihood = -43357.157
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.157 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448509 .037895 14.16 0.000 1.376108 1.524719
_rcs1 | 2.615358 .0535217 46.98 0.000 2.512533 2.722391
_rcs2 | 1.090703 .0173072 5.47 0.000 1.057304 1.125158
_rcs3 | 1.039025 .0116309 3.42 0.001 1.016477 1.062073
_rcs4 | 1.011776 .006751 1.75 0.079 .9986304 1.025095
_rcs5 | 1.006082 .0058155 1.05 0.294 .9947483 1.017545
_rcs6 | 1.005099 .0041534 1.23 0.218 .9969917 1.013273
_rcs7 | 1.005546 .0036747 1.51 0.130 .9983692 1.012774
_rcs8 | 1.003852 .0021005 1.84 0.066 .9997438 1.007978
_rcs9 | 1.002665 .0012975 2.06 0.040 1.000125 1.005211
_rcs_tr_outcome1 | .9196483 .0203671 -3.78 0.000 .8805835 .960446
_rcs_tr_outcome2 | 1.006118 .0176471 0.35 0.728 .9721181 1.041307
_rcs_tr_outcome3 | .9923805 .0121196 -0.63 0.531 .9689085 1.016421
_rcs_tr_outcome4 | 1.004208 .0090299 0.47 0.641 .9866649 1.022063
_rcs_tr_outcome5 | .9988201 .0057612 -0.20 0.838 .987592 1.010176
_cons | .1619563 .0039206 -75.20 0.000 .1544514 .1698258
------------------------------------------------------------------------------------
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 = -43373.363
Iteration 1: log pseudolikelihood = -43354.4
Iteration 2: log pseudolikelihood = -43354.31
Iteration 3: log pseudolikelihood = -43354.31
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43354.31 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448692 .0378551 14.18 0.000 1.376365 1.524819
_rcs1 | 2.615261 .0533298 47.14 0.000 2.512798 2.721902
_rcs2 | 1.089162 .0162292 5.73 0.000 1.057813 1.12144
_rcs3 | 1.043998 .012372 3.63 0.000 1.020029 1.068531
_rcs4 | 1.007298 .0075427 0.97 0.331 .9926228 1.022191
_rcs5 | 1.006781 .0056962 1.19 0.232 .9956784 1.018007
_rcs6 | 1.008003 .0045596 1.76 0.078 .9991062 1.01698
_rcs7 | 1.003273 .0038954 0.84 0.400 .995667 1.010937
_rcs8 | 1.000858 .0032619 0.26 0.792 .9944849 1.007271
_rcs9 | 1.002218 .0013889 1.60 0.110 .9994998 1.004944
_rcs_tr_outcome1 | .9197547 .020312 -3.79 0.000 .8807932 .9604397
_rcs_tr_outcome2 | 1.007913 .0168646 0.47 0.638 .975395 1.041515
_rcs_tr_outcome3 | .98746 .0124584 -1.00 0.317 .9633414 1.012182
_rcs_tr_outcome4 | 1.008581 .0095516 0.90 0.367 .990033 1.027477
_rcs_tr_outcome5 | .9957171 .0059792 -0.71 0.475 .9840667 1.007505
_rcs_tr_outcome6 | 1.005403 .0048886 1.11 0.268 .9958666 1.01503
_cons | .1619289 .0039147 -75.31 0.000 .1544351 .1697863
------------------------------------------------------------------------------------
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 = -43373.3
Iteration 1: log pseudolikelihood = -43355.255
Iteration 2: log pseudolikelihood = -43355.178
Iteration 3: log pseudolikelihood = -43355.178
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43355.178 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448678 .037877 14.18 0.000 1.37631 1.524851
_rcs1 | 2.615349 .0534009 47.09 0.000 2.512752 2.722136
_rcs2 | 1.089246 .0161022 5.78 0.000 1.058139 1.121267
_rcs3 | 1.04399 .0125116 3.59 0.000 1.019754 1.068803
_rcs4 | 1.007158 .0083718 0.86 0.391 .9908824 1.023701
_rcs5 | 1.007156 .0057544 1.25 0.212 .9959402 1.018497
_rcs6 | 1.007068 .0044237 1.60 0.109 .998435 1.015776
_rcs7 | 1.004451 .0039896 1.12 0.264 .9966616 1.012301
_rcs8 | 1.001008 .0037022 0.27 0.785 .9937776 1.00829
_rcs9 | 1.001496 .0018596 0.81 0.421 .9978582 1.005148
_rcs_tr_outcome1 | .9197169 .0203301 -3.79 0.000 .8807214 .9604389
_rcs_tr_outcome2 | 1.008101 .0167051 0.49 0.626 .9758854 1.04138
_rcs_tr_outcome3 | .9869074 .0126291 -1.03 0.303 .9624626 1.011973
_rcs_tr_outcome4 | 1.008143 .0096036 0.85 0.395 .9894952 1.027143
_rcs_tr_outcome5 | .9973915 .0062068 -0.42 0.675 .9853003 1.009631
_rcs_tr_outcome6 | 1.000632 .0049178 0.13 0.898 .9910391 1.010317
_rcs_tr_outcome7 | 1.003894 .0040371 0.97 0.334 .9960125 1.011838
_cons | .1619315 .0039171 -75.26 0.000 .1544332 .1697938
------------------------------------------------------------------------------------
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 = -43374.238
Iteration 1: log pseudolikelihood = -43358.05
Iteration 2: log pseudolikelihood = -43358.003
Iteration 3: log pseudolikelihood = -43358.003
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.003 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448423 .0379611 14.14 0.000 1.375899 1.524769
_rcs1 | 2.618006 .0496887 50.71 0.000 2.522407 2.717228
_rcs2 | 1.096249 .0072053 13.98 0.000 1.082217 1.110462
_rcs3 | 1.032602 .005147 6.44 0.000 1.022563 1.042739
_rcs4 | 1.013247 .0035334 3.77 0.000 1.006345 1.020196
_rcs5 | 1.007972 .0026165 3.06 0.002 1.002856 1.013113
_rcs6 | 1.005716 .0019004 3.02 0.003 1.001998 1.009447
_rcs7 | 1.004698 .0016236 2.90 0.004 1.001521 1.007885
_rcs8 | 1.004302 .001526 2.83 0.005 1.001316 1.007298
_rcs9 | 1.003175 .0014185 2.24 0.025 1.000399 1.005959
_rcs10 | 1.001951 .001222 1.60 0.110 .9995585 1.004349
_rcs_tr_outcome1 | .918537 .0186191 -4.19 0.000 .8827598 .9557643
_cons | .1619626 .0039276 -75.07 0.000 .1544448 .1698465
------------------------------------------------------------------------------------
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 = -43373.668
Iteration 1: log pseudolikelihood = -43358.045
Iteration 2: log pseudolikelihood = -43358.001
Iteration 3: log pseudolikelihood = -43358.001
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43358.001 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448274 .0378984 14.15 0.000 1.375867 1.524491
_rcs1 | 2.616794 .0545916 46.11 0.000 2.511954 2.726009
_rcs2 | 1.095476 .0179565 5.56 0.000 1.060841 1.131241
_rcs3 | 1.032489 .0060105 5.49 0.000 1.020776 1.044337
_rcs4 | 1.013219 .0035751 3.72 0.000 1.006236 1.02025
_rcs5 | 1.007962 .0026193 3.05 0.002 1.002842 1.013109
_rcs6 | 1.005716 .0019 3.02 0.003 1.001999 1.009446
_rcs7 | 1.004699 .0016218 2.90 0.004 1.001525 1.007883
_rcs8 | 1.004304 .0015245 2.83 0.005 1.00132 1.007296
_rcs9 | 1.003176 .0014168 2.25 0.025 1.000403 1.005957
_rcs10 | 1.001951 .001221 1.60 0.110 .999561 1.004347
_rcs_tr_outcome1 | .9190729 .0207103 -3.75 0.000 .8793647 .9605742
_rcs_tr_outcome2 | 1.000919 .017943 0.05 0.959 .9663621 1.036712
_cons | .1619763 .0039224 -75.17 0.000 .1544682 .1698494
------------------------------------------------------------------------------------
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 = -43374.122
Iteration 1: log pseudolikelihood = -43358.024
Iteration 2: log pseudolikelihood = -43357.977
Iteration 3: log pseudolikelihood = -43357.977
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.977 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448414 .0379361 14.14 0.000 1.375937 1.524709
_rcs1 | 2.616509 .054327 46.32 0.000 2.512168 2.725184
_rcs2 | 1.094061 .0191848 5.13 0.000 1.057098 1.132316
_rcs3 | 1.033531 .0087626 3.89 0.000 1.016499 1.050849
_rcs4 | 1.013991 .0063636 2.21 0.027 1.001595 1.02654
_rcs5 | 1.008379 .0039929 2.11 0.035 1.000583 1.016235
_rcs6 | 1.005893 .0023189 2.55 0.011 1.001359 1.010448
_rcs7 | 1.004758 .0017125 2.78 0.005 1.001407 1.00812
_rcs8 | 1.004312 .0015281 2.83 0.005 1.001322 1.007312
_rcs9 | 1.003171 .0014155 2.24 0.025 1.000401 1.005949
_rcs10 | 1.001948 .0012202 1.60 0.110 .9995588 1.004342
_rcs_tr_outcome1 | .9191696 .0206243 -3.76 0.000 .8796227 .9604944
_rcs_tr_outcome2 | 1.002341 .019034 0.12 0.902 .9657211 1.04035
_rcs_tr_outcome3 | .9981232 .0113298 -0.17 0.869 .9761624 1.020578
_cons | .1619656 .0039247 -75.12 0.000 .1544531 .1698436
------------------------------------------------------------------------------------
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 = -43373.905
Iteration 1: log pseudolikelihood = -43357.975
Iteration 2: log pseudolikelihood = -43357.93
Iteration 3: log pseudolikelihood = -43357.93
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.93 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448467 .0379285 14.15 0.000 1.376004 1.524746
_rcs1 | 2.615892 .0540262 46.56 0.000 2.512117 2.723954
_rcs2 | 1.092702 .0185606 5.22 0.000 1.056923 1.129692
_rcs3 | 1.035012 .010193 3.49 0.000 1.015225 1.055184
_rcs4 | 1.014264 .0062052 2.32 0.021 1.002175 1.026499
_rcs5 | 1.007987 .0052211 1.54 0.125 .9978051 1.018272
_rcs6 | 1.005434 .0041774 1.30 0.192 .9972796 1.013655
_rcs7 | 1.004482 .0028307 1.59 0.113 .9989492 1.010045
_rcs8 | 1.004221 .001755 2.41 0.016 1.000787 1.007666
_rcs9 | 1.003162 .0014297 2.22 0.027 1.000364 1.005968
_rcs10 | 1.001954 .0012168 1.61 0.108 .9995714 1.004341
_rcs_tr_outcome1 | .9194192 .0205321 -3.76 0.000 .8800449 .9605551
_rcs_tr_outcome2 | 1.003786 .0186036 0.20 0.838 .9679781 1.040919
_rcs_tr_outcome3 | .9964478 .0115342 -0.31 0.759 .9740956 1.019313
_rcs_tr_outcome4 | 1.000847 .0080949 0.10 0.917 .9851058 1.016839
_cons | .1619605 .0039236 -75.14 0.000 .1544501 .1698361
------------------------------------------------------------------------------------
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 = -43373.944
Iteration 1: log pseudolikelihood = -43357.629
Iteration 2: log pseudolikelihood = -43357.58
Iteration 3: log pseudolikelihood = -43357.58
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43357.58 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.4486 .0379074 14.16 0.000 1.376176 1.524836
_rcs1 | 2.615423 .0536136 46.90 0.000 2.512426 2.722644
_rcs2 | 1.09037 .0173491 5.44 0.000 1.056891 1.124909
_rcs3 | 1.038823 .0115508 3.43 0.001 1.016429 1.061711
_rcs4 | 1.013347 .0065107 2.06 0.039 1.000666 1.026188
_rcs5 | 1.006197 .0059844 1.04 0.299 .994536 1.017995
_rcs6 | 1.00499 .0040282 1.24 0.214 .997126 1.012916
_rcs7 | 1.005018 .0038397 1.31 0.190 .9975202 1.012572
_rcs8 | 1.004688 .0028859 1.63 0.103 .9990474 1.01036
_rcs9 | 1.003287 .0016164 2.04 0.042 1.000124 1.00646
_rcs10 | 1.00195 .0012157 1.61 0.108 .99957 1.004335
_rcs_tr_outcome1 | .9196064 .0204078 -3.78 0.000 .8804653 .9604874
_rcs_tr_outcome2 | 1.006254 .0177124 0.35 0.723 .9721306 1.041576
_rcs_tr_outcome3 | .9924429 .0121445 -0.62 0.535 .9689233 1.016533
_rcs_tr_outcome4 | 1.004014 .0090679 0.44 0.657 .9863978 1.021945
_rcs_tr_outcome5 | .9989096 .0058053 -0.19 0.851 .987596 1.010353
_cons | .1619498 .0039215 -75.18 0.000 .1544433 .1698211
------------------------------------------------------------------------------------
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 = -43373.695
Iteration 1: log pseudolikelihood = -43354.96
Iteration 2: log pseudolikelihood = -43354.872
Iteration 3: log pseudolikelihood = -43354.872
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43354.872 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448786 .0378629 14.19 0.000 1.376445 1.52493
_rcs1 | 2.615323 .053401 47.08 0.000 2.512725 2.72211
_rcs2 | 1.088617 .0162098 5.70 0.000 1.057306 1.120856
_rcs3 | 1.044254 .0124599 3.63 0.000 1.020116 1.068963
_rcs4 | 1.009242 .0070914 1.31 0.190 .9954385 1.023237
_rcs5 | 1.005263 .006016 0.88 0.380 .993541 1.017124
_rcs6 | 1.008082 .0045109 1.80 0.072 .9992798 1.016963
_rcs7 | 1.005114 .0037133 1.38 0.167 .9978621 1.012418
_rcs8 | 1.001703 .0037636 0.45 0.651 .9943532 1.009106
_rcs9 | 1.00148 .0023522 0.63 0.529 .9968801 1.006101
_rcs10 | 1.00183 .001237 1.48 0.139 .9994084 1.004257
_rcs_tr_outcome1 | .9197217 .0203481 -3.78 0.000 .8806925 .9604806
_rcs_tr_outcome2 | 1.008275 .0169063 0.49 0.623 .9756775 1.041961
_rcs_tr_outcome3 | .9871077 .0125936 -1.02 0.309 .9627308 1.012102
_rcs_tr_outcome4 | 1.008848 .0096855 0.92 0.359 .9900424 1.028011
_rcs_tr_outcome5 | .9957365 .006015 -0.71 0.479 .9840168 1.007596
_rcs_tr_outcome6 | 1.005063 .0049163 1.03 0.302 .9954736 1.014745
_cons | .1619227 .0039151 -75.30 0.000 .1544282 .169781
------------------------------------------------------------------------------------
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 = -43373.841
Iteration 1: log pseudolikelihood = -43355.574
Iteration 2: log pseudolikelihood = -43355.494
Iteration 3: log pseudolikelihood = -43355.494
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -43355.494 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448822 .0378798 14.18 0.000 1.376449 1.525
_rcs1 | 2.615607 .0535178 46.99 0.000 2.51279 2.722632
_rcs2 | 1.088702 .0159997 5.78 0.000 1.057791 1.120517
_rcs3 | 1.044705 .0127279 3.59 0.000 1.020054 1.069951
_rcs4 | 1.008211 .0080323 1.03 0.305 .9925906 1.024078
_rcs5 | 1.006314 .0058281 1.09 0.277 .9949553 1.017802
_rcs6 | 1.00769 .0046348 1.67 0.096 .9986471 1.016815
_rcs7 | 1.00488 .003906 1.25 0.210 .9972531 1.012565
_rcs8 | 1.002428 .0036355 0.67 0.504 .9953282 1.009579
_rcs9 | 1.001082 .0031395 0.34 0.730 .994948 1.007255
_rcs10 | 1.001459 .0014082 1.04 0.300 .9987025 1.004223
_rcs_tr_outcome1 | .919618 .0203799 -3.78 0.000 .8805292 .960442
_rcs_tr_outcome2 | 1.008461 .0166861 0.51 0.611 .9762816 1.041702
_rcs_tr_outcome3 | .9862826 .0127903 -1.07 0.287 .9615299 1.011672
_rcs_tr_outcome4 | 1.008943 .009884 0.91 0.363 .9897551 1.028502
_rcs_tr_outcome5 | .99653 .0062976 -0.55 0.582 .984263 1.00895
_rcs_tr_outcome6 | 1.001462 .0049736 0.29 0.769 .9917616 1.011258
_rcs_tr_outcome7 | 1.003557 .0040944 0.87 0.384 .9955642 1.011614
_cons | .1619191 .0039167 -75.27 0.000 .1544217 .1697805
------------------------------------------------------------------------------------
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 m
> zone3 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_nac_corr cohab2 coh
> ab3 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 = -44581.624
Iteration 1: log pseudolikelihood = -44438.858
Iteration 2: log pseudolikelihood = -44437.742
Iteration 3: log pseudolikelihood = -44437.742
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 46,864
Wald chi2(1) = 169.07
Log pseudolikelihood = -44437.742 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.386407 .0348356 13.00 0.000 1.319785 1.456393
_cons | .0704305 .0016204 -115.32 0.000 .0673252 .0736791
------------------------------------------------------------------------------
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 = -44581.624
Iteration 1: log pseudolikelihood = -43716.756
Iteration 2: log pseudolikelihood = -43706.292
Iteration 3: log pseudolikelihood = -43706.29
Fitting full model:
Iteration 0: log pseudolikelihood = -43706.29
Iteration 1: log pseudolikelihood = -43562.656
Iteration 2: log pseudolikelihood = -43561.527
Iteration 3: log pseudolikelihood = -43561.527
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 46,864
Wald chi2(1) = 185.04
Log pseudolikelihood = -43561.527 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.387764 .0334307 13.60 0.000 1.323764 1.454858
_cons | .1008517 .0023174 -99.84 0.000 .0964105 .1054975
-------------+----------------------------------------------------------------
/ln_p | -.3073712 .0075497 -40.71 0.000 -.3221683 -.2925741
-------------+----------------------------------------------------------------
p | .7353776 .0055519 .7245763 .7463399
1/p | 1.359846 .0102664 1.339872 1.380117
------------------------------------------------------------------------------
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 = -44580.281
Iteration 1: log pseudolikelihood = -43632.46
Iteration 2: log pseudolikelihood = -43587.871
Iteration 3: log pseudolikelihood = -43587.756
Iteration 4: log pseudolikelihood = -43587.756
Fitting full model:
Iteration 0: log pseudolikelihood = -43587.756
Iteration 1: log pseudolikelihood = -43445.296
Iteration 2: log pseudolikelihood = -43444.184
Iteration 3: log pseudolikelihood = -43444.184
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 46,864
Wald chi2(1) = 187.28
Log pseudolikelihood = -43444.184 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.385928 .0330527 13.68 0.000 1.322637 1.452248
_cons | .1150119 .0028866 -86.17 0.000 .1094913 .1208109
-------------+----------------------------------------------------------------
/gamma | -.2429196 .0073671 -32.97 0.000 -.2573589 -.2284804
------------------------------------------------------------------------------
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 = -59839.317
Iteration 1: log pseudolikelihood = -43608.027
Iteration 2: log pseudolikelihood = -43557.955
Iteration 3: log pseudolikelihood = -43557.852
Iteration 4: log pseudolikelihood = -43557.852
Fitting full model:
Iteration 0: log pseudolikelihood = -43557.852
Iteration 1: log pseudolikelihood = -43400.462
Iteration 2: log pseudolikelihood = -43397.561
Iteration 3: log pseudolikelihood = -43397.559
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 46,864
Wald chi2(1) = 216.14
Log pseudolikelihood = -43397.559 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5875297 .0212535 -14.70 0.000 .547316 .6306982
_cons | 19.68705 .7372387 79.58 0.000 18.29384 21.18636
-------------+----------------------------------------------------------------
/lnsigma | .8421764 .0083102 101.34 0.000 .8258888 .858464
-------------+----------------------------------------------------------------
sigma | 2.321414 .0192913 2.28391 2.359534
------------------------------------------------------------------------------
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 = -44117.109
Iteration 1: log pseudolikelihood = -43594.931
Iteration 2: log pseudolikelihood = -43588.497
Iteration 3: log pseudolikelihood = -43588.495
Fitting full model:
Iteration 0: log pseudolikelihood = -43588.495
Iteration 1: log pseudolikelihood = -43437.41
Iteration 2: log pseudolikelihood = -43434.587
Iteration 3: log pseudolikelihood = -43434.585
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 46,864
Wald chi2(1) = 199.92
Log pseudolikelihood = -43434.585 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .6167004 .0210828 -14.14 0.000 .5767328 .6594378
_cons | 15.66913 .5317522 81.08 0.000 14.66082 16.74678
-------------+----------------------------------------------------------------
/lngamma | .2018459 .0078487 25.72 0.000 .1864627 .2172291
-------------+----------------------------------------------------------------
gamma | 1.223659 .0096042 1.20498 1.242629
------------------------------------------------------------------------------
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 | 16,157 . -43550.55 4 87109.09 87139.85
m_stipw_no~2 | 16,157 . -43418.68 5 86847.36 86885.81
m_stipw_no~3 | 16,157 . -43410.47 6 86832.94 86879.08
m_stipw_no~4 | 16,157 . -43409.23 7 86832.47 86886.3
m_stipw_no~5 | 16,157 . -43407.8 8 86831.6 86893.12
m_stipw_no~6 | 16,157 . -43405.46 9 86828.92 86898.13
m_stipw_no~7 | 16,157 . -43405.11 10 86830.23 86907.13
m_stipw_no~1 | 16,157 . -43376.3 5 86762.59 86801.05
m_stipw_no~2 | 16,157 . -43376.27 6 86764.55 86810.69
m_stipw_no~3 | 16,157 . -43368.33 7 86750.65 86804.48
m_stipw_no~4 | 16,157 . -43366.83 8 86749.65 86811.17
m_stipw_no~5 | 16,157 . -43365.29 9 86748.59 86817.8
m_stipw_no~6 | 16,157 . -43363.05 10 86746.11 86823.01
m_stipw_no~7 | 16,157 . -43362.7 11 86747.39 86831.98
m_stipw_no~1 | 16,157 . -43364.49 6 86740.98 86787.12
m_stipw_no~2 | 16,157 . -43364.49 7 86742.98 86796.81
m_stipw_no~3 | 16,157 . -43364.44 8 86744.89 86806.41
m_stipw_no~4 | 16,157 . -43363.81 9 86745.62 86814.83
m_stipw_no~5 | 16,157 . -43361.92 10 86743.83 86820.73
m_stipw_no~6 | 16,157 . -43359.43 11 86740.87 86825.46
m_stipw_no~7 | 16,157 . -43359.07 12 86742.15 86834.43
m_stipw_no~1 | 16,157 . -43362.88 7 86739.76 86793.59
m_stipw_no~2 | 16,157 . -43362.88 8 86741.76 86803.28
m_stipw_no~3 | 16,157 . -43362.75 9 86743.5 86812.71
m_stipw_no~4 | 16,157 . -43362.81 10 86745.62 86822.52
m_stipw_no~5 | 16,157 . -43361.6 11 86745.19 86829.78
m_stipw_no~6 | 16,157 . -43359.15 12 86742.3 86834.58
m_stipw_no~7 | 16,157 . -43358.73 13 86743.47 86843.44
m_stipw_no~1 | 16,157 . -43360.67 8 86737.35 86798.87
m_stipw_no~2 | 16,157 . -43360.67 9 86739.34 86808.55
m_stipw_no~3 | 16,157 . -43360.63 10 86741.27 86818.17
m_stipw_no~4 | 16,157 . -43360.6 11 86743.2 86827.79
m_stipw_no~5 | 16,157 . -43360.24 12 86744.47 86836.75
m_stipw_no~6 | 16,157 . -43358.47 13 86742.95 86842.92
m_stipw_no~7 | 16,157 . -43357.86 14 86743.72 86851.38
m_stipw_no~1 | 16,157 . -43359.53 9 86737.05 86806.26
m_stipw_no~2 | 16,157 . -43359.52 10 86739.05 86815.95
m_stipw_no~3 | 16,157 . -43359.49 11 86740.99 86825.58
m_stipw_no~4 | 16,157 . -43359.44 12 86742.89 86835.17
m_stipw_no~5 | 16,157 . -43358.92 13 86743.83 86843.81
m_stipw_no~6 | 16,157 . -43356.44 14 86740.88 86848.55
m_stipw_no~7 | 16,157 . -43357.01 15 86744.02 86859.37
m_stipw_no~1 | 16,157 . -43358.69 10 86737.38 86814.28
m_stipw_no~2 | 16,157 . -43358.69 11 86739.38 86823.97
m_stipw_no~3 | 16,157 . -43358.66 12 86741.33 86833.61
m_stipw_no~4 | 16,157 . -43358.62 13 86743.24 86843.22
m_stipw_no~5 | 16,157 . -43358.1 14 86744.21 86851.87
m_stipw_no~6 | 16,157 . -43356.2 15 86742.4 86857.75
m_stipw_no~7 | 16,157 . -43356.14 16 86744.29 86867.33
m_stipw_no~1 | 16,157 . -43358.06 11 86738.12 86822.71
m_stipw_no~2 | 16,157 . -43358.06 12 86740.11 86832.4
m_stipw_no~3 | 16,157 . -43358.03 13 86742.07 86842.04
m_stipw_no~4 | 16,157 . -43357.99 14 86743.98 86851.64
m_stipw_no~5 | 16,157 . -43357.68 15 86745.37 86860.72
m_stipw_no~6 | 16,157 . -43355.38 16 86742.77 86865.81
m_stipw_no~7 | 16,157 . -43355.76 17 86745.52 86876.25
m_stipw_no~1 | 16,157 . -43357.61 12 86739.22 86831.5
m_stipw_no~2 | 16,157 . -43357.61 13 86741.21 86841.18
m_stipw_no~3 | 16,157 . -43357.58 14 86743.16 86850.83
m_stipw_no~4 | 16,157 . -43357.54 15 86745.08 86860.43
m_stipw_no~5 | 16,157 . -43357.16 16 86746.31 86869.36
m_stipw_no~6 | 16,157 . -43354.31 17 86742.62 86873.35
m_stipw_no~7 | 16,157 . -43355.18 18 86746.36 86884.78
m_stipw_no~1 | 16,157 . -43358 13 86742.01 86841.98
m_stipw_no~2 | 16,157 . -43358 14 86744 86851.66
m_stipw_no~3 | 16,157 . -43357.98 15 86745.95 86861.31
m_stipw_no~4 | 16,157 . -43357.93 16 86747.86 86870.9
m_stipw_no~5 | 16,157 . -43357.58 17 86749.16 86879.89
m_stipw_no~6 | 16,157 . -43354.87 18 86745.74 86884.16
m_stipw_no~7 | 16,157 . -43355.49 19 86748.99 86895.1
m_stipw_no~p | 16,157 -44581.62 -44437.74 2 88879.48 88894.86
m_stipw_no~i | 16,157 -43706.29 -43561.53 3 87129.05 87152.12
m_stipw_no~m | 16,157 -43587.76 -43444.18 3 86894.37 86917.44
m_stipw_no~n | 16,157 -43557.85 -43397.56 3 86801.12 86824.19
m_stipw_no~g | 16,157 -43588.49 -43434.59 3 86875.17 86898.24
-----------------------------------------------------------------------------
. //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.csv", replace
(output written to testreg_aic_bic_mrl_23_2.csv)
. esttab matrix(stats_2) using "testreg_aic_bic_mrl_23_2.html", replace
(output written to testreg_aic_bic_mrl_23_2.html)
.
. *m_stipw_nostag_rp5_tvcdf1 m_stipw_nostag_rp5_tvcdf1 confirmed
| stats_2 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m_stipw_nostag_rp6_tvcdf1 | 16157 | . | -43359.53 | 9 | 86737.05 | 86806.26 |
| m_stipw_nostag_rp5_tvcdf1 | 16157 | . | -43360.67 | 8 | 86737.35 | 86798.87 |
| m_stipw_nostag_rp7_tvcdf1 | 16157 | . | -43358.69 | 10 | 86737.38 | 86814.28 |
| m_stipw_nostag_rp8_tvcdf1 | 16157 | . | -43358.06 | 11 | 86738.12 | 86822.71 |
| m_stipw_nostag_rp6_tvcdf2 | 16157 | . | -43359.52 | 10 | 86739.05 | 86815.95 |
| m_stipw_nostag_rp9_tvcdf1 | 16157 | . | -43357.61 | 12 | 86739.22 | 86831.5 |
| m_stipw_nostag_rp5_tvcdf2 | 16157 | . | -43360.67 | 9 | 86739.34 | 86808.55 |
| m_stipw_nostag_rp7_tvcdf2 | 16157 | . | -43358.69 | 11 | 86739.38 | 86823.97 |
| m_stipw_nostag_rp4_tvcdf1 | 16157 | . | -43362.88 | 7 | 86739.76 | 86793.59 |
| m_stipw_nostag_rp8_tvcdf2 | 16157 | . | -43358.06 | 12 | 86740.11 | 86832.4 |
| m_stipw_nostag_rp3_tvcdf6 | 16157 | . | -43359.43 | 11 | 86740.87 | 86825.46 |
| m_stipw_nostag_rp6_tvcdf6 | 16157 | . | -43356.44 | 14 | 86740.88 | 86848.55 |
| m_stipw_nostag_rp3_tvcdf1 | 16157 | . | -43364.49 | 6 | 86740.98 | 86787.12 |
| m_stipw_nostag_rp6_tvcdf3 | 16157 | . | -43359.49 | 11 | 86740.99 | 86825.58 |
| m_stipw_nostag_rp9_tvcdf2 | 16157 | . | -43357.61 | 13 | 86741.21 | 86841.18 |
| m_stipw_nostag_rp5_tvcdf3 | 16157 | . | -43360.63 | 10 | 86741.27 | 86818.17 |
| m_stipw_nostag_rp7_tvcdf3 | 16157 | . | -43358.66 | 12 | 86741.33 | 86833.61 |
| m_stipw_nostag_rp4_tvcdf2 | 16157 | . | -43362.88 | 8 | 86741.76 | 86803.28 |
| m_stipw_nostag_rp10_tvcdf1 | 16157 | . | -43358 | 13 | 86742.01 | 86841.98 |
| m_stipw_nostag_rp8_tvcdf3 | 16157 | . | -43358.03 | 13 | 86742.07 | 86842.04 |
| m_stipw_nostag_rp3_tvcdf7 | 16157 | . | -43359.07 | 12 | 86742.15 | 86834.43 |
| m_stipw_nostag_rp4_tvcdf6 | 16157 | . | -43359.15 | 12 | 86742.3 | 86834.58 |
| m_stipw_nostag_rp7_tvcdf6 | 16157 | . | -43356.2 | 15 | 86742.4 | 86857.75 |
| m_stipw_nostag_rp9_tvcdf6 | 16157 | . | -43354.31 | 17 | 86742.62 | 86873.35 |
| m_stipw_nostag_rp8_tvcdf6 | 16157 | . | -43355.38 | 16 | 86742.77 | 86865.81 |
| m_stipw_nostag_rp6_tvcdf4 | 16157 | . | -43359.44 | 12 | 86742.89 | 86835.17 |
| m_stipw_nostag_rp5_tvcdf6 | 16157 | . | -43358.47 | 13 | 86742.95 | 86842.92 |
| m_stipw_nostag_rp3_tvcdf2 | 16157 | . | -43364.49 | 7 | 86742.98 | 86796.81 |
| m_stipw_nostag_rp9_tvcdf3 | 16157 | . | -43357.58 | 14 | 86743.16 | 86850.83 |
| m_stipw_nostag_rp5_tvcdf4 | 16157 | . | -43360.6 | 11 | 86743.2 | 86827.79 |
| m_stipw_nostag_rp7_tvcdf4 | 16157 | . | -43358.62 | 13 | 86743.24 | 86843.22 |
| m_stipw_nostag_rp4_tvcdf7 | 16157 | . | -43358.73 | 13 | 86743.47 | 86843.44 |
| m_stipw_nostag_rp4_tvcdf3 | 16157 | . | -43362.75 | 9 | 86743.5 | 86812.71 |
| m_stipw_nostag_rp5_tvcdf7 | 16157 | . | -43357.86 | 14 | 86743.72 | 86851.38 |
| m_stipw_nostag_rp3_tvcdf5 | 16157 | . | -43361.92 | 10 | 86743.83 | 86820.73 |
| m_stipw_nostag_rp6_tvcdf5 | 16157 | . | -43358.92 | 13 | 86743.83 | 86843.81 |
| m_stipw_nostag_rp8_tvcdf4 | 16157 | . | -43357.99 | 14 | 86743.98 | 86851.64 |
| m_stipw_nostag_rp10_tvcdf2 | 16157 | . | -43358 | 14 | 86744 | 86851.66 |
| m_stipw_nostag_rp6_tvcdf7 | 16157 | . | -43357.01 | 15 | 86744.02 | 86859.37 |
| m_stipw_nostag_rp7_tvcdf5 | 16157 | . | -43358.1 | 14 | 86744.21 | 86851.87 |
| m_stipw_nostag_rp7_tvcdf7 | 16157 | . | -43356.14 | 16 | 86744.29 | 86867.33 |
| m_stipw_nostag_rp5_tvcdf5 | 16157 | . | -43360.24 | 12 | 86744.47 | 86836.75 |
| m_stipw_nostag_rp3_tvcdf3 | 16157 | . | -43364.44 | 8 | 86744.89 | 86806.41 |
| m_stipw_nostag_rp9_tvcdf4 | 16157 | . | -43357.54 | 15 | 86745.08 | 86860.43 |
| m_stipw_nostag_rp4_tvcdf5 | 16157 | . | -43361.6 | 11 | 86745.19 | 86829.78 |
| m_stipw_nostag_rp8_tvcdf5 | 16157 | . | -43357.68 | 15 | 86745.37 | 86860.72 |
| m_stipw_nostag_rp8_tvcdf7 | 16157 | . | -43355.76 | 17 | 86745.52 | 86876.25 |
| m_stipw_nostag_rp4_tvcdf4 | 16157 | . | -43362.81 | 10 | 86745.62 | 86822.52 |
| m_stipw_nostag_rp3_tvcdf4 | 16157 | . | -43363.81 | 9 | 86745.62 | 86814.83 |
| m_stipw_nostag_rp10_tvcdf6 | 16157 | . | -43354.87 | 18 | 86745.74 | 86884.16 |
| m_stipw_nostag_rp10_tvcdf3 | 16157 | . | -43357.98 | 15 | 86745.95 | 86861.31 |
| m_stipw_nostag_rp2_tvcdf6 | 16157 | . | -43363.05 | 10 | 86746.11 | 86823.01 |
| m_stipw_nostag_rp9_tvcdf5 | 16157 | . | -43357.16 | 16 | 86746.31 | 86869.36 |
| m_stipw_nostag_rp9_tvcdf7 | 16157 | . | -43355.18 | 18 | 86746.36 | 86884.78 |
| m_stipw_nostag_rp2_tvcdf7 | 16157 | . | -43362.7 | 11 | 86747.39 | 86831.98 |
| m_stipw_nostag_rp10_tvcdf4 | 16157 | . | -43357.93 | 16 | 86747.86 | 86870.9 |
| m_stipw_nostag_rp2_tvcdf5 | 16157 | . | -43365.29 | 9 | 86748.59 | 86817.8 |
| m_stipw_nostag_rp10_tvcdf7 | 16157 | . | -43355.49 | 19 | 86748.99 | 86895.1 |
| m_stipw_nostag_rp10_tvcdf5 | 16157 | . | -43357.58 | 17 | 86749.16 | 86879.89 |
| m_stipw_nostag_rp2_tvcdf4 | 16157 | . | -43366.83 | 8 | 86749.65 | 86811.17 |
| m_stipw_nostag_rp2_tvcdf3 | 16157 | . | -43368.33 | 7 | 86750.65 | 86804.48 |
| m_stipw_nostag_rp2_tvcdf1 | 16157 | . | -43376.3 | 5 | 86762.59 | 86801.05 |
| m_stipw_nostag_rp2_tvcdf2 | 16157 | . | -43376.27 | 6 | 86764.55 | 86810.69 |
| m_stipw_nostag_logn | 16157 | -43557.85 | -43397.56 | 3 | 86801.12 | 86824.19 |
| m_stipw_nostag_rp1_tvcdf6 | 16157 | . | -43405.46 | 9 | 86828.92 | 86898.13 |
| m_stipw_nostag_rp1_tvcdf7 | 16157 | . | -43405.11 | 10 | 86830.23 | 86907.13 |
| m_stipw_nostag_rp1_tvcdf5 | 16157 | . | -43407.8 | 8 | 86831.6 | 86893.12 |
| m_stipw_nostag_rp1_tvcdf4 | 16157 | . | -43409.23 | 7 | 86832.47 | 86886.3 |
| m_stipw_nostag_rp1_tvcdf3 | 16157 | . | -43410.47 | 6 | 86832.94 | 86879.08 |
| m_stipw_nostag_rp1_tvcdf2 | 16157 | . | -43418.68 | 5 | 86847.36 | 86885.81 |
| m_stipw_nostag_llog | 16157 | -43588.49 | -43434.59 | 3 | 86875.17 | 86898.24 |
| m_stipw_nostag_gom | 16157 | -43587.76 | -43444.18 | 3 | 86894.37 | 86917.44 |
| m_stipw_nostag_rp1_tvcdf1 | 16157 | . | -43550.55 | 4 | 87109.09 | 87139.85 |
| m_stipw_nostag_wei | 16157 | -43706.29 | -43561.53 | 3 | 87129.05 | 87152.12 |
| m_stipw_nostag_exp | 16157 | -44581.62 | -44437.74 | 2 | 88879.48 | 88894.86 |
. estimates replay m_stipw_nostag_rp4_tvcdf1, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_stipw_nostag_rp4_tvcdf1
------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -43362.879 Number of obs = 46,864
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.448677 .0379201 14.16 0.000 1.376229 1.524938
_rcs1 | 2.616854 .0495581 50.80 0.000 2.521502 2.715811
_rcs2 | 1.100584 .0075544 13.96 0.000 1.085877 1.115491
_rcs3 | 1.026096 .0045617 5.79 0.000 1.017195 1.035076
_rcs4 | 1.007958 .0030341 2.63 0.008 1.002029 1.013922
_rcs_tr_outcome1 | .9192227 .0185527 -4.17 0.000 .8835698 .9563142
_cons | .1619498 .0039233 -75.15 0.000 .15444 .1698248
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_stipw_nostag_rp4_tvcdf1
(results m_stipw_nostag_rp4_tvcdf1 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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_a.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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdiff_rmst_a.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_pr
> in3 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 mzone
> 2 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_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(mes
> timation) 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 = -28936.9
Iteration 1: log pseudolikelihood = -28698.356
Iteration 2: log pseudolikelihood = -28694.422
Iteration 3: log pseudolikelihood = -28694.419
Iteration 4: log pseudolikelihood = -28694.419
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28694.419 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.282743 .0954805 3.35 0.001 1.108615 1.484221
_rcs1 | 2.406017 .0923953 22.86 0.000 2.231573 2.594098
_rcs_tr_outcome1 | .9341192 .037523 -1.70 0.090 .8633962 1.010635
_cons | .1860564 .0134799 -23.21 0.000 .1614264 .2144443
------------------------------------------------------------------------------------
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 = -28686.283
Iteration 1: log pseudolikelihood = -28625.545
Iteration 2: log pseudolikelihood = -28625.343
Iteration 3: log pseudolikelihood = -28625.343
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28625.343 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291763 .0961926 3.44 0.001 1.116341 1.49475
_rcs1 | 2.406017 .0923953 22.86 0.000 2.231573 2.594098
_rcs_tr_outcome1 | .9763219 .0401354 -0.58 0.560 .9007436 1.058242
_rcs_tr_outcome2 | 1.116166 .0134489 9.12 0.000 1.090115 1.142839
_cons | .1860564 .0134799 -23.21 0.000 .1614264 .2144443
------------------------------------------------------------------------------------
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 = -28680.909
Iteration 1: log pseudolikelihood = -28622.739
Iteration 2: log pseudolikelihood = -28622.535
Iteration 3: log pseudolikelihood = -28622.535
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28622.535 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291811 .0961974 3.44 0.001 1.116381 1.494808
_rcs1 | 2.406017 .0923953 22.86 0.000 2.231573 2.594098
_rcs_tr_outcome1 | .9761781 .0400049 -0.59 0.556 .9008362 1.057821
_rcs_tr_outcome2 | 1.105768 .0129842 8.56 0.000 1.08061 1.131511
_rcs_tr_outcome3 | 1.021953 .0082017 2.71 0.007 1.006004 1.038155
_cons | .1860564 .0134799 -23.21 0.000 .1614264 .2144443
------------------------------------------------------------------------------------
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 = -28699.22
Iteration 1: log pseudolikelihood = -28621.96
Iteration 2: log pseudolikelihood = -28621.455
Iteration 3: log pseudolikelihood = -28621.455
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28621.455 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.29191 .096205 3.44 0.001 1.116466 1.494924
_rcs1 | 2.406017 .0923953 22.86 0.000 2.231573 2.594098
_rcs_tr_outcome1 | .9764606 .0400255 -0.58 0.561 .9010806 1.058147
_rcs_tr_outcome2 | 1.10536 .0132297 8.37 0.000 1.079732 1.131596
_rcs_tr_outcome3 | 1.022892 .0082907 2.79 0.005 1.006771 1.039271
_rcs_tr_outcome4 | 1.008323 .0056784 1.47 0.141 .997255 1.019514
_cons | .1860564 .0134799 -23.21 0.000 .1614264 .2144443
------------------------------------------------------------------------------------
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 = -28679.115
Iteration 1: log pseudolikelihood = -28617.909
Iteration 2: log pseudolikelihood = -28617.627
Iteration 3: log pseudolikelihood = -28617.627
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28617.627 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291859 .0962022 3.44 0.001 1.11642 1.494866
_rcs1 | 2.406017 .0923953 22.86 0.000 2.231573 2.594098
_rcs_tr_outcome1 | .9762202 .03997 -0.59 0.557 .9009413 1.057789
_rcs_tr_outcome2 | 1.101831 .0122946 8.69 0.000 1.077995 1.126193
_rcs_tr_outcome3 | 1.028254 .0083597 3.43 0.001 1.012 1.04477
_rcs_tr_outcome4 | 1.003605 .0057289 0.63 0.528 .992439 1.014896
_rcs_tr_outcome5 | 1.010405 .0042373 2.47 0.014 1.002135 1.018745
_cons | .1860564 .0134799 -23.21 0.000 .1614264 .2144443
------------------------------------------------------------------------------------
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 = -28678.468
Iteration 1: log pseudolikelihood = -28617.938
Iteration 2: log pseudolikelihood = -28617.691
Iteration 3: log pseudolikelihood = -28617.691
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28617.691 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291747 .096193 3.44 0.001 1.116325 1.494735
_rcs1 | 2.406017 .0923953 22.86 0.000 2.231573 2.594098
_rcs_tr_outcome1 | .9764088 .039976 -0.58 0.560 .9011185 1.05799
_rcs_tr_outcome2 | 1.10102 .0118962 8.91 0.000 1.077949 1.124584
_rcs_tr_outcome3 | 1.031726 .0084855 3.80 0.000 1.015228 1.048492
_rcs_tr_outcome4 | 1.00216 .0060692 0.36 0.722 .9903347 1.014126
_rcs_tr_outcome5 | 1.010623 .0043872 2.43 0.015 1.002061 1.019258
_rcs_tr_outcome6 | 1.002509 .0035092 0.72 0.474 .9956549 1.009411
_cons | .1860564 .0134799 -23.21 0.000 .1614264 .2144443
------------------------------------------------------------------------------------
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 = -28689.325
Iteration 1: log pseudolikelihood = -28615.477
Iteration 2: log pseudolikelihood = -28614.984
Iteration 3: log pseudolikelihood = -28614.984
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28614.984 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291787 .0961956 3.44 0.001 1.11636 1.494781
_rcs1 | 2.406017 .0923953 22.86 0.000 2.231573 2.594098
_rcs_tr_outcome1 | .9763445 .0399802 -0.58 0.559 .9010467 1.057935
_rcs_tr_outcome2 | 1.101048 .0120905 8.77 0.000 1.077604 1.125002
_rcs_tr_outcome3 | 1.031795 .0086654 3.73 0.000 1.014951 1.04892
_rcs_tr_outcome4 | 1.004483 .0062567 0.72 0.473 .9922943 1.016821
_rcs_tr_outcome5 | 1.007809 .0044546 1.76 0.078 .9991161 1.016578
_rcs_tr_outcome6 | 1.007723 .003602 2.15 0.031 1.000688 1.014808
_rcs_tr_outcome7 | .9971074 .0030951 -0.93 0.351 .9910595 1.003192
_cons | .1860564 .0134799 -23.21 0.000 .1614264 .2144443
------------------------------------------------------------------------------------
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 = -28542.287
Iteration 1: log pseudolikelihood = -28528.118
Iteration 2: log pseudolikelihood = -28528.083
Iteration 3: log pseudolikelihood = -28528.083
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28528.083 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.292622 .0984114 3.37 0.001 1.11344 1.500639
_rcs1 | 2.551708 .1123534 21.28 0.000 2.340733 2.781698
_rcs2 | 1.139091 .0229708 6.46 0.000 1.094948 1.185015
_rcs_tr_outcome1 | .9316319 .0454272 -1.45 0.146 .8467184 1.025061
_cons | .185728 .0136648 -22.88 0.000 .160787 .2145378
------------------------------------------------------------------------------------
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 = -28542.815
Iteration 1: log pseudolikelihood = -28523.4
Iteration 2: log pseudolikelihood = -28523.304
Iteration 3: log pseudolikelihood = -28523.304
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28523.304 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298288 .096882 3.50 0.000 1.121636 1.502762
_rcs1 | 2.595439 .1253408 19.75 0.000 2.361044 2.853105
_rcs2 | 1.170075 .0514102 3.57 0.000 1.073529 1.275303
_rcs_tr_outcome1 | .9050674 .0456886 -1.98 0.048 .8198067 .9991952
_rcs_tr_outcome2 | .953927 .0434524 -1.04 0.300 .872453 1.043009
_cons | .1851212 .0134418 -23.23 0.000 .1605646 .2134335
------------------------------------------------------------------------------------
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 = -28537.191
Iteration 1: log pseudolikelihood = -28521.031
Iteration 2: log pseudolikelihood = -28520.936
Iteration 3: log pseudolikelihood = -28520.936
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28520.936 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298174 .0968797 3.50 0.000 1.121527 1.502644
_rcs1 | 2.594694 .1251152 19.77 0.000 2.360704 2.851876
_rcs2 | 1.169565 .0513216 3.57 0.000 1.073181 1.274606
_rcs_tr_outcome1 | .9051608 .0455311 -1.98 0.048 .8201795 .9989473
_rcs_tr_outcome2 | .9453863 .0429162 -1.24 0.216 .8649054 1.033356
_rcs_tr_outcome3 | 1.011626 .0086186 1.36 0.175 .9948738 1.02866
_cons | .1851323 .0134421 -23.23 0.000 .160575 .2134451
------------------------------------------------------------------------------------
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 = -28555.752
Iteration 1: log pseudolikelihood = -28519.815
Iteration 2: log pseudolikelihood = -28519.416
Iteration 3: log pseudolikelihood = -28519.416
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28519.416 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298437 .0968945 3.50 0.000 1.121762 1.502937
_rcs1 | 2.595439 .1253408 19.75 0.000 2.361044 2.853105
_rcs2 | 1.170075 .0514102 3.57 0.000 1.073529 1.275303
_rcs_tr_outcome1 | .9051959 .0456072 -1.98 0.048 .8200793 .9991469
_rcs_tr_outcome2 | .9453805 .0428507 -1.24 0.215 .8650172 1.03321
_rcs_tr_outcome3 | 1.007549 .0092143 0.82 0.411 .9896505 1.025772
_rcs_tr_outcome4 | 1.008323 .0056784 1.47 0.141 .997255 1.019514
_cons | .1851212 .0134418 -23.23 0.000 .1605646 .2134335
------------------------------------------------------------------------------------
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 = -28535.72
Iteration 1: log pseudolikelihood = -28515.805
Iteration 2: log pseudolikelihood = -28515.63
Iteration 3: log pseudolikelihood = -28515.63
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28515.63 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298376 .0968907 3.50 0.000 1.121709 1.502869
_rcs1 | 2.595375 .1253222 19.75 0.000 2.361013 2.853001
_rcs2 | 1.170031 .0514045 3.57 0.000 1.073496 1.275247
_rcs_tr_outcome1 | .9049969 .0455577 -1.98 0.047 .819969 .9988418
_rcs_tr_outcome2 | .9426544 .0424631 -1.31 0.190 .8629963 1.029665
_rcs_tr_outcome3 | 1.010327 .0096077 1.08 0.280 .9916703 1.029334
_rcs_tr_outcome4 | 1.002001 .0057337 0.35 0.727 .9908263 1.013303
_rcs_tr_outcome5 | 1.010605 .0042397 2.51 0.012 1.002329 1.018949
_cons | .1851221 .0134418 -23.23 0.000 .1605655 .2134345
------------------------------------------------------------------------------------
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 = -28534.999
Iteration 1: log pseudolikelihood = -28515.793
Iteration 2: log pseudolikelihood = -28515.652
Iteration 3: log pseudolikelihood = -28515.652
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28515.652 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298273 .0968824 3.50 0.000 1.12162 1.502748
_rcs1 | 2.595439 .1253408 19.75 0.000 2.361044 2.853105
_rcs2 | 1.170075 .0514102 3.57 0.000 1.073529 1.275303
_rcs_tr_outcome1 | .9051479 .0455695 -1.98 0.048 .8200985 .9990175
_rcs_tr_outcome2 | .9421553 .0422966 -1.33 0.184 .8627979 1.028812
_rcs_tr_outcome3 | 1.011874 .0099829 1.20 0.231 .9924962 1.031631
_rcs_tr_outcome4 | .998649 .0061254 -0.22 0.826 .9867152 1.010727
_rcs_tr_outcome5 | 1.010623 .0043872 2.43 0.015 1.002061 1.019258
_rcs_tr_outcome6 | 1.002509 .0035092 0.72 0.474 .9956549 1.009411
_cons | .1851212 .0134418 -23.23 0.000 .1605646 .2134335
------------------------------------------------------------------------------------
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 = -28545.858
Iteration 1: log pseudolikelihood = -28513.289
Iteration 2: log pseudolikelihood = -28512.901
Iteration 3: log pseudolikelihood = -28512.901
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28512.901 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298324 .096886 3.50 0.000 1.121665 1.502807
_rcs1 | 2.595504 .125359 19.75 0.000 2.361076 2.853208
_rcs2 | 1.170119 .0514146 3.58 0.000 1.073565 1.275356
_rcs_tr_outcome1 | .9050591 .0455753 -1.98 0.048 .8199996 .9989419
_rcs_tr_outcome2 | .9424517 .0422674 -1.32 0.186 .8631457 1.029044
_rcs_tr_outcome3 | 1.009624 .0104734 0.92 0.356 .9893037 1.030362
_rcs_tr_outcome4 | 1.000005 .0063516 0.00 0.999 .987633 1.012532
_rcs_tr_outcome5 | 1.00733 .0044538 1.65 0.099 .9986385 1.016097
_rcs_tr_outcome6 | 1.007798 .0036028 2.17 0.030 1.000761 1.014884
_rcs_tr_outcome7 | .9970844 .003095 -0.94 0.347 .9910367 1.003169
_cons | .1851202 .0134418 -23.23 0.000 .1605636 .2134326
------------------------------------------------------------------------------------
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 = -28557.486
Iteration 1: log pseudolikelihood = -28527.685
Iteration 2: log pseudolikelihood = -28527.511
Iteration 3: log pseudolikelihood = -28527.511
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28527.511 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.29257 .0984316 3.37 0.001 1.113355 1.500634
_rcs1 | 2.556141 .1103664 21.74 0.000 2.348726 2.781871
_rcs2 | 1.145689 .0265677 5.87 0.000 1.094783 1.198962
_rcs3 | 1.002146 .0140442 0.15 0.878 .9749944 1.030053
_rcs_tr_outcome1 | .9312972 .0452798 -1.46 0.143 .8466478 1.02441
_cons | .1856959 .0136321 -22.93 0.000 .1608106 .2144322
------------------------------------------------------------------------------------
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 = -28557.639
Iteration 1: log pseudolikelihood = -28522.588
Iteration 2: log pseudolikelihood = -28522.357
Iteration 3: log pseudolikelihood = -28522.357
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28522.357 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298339 .0969334 3.50 0.000 1.121599 1.502929
_rcs1 | 2.603097 .1246687 19.98 0.000 2.369868 2.859279
_rcs2 | 1.180041 .0558122 3.50 0.000 1.075568 1.294662
_rcs3 | 1.002642 .0140205 0.19 0.850 .9755354 1.030502
_rcs_tr_outcome1 | .9033809 .0456984 -2.01 0.045 .8181107 .9975386
_rcs_tr_outcome2 | .9514072 .0446953 -1.06 0.289 .867718 1.043168
_cons | .1850631 .0133981 -23.30 0.000 .1605813 .2132773
------------------------------------------------------------------------------------
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 = -28553.826
Iteration 1: log pseudolikelihood = -28508.498
Iteration 2: log pseudolikelihood = -28507.934
Iteration 3: log pseudolikelihood = -28507.934
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28507.934 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.30251 .095786 3.59 0.000 1.127675 1.504451
_rcs1 | 2.642654 .1383303 18.56 0.000 2.384976 2.928172
_rcs2 | 1.224285 .0792492 3.13 0.002 1.078409 1.389894
_rcs3 | .9704782 .030742 -0.95 0.344 .9120574 1.032641
_rcs_tr_outcome1 | .8887661 .0482423 -2.17 0.030 .7990689 .9885321
_rcs_tr_outcome2 | .9031946 .0594029 -1.55 0.122 .7939588 1.027459
_rcs_tr_outcome3 | 1.053041 .0344034 1.58 0.114 .987725 1.122676
_cons | .1845281 .0131913 -23.64 0.000 .1604033 .2122814
------------------------------------------------------------------------------------
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 = -28571.734
Iteration 1: log pseudolikelihood = -28506.93
Iteration 2: log pseudolikelihood = -28505.407
Iteration 3: log pseudolikelihood = -28505.405
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28505.405 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.303112 .09581 3.60 0.000 1.128231 1.505101
_rcs1 | 2.645716 .139549 18.45 0.000 2.385867 2.933864
_rcs2 | 1.227185 .0798888 3.14 0.002 1.080184 1.394192
_rcs3 | .9685777 .0304464 -1.02 0.310 .9107048 1.030128
_rcs_tr_outcome1 | .8877569 .0485492 -2.18 0.029 .7975244 .9881984
_rcs_tr_outcome2 | .8995896 .0600523 -1.59 0.113 .789264 1.025337
_rcs_tr_outcome3 | 1.050367 .0329677 1.57 0.117 .9876989 1.117012
_rcs_tr_outcome4 | 1.014932 .0083207 1.81 0.071 .9987539 1.031372
_cons | .1844764 .0131861 -23.65 0.000 .1603608 .2122185
------------------------------------------------------------------------------------
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 = -28552.096
Iteration 1: log pseudolikelihood = -28503.838
Iteration 2: log pseudolikelihood = -28502.955
Iteration 3: log pseudolikelihood = -28502.955
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28502.955 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.302556 .095797 3.59 0.000 1.127702 1.504521
_rcs1 | 2.642707 .1383285 18.57 0.000 2.385032 2.928221
_rcs2 | 1.224293 .0792042 3.13 0.002 1.078494 1.389801
_rcs3 | .970567 .0307119 -0.94 0.345 .9122014 1.032667
_rcs_tr_outcome1 | .8887805 .0482151 -2.17 0.030 .799131 .9884872
_rcs_tr_outcome2 | .8989851 .0595748 -1.61 0.108 .7894857 1.023672
_rcs_tr_outcome3 | 1.048686 .0311709 1.60 0.110 .9893379 1.111595
_rcs_tr_outcome4 | 1.013921 .0119548 1.17 0.241 .9907588 1.037625
_rcs_tr_outcome5 | 1.010851 .0042782 2.55 0.011 1.0025 1.019271
_cons | .1845278 .0131918 -23.64 0.000 .1604021 .2122823
------------------------------------------------------------------------------------
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 = -28551.385
Iteration 1: log pseudolikelihood = -28503.845
Iteration 2: log pseudolikelihood = -28503.09
Iteration 3: log pseudolikelihood = -28503.09
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28503.09 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.302446 .0957817 3.59 0.000 1.127619 1.504378
_rcs1 | 2.642654 .1383303 18.56 0.000 2.384976 2.928172
_rcs2 | 1.224285 .0792492 3.13 0.002 1.078409 1.389894
_rcs3 | .9704782 .030742 -0.95 0.344 .9120574 1.032641
_rcs_tr_outcome1 | .8889762 .0482276 -2.17 0.030 .7993038 .9887087
_rcs_tr_outcome2 | .8982192 .0596236 -1.62 0.106 .7886418 1.023022
_rcs_tr_outcome3 | 1.047722 .0293324 1.67 0.096 .9917801 1.106819
_rcs_tr_outcome4 | 1.014836 .0148161 1.01 0.313 .9862086 1.044295
_rcs_tr_outcome5 | 1.013455 .0053174 2.55 0.011 1.003086 1.02393
_rcs_tr_outcome6 | 1.002509 .0035092 0.72 0.474 .9956549 1.009411
_cons | .1845281 .0131913 -23.64 0.000 .1604033 .2122814
------------------------------------------------------------------------------------
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 = -28562.246
Iteration 1: log pseudolikelihood = -28501.669
Iteration 2: log pseudolikelihood = -28500.314
Iteration 3: log pseudolikelihood = -28500.313
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28500.313 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.302517 .0957838 3.59 0.000 1.127686 1.504453
_rcs1 | 2.642822 .1384014 18.56 0.000 2.385017 2.928494
_rcs2 | 1.224449 .0792977 3.13 0.002 1.078488 1.390165
_rcs3 | .9703617 .0307374 -0.95 0.342 .9119496 1.032515
_rcs_tr_outcome1 | .8888461 .0482471 -2.17 0.030 .7991399 .988622
_rcs_tr_outcome2 | .8979495 .059807 -1.62 0.106 .7880586 1.023164
_rcs_tr_outcome3 | 1.043555 .0279678 1.59 0.112 .9901536 1.099836
_rcs_tr_outcome4 | 1.017962 .0159521 1.14 0.256 .9871713 1.049712
_rcs_tr_outcome5 | 1.012498 .0066325 1.90 0.058 .9995819 1.025581
_rcs_tr_outcome6 | 1.008382 .0036722 2.29 0.022 1.001211 1.015605
_rcs_tr_outcome7 | .9970669 .0030948 -0.95 0.344 .9910195 1.003151
_cons | .1845252 .0131909 -23.64 0.000 .1604011 .2122776
------------------------------------------------------------------------------------
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 = -28547.47
Iteration 1: log pseudolikelihood = -28527.178
Iteration 2: log pseudolikelihood = -28527.111
Iteration 3: log pseudolikelihood = -28527.111
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28527.111 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291819 .0988353 3.35 0.001 1.111929 1.50081
_rcs1 | 2.549845 .1097648 21.74 0.000 2.343536 2.774317
_rcs2 | 1.138589 .0229255 6.45 0.000 1.094531 1.184421
_rcs3 | 1.012393 .0151204 0.82 0.410 .9831866 1.042466
_rcs4 | .9957136 .0101257 -0.42 0.673 .9760641 1.015759
_rcs_tr_outcome1 | .932316 .0454401 -1.44 0.150 .8473765 1.02577
_cons | .1857739 .013684 -22.85 0.000 .1607998 .2146267
------------------------------------------------------------------------------------
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 = -28547.443
Iteration 1: log pseudolikelihood = -28522.449
Iteration 2: log pseudolikelihood = -28522.3
Iteration 3: log pseudolikelihood = -28522.3
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28522.3 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.297538 .0972403 3.48 0.001 1.120286 1.502833
_rcs1 | 2.595537 .1211201 20.44 0.000 2.368678 2.844123
_rcs2 | 1.171873 .0505191 3.68 0.000 1.076925 1.275192
_rcs3 | 1.013214 .0149575 0.89 0.374 .9843178 1.042958
_rcs4 | .9959686 .0100663 -0.40 0.689 .9764331 1.015895
_rcs_tr_outcome1 | .9051947 .0447326 -2.02 0.044 .8216326 .9972553
_rcs_tr_outcome2 | .9531812 .0430183 -1.06 0.288 .8724884 1.041337
_cons | .1851513 .0134437 -23.23 0.000 .1605911 .2134676
------------------------------------------------------------------------------------
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 = -28548.244
Iteration 1: log pseudolikelihood = -28512.696
Iteration 2: log pseudolikelihood = -28511.949
Iteration 3: log pseudolikelihood = -28511.948
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28511.948 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.300783 .0961696 3.56 0.000 1.125314 1.503613
_rcs1 | 2.628996 .129765 19.58 0.000 2.386576 2.89604
_rcs2 | 1.211265 .0708121 3.28 0.001 1.080132 1.358318
_rcs3 | .9849005 .0300669 -0.50 0.618 .9276988 1.045629
_rcs4 | .9922548 .0111604 -0.69 0.489 .9706202 1.014372
_rcs_tr_outcome1 | .8935513 .0459203 -2.19 0.029 .8079333 .9882423
_rcs_tr_outcome2 | .9127921 .0542996 -1.53 0.125 .8123368 1.02567
_rcs_tr_outcome3 | 1.044931 .0333293 1.38 0.168 .9816071 1.112341
_cons | .1847195 .0132599 -23.53 0.000 .1604761 .2126254
------------------------------------------------------------------------------------
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 = -28548.756
Iteration 1: log pseudolikelihood = -28507.418
Iteration 2: log pseudolikelihood = -28506.614
Iteration 3: log pseudolikelihood = -28506.614
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28506.614 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.300928 .0961346 3.56 0.000 1.125517 1.503677
_rcs1 | 2.619891 .1252074 20.15 0.000 2.385631 2.877153
_rcs2 | 1.201322 .0667271 3.30 0.001 1.077407 1.339489
_rcs3 | .9909512 .0337785 -0.27 0.790 .9269097 1.059417
_rcs4 | .9836591 .0198259 -0.82 0.414 .9455586 1.023295
_rcs_tr_outcome1 | .8967478 .0447533 -2.18 0.029 .8131864 .9888959
_rcs_tr_outcome2 | .9201196 .0522562 -1.47 0.143 .8231939 1.028458
_rcs_tr_outcome3 | 1.032233 .03616 0.91 0.365 .9637386 1.105595
_rcs_tr_outcome4 | 1.025074 .0214458 1.18 0.237 .9838909 1.06798
_cons | .1847667 .013277 -23.50 0.000 .1604936 .2127107
------------------------------------------------------------------------------------
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 = -28531.182
Iteration 1: log pseudolikelihood = -28504.224
Iteration 2: log pseudolikelihood = -28503.586
Iteration 3: log pseudolikelihood = -28503.585
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28503.585 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.300839 .0961445 3.56 0.000 1.125412 1.503611
_rcs1 | 2.621628 .1259531 20.06 0.000 2.386031 2.880488
_rcs2 | 1.203409 .0674425 3.30 0.001 1.078225 1.343127
_rcs3 | .989674 .0334644 -0.31 0.759 .926211 1.057485
_rcs4 | .9851104 .0193325 -0.76 0.445 .9479388 1.02374
_rcs_tr_outcome1 | .8960141 .0448918 -2.19 0.028 .8122099 .9884653
_rcs_tr_outcome2 | .9155264 .0525228 -1.54 0.124 .8181602 1.02448
_rcs_tr_outcome3 | 1.033638 .0361686 0.95 0.344 .9651249 1.107014
_rcs_tr_outcome4 | 1.019579 .0196153 1.01 0.314 .9818492 1.058758
_rcs_tr_outcome5 | 1.015665 .0085015 1.86 0.063 .9991384 1.032465
_cons | .1847607 .0132762 -23.50 0.000 .160489 .212703
------------------------------------------------------------------------------------
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 = -28528.242
Iteration 1: log pseudolikelihood = -28503.8
Iteration 2: log pseudolikelihood = -28503.254
Iteration 3: log pseudolikelihood = -28503.254
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28503.254 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.300643 .0961291 3.56 0.000 1.125244 1.503383
_rcs1 | 2.619638 .1251099 20.16 0.000 2.385553 2.876692
_rcs2 | 1.201134 .0666648 3.30 0.001 1.077329 1.339166
_rcs3 | .9911317 .0337466 -0.26 0.794 .9271482 1.059531
_rcs4 | .9840252 .019781 -0.80 0.423 .9460089 1.023569
_rcs_tr_outcome1 | .8968445 .0446922 -2.18 0.029 .8133912 .9888599
_rcs_tr_outcome2 | .916566 .0521694 -1.53 0.126 .8198129 1.024738
_rcs_tr_outcome3 | 1.031599 .0354495 0.91 0.365 .9644069 1.103472
_rcs_tr_outcome4 | 1.017 .0173626 0.99 0.323 .9835327 1.051606
_rcs_tr_outcome5 | 1.020705 .0135187 1.55 0.122 .9945493 1.047548
_rcs_tr_outcome6 | 1.004065 .0039975 1.02 0.308 .9962605 1.011931
_cons | .184776 .0132786 -23.50 0.000 .1605001 .2127236
------------------------------------------------------------------------------------
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 = -28538.319
Iteration 1: log pseudolikelihood = -28500.612
Iteration 2: log pseudolikelihood = -28499.842
Iteration 3: log pseudolikelihood = -28499.841
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28499.841 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.300784 .0961258 3.56 0.000 1.125389 1.503514
_rcs1 | 2.618889 .1248044 20.20 0.000 2.385353 2.875289
_rcs2 | 1.200137 .0662546 3.30 0.001 1.077059 1.337279
_rcs3 | .9917221 .033759 -0.24 0.807 .9277147 1.060146
_rcs4 | .9830905 .0197538 -0.85 0.396 .9451262 1.02258
_rcs_tr_outcome1 | .8969463 .0446198 -2.19 0.029 .8136211 .988805
_rcs_tr_outcome2 | .9174679 .0521556 -1.52 0.130 .8207339 1.025603
_rcs_tr_outcome3 | 1.027067 .0345205 0.79 0.427 .9615883 1.097004
_rcs_tr_outcome4 | 1.018236 .0164377 1.12 0.263 .9865231 1.050969
_rcs_tr_outcome5 | 1.019995 .014764 1.37 0.171 .9914643 1.049346
_rcs_tr_outcome6 | 1.012328 .0065706 1.89 0.059 .9995317 1.025289
_rcs_tr_outcome7 | .9975093 .0031326 -0.79 0.427 .9913884 1.003668
_cons | .1847734 .0132782 -23.50 0.000 .1604982 .2127202
------------------------------------------------------------------------------------
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 = -28513.184
Iteration 1: log pseudolikelihood = -28501.553
Iteration 2: log pseudolikelihood = -28501.522
Iteration 3: log pseudolikelihood = -28501.522
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28501.522 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.289071 .0995201 3.29 0.001 1.108055 1.499657
_rcs1 | 2.538927 .1111061 21.29 0.000 2.330241 2.766303
_rcs2 | 1.126963 .0182168 7.39 0.000 1.091819 1.163239
_rcs3 | 1.028166 .0170291 1.68 0.094 .9953258 1.06209
_rcs4 | .9840195 .0115701 -1.37 0.171 .9616018 1.00696
_rcs5 | 1.011053 .0058513 1.90 0.058 .9996497 1.022587
_rcs_tr_outcome1 | .936209 .0468231 -1.32 0.188 .8487919 1.032629
_cons | .1859777 .0137558 -22.74 0.000 .1608799 .2149909
------------------------------------------------------------------------------------
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 = -28513.039
Iteration 1: log pseudolikelihood = -28498.311
Iteration 2: log pseudolikelihood = -28498.259
Iteration 3: log pseudolikelihood = -28498.259
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28498.259 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.293867 .0980198 3.40 0.001 1.115334 1.500978
_rcs1 | 2.576565 .1181868 20.63 0.000 2.355031 2.818939
_rcs2 | 1.154256 .0429243 3.86 0.000 1.073119 1.241528
_rcs3 | 1.028819 .0168732 1.73 0.083 .9962744 1.062428
_rcs4 | .9846119 .0113419 -1.35 0.178 .9626313 1.007094
_rcs5 | 1.010759 .0058064 1.86 0.062 .9994427 1.022204
_rcs_tr_outcome1 | .9136313 .0443955 -1.86 0.063 .8306328 1.004923
_rcs_tr_outcome2 | .9613355 .0392039 -0.97 0.334 .8874878 1.041328
_cons | .1854578 .0135399 -23.08 0.000 .1607313 .2139882
------------------------------------------------------------------------------------
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 = -28513.868
Iteration 1: log pseudolikelihood = -28487.007
Iteration 2: log pseudolikelihood = -28486.433
Iteration 3: log pseudolikelihood = -28486.432
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28486.432 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.297991 .0967825 3.50 0.000 1.12151 1.502243
_rcs1 | 2.609952 .1240286 20.19 0.000 2.377838 2.864725
_rcs2 | 1.192361 .0597134 3.51 0.000 1.080885 1.315333
_rcs3 | 1.000761 .0279166 0.03 0.978 .9475144 1.057
_rcs4 | .9771401 .013713 -1.65 0.099 .9506293 1.00439
_rcs5 | 1.010466 .0058646 1.79 0.073 .9990362 1.022026
_rcs_tr_outcome1 | .9016125 .0444937 -2.10 0.036 .8184911 .9931751
_rcs_tr_outcome2 | .9222767 .0475439 -1.57 0.117 .8336453 1.020331
_rcs_tr_outcome3 | 1.046403 .0307852 1.54 0.123 .9877717 1.108514
_cons | .18497 .0133408 -23.40 0.000 .1605865 .2130559
------------------------------------------------------------------------------------
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 = -28509.929
Iteration 1: log pseudolikelihood = -28480.19
Iteration 2: log pseudolikelihood = -28479.806
Iteration 3: log pseudolikelihood = -28479.805
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28479.805 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.29742 .0968692 3.49 0.000 1.120798 1.501875
_rcs1 | 2.59404 .1167126 21.19 0.000 2.375083 2.833181
_rcs2 | 1.174166 .0507078 3.72 0.000 1.07887 1.277879
_rcs3 | 1.016218 .034328 0.48 0.634 .9511157 1.085777
_rcs4 | .9677231 .019674 -1.61 0.107 .929921 1.007062
_rcs5 | 1.004925 .0077488 0.64 0.524 .9898515 1.020227
_rcs_tr_outcome1 | .9069282 .0427542 -2.07 0.038 .8268862 .9947181
_rcs_tr_outcome2 | .9369663 .0420353 -1.45 0.147 .858097 1.023085
_rcs_tr_outcome3 | 1.020969 .0332204 0.64 0.524 .957891 1.088201
_rcs_tr_outcome4 | 1.032135 .0211435 1.54 0.123 .9915154 1.074419
_cons | .1851022 .0133848 -23.33 0.000 .1606426 .213286
------------------------------------------------------------------------------------
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 = -28511.637
Iteration 1: log pseudolikelihood = -28476.699
Iteration 2: log pseudolikelihood = -28476.307
Iteration 3: log pseudolikelihood = -28476.307
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28476.307 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.299244 .0964261 3.53 0.000 1.123355 1.502673
_rcs1 | 2.593475 .1156669 21.37 0.000 2.376398 2.830382
_rcs2 | 1.166936 .0457007 3.94 0.000 1.080715 1.260035
_rcs3 | 1.022701 .0379255 0.61 0.545 .9510052 1.099801
_rcs4 | .9634322 .0221948 -1.62 0.106 .9208985 1.00793
_rcs5 | 1.011841 .01173 1.02 0.310 .9891102 1.035095
_rcs_tr_outcome1 | .9056584 .0423981 -2.12 0.034 .826258 .9926889
_rcs_tr_outcome2 | .9442087 .0384266 -1.41 0.158 .8718193 1.022609
_rcs_tr_outcome3 | 1.005431 .0381641 0.14 0.887 .933345 1.083083
_rcs_tr_outcome4 | 1.041697 .0247183 1.72 0.085 .9943598 1.091289
_rcs_tr_outcome5 | .9985808 .0123117 -0.12 0.908 .9747396 1.023005
_cons | .1849987 .0133547 -23.38 0.000 .1605912 .2131157
------------------------------------------------------------------------------------
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 = -28510.83
Iteration 1: log pseudolikelihood = -28475.712
Iteration 2: log pseudolikelihood = -28475.237
Iteration 3: log pseudolikelihood = -28475.237
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28475.237 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.299003 .0964995 3.52 0.000 1.122992 1.502601
_rcs1 | 2.593528 .1156664 21.37 0.000 2.376451 2.830433
_rcs2 | 1.167559 .0462061 3.91 0.000 1.08042 1.261726
_rcs3 | 1.021997 .0372706 0.60 0.551 .9514977 1.09772
_rcs4 | .9640052 .0217455 -1.63 0.104 .9223132 1.007582
_rcs5 | 1.011773 .0110017 1.08 0.282 .9904384 1.033568
_rcs_tr_outcome1 | .9058754 .0423623 -2.11 0.035 .8265382 .9928281
_rcs_tr_outcome2 | .9438203 .0387703 -1.41 0.159 .8708104 1.022951
_rcs_tr_outcome3 | 1.001826 .0385987 0.05 0.962 .9289595 1.080407
_rcs_tr_outcome4 | 1.039372 .0222757 1.80 0.072 .9966165 1.083961
_rcs_tr_outcome5 | 1.012128 .0124273 0.98 0.326 .9880619 1.036781
_rcs_tr_outcome6 | .9940055 .0071818 -0.83 0.405 .9800287 1.008182
_cons | .1850106 .013361 -23.36 0.000 .1605924 .2131416
------------------------------------------------------------------------------------
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 = -28519.814
Iteration 1: log pseudolikelihood = -28473.878
Iteration 2: log pseudolikelihood = -28473.312
Iteration 3: log pseudolikelihood = -28473.312
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28473.312 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.299195 .0964336 3.53 0.000 1.123294 1.502642
_rcs1 | 2.593113 .1156044 21.37 0.000 2.376149 2.829887
_rcs2 | 1.166533 .0455813 3.94 0.000 1.08053 1.25938
_rcs3 | 1.023051 .0377435 0.62 0.537 .9516865 1.099767
_rcs4 | .9632881 .0220731 -1.63 0.103 .9209827 1.007537
_rcs5 | 1.0117 .0115928 1.02 0.310 .9892323 1.034679
_rcs_tr_outcome1 | .9058187 .0423993 -2.11 0.035 .8264156 .992851
_rcs_tr_outcome2 | .9449641 .0384853 -1.39 0.165 .8724663 1.023486
_rcs_tr_outcome3 | .9969434 .038988 -0.08 0.938 .9233834 1.076363
_rcs_tr_outcome4 | 1.03709 .0199013 1.90 0.058 .9988085 1.076839
_rcs_tr_outcome5 | 1.020809 .0137974 1.52 0.128 .9941213 1.048212
_rcs_tr_outcome6 | .998556 .0098178 -0.15 0.883 .9794978 1.017985
_rcs_tr_outcome7 | .9946189 .0039697 -1.35 0.176 .9868687 1.00243
_cons | .1850023 .0133563 -23.37 0.000 .1605922 .2131228
------------------------------------------------------------------------------------
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 = -28512.035
Iteration 1: log pseudolikelihood = -28485.685
Iteration 2: log pseudolikelihood = -28485.534
Iteration 3: log pseudolikelihood = -28485.534
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28485.534 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.287324 .0994604 3.27 0.001 1.106427 1.497796
_rcs1 | 2.538594 .1112927 21.25 0.000 2.329573 2.766369
_rcs2 | 1.124888 .0169111 7.83 0.000 1.092226 1.158526
_rcs3 | 1.041436 .0183187 2.31 0.021 1.006143 1.077966
_rcs4 | .9772126 .0127638 -1.76 0.078 .9525136 1.002552
_rcs5 | 1.009415 .0070466 1.34 0.179 .995698 1.023321
_rcs6 | .9972517 .0052961 -0.52 0.604 .9869253 1.007686
_rcs_tr_outcome1 | .9387696 .0466229 -1.27 0.203 .8516969 1.034744
_cons | .1859883 .0137273 -22.79 0.000 .1609388 .2149367
------------------------------------------------------------------------------------
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 = -28511.819
Iteration 1: log pseudolikelihood = -28483.008
Iteration 2: log pseudolikelihood = -28482.79
Iteration 3: log pseudolikelihood = -28482.79
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28482.79 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291655 .0980758 3.37 0.001 1.11305 1.49892
_rcs1 | 2.57277 .118518 20.51 0.000 2.350657 2.81587
_rcs2 | 1.149802 .0412175 3.89 0.000 1.071789 1.233492
_rcs3 | 1.042057 .0181846 2.36 0.018 1.007019 1.078315
_rcs4 | .9780395 .0124407 -1.75 0.081 .9539577 1.002729
_rcs5 | 1.009227 .0070541 1.31 0.189 .9954958 1.023148
_rcs6 | .9971648 .005335 -0.53 0.596 .9867631 1.007676
_rcs_tr_outcome1 | .9181372 .0445094 -1.76 0.078 .8349166 1.009653
_rcs_tr_outcome2 | .964614 .0379634 -0.92 0.360 .8930044 1.041966
_cons | .1855186 .0135289 -23.10 0.000 .1608103 .2140233
------------------------------------------------------------------------------------
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 = -28512.729
Iteration 1: log pseudolikelihood = -28473.057
Iteration 2: log pseudolikelihood = -28472.332
Iteration 3: log pseudolikelihood = -28472.331
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28472.331 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.295754 .0968743 3.47 0.001 1.11914 1.500241
_rcs1 | 2.60465 .1225024 20.35 0.000 2.375284 2.856165
_rcs2 | 1.186066 .0557035 3.63 0.000 1.081763 1.300425
_rcs3 | 1.016616 .026992 0.62 0.535 .9650652 1.07092
_rcs4 | .968883 .0158017 -1.94 0.053 .938402 1.000354
_rcs5 | 1.007161 .0075513 0.95 0.341 .9924693 1.022071
_rcs6 | .9973077 .0053059 -0.51 0.612 .9869623 1.007762
_rcs_tr_outcome1 | .9064665 .0438135 -2.03 0.042 .8245358 .9965384
_rcs_tr_outcome2 | .9277977 .0442498 -1.57 0.116 .8449998 1.018709
_rcs_tr_outcome3 | 1.04305 .0297611 1.48 0.140 .9863206 1.103043
_cons | .1850446 .013339 -23.41 0.000 .1606636 .2131256
------------------------------------------------------------------------------------
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 = -28510.914
Iteration 1: log pseudolikelihood = -28468.324
Iteration 2: log pseudolikelihood = -28467.702
Iteration 3: log pseudolikelihood = -28467.702
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28467.702 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.294731 .0970248 3.45 0.001 1.117872 1.499571
_rcs1 | 2.590614 .1161669 21.23 0.000 2.37265 2.828602
_rcs2 | 1.172127 .0474075 3.93 0.000 1.082797 1.268826
_rcs3 | 1.028259 .0331416 0.86 0.387 .9653121 1.095311
_rcs4 | .9637503 .0186951 -1.90 0.057 .9277964 1.001097
_rcs5 | 1.00021 .0124956 0.02 0.987 .9760162 1.025003
_rcs6 | .9955477 .005378 -0.83 0.409 .9850626 1.006144
_rcs_tr_outcome1 | .9117197 .0423471 -1.99 0.047 .8323867 .9986138
_rcs_tr_outcome2 | .9384447 .0389563 -1.53 0.126 .8651154 1.01799
_rcs_tr_outcome3 | 1.024531 .0317608 0.78 0.434 .9641341 1.088711
_rcs_tr_outcome4 | 1.028326 .021917 1.31 0.190 .9862548 1.072193
_cons | .1851923 .0133821 -23.34 0.000 .1607365 .213369
------------------------------------------------------------------------------------
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 = -28509.204
Iteration 1: log pseudolikelihood = -28463.69
Iteration 2: log pseudolikelihood = -28463.074
Iteration 3: log pseudolikelihood = -28463.074
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28463.074 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.295682 .0967621 3.47 0.001 1.119258 1.499915
_rcs1 | 2.587649 .1153186 21.33 0.000 2.371218 2.823834
_rcs2 | 1.163824 .0409331 4.31 0.000 1.0863 1.246881
_rcs3 | 1.038315 .0382591 1.02 0.308 .965972 1.116075
_rcs4 | .957763 .0227237 -1.82 0.069 .914245 1.003352
_rcs5 | 1.001256 .0124522 0.10 0.920 .9771451 1.025962
_rcs6 | .9963515 .0077063 -0.47 0.637 .9813613 1.011571
_rcs_tr_outcome1 | .911808 .0420925 -2.00 0.046 .8329305 .9981551
_rcs_tr_outcome2 | .9454235 .0345338 -1.54 0.124 .8801046 1.01559
_rcs_tr_outcome3 | 1.00683 .0352038 0.19 0.846 .940143 1.078247
_rcs_tr_outcome4 | 1.040767 .0254704 1.63 0.103 .9920244 1.091905
_rcs_tr_outcome5 | 1.004946 .0122286 0.41 0.685 .9812624 1.029202
_cons | .1851416 .0133668 -23.36 0.000 .1607123 .2132844
------------------------------------------------------------------------------------
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 = -28506.78
Iteration 1: log pseudolikelihood = -28450.084
Iteration 2: log pseudolikelihood = -28448.791
Iteration 3: log pseudolikelihood = -28448.79
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28448.79 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.30114 .0963335 3.56 0.000 1.12539 1.504337
_rcs1 | 2.598132 .1218657 20.36 0.000 2.36993 2.848308
_rcs2 | 1.158774 .036928 4.62 0.000 1.08861 1.23346
_rcs3 | 1.052582 .0418456 1.29 0.197 .9736797 1.137877
_rcs4 | .9482385 .0254466 -1.98 0.048 .8996529 .9994479
_rcs5 | 1.007323 .0139419 0.53 0.598 .9803648 1.035023
_rcs6 | .9908075 .0105379 -0.87 0.385 .9703675 1.011678
_rcs_tr_outcome1 | .9042099 .04432 -2.05 0.040 .8213864 .9953848
_rcs_tr_outcome2 | .950159 .0319518 -1.52 0.128 .8895537 1.014893
_rcs_tr_outcome3 | .9801865 .0397929 -0.49 0.622 .905216 1.061366
_rcs_tr_outcome4 | 1.056865 .0290762 2.01 0.044 1.001386 1.115417
_rcs_tr_outcome5 | 1.003276 .0145538 0.23 0.822 .9751526 1.03221
_rcs_tr_outcome6 | 1.01181 .011325 1.05 0.294 .9898555 1.034252
_cons | .1847133 .0132995 -23.46 0.000 .1604024 .2127087
------------------------------------------------------------------------------------
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 = -28508.055
Iteration 1: log pseudolikelihood = -28449.515
Iteration 2: log pseudolikelihood = -28448.424
Iteration 3: log pseudolikelihood = -28448.424
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28448.424 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.299922 .0963217 3.54 0.000 1.124204 1.503107
_rcs1 | 2.594916 .1200062 20.62 0.000 2.370054 2.841113
_rcs2 | 1.158654 .0371667 4.59 0.000 1.088051 1.233838
_rcs3 | 1.051097 .0411417 1.27 0.203 .973476 1.134907
_rcs4 | .9491297 .0249305 -1.99 0.047 .9015033 .9992723
_rcs5 | 1.006259 .0136243 0.46 0.645 .9799071 1.033319
_rcs6 | .9927953 .0099664 -0.72 0.471 .9734524 1.012522
_rcs_tr_outcome1 | .9059775 .0435848 -2.05 0.040 .8244565 .9955591
_rcs_tr_outcome2 | .9518108 .0320757 -1.47 0.143 .8909748 1.016801
_rcs_tr_outcome3 | .9740425 .0404371 -0.63 0.526 .8979258 1.056611
_rcs_tr_outcome4 | 1.058655 .0274898 2.20 0.028 1.006125 1.113929
_rcs_tr_outcome5 | 1.009612 .0149218 0.65 0.517 .980785 1.039285
_rcs_tr_outcome6 | 1.008767 .0099594 0.88 0.377 .9894345 1.028477
_rcs_tr_outcome7 | 1.002864 .0073246 0.39 0.695 .9886106 1.017324
_cons | .184821 .0133085 -23.45 0.000 .1604938 .2128357
------------------------------------------------------------------------------------
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 = -28495.988
Iteration 1: log pseudolikelihood = -28476.832
Iteration 2: log pseudolikelihood = -28476.744
Iteration 3: log pseudolikelihood = -28476.744
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28476.744 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.286185 .099578 3.25 0.001 1.105102 1.49694
_rcs1 | 2.534659 .1096643 21.50 0.000 2.328582 2.758973
_rcs2 | 1.124682 .0170038 7.77 0.000 1.091844 1.158508
_rcs3 | 1.041946 .0183112 2.34 0.019 1.006668 1.078461
_rcs4 | .9811292 .0129154 -1.45 0.148 .9561392 1.006772
_rcs5 | 1.002024 .0083391 0.24 0.808 .9858127 1.018503
_rcs6 | 1.006107 .0049462 1.24 0.216 .9964595 1.015848
_rcs7 | .9922138 .004657 -1.67 0.096 .983128 1.001384
_rcs_tr_outcome1 | .9403412 .0461247 -1.25 0.210 .8541482 1.035232
_cons | .186106 .0137606 -22.74 0.000 .1609988 .2151285
------------------------------------------------------------------------------------
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 = -28495.877
Iteration 1: log pseudolikelihood = -28473.926
Iteration 2: log pseudolikelihood = -28473.769
Iteration 3: log pseudolikelihood = -28473.769
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28473.769 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.290733 .0981284 3.36 0.001 1.112048 1.498129
_rcs1 | 2.570296 .1169337 20.75 0.000 2.351031 2.810011
_rcs2 | 1.150644 .0412934 3.91 0.000 1.072491 1.234492
_rcs3 | 1.042858 .0183458 2.39 0.017 1.007514 1.079442
_rcs4 | .9820632 .0126253 -1.41 0.159 .9576272 1.007123
_rcs5 | 1.002001 .0083102 0.24 0.809 .9858453 1.018422
_rcs6 | 1.005971 .0049593 1.21 0.227 .9962973 1.015738
_rcs7 | .9921527 .0047066 -1.66 0.097 .9829706 1.001421
_rcs_tr_outcome1 | .9187223 .0438772 -1.77 0.076 .8366268 1.008874
_rcs_tr_outcome2 | .9631171 .038234 -0.95 0.344 .891021 1.041047
_cons | .1856137 .0135534 -23.06 0.000 .1608629 .2141728
------------------------------------------------------------------------------------
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 = -28496.361
Iteration 1: log pseudolikelihood = -28462.918
Iteration 2: log pseudolikelihood = -28462.279
Iteration 3: log pseudolikelihood = -28462.278
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28462.278 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.295075 .0968773 3.46 0.001 1.118463 1.499577
_rcs1 | 2.604191 .1223296 20.38 0.000 2.375136 2.855336
_rcs2 | 1.189868 .0573356 3.61 0.000 1.082636 1.307722
_rcs3 | 1.016954 .0254997 0.67 0.503 .9681841 1.068181
_rcs4 | .9716314 .0164346 -1.70 0.089 .9399483 1.004382
_rcs5 | .9981582 .0091294 -0.20 0.840 .9804243 1.016213
_rcs6 | 1.00581 .004982 1.17 0.242 .9960923 1.015622
_rcs7 | .9921129 .0046659 -1.68 0.092 .98301 1.0013
_rcs_tr_outcome1 | .9061544 .0436647 -2.05 0.041 .8244903 .9959072
_rcs_tr_outcome2 | .9238846 .0454675 -1.61 0.108 .8389329 1.017439
_rcs_tr_outcome3 | 1.045292 .0290079 1.60 0.110 .9899562 1.103721
_cons | .1851139 .0133538 -23.38 0.000 .1607071 .2132275
------------------------------------------------------------------------------------
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 = -28495.446
Iteration 1: log pseudolikelihood = -28459.102
Iteration 2: log pseudolikelihood = -28458.688
Iteration 3: log pseudolikelihood = -28458.688
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28458.688 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.293872 .0970114 3.44 0.001 1.117044 1.498692
_rcs1 | 2.589823 .1154783 21.34 0.000 2.373097 2.826341
_rcs2 | 1.17543 .0489905 3.88 0.000 1.083228 1.275481
_rcs3 | 1.028742 .0325666 0.90 0.371 .9668523 1.094593
_rcs4 | .9685581 .0176981 -1.75 0.080 .9344844 1.003874
_rcs5 | .9915386 .0147694 -0.57 0.568 .9630096 1.020913
_rcs6 | 1.002331 .0063802 0.37 0.715 .9899034 1.014914
_rcs7 | .9914712 .0046848 -1.81 0.070 .9823317 1.000696
_rcs_tr_outcome1 | .9116028 .0419813 -2.01 0.044 .832925 .9977124
_rcs_tr_outcome2 | .9346718 .0401446 -1.57 0.116 .8592107 1.01676
_rcs_tr_outcome3 | 1.026217 .0318076 0.83 0.404 .9657308 1.090491
_rcs_tr_outcome4 | 1.02785 .022575 1.25 0.211 .9845432 1.073063
_cons | .1852739 .0133984 -23.31 0.000 .1607898 .2134864
------------------------------------------------------------------------------------
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 = -28496.053
Iteration 1: log pseudolikelihood = -28451.233
Iteration 2: log pseudolikelihood = -28450.644
Iteration 3: log pseudolikelihood = -28450.644
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28450.644 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.296243 .0965969 3.48 0.000 1.120093 1.500094
_rcs1 | 2.588571 .1149493 21.42 0.000 2.372801 2.823963
_rcs2 | 1.164158 .0413613 4.28 0.000 1.08585 1.248114
_rcs3 | 1.042572 .0389946 1.11 0.265 .9688779 1.121871
_rcs4 | .961348 .0198949 -1.90 0.057 .9231348 1.001143
_rcs5 | .9913561 .0139376 -0.62 0.537 .9644118 1.019053
_rcs6 | 1.006464 .0091851 0.71 0.480 .9886216 1.024628
_rcs7 | .9919919 .0053108 -1.50 0.133 .9816374 1.002456
_rcs_tr_outcome1 | .9099094 .0419242 -2.05 0.040 .8313404 .9959039
_rcs_tr_outcome2 | .9441599 .0350751 -1.55 0.122 .8778571 1.01547
_rcs_tr_outcome3 | 1.004006 .0364658 0.11 0.912 .9350189 1.078083
_rcs_tr_outcome4 | 1.044039 .0246595 1.82 0.068 .9968087 1.093507
_rcs_tr_outcome5 | .9990546 .0122522 -0.08 0.939 .9753271 1.023359
_cons | .1851303 .0133679 -23.36 0.000 .1606993 .2132755
------------------------------------------------------------------------------------
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 = -28500.394
Iteration 1: log pseudolikelihood = -28445.554
Iteration 2: log pseudolikelihood = -28444.405
Iteration 3: log pseudolikelihood = -28444.405
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28444.405 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298648 .0965928 3.51 0.000 1.122482 1.502461
_rcs1 | 2.592517 .1187867 20.79 0.000 2.369847 2.836109
_rcs2 | 1.16063 .0379456 4.56 0.000 1.08859 1.237436
_rcs3 | 1.05179 .0414366 1.28 0.200 .9736319 1.136222
_rcs4 | .9534206 .0244161 -1.86 0.063 .906747 1.002497
_rcs5 | .995879 .0152961 -0.27 0.788 .966346 1.026315
_rcs6 | 1.002388 .0093452 0.26 0.798 .9842383 1.020873
_rcs7 | .9886852 .0073519 -1.53 0.126 .9743802 1.0032
_rcs_tr_outcome1 | .9075693 .0431345 -2.04 0.041 .8268455 .996174
_rcs_tr_outcome2 | .947613 .0327162 -1.56 0.119 .8856119 1.013955
_rcs_tr_outcome3 | .9877891 .0379399 -0.32 0.749 .9161582 1.065021
_rcs_tr_outcome4 | 1.053229 .0271752 2.01 0.044 1.001291 1.107861
_rcs_tr_outcome5 | 1.005128 .0151015 0.34 0.734 .9759613 1.035167
_rcs_tr_outcome6 | 1.009124 .0103873 0.88 0.378 .9889689 1.029689
_cons | .1849155 .013345 -23.39 0.000 .1605254 .2130115
------------------------------------------------------------------------------------
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 = -28495.518
Iteration 1: log pseudolikelihood = -28441.359
Iteration 2: log pseudolikelihood = -28440.264
Iteration 3: log pseudolikelihood = -28440.263
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28440.263 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.300673 .0964846 3.54 0.000 1.124671 1.504218
_rcs1 | 2.594784 .1198915 20.64 0.000 2.370127 2.840735
_rcs2 | 1.158844 .0373904 4.57 0.000 1.087829 1.234494
_rcs3 | 1.053523 .0424491 1.29 0.196 .9735243 1.140095
_rcs4 | .9529994 .0266111 -1.72 0.085 .9022442 1.00661
_rcs5 | .9952729 .0164765 -0.29 0.775 .9634979 1.028096
_rcs6 | 1.003603 .0098233 0.37 0.713 .9845328 1.023042
_rcs7 | .9864676 .009072 -1.48 0.138 .968846 1.00441
_rcs_tr_outcome1 | .905317 .0437737 -2.06 0.040 .8234619 .9953088
_rcs_tr_outcome2 | .9501261 .032361 -1.50 0.133 .8887705 1.015717
_rcs_tr_outcome3 | .9793765 .0403123 -0.51 0.613 .9034689 1.061662
_rcs_tr_outcome4 | 1.054022 .0301555 1.84 0.066 .9965453 1.114814
_rcs_tr_outcome5 | 1.012596 .0173482 0.73 0.465 .9791586 1.047175
_rcs_tr_outcome6 | 1.004106 .0104588 0.39 0.694 .9838149 1.024816
_rcs_tr_outcome7 | 1.010786 .0098094 1.11 0.269 .9917414 1.030196
_cons | .1847853 .0133322 -23.40 0.000 .1604181 .2128539
------------------------------------------------------------------------------------
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 = -28507.389
Iteration 1: log pseudolikelihood = -28485.744
Iteration 2: log pseudolikelihood = -28485.608
Iteration 3: log pseudolikelihood = -28485.608
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28485.608 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.287313 .0998133 3.26 0.001 1.105822 1.498591
_rcs1 | 2.535771 .1103499 21.38 0.000 2.328456 2.761544
_rcs2 | 1.124784 .0172178 7.68 0.000 1.091539 1.159042
_rcs3 | 1.040993 .0183102 2.28 0.022 1.005717 1.077506
_rcs4 | .9887695 .0130073 -0.86 0.391 .9636015 1.014595
_rcs5 | .9932274 .0096064 -0.70 0.482 .9745764 1.012235
_rcs6 | 1.0084 .0055896 1.51 0.131 .9975042 1.019416
_rcs7 | .9984068 .0048294 -0.33 0.742 .9889861 1.007917
_rcs8 | .9955853 .0037086 -1.19 0.235 .988343 1.002881
_rcs_tr_outcome1 | .9388557 .0465783 -1.27 0.203 .851862 1.034733
_cons | .1860619 .0137831 -22.70 0.000 .1609172 .2151358
------------------------------------------------------------------------------------
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 = -28507.377
Iteration 1: log pseudolikelihood = -28482.788
Iteration 2: log pseudolikelihood = -28482.578
Iteration 3: log pseudolikelihood = -28482.578
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28482.578 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291889 .098396 3.36 0.001 1.112741 1.49988
_rcs1 | 2.571773 .1172369 20.72 0.000 2.351959 2.812131
_rcs2 | 1.151012 .0412383 3.93 0.000 1.072959 1.234743
_rcs3 | 1.042171 .0185231 2.32 0.020 1.006491 1.079115
_rcs4 | .9897155 .0127981 -0.80 0.424 .9649468 1.01512
_rcs5 | .9934145 .0094859 -0.69 0.489 .9749954 1.012181
_rcs6 | 1.00826 .0055949 1.48 0.138 .9973532 1.019285
_rcs7 | .9983151 .0048779 -0.35 0.730 .9888002 1.007921
_rcs8 | .9955493 .0037621 -1.18 0.238 .988203 1.00295
_rcs_tr_outcome1 | .9170856 .0441774 -1.80 0.072 .8344613 1.007891
_rcs_tr_outcome2 | .962746 .0382779 -0.95 0.340 .8905714 1.04077
_cons | .1855661 .0135777 -23.02 0.000 .1607746 .2141806
------------------------------------------------------------------------------------
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 = -28507.394
Iteration 1: log pseudolikelihood = -28471.585
Iteration 2: log pseudolikelihood = -28470.957
Iteration 3: log pseudolikelihood = -28470.957
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28470.957 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.296213 .0970821 3.46 0.001 1.119243 1.501166
_rcs1 | 2.605871 .1223023 20.41 0.000 2.376857 2.85695
_rcs2 | 1.190966 .0582299 3.57 0.000 1.082135 1.310741
_rcs3 | 1.017007 .0252411 0.68 0.497 .9687189 1.067701
_rcs4 | .9783975 .0170982 -1.25 0.211 .945453 1.01249
_rcs5 | .9881175 .0108395 -1.09 0.276 .9670993 1.009593
_rcs6 | 1.007021 .0059236 1.19 0.234 .9954777 1.018698
_rcs7 | .998261 .0048264 -0.36 0.719 .9888461 1.007765
_rcs8 | .995613 .0036593 -1.20 0.232 .9884667 1.002811
_rcs_tr_outcome1 | .9044208 .0438087 -2.07 0.038 .8225072 .9944922
_rcs_tr_outcome2 | .9230271 .04638 -1.59 0.111 .8364567 1.018557
_rcs_tr_outcome3 | 1.04579 .0298986 1.57 0.117 .9888013 1.106063
_cons | .1850678 .0133719 -23.35 0.000 .1606306 .2132227
------------------------------------------------------------------------------------
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 = -28506.885
Iteration 1: log pseudolikelihood = -28467.696
Iteration 2: log pseudolikelihood = -28467.247
Iteration 3: log pseudolikelihood = -28467.246
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28467.246 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.29511 .0972339 3.44 0.001 1.117893 1.50042
_rcs1 | 2.591625 .115845 21.30 0.000 2.374234 2.828919
_rcs2 | 1.176097 .0503395 3.79 0.000 1.081458 1.279017
_rcs3 | 1.029005 .0320776 0.92 0.359 .9680158 1.093836
_rcs4 | .9767646 .017361 -1.32 0.186 .9433235 1.011391
_rcs5 | .9825049 .0153297 -1.13 0.258 .952914 1.013015
_rcs6 | 1.001964 .0090442 0.22 0.828 .9843934 1.019848
_rcs7 | .9964062 .0051538 -0.70 0.486 .9863559 1.006559
_rcs8 | .9954825 .0036535 -1.23 0.217 .9883474 1.002669
_rcs_tr_outcome1 | .9098261 .0422717 -2.03 0.042 .8306355 .9965665
_rcs_tr_outcome2 | .9341859 .0414356 -1.53 0.125 .8564036 1.019033
_rcs_tr_outcome3 | 1.026413 .0321917 0.83 0.406 .9652181 1.091487
_rcs_tr_outcome4 | 1.027918 .0222019 1.27 0.202 .985311 1.072367
_cons | .1852201 .0134151 -23.28 0.000 .160708 .2134711
------------------------------------------------------------------------------------
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 = -28506.933
Iteration 1: log pseudolikelihood = -28461.357
Iteration 2: log pseudolikelihood = -28460.723
Iteration 3: log pseudolikelihood = -28460.723
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28460.723 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.297122 .0967867 3.49 0.000 1.120643 1.501393
_rcs1 | 2.589386 .1145849 21.50 0.000 2.374268 2.823996
_rcs2 | 1.164895 .0428324 4.15 0.000 1.083899 1.251944
_rcs3 | 1.042022 .0391669 1.10 0.273 .9680157 1.121686
_rcs4 | .971569 .0184647 -1.52 0.129 .9360446 1.008442
_rcs5 | .9798582 .0156892 -1.27 0.204 .9495855 1.011096
_rcs6 | 1.00507 .0102558 0.50 0.620 .9851685 1.025373
_rcs7 | .9984126 .0070723 -0.22 0.823 .9846469 1.012371
_rcs8 | .99542 .0038559 -1.19 0.236 .9878911 1.003006
_rcs_tr_outcome1 | .9087691 .0418921 -2.08 0.038 .830262 .9946995
_rcs_tr_outcome2 | .9436173 .0364381 -1.50 0.133 .8748356 1.017807
_rcs_tr_outcome3 | 1.005183 .0374496 0.14 0.890 .9343991 1.081329
_rcs_tr_outcome4 | 1.04298 .0258507 1.70 0.090 .9935248 1.094898
_rcs_tr_outcome5 | 1.000194 .0124945 0.02 0.988 .9760026 1.024985
_cons | .1851069 .013387 -23.32 0.000 .1606436 .2132956
------------------------------------------------------------------------------------
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 = -28508.715
Iteration 1: log pseudolikelihood = -28457.831
Iteration 2: log pseudolikelihood = -28456.779
Iteration 3: log pseudolikelihood = -28456.779
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28456.779 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298792 .0966135 3.51 0.000 1.12259 1.502651
_rcs1 | 2.591839 .1173913 21.03 0.000 2.371673 2.832443
_rcs2 | 1.16133 .03938 4.41 0.000 1.086656 1.241136
_rcs3 | 1.05042 .0420621 1.23 0.219 .971132 1.136182
_rcs4 | .9652424 .0219885 -1.55 0.120 .9230936 1.009316
_rcs5 | .9827848 .0155416 -1.10 0.272 .9527911 1.013723
_rcs6 | 1.005044 .0104433 0.48 0.628 .9847825 1.025722
_rcs7 | .9943318 .0093556 -0.60 0.546 .9761631 1.012839
_rcs8 | .9942402 .0047933 -1.20 0.231 .9848897 1.003679
_rcs_tr_outcome1 | .9073249 .0428765 -2.06 0.040 .8270628 .995376
_rcs_tr_outcome2 | .9470446 .0340126 -1.51 0.130 .8826733 1.01611
_rcs_tr_outcome3 | .9908556 .0388243 -0.23 0.815 .9176098 1.069948
_rcs_tr_outcome4 | 1.049373 .0267181 1.89 0.058 .9982919 1.103068
_rcs_tr_outcome5 | 1.006457 .0148446 0.44 0.663 .9777784 1.035976
_rcs_tr_outcome6 | 1.007945 .0110823 0.72 0.472 .9864565 1.029901
_cons | .1849557 .0133592 -23.37 0.000 .1605411 .2130832
------------------------------------------------------------------------------------
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 = -28506.098
Iteration 1: log pseudolikelihood = -28457.98
Iteration 2: log pseudolikelihood = -28457.057
Iteration 3: log pseudolikelihood = -28457.057
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28457.057 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298894 .0967233 3.51 0.000 1.122504 1.503001
_rcs1 | 2.59111 .1175829 20.98 0.000 2.370603 2.832128
_rcs2 | 1.160266 .0387046 4.46 0.000 1.086833 1.23866
_rcs3 | 1.051476 .0428805 1.23 0.218 .9707033 1.138971
_rcs4 | .9644474 .0252095 -1.38 0.166 .9162821 1.015145
_rcs5 | .9824658 .0169193 -1.03 0.304 .949858 1.016193
_rcs6 | 1.004807 .0103681 0.46 0.642 .9846904 1.025335
_rcs7 | .9954057 .009109 -0.50 0.615 .9777116 1.01342
_rcs8 | .9933952 .0062187 -1.06 0.290 .9812813 1.005659
_rcs_tr_outcome1 | .9074549 .0430346 -2.05 0.041 .8269099 .9958454
_rcs_tr_outcome2 | .9484205 .0334456 -1.50 0.133 .8850824 1.016291
_rcs_tr_outcome3 | .9849937 .0398211 -0.37 0.708 .9099579 1.066217
_rcs_tr_outcome4 | 1.048972 .0278333 1.80 0.072 .9958135 1.104967
_rcs_tr_outcome5 | 1.014518 .0171558 0.85 0.394 .9814447 1.048706
_rcs_tr_outcome6 | 1.004798 .0106304 0.45 0.651 .9841776 1.025851
_rcs_tr_outcome7 | 1.005556 .0088379 0.63 0.528 .9883823 1.023028
_cons | .1849532 .0133722 -23.34 0.000 .1605166 .2131101
------------------------------------------------------------------------------------
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 = -28475.602
Iteration 1: log pseudolikelihood = -28464.621
Iteration 2: log pseudolikelihood = -28464.599
Iteration 3: log pseudolikelihood = -28464.599
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28464.599 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.284283 .1000911 3.21 0.001 1.102356 1.496234
_rcs1 | 2.530357 .1088475 21.58 0.000 2.325766 2.752946
_rcs2 | 1.125912 .018037 7.40 0.000 1.091109 1.161825
_rcs3 | 1.038447 .0182962 2.14 0.032 1.003199 1.074933
_rcs4 | .9983247 .0118968 -0.14 0.888 .9752775 1.021917
_rcs5 | .9851068 .0102792 -1.44 0.150 .9651646 1.005461
_rcs6 | 1.007768 .0060748 1.28 0.199 .9959318 1.019745
_rcs7 | 1.003375 .0041794 0.81 0.419 .9952168 1.0116
_rcs8 | .9940663 .0048104 -1.23 0.219 .9846827 1.003539
_rcs9 | .9998596 .0036307 -0.04 0.969 .9927689 1.007001
_rcs_tr_outcome1 | .9428021 .04659 -1.19 0.233 .8557701 1.038685
_cons | .1862963 .0138248 -22.64 0.000 .1610785 .215462
------------------------------------------------------------------------------------
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 = -28475.686
Iteration 1: log pseudolikelihood = -28461.742
Iteration 2: log pseudolikelihood = -28461.688
Iteration 3: log pseudolikelihood = -28461.688
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28461.688 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.288658 .0987521 3.31 0.001 1.108942 1.4975
_rcs1 | 2.565225 .1150456 21.01 0.000 2.349366 2.800917
_rcs2 | 1.151464 .0409704 3.96 0.000 1.073899 1.234631
_rcs3 | 1.039892 .0187144 2.17 0.030 1.003852 1.077226
_rcs4 | .9991723 .0117731 -0.07 0.944 .9763619 1.022516
_rcs5 | .9854854 .0100941 -1.43 0.153 .9658986 1.005469
_rcs6 | 1.00767 .0060835 1.27 0.206 .9958169 1.019665
_rcs7 | 1.003268 .0042139 0.78 0.437 .995043 1.011562
_rcs8 | .993983 .0048608 -1.23 0.217 .9845016 1.003556
_rcs9 | .9998479 .0036845 -0.04 0.967 .9926525 1.007095
_rcs_tr_outcome1 | .9215228 .0439437 -1.71 0.087 .8392972 1.011804
_rcs_tr_outcome2 | .9635062 .0379149 -0.94 0.345 .8919878 1.040759
_cons | .1858165 .0136266 -22.95 0.000 .1609394 .214539
------------------------------------------------------------------------------------
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 = -28475.249
Iteration 1: log pseudolikelihood = -28449.134
Iteration 2: log pseudolikelihood = -28448.644
Iteration 3: log pseudolikelihood = -28448.643
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28448.643 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.293304 .0974059 3.41 0.001 1.115814 1.499027
_rcs1 | 2.601677 .1208558 20.58 0.000 2.375267 2.849668
_rcs2 | 1.194229 .0587862 3.61 0.000 1.084394 1.315189
_rcs3 | 1.014474 .0250178 0.58 0.560 .9666066 1.064713
_rcs4 | .9867401 .0162529 -0.81 0.418 .9553937 1.019115
_rcs5 | .978847 .0118494 -1.77 0.077 .9558959 1.002349
_rcs6 | 1.005078 .0066728 0.76 0.446 .9920842 1.018242
_rcs7 | 1.002892 .0042416 0.68 0.495 .9946133 1.01124
_rcs8 | .9939575 .0047972 -1.26 0.209 .9845995 1.003404
_rcs9 | 1.000001 .003559 0.00 1.000 .9930502 1.007001
_rcs_tr_outcome1 | .9077336 .0437498 -2.01 0.045 .8259111 .9976622
_rcs_tr_outcome2 | .9211152 .0464163 -1.63 0.103 .8344891 1.016734
_rcs_tr_outcome3 | 1.048619 .0298304 1.67 0.095 .9917524 1.108746
_cons | .1852802 .0134088 -23.30 0.000 .1607782 .2135161
------------------------------------------------------------------------------------
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 = -28475.45
Iteration 1: log pseudolikelihood = -28445.667
Iteration 2: log pseudolikelihood = -28445.337
Iteration 3: log pseudolikelihood = -28445.337
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28445.337 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.292051 .097528 3.39 0.001 1.114367 1.498066
_rcs1 | 2.587462 .1143562 21.51 0.000 2.372761 2.82159
_rcs2 | 1.17964 .0522735 3.73 0.000 1.081509 1.286675
_rcs3 | 1.025846 .0320071 0.82 0.413 .9649927 1.090537
_rcs4 | .9860741 .0162156 -0.85 0.394 .9547989 1.018374
_rcs5 | .9745982 .0148479 -1.69 0.091 .9459269 1.004138
_rcs6 | .9995009 .0108628 -0.05 0.963 .9784354 1.02102
_rcs7 | .9997928 .0057497 -0.04 0.971 .9885869 1.011126
_rcs8 | .9929544 .0048337 -1.45 0.146 .9835255 1.002474
_rcs9 | 1.000143 .0035104 0.04 0.967 .9932864 1.007047
_rcs_tr_outcome1 | .9133335 .0421802 -1.96 0.050 .8342931 .9998622
_rcs_tr_outcome2 | .9318799 .0426466 -1.54 0.123 .8519331 1.019329
_rcs_tr_outcome3 | 1.029919 .0328044 0.93 0.355 .9675897 1.096264
_rcs_tr_outcome4 | 1.027963 .021602 1.31 0.189 .9864835 1.071186
_cons | .1854411 .0134494 -23.23 0.000 .1608686 .2137671
------------------------------------------------------------------------------------
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 = -28475.222
Iteration 1: log pseudolikelihood = -28437.577
Iteration 2: log pseudolikelihood = -28437.117
Iteration 3: log pseudolikelihood = -28437.117
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28437.117 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.294506 .0970922 3.44 0.001 1.117535 1.499502
_rcs1 | 2.584609 .1129828 21.72 0.000 2.372387 2.815814
_rcs2 | 1.165813 .0441221 4.05 0.000 1.082465 1.255579
_rcs3 | 1.041831 .0398693 1.07 0.284 .9665475 1.122979
_rcs4 | .9816609 .016245 -1.12 0.263 .9503322 1.014022
_rcs5 | .9700379 .0161203 -1.83 0.067 .9389517 1.002153
_rcs6 | 1.000149 .0104813 0.01 0.989 .9798153 1.020904
_rcs7 | 1.002694 .0077359 0.35 0.727 .9876457 1.017971
_rcs8 | .9936415 .0058295 -1.09 0.277 .9822813 1.005133
_rcs9 | 1.000085 .0035533 0.02 0.981 .9931453 1.007074
_rcs_tr_outcome1 | .9121001 .0418002 -2.01 0.045 .833745 .997819
_rcs_tr_outcome2 | .9434336 .0375948 -1.46 0.144 .8725532 1.020072
_rcs_tr_outcome3 | 1.004331 .0386959 0.11 0.911 .931281 1.08311
_rcs_tr_outcome4 | 1.046357 .0250735 1.89 0.059 .9983505 1.096673
_rcs_tr_outcome5 | 1.000585 .0119609 0.05 0.961 .9774149 1.024305
_cons | .185304 .0134227 -23.27 0.000 .1607781 .2135711
------------------------------------------------------------------------------------
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 = -28474.78
Iteration 1: log pseudolikelihood = -28429.239
Iteration 2: log pseudolikelihood = -28428.28
Iteration 3: log pseudolikelihood = -28428.279
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28428.279 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.298417 .0969228 3.50 0.000 1.121694 1.502982
_rcs1 | 2.590445 .1168279 21.11 0.000 2.371295 2.829848
_rcs2 | 1.160053 .0396068 4.35 0.000 1.084965 1.240337
_rcs3 | 1.053456 .0438102 1.25 0.210 .9709957 1.142919
_rcs4 | .9742101 .0190247 -1.34 0.181 .937627 1.012221
_rcs5 | .9715196 .0163972 -1.71 0.087 .9399074 1.004195
_rcs6 | 1.004333 .0118591 0.37 0.714 .9813563 1.027847
_rcs7 | .9983595 .0081329 -0.20 0.840 .9825459 1.014428
_rcs8 | .989199 .0080568 -1.33 0.182 .9735334 1.005117
_rcs9 | .9990855 .0037901 -0.24 0.809 .9916845 1.006542
_rcs_tr_outcome1 | .908178 .043091 -2.03 0.042 .8275293 .9966864
_rcs_tr_outcome2 | .9494724 .0346801 -1.42 0.156 .8838766 1.019936
_rcs_tr_outcome3 | .9856531 .0409818 -0.35 0.728 .908516 1.06934
_rcs_tr_outcome4 | 1.05419 .0273166 2.04 0.042 1.001987 1.109112
_rcs_tr_outcome5 | 1.003903 .0146969 0.27 0.790 .9755071 1.033126
_rcs_tr_outcome6 | 1.012825 .0110659 1.17 0.243 .991367 1.034748
_cons | .1849905 .0133821 -23.33 0.000 .1605366 .2131693
------------------------------------------------------------------------------------
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 = -28475.122
Iteration 1: log pseudolikelihood = -28433.093
Iteration 2: log pseudolikelihood = -28432.315
Iteration 3: log pseudolikelihood = -28432.314
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28432.314 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.29714 .0969656 3.48 0.001 1.120358 1.501817
_rcs1 | 2.587578 .1154711 21.30 0.000 2.370873 2.824089
_rcs2 | 1.161347 .0402236 4.32 0.000 1.085126 1.242921
_rcs3 | 1.050081 .0440542 1.16 0.244 .967191 1.140075
_rcs4 | .9760876 .0227997 -1.04 0.300 .9324085 1.021813
_rcs5 | .9714311 .0158699 -1.77 0.076 .9408193 1.003039
_rcs6 | 1.002121 .0117539 0.18 0.857 .9793471 1.025425
_rcs7 | 1.000955 .0083037 0.12 0.908 .9848119 1.017363
_rcs8 | .9892182 .009107 -1.18 0.239 .9715289 1.00723
_rcs9 | .9979471 .0045524 -0.45 0.652 .9890643 1.00691
_rcs_tr_outcome1 | .9098011 .0427527 -2.01 0.044 .8297503 .9975748
_rcs_tr_outcome2 | .9482854 .0346972 -1.45 0.147 .8826614 1.018789
_rcs_tr_outcome3 | .9856626 .0412823 -0.34 0.730 .9079827 1.069988
_rcs_tr_outcome4 | 1.048581 .0277301 1.79 0.073 .9956156 1.104364
_rcs_tr_outcome5 | 1.015663 .0164997 0.96 0.339 .9838335 1.048522
_rcs_tr_outcome6 | 1.004216 .0100791 0.42 0.675 .9846547 1.024167
_rcs_tr_outcome7 | 1.00938 .009809 0.96 0.337 .9903363 1.028789
_cons | .1850957 .0133991 -23.30 0.000 .1606119 .2133119
------------------------------------------------------------------------------------
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 = -28456.564
Iteration 1: log pseudolikelihood = -28448.54
Iteration 2: log pseudolikelihood = -28448.527
Iteration 3: log pseudolikelihood = -28448.527
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28448.527 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.283713 .0997991 3.21 0.001 1.102283 1.495004
_rcs1 | 2.530243 .1082071 21.71 0.000 2.326806 2.751467
_rcs2 | 1.127491 .018819 7.19 0.000 1.091203 1.164985
_rcs3 | 1.035994 .0184973 1.98 0.048 1.000367 1.07289
_rcs4 | 1.003518 .011007 0.32 0.749 .9821744 1.025324
_rcs5 | .9806223 .0103709 -1.85 0.064 .9605049 1.001161
_rcs6 | 1.006224 .0063536 0.98 0.326 .9938481 1.018755
_rcs7 | 1.005078 .0048709 1.05 0.296 .9955767 1.01467
_rcs8 | 1.00079 .0040551 0.19 0.845 .9928736 1.008769
_rcs9 | .9929952 .0042102 -1.66 0.097 .9847775 1.001281
_rcs10 | 1.001725 .0035498 0.49 0.627 .9947917 1.008707
_rcs_tr_outcome1 | .9433964 .0465482 -1.18 0.238 .8564361 1.039186
_cons | .1863347 .0137967 -22.69 0.000 .1611643 .2154363
------------------------------------------------------------------------------------
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 = -28456.68
Iteration 1: log pseudolikelihood = -28445.749
Iteration 2: log pseudolikelihood = -28445.714
Iteration 3: log pseudolikelihood = -28445.714
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28445.714 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.287992 .0984462 3.31 0.001 1.108798 1.496145
_rcs1 | 2.564402 .1144324 21.10 0.000 2.349646 2.798785
_rcs2 | 1.152515 .0413614 3.96 0.000 1.074234 1.236501
_rcs3 | 1.037616 .019057 2.01 0.044 1.000929 1.075647
_rcs4 | 1.004316 .0109242 0.40 0.692 .9831315 1.025957
_rcs5 | .9810778 .0101534 -1.85 0.065 .961378 1.001181
_rcs6 | 1.006189 .0063366 0.98 0.327 .9938455 1.018685
_rcs7 | 1.004975 .0048817 1.02 0.307 .9954523 1.014589
_rcs8 | 1.000694 .0040932 0.17 0.865 .9927031 1.008748
_rcs9 | .9929316 .0042645 -1.65 0.099 .9846084 1.001325
_rcs10 | 1.001711 .0036058 0.47 0.635 .9946686 1.008803
_rcs_tr_outcome1 | .9225026 .0440848 -1.69 0.091 .840021 1.013083
_rcs_tr_outcome2 | .9641872 .0381162 -0.92 0.356 .8923018 1.041864
_cons | .185865 .0135975 -23.00 0.000 .1610368 .214521
------------------------------------------------------------------------------------
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 = -28455.98
Iteration 1: log pseudolikelihood = -28432.909
Iteration 2: log pseudolikelihood = -28432.431
Iteration 3: log pseudolikelihood = -28432.43
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28432.43 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.292483 .0971783 3.41 0.001 1.115386 1.497699
_rcs1 | 2.600407 .1200497 20.70 0.000 2.375445 2.846673
_rcs2 | 1.195586 .0591894 3.61 0.000 1.085028 1.31741
_rcs3 | 1.012735 .0251162 0.51 0.610 .9646856 1.063178
_rcs4 | .9912296 .0158018 -0.55 0.581 .9607375 1.022689
_rcs5 | .973924 .0120756 -2.13 0.033 .9505416 .9978816
_rcs6 | 1.002939 .0070803 0.42 0.678 .9891578 1.016913
_rcs7 | 1.003973 .0051655 0.77 0.441 .9938996 1.014149
_rcs8 | 1.000641 .0040277 0.16 0.873 .992778 1.008567
_rcs9 | .9929407 .0041518 -1.69 0.090 .9848366 1.001112
_rcs10 | 1.001875 .0034586 0.54 0.587 .9951192 1.008677
_rcs_tr_outcome1 | .9089919 .0438079 -1.98 0.048 .8270606 .9990397
_rcs_tr_outcome2 | .9216264 .0464765 -1.62 0.106 .8348912 1.017372
_rcs_tr_outcome3 | 1.049315 .0301925 1.67 0.094 .9917769 1.110192
_cons | .1853379 .0133867 -23.34 0.000 .1608729 .2135233
------------------------------------------------------------------------------------
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 = -28456.586
Iteration 1: log pseudolikelihood = -28429.616
Iteration 2: log pseudolikelihood = -28429.271
Iteration 3: log pseudolikelihood = -28429.27
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28429.27 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.291444 .0972615 3.40 0.001 1.114217 1.496861
_rcs1 | 2.586512 .1135897 21.64 0.000 2.373193 2.819006
_rcs2 | 1.180681 .0530566 3.70 0.000 1.08114 1.289387
_rcs3 | 1.02399 .0325757 0.75 0.456 .9620923 1.089869
_rcs4 | .9912744 .0157785 -0.55 0.582 .9608266 1.022687
_rcs5 | .9704238 .0144329 -2.02 0.044 .9425442 .9991281
_rcs6 | .9978205 .0113293 -0.19 0.848 .9758608 1.020274
_rcs7 | 1.000036 .0075229 0.00 0.996 .9853999 1.01489
_rcs8 | .9987998 .0045338 -0.26 0.791 .9899532 1.007726
_rcs9 | .9924233 .0041552 -1.82 0.069 .9843126 1.000601
_rcs10 | 1.002044 .0033849 0.60 0.545 .9954319 1.008701
_rcs_tr_outcome1 | .9143174 .0422999 -1.94 0.053 .8350587 1.001099
_rcs_tr_outcome2 | .9326424 .0430277 -1.51 0.131 .8520102 1.020905
_rcs_tr_outcome3 | 1.030316 .0338622 0.91 0.363 .96604 1.098869
_rcs_tr_outcome4 | 1.027765 .0219876 1.28 0.200 .9855614 1.071776
_cons | .1854858 .0134235 -23.28 0.000 .1609569 .2137528
------------------------------------------------------------------------------------
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 = -28456.448
Iteration 1: log pseudolikelihood = -28422.97
Iteration 2: log pseudolikelihood = -28422.6
Iteration 3: log pseudolikelihood = -28422.6
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28422.6 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.293359 .0969025 3.43 0.001 1.116721 1.497938
_rcs1 | 2.583214 .1120903 21.87 0.000 2.372603 2.812519
_rcs2 | 1.168072 .0460515 3.94 0.000 1.081212 1.26191
_rcs3 | 1.037811 .0403366 0.95 0.340 .9616893 1.119959
_rcs4 | .9879727 .0154745 -0.77 0.440 .958104 1.018773
_rcs5 | .9663295 .0159321 -2.08 0.038 .9356024 .9980657
_rcs6 | .997338 .0107211 -0.25 0.804 .9765449 1.018574
_rcs7 | 1.002645 .0087992 0.30 0.763 .9855461 1.02004
_rcs8 | 1.000471 .0062332 0.08 0.940 .9883281 1.012762
_rcs9 | .9927119 .0047131 -1.54 0.123 .9835172 1.001993
_rcs10 | 1.001982 .0033931 0.58 0.559 .9953535 1.008655
_rcs_tr_outcome1 | .913748 .0419145 -1.97 0.049 .8351818 .9997051
_rcs_tr_outcome2 | .9431311 .038824 -1.42 0.155 .8700262 1.022379
_rcs_tr_outcome3 | 1.007773 .039958 0.20 0.845 .9324226 1.089213
_rcs_tr_outcome4 | 1.044048 .0252136 1.78 0.074 .9957811 1.094654
_rcs_tr_outcome5 | 1.000789 .0122166 0.06 0.948 .9771294 1.025022
_cons | .1853843 .0134037 -23.31 0.000 .1608901 .2136076
------------------------------------------------------------------------------------
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 = -28455.957
Iteration 1: log pseudolikelihood = -28414.159
Iteration 2: log pseudolikelihood = -28413.3
Iteration 3: log pseudolikelihood = -28413.3
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28413.3 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.297206 .0967423 3.49 0.000 1.1208 1.501375
_rcs1 | 2.587947 .1156863 21.27 0.000 2.370855 2.824918
_rcs2 | 1.160293 .0405535 4.25 0.000 1.083471 1.242562
_rcs3 | 1.052666 .0451568 1.20 0.232 .9677789 1.144999
_rcs4 | .9803049 .0177721 -1.10 0.273 .9460839 1.015764
_rcs5 | .9647535 .0167288 -2.07 0.039 .9325166 .9981047
_rcs6 | 1.001483 .0117873 0.13 0.900 .978645 1.024854
_rcs7 | 1.001559 .008647 0.18 0.857 .984754 1.018651
_rcs8 | .99613 .0077191 -0.50 0.617 .9811152 1.011375
_rcs9 | .9900851 .0060337 -1.64 0.102 .9783297 1.001982
_rcs10 | 1.001543 .003511 0.44 0.660 .9946851 1.008448
_rcs_tr_outcome1 | .9101479 .043057 -1.99 0.047 .829552 .9985742
_rcs_tr_outcome2 | .9507875 .0355602 -1.35 0.177 .883584 1.023102
_rcs_tr_outcome3 | .9847312 .0426895 -0.35 0.723 .9045173 1.072059
_rcs_tr_outcome4 | 1.056543 .0281244 2.07 0.039 1.002833 1.113129
_rcs_tr_outcome5 | 1.004459 .0148932 0.30 0.764 .9756888 1.034077
_rcs_tr_outcome6 | 1.011077 .0104748 1.06 0.288 .9907542 1.031817
_cons | .1850794 .0133659 -23.36 0.000 .1606523 .2132207
------------------------------------------------------------------------------------
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 = -28456.075
Iteration 1: log pseudolikelihood = -28414.798
Iteration 2: log pseudolikelihood = -28413.976
Iteration 3: log pseudolikelihood = -28413.975
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -28413.975 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.297429 .0968593 3.49 0.000 1.120823 1.501861
_rcs1 | 2.587453 .1153144 21.33 0.000 2.371031 2.82363
_rcs2 | 1.160486 .0407534 4.24 0.000 1.083298 1.243174
_rcs3 | 1.050647 .0453259 1.15 0.252 .9654625 1.143348
_rcs4 | .9802742 .0209838 -0.93 0.352 .9399976 1.022277
_rcs5 | .9661607 .0158863 -2.09 0.036 .9355205 .9978044
_rcs6 | 1.000495 .0122953 0.04 0.968 .9766844 1.024886
_rcs7 | 1.002848 .0092501 0.31 0.758 .9848806 1.021142
_rcs8 | .9968803 .0075416 -0.41 0.680 .9822081 1.011772
_rcs9 | .9878505 .0076881 -1.57 0.116 .9728964 1.003034
_rcs10 | 1.000668 .0038366 0.17 0.862 .9931763 1.008216
_rcs_tr_outcome1 | .9101993 .0430788 -1.99 0.047 .8295642 .9986721
_rcs_tr_outcome2 | .9510552 .035485 -1.34 0.179 .8839881 1.023211
_rcs_tr_outcome3 | .9836319 .0425667 -0.38 0.703 .9036429 1.070701
_rcs_tr_outcome4 | 1.052115 .0280748 1.90 0.057 .9985037 1.108605
_rcs_tr_outcome5 | 1.012554 .0165959 0.76 0.447 .9805434 1.045609
_rcs_tr_outcome6 | 1.004657 .0103484 0.45 0.652 .9845778 1.025146
_rcs_tr_outcome7 | 1.011312 .0098051 1.16 0.246 .9922757 1.030713
_cons | .1850732 .0133787 -23.34 0.000 .1606243 .2132436
------------------------------------------------------------------------------------
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_pr
> in3 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 mzone
> 2 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_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 = -29460.662
Iteration 1: log pseudolikelihood = -29423.761
Iteration 2: log pseudolikelihood = -29423.677
Iteration 3: log pseudolikelihood = -29423.677
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 29,848
Wald chi2(1) = 7.49
Log pseudolikelihood = -29423.677 Prob > chi2 = 0.0062
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.207003 .0829812 2.74 0.006 1.054845 1.38111
_cons | .0839327 .005538 -37.55 0.000 .073751 .09552
------------------------------------------------------------------------------
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 = -29460.662
Iteration 1: log pseudolikelihood = -28759.604
Iteration 2: log pseudolikelihood = -28749.929
Iteration 3: log pseudolikelihood = -28749.927
Fitting full model:
Iteration 0: log pseudolikelihood = -28749.927
Iteration 1: log pseudolikelihood = -28703.333
Iteration 2: log pseudolikelihood = -28703.199
Iteration 3: log pseudolikelihood = -28703.199
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 29,848
Wald chi2(1) = 10.30
Log pseudolikelihood = -28703.199 Prob > chi2 = 0.0013
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.235626 .0814545 3.21 0.001 1.085862 1.406046
_cons | .1221219 .0085947 -29.88 0.000 .1063869 .1401843
-------------+----------------------------------------------------------------
/ln_p | -.3398481 .0220005 -15.45 0.000 -.3829684 -.2967279
-------------+----------------------------------------------------------------
p | .7118784 .0156617 .6818345 .7432462
1/p | 1.404734 .0309049 1.345449 1.466632
------------------------------------------------------------------------------
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 = -29436.832
Iteration 1: log pseudolikelihood = -28741.066
Iteration 2: log pseudolikelihood = -28703.412
Iteration 3: log pseudolikelihood = -28703.291
Iteration 4: log pseudolikelihood = -28703.291
Fitting full model:
Iteration 0: log pseudolikelihood = -28703.291
Iteration 1: log pseudolikelihood = -28653.341
Iteration 2: log pseudolikelihood = -28653.186
Iteration 3: log pseudolikelihood = -28653.186
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 29,848
Wald chi2(1) = 11.12
Log pseudolikelihood = -28653.186 Prob > chi2 = 0.0009
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.244993 .0818101 3.33 0.001 1.094544 1.416121
_cons | .138599 .0119115 -22.99 0.000 .1171134 .1640265
-------------+----------------------------------------------------------------
/gamma | -.2586473 .0261315 -9.90 0.000 -.3098641 -.2074306
------------------------------------------------------------------------------
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 = -39763.535
Iteration 1: log pseudolikelihood = -29520.767
Iteration 2: log pseudolikelihood = -28632.867
Iteration 3: log pseudolikelihood = -28576.097
Iteration 4: log pseudolikelihood = -28575.952
Iteration 5: log pseudolikelihood = -28575.952
Fitting full model:
Iteration 0: log pseudolikelihood = -28575.952
Iteration 1: log pseudolikelihood = -28516.291
Iteration 2: log pseudolikelihood = -28515.671
Iteration 3: log pseudolikelihood = -28515.671
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 29,848
Wald chi2(1) = 12.89
Log pseudolikelihood = -28515.671 Prob > chi2 = 0.0003
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .6812799 .072838 -3.59 0.000 .5524851 .8400991
_cons | 15.83709 1.759833 24.86 0.000 12.73764 19.69072
-------------+----------------------------------------------------------------
/lnsigma | .8617207 .0211764 40.69 0.000 .8202157 .9032256
-------------+----------------------------------------------------------------
sigma | 2.36723 .0501294 2.27099 2.46755
------------------------------------------------------------------------------
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 = -29019.97
Iteration 1: log pseudolikelihood = -28655.698
Iteration 2: log pseudolikelihood = -28645.454
Iteration 3: log pseudolikelihood = -28645.447
Iteration 4: log pseudolikelihood = -28645.447
Fitting full model:
Iteration 0: log pseudolikelihood = -28645.447
Iteration 1: log pseudolikelihood = -28593.144
Iteration 2: log pseudolikelihood = -28592.632
Iteration 3: log pseudolikelihood = -28592.632
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 29,848
Wald chi2(1) = 11.33
Log pseudolikelihood = -28592.632 Prob > chi2 = 0.0008
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .7144461 .071364 -3.37 0.001 .5874155 .8689476
_cons | 12.94776 1.259397 26.33 0.000 10.70041 15.66711
-------------+----------------------------------------------------------------
/lngamma | .2332321 .0221237 10.54 0.000 .1898705 .2765937
-------------+----------------------------------------------------------------
gamma | 1.262675 .027935 1.209093 1.318631
------------------------------------------------------------------------------
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 colinear 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 | 9,961 . -28694.42 4 57396.84 57425.66
m2_stipw_n~2 | 9,961 . -28625.34 5 57260.69 57296.72
m2_stipw_n~3 | 9,961 . -28622.53 6 57257.07 57300.31
m2_stipw_n~4 | 9,961 . -28621.45 7 57256.91 57307.35
m2_stipw_n~5 | 9,961 . -28617.63 8 57251.25 57308.91
m2_stipw_n~6 | 9,961 . -28617.69 9 57253.38 57318.24
m2_stipw_n~7 | 9,961 . -28614.98 10 57249.97 57322.03
m2_stipw_n~1 | 9,961 . -28528.08 5 57066.17 57102.2
m2_stipw_n~2 | 9,961 . -28523.3 6 57058.61 57101.85
m2_stipw_n~3 | 9,961 . -28520.94 7 57055.87 57106.32
m2_stipw_n~4 | 9,961 . -28519.42 8 57054.83 57112.48
m2_stipw_n~5 | 9,961 . -28515.63 9 57049.26 57114.12
m2_stipw_n~6 | 9,961 . -28515.65 10 57051.3 57123.37
m2_stipw_n~7 | 9,961 . -28512.9 11 57047.8 57127.07
m2_stipw_n~1 | 9,961 . -28527.51 6 57067.02 57110.26
m2_stipw_n~2 | 9,961 . -28522.36 7 57058.71 57109.16
m2_stipw_n~3 | 9,961 . -28507.93 8 57031.87 57089.52
m2_stipw_n~4 | 9,961 . -28505.4 9 57028.81 57093.67
m2_stipw_n~5 | 9,961 . -28502.95 10 57025.91 57097.97
m2_stipw_n~6 | 9,961 . -28503.09 11 57028.18 57107.45
m2_stipw_n~7 | 9,961 . -28500.31 12 57024.63 57111.1
m2_stipw_n~1 | 9,961 . -28527.11 7 57068.22 57118.67
m2_stipw_n~2 | 9,961 . -28522.3 8 57060.6 57118.25
m2_stipw_n~3 | 9,961 . -28511.95 9 57041.9 57106.75
m2_stipw_n~4 | 9,961 . -28506.61 10 57033.23 57105.29
m2_stipw_n~5 | 9,961 . -28503.58 11 57029.17 57108.44
m2_stipw_n~6 | 9,961 . -28503.25 12 57030.51 57116.98
m2_stipw_n~7 | 9,961 . -28499.84 13 57025.68 57119.37
m2_stipw_n~1 | 9,961 . -28501.52 8 57019.04 57076.7
m2_stipw_n~2 | 9,961 . -28498.26 9 57014.52 57079.38
m2_stipw_n~3 | 9,961 . -28486.43 10 56992.86 57064.93
m2_stipw_n~4 | 9,961 . -28479.81 11 56981.61 57060.88
m2_stipw_n~5 | 9,961 . -28476.31 12 56976.61 57063.09
m2_stipw_n~6 | 9,961 . -28475.24 13 56976.47 57070.16
m2_stipw_n~7 | 9,961 . -28473.31 14 56974.62 57075.51
m2_stipw_n~1 | 9,961 . -28485.53 9 56989.07 57053.93
m2_stipw_n~2 | 9,961 . -28482.79 10 56985.58 57057.65
m2_stipw_n~3 | 9,961 . -28472.33 11 56966.66 57045.93
m2_stipw_n~4 | 9,961 . -28467.7 12 56959.4 57045.88
m2_stipw_n~5 | 9,961 . -28463.07 13 56952.15 57045.83
m2_stipw_n~6 | 9,961 . -28448.79 14 56925.58 57026.47
m2_stipw_n~7 | 9,961 . -28448.42 15 56926.85 57034.94
m2_stipw_n~1 | 9,961 . -28476.74 10 56973.49 57045.55
m2_stipw_n~2 | 9,961 . -28473.77 11 56969.54 57048.81
m2_stipw_n~3 | 9,961 . -28462.28 12 56948.56 57035.03
m2_stipw_n~4 | 9,961 . -28458.69 13 56943.38 57037.06
m2_stipw_n~5 | 9,961 . -28450.64 14 56929.29 57030.18
m2_stipw_n~6 | 9,961 . -28444.4 15 56918.81 57026.91
m2_stipw_n~7 | 9,961 . -28440.26 16 56912.53 57027.83
m2_stipw_n~1 | 9,961 . -28485.61 11 56993.22 57072.49
m2_stipw_n~2 | 9,961 . -28482.58 12 56989.16 57075.63
m2_stipw_n~3 | 9,961 . -28470.96 13 56967.91 57061.6
m2_stipw_n~4 | 9,961 . -28467.25 14 56962.49 57063.38
m2_stipw_n~5 | 9,961 . -28460.72 15 56951.45 57059.54
m2_stipw_n~6 | 9,961 . -28456.78 16 56945.56 57060.86
m2_stipw_n~7 | 9,961 . -28457.06 17 56948.11 57070.62
m2_stipw_n~1 | 9,961 . -28464.6 12 56953.2 57039.67
m2_stipw_n~2 | 9,961 . -28461.69 13 56949.38 57043.06
m2_stipw_n~3 | 9,961 . -28448.64 14 56925.29 57026.18
m2_stipw_n~4 | 9,961 . -28445.34 15 56920.67 57028.77
m2_stipw_n~5 | 9,961 . -28437.12 16 56906.23 57021.54
m2_stipw_n~6 | 9,961 . -28428.28 17 56890.56 57013.07
m2_stipw_n~7 | 9,961 . -28432.31 18 56900.63 57030.34
m2_stipw_n~1 | 9,961 . -28448.53 13 56923.05 57016.74
m2_stipw_n~2 | 9,961 . -28445.71 14 56919.43 57020.32
m2_stipw_n~3 | 9,961 . -28432.43 15 56894.86 57002.96
m2_stipw_n~4 | 9,961 . -28429.27 16 56890.54 57005.84
m2_stipw_n~5 | 9,961 . -28422.6 17 56879.2 57001.71
m2_stipw_n~6 | 9,961 . -28413.3 18 56862.6 56992.32
m2_stipw_n~7 | 9,961 . -28413.98 19 56865.95 57002.87
m2_stipw_n~p | 9,961 -29460.66 -29423.68 2 58851.35 58865.77
m2_stipw_n~i | 9,961 -28749.93 -28703.2 3 57412.4 57434.02
m2_stipw_n~m | 9,961 -28703.29 -28653.19 3 57312.37 57333.99
m2_stipw_n~n | 9,961 -28575.95 -28515.67 3 57037.34 57058.96
m2_stipw_n~g | 9,961 -28645.45 -28592.63 3 57191.26 57212.88
-----------------------------------------------------------------------------
. //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.csv", replace
(output written to testreg_aic_bic_mrl_23_3.csv)
. esttab matrix(stats_3) using "testreg_aic_bic_mrl_23_3.html", replace
(output written to testreg_aic_bic_mrl_23_3.html)
.
. *m2_stipw_nostag_rp5_tvcdf1
.
| stats_3 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m2_stipw_nostag_rp10_tvcdf6 | 9961 | . | -28413.3 | 18 | 56862.6 | 56992.32 |
| m2_stipw_nostag_rp10_tvcdf7 | 9961 | . | -28413.98 | 19 | 56865.95 | 57002.87 |
| m2_stipw_nostag_rp10_tvcdf5 | 9961 | . | -28422.6 | 17 | 56879.2 | 57001.71 |
| m2_stipw_nostag_rp10_tvcdf4 | 9961 | . | -28429.27 | 16 | 56890.54 | 57005.84 |
| m2_stipw_nostag_rp9_tvcdf6 | 9961 | . | -28428.28 | 17 | 56890.56 | 57013.07 |
| m2_stipw_nostag_rp10_tvcdf3 | 9961 | . | -28432.43 | 15 | 56894.86 | 57002.96 |
| m2_stipw_nostag_rp9_tvcdf7 | 9961 | . | -28432.31 | 18 | 56900.63 | 57030.34 |
| m2_stipw_nostag_rp9_tvcdf5 | 9961 | . | -28437.12 | 16 | 56906.23 | 57021.54 |
| m2_stipw_nostag_rp7_tvcdf7 | 9961 | . | -28440.26 | 16 | 56912.53 | 57027.83 |
| m2_stipw_nostag_rp7_tvcdf6 | 9961 | . | -28444.4 | 15 | 56918.81 | 57026.91 |
| m2_stipw_nostag_rp10_tvcdf2 | 9961 | . | -28445.71 | 14 | 56919.43 | 57020.32 |
| m2_stipw_nostag_rp9_tvcdf4 | 9961 | . | -28445.34 | 15 | 56920.67 | 57028.77 |
| m2_stipw_nostag_rp10_tvcdf1 | 9961 | . | -28448.53 | 13 | 56923.05 | 57016.74 |
| m2_stipw_nostag_rp9_tvcdf3 | 9961 | . | -28448.64 | 14 | 56925.29 | 57026.18 |
| m2_stipw_nostag_rp6_tvcdf6 | 9961 | . | -28448.79 | 14 | 56925.58 | 57026.47 |
| m2_stipw_nostag_rp6_tvcdf7 | 9961 | . | -28448.42 | 15 | 56926.85 | 57034.94 |
| m2_stipw_nostag_rp7_tvcdf5 | 9961 | . | -28450.64 | 14 | 56929.29 | 57030.18 |
| m2_stipw_nostag_rp7_tvcdf4 | 9961 | . | -28458.69 | 13 | 56943.38 | 57037.06 |
| m2_stipw_nostag_rp8_tvcdf6 | 9961 | . | -28456.78 | 16 | 56945.56 | 57060.86 |
| m2_stipw_nostag_rp8_tvcdf7 | 9961 | . | -28457.06 | 17 | 56948.11 | 57070.62 |
| m2_stipw_nostag_rp7_tvcdf3 | 9961 | . | -28462.28 | 12 | 56948.56 | 57035.03 |
| m2_stipw_nostag_rp9_tvcdf2 | 9961 | . | -28461.69 | 13 | 56949.38 | 57043.06 |
| m2_stipw_nostag_rp8_tvcdf5 | 9961 | . | -28460.72 | 15 | 56951.45 | 57059.54 |
| m2_stipw_nostag_rp6_tvcdf5 | 9961 | . | -28463.07 | 13 | 56952.15 | 57045.83 |
| m2_stipw_nostag_rp9_tvcdf1 | 9961 | . | -28464.6 | 12 | 56953.2 | 57039.67 |
| m2_stipw_nostag_rp6_tvcdf4 | 9961 | . | -28467.7 | 12 | 56959.4 | 57045.88 |
| m2_stipw_nostag_rp8_tvcdf4 | 9961 | . | -28467.25 | 14 | 56962.49 | 57063.38 |
| m2_stipw_nostag_rp6_tvcdf3 | 9961 | . | -28472.33 | 11 | 56966.66 | 57045.93 |
| m2_stipw_nostag_rp8_tvcdf3 | 9961 | . | -28470.96 | 13 | 56967.91 | 57061.6 |
| m2_stipw_nostag_rp7_tvcdf2 | 9961 | . | -28473.77 | 11 | 56969.54 | 57048.81 |
| m2_stipw_nostag_rp7_tvcdf1 | 9961 | . | -28476.74 | 10 | 56973.49 | 57045.55 |
| m2_stipw_nostag_rp5_tvcdf7 | 9961 | . | -28473.31 | 14 | 56974.62 | 57075.51 |
| m2_stipw_nostag_rp5_tvcdf6 | 9961 | . | -28475.24 | 13 | 56976.47 | 57070.16 |
| m2_stipw_nostag_rp5_tvcdf5 | 9961 | . | -28476.31 | 12 | 56976.61 | 57063.09 |
| m2_stipw_nostag_rp5_tvcdf4 | 9961 | . | -28479.81 | 11 | 56981.61 | 57060.88 |
| m2_stipw_nostag_rp6_tvcdf2 | 9961 | . | -28482.79 | 10 | 56985.58 | 57057.65 |
| m2_stipw_nostag_rp6_tvcdf1 | 9961 | . | -28485.53 | 9 | 56989.07 | 57053.93 |
| m2_stipw_nostag_rp8_tvcdf2 | 9961 | . | -28482.58 | 12 | 56989.16 | 57075.63 |
| m2_stipw_nostag_rp5_tvcdf3 | 9961 | . | -28486.43 | 10 | 56992.86 | 57064.93 |
| m2_stipw_nostag_rp8_tvcdf1 | 9961 | . | -28485.61 | 11 | 56993.22 | 57072.49 |
| m2_stipw_nostag_rp5_tvcdf2 | 9961 | . | -28498.26 | 9 | 57014.52 | 57079.38 |
| m2_stipw_nostag_rp5_tvcdf1 | 9961 | . | -28501.52 | 8 | 57019.04 | 57076.7 |
| m2_stipw_nostag_rp3_tvcdf7 | 9961 | . | -28500.31 | 12 | 57024.63 | 57111.1 |
| m2_stipw_nostag_rp4_tvcdf7 | 9961 | . | -28499.84 | 13 | 57025.68 | 57119.37 |
| m2_stipw_nostag_rp3_tvcdf5 | 9961 | . | -28502.95 | 10 | 57025.91 | 57097.97 |
| m2_stipw_nostag_rp3_tvcdf6 | 9961 | . | -28503.09 | 11 | 57028.18 | 57107.45 |
| m2_stipw_nostag_rp3_tvcdf4 | 9961 | . | -28505.4 | 9 | 57028.81 | 57093.67 |
| m2_stipw_nostag_rp4_tvcdf5 | 9961 | . | -28503.58 | 11 | 57029.17 | 57108.44 |
| m2_stipw_nostag_rp4_tvcdf6 | 9961 | . | -28503.25 | 12 | 57030.51 | 57116.98 |
| m2_stipw_nostag_rp3_tvcdf3 | 9961 | . | -28507.93 | 8 | 57031.87 | 57089.52 |
| m2_stipw_nostag_rp4_tvcdf4 | 9961 | . | -28506.61 | 10 | 57033.23 | 57105.29 |
| m2_stipw_nostag_logn | 9961 | -28575.95 | -28515.67 | 3 | 57037.34 | 57058.96 |
| m2_stipw_nostag_rp4_tvcdf3 | 9961 | . | -28511.95 | 9 | 57041.9 | 57106.75 |
| m2_stipw_nostag_rp2_tvcdf7 | 9961 | . | -28512.9 | 11 | 57047.8 | 57127.07 |
| m2_stipw_nostag_rp2_tvcdf5 | 9961 | . | -28515.63 | 9 | 57049.26 | 57114.12 |
| m2_stipw_nostag_rp2_tvcdf6 | 9961 | . | -28515.65 | 10 | 57051.3 | 57123.37 |
| m2_stipw_nostag_rp2_tvcdf4 | 9961 | . | -28519.42 | 8 | 57054.83 | 57112.48 |
| m2_stipw_nostag_rp2_tvcdf3 | 9961 | . | -28520.94 | 7 | 57055.87 | 57106.32 |
| m2_stipw_nostag_rp2_tvcdf2 | 9961 | . | -28523.3 | 6 | 57058.61 | 57101.85 |
| m2_stipw_nostag_rp3_tvcdf2 | 9961 | . | -28522.36 | 7 | 57058.71 | 57109.16 |
| m2_stipw_nostag_rp4_tvcdf2 | 9961 | . | -28522.3 | 8 | 57060.6 | 57118.25 |
| m2_stipw_nostag_rp2_tvcdf1 | 9961 | . | -28528.08 | 5 | 57066.17 | 57102.2 |
| m2_stipw_nostag_rp3_tvcdf1 | 9961 | . | -28527.51 | 6 | 57067.02 | 57110.26 |
| m2_stipw_nostag_rp4_tvcdf1 | 9961 | . | -28527.11 | 7 | 57068.22 | 57118.67 |
| m2_stipw_nostag_llog | 9961 | -28645.45 | -28592.63 | 3 | 57191.26 | 57212.88 |
| m2_stipw_nostag_rp1_tvcdf7 | 9961 | . | -28614.98 | 10 | 57249.97 | 57322.03 |
| m2_stipw_nostag_rp1_tvcdf5 | 9961 | . | -28617.63 | 8 | 57251.25 | 57308.91 |
| m2_stipw_nostag_rp1_tvcdf6 | 9961 | . | -28617.69 | 9 | 57253.38 | 57318.24 |
| m2_stipw_nostag_rp1_tvcdf4 | 9961 | . | -28621.45 | 7 | 57256.91 | 57307.35 |
| m2_stipw_nostag_rp1_tvcdf3 | 9961 | . | -28622.53 | 6 | 57257.07 | 57300.31 |
| m2_stipw_nostag_rp1_tvcdf2 | 9961 | . | -28625.34 | 5 | 57260.69 | 57296.72 |
| m2_stipw_nostag_gom | 9961 | -28703.29 | -28653.19 | 3 | 57312.37 | 57333.99 |
| m2_stipw_nostag_rp1_tvcdf1 | 9961 | . | -28694.42 | 4 | 57396.84 | 57425.66 |
| m2_stipw_nostag_wei | 9961 | -28749.93 | -28703.2 | 3 | 57412.4 | 57434.02 |
| m2_stipw_nostag_exp | 9961 | -29460.66 | -29423.68 | 2 | 58851.35 | 58865.77 |
. estimates replay m2_stipw_nostag_rp10_tvcdf6, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m2_stipw_nostag_rp10_tvcdf6
------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -28413.3 Number of obs = 29,848
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.297206 .0967423 3.49 0.000 1.1208 1.501375
_rcs1 | 2.587947 .1156863 21.27 0.000 2.370855 2.824918
_rcs2 | 1.160293 .0405535 4.25 0.000 1.083471 1.242562
_rcs3 | 1.052666 .0451568 1.20 0.232 .9677789 1.144999
_rcs4 | .9803049 .0177721 -1.10 0.273 .9460839 1.015764
_rcs5 | .9647535 .0167288 -2.07 0.039 .9325166 .9981047
_rcs6 | 1.001483 .0117873 0.13 0.900 .978645 1.024854
_rcs7 | 1.001559 .008647 0.18 0.857 .984754 1.018651
_rcs8 | .99613 .0077191 -0.50 0.617 .9811152 1.011375
_rcs9 | .9900851 .0060337 -1.64 0.102 .9783297 1.001982
_rcs10 | 1.001543 .003511 0.44 0.660 .9946851 1.008448
_rcs_tr_outcome1 | .9101479 .043057 -1.99 0.047 .829552 .9985742
_rcs_tr_outcome2 | .9507875 .0355602 -1.35 0.177 .883584 1.023102
_rcs_tr_outcome3 | .9847312 .0426895 -0.35 0.723 .9045173 1.072059
_rcs_tr_outcome4 | 1.056543 .0281244 2.07 0.039 1.002833 1.113129
_rcs_tr_outcome5 | 1.004459 .0148932 0.30 0.764 .9756888 1.034077
_rcs_tr_outcome6 | 1.011077 .0104748 1.06 0.288 .9907542 1.031817
_cons | .1850794 .0133659 -23.36 0.000 .1606523 .2132207
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m2_stipw_nostag_rp10_tvcdf6
(results m2_stipw_nostag_rp10_tvcdf6 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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_22_b.gph saved)
.
.
. 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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_b.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_pr
> in3 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 mzone
> 2 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_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(mes
> timation) 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 = -48306.677
Iteration 1: log pseudolikelihood = -48004.266
Iteration 2: log pseudolikelihood = -48001.255
Iteration 3: log pseudolikelihood = -48001.254
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -48001.254 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.07568 .0246336 3.19 0.001 1.028467 1.125061
_rcs1 | 2.411842 .0194809 109.00 0.000 2.373961 2.450328
_rcs_tr_outcome1 | .9520947 .0142355 -3.28 0.001 .9245986 .9804086
_cons | .250868 .0031788 -109.13 0.000 .2447145 .2571763
------------------------------------------------------------------------------------
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 = -48106.836
Iteration 1: log pseudolikelihood = -47926.572
Iteration 2: log pseudolikelihood = -47925.6
Iteration 3: log pseudolikelihood = -47925.6
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47925.6 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.080181 .0249312 3.34 0.001 1.032405 1.130167
_rcs1 | 2.411842 .0194809 109.00 0.000 2.373961 2.450328
_rcs_tr_outcome1 | 1.001759 .0182861 0.10 0.923 .9665526 1.038248
_rcs_tr_outcome2 | 1.117996 .0146235 8.53 0.000 1.089699 1.147028
_cons | .250868 .0031788 -109.13 0.000 .2447145 .2571763
------------------------------------------------------------------------------------
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 = -48081.166
Iteration 1: log pseudolikelihood = -47924.329
Iteration 2: log pseudolikelihood = -47923.556
Iteration 3: log pseudolikelihood = -47923.556
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47923.556 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08044 .0249249 3.35 0.001 1.032676 1.130414
_rcs1 | 2.411842 .0194809 109.00 0.000 2.373961 2.450328
_rcs_tr_outcome1 | 1.001177 .0179185 0.07 0.948 .966666 1.03692
_rcs_tr_outcome2 | 1.108584 .0143075 7.99 0.000 1.080893 1.136983
_rcs_tr_outcome3 | 1.017669 .0083071 2.15 0.032 1.001517 1.034082
_cons | .250868 .0031788 -109.13 0.000 .2447145 .2571763
------------------------------------------------------------------------------------
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 = -48080.079
Iteration 1: log pseudolikelihood = -47923.927
Iteration 2: log pseudolikelihood = -47923.157
Iteration 3: log pseudolikelihood = -47923.157
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47923.157 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.080546 .0249258 3.36 0.001 1.032781 1.130521
_rcs1 | 2.411842 .0194809 109.00 0.000 2.373961 2.450328
_rcs_tr_outcome1 | 1.001252 .0178831 0.07 0.944 .9668078 1.036923
_rcs_tr_outcome2 | 1.107374 .0143792 7.85 0.000 1.079547 1.135919
_rcs_tr_outcome3 | 1.020941 .0086643 2.44 0.015 1.004099 1.038064
_rcs_tr_outcome4 | 1.004826 .0056848 0.85 0.395 .9937455 1.01603
_cons | .250868 .0031788 -109.13 0.000 .2447145 .2571763
------------------------------------------------------------------------------------
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 = -48076.49
Iteration 1: log pseudolikelihood = -47921.579
Iteration 2: log pseudolikelihood = -47920.812
Iteration 3: log pseudolikelihood = -47920.812
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47920.812 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.080699 .0249283 3.36 0.001 1.032928 1.130679
_rcs1 | 2.411842 .0194809 109.00 0.000 2.373961 2.450328
_rcs_tr_outcome1 | 1.001082 .0177422 0.06 0.951 .9669046 1.036467
_rcs_tr_outcome2 | 1.103861 .0135293 8.06 0.000 1.07766 1.130699
_rcs_tr_outcome3 | 1.026904 .0090055 3.03 0.002 1.009404 1.044707
_rcs_tr_outcome4 | 1.001796 .0059216 0.30 0.762 .9902565 1.013469
_rcs_tr_outcome5 | 1.006885 .0041723 1.66 0.098 .9987404 1.015096
_cons | .250868 .0031788 -109.13 0.000 .2447145 .2571763
------------------------------------------------------------------------------------
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 = -48077.836
Iteration 1: log pseudolikelihood = -47922.092
Iteration 2: log pseudolikelihood = -47921.302
Iteration 3: log pseudolikelihood = -47921.302
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47921.302 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.080495 .0249265 3.36 0.001 1.032728 1.130471
_rcs1 | 2.411842 .0194809 109.00 0.000 2.373961 2.450328
_rcs_tr_outcome1 | 1.001119 .0177379 0.06 0.950 .9669504 1.036496
_rcs_tr_outcome2 | 1.103275 .0133174 8.14 0.000 1.07748 1.129688
_rcs_tr_outcome3 | 1.029543 .009273 3.23 0.001 1.011528 1.047879
_rcs_tr_outcome4 | 1.002099 .006234 0.34 0.736 .9899553 1.014393
_rcs_tr_outcome5 | 1.00657 .0043626 1.51 0.131 .9980559 1.015157
_rcs_tr_outcome6 | 1.000619 .0034285 0.18 0.857 .9939216 1.007361
_cons | .250868 .0031788 -109.13 0.000 .2447145 .2571763
------------------------------------------------------------------------------------
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 = -48076.587
Iteration 1: log pseudolikelihood = -47918.463
Iteration 2: log pseudolikelihood = -47917.63
Iteration 3: log pseudolikelihood = -47917.63
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47917.63 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.080509 .0249281 3.36 0.001 1.032739 1.130489
_rcs1 | 2.411842 .0194809 109.00 0.000 2.373961 2.450328
_rcs_tr_outcome1 | 1.000986 .0177823 0.06 0.956 .9667332 1.036453
_rcs_tr_outcome2 | 1.104209 .0138054 7.93 0.000 1.07748 1.131602
_rcs_tr_outcome3 | 1.02843 .0096351 2.99 0.003 1.009718 1.047489
_rcs_tr_outcome4 | 1.006585 .0064565 1.02 0.306 .9940096 1.019319
_rcs_tr_outcome5 | 1.003222 .0045412 0.71 0.477 .9943602 1.012162
_rcs_tr_outcome6 | 1.005374 .0035744 1.51 0.132 .9983929 1.012404
_rcs_tr_outcome7 | .9963082 .0029903 -1.23 0.218 .9904646 1.002186
_cons | .250868 .0031788 -109.13 0.000 .2447145 .2571763
------------------------------------------------------------------------------------
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 = -47825.611
Iteration 1: log pseudolikelihood = -47769.094
Iteration 2: log pseudolikelihood = -47768.857
Iteration 3: log pseudolikelihood = -47768.857
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47768.857 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083333 .0250689 3.46 0.001 1.035296 1.133598
_rcs1 | 2.553904 .0286511 83.58 0.000 2.498362 2.610681
_rcs2 | 1.126787 .0091221 14.74 0.000 1.109049 1.144808
_rcs_tr_outcome1 | .9506638 .0167025 -2.88 0.004 .9184848 .9839702
_cons | .2500058 .0032316 -107.24 0.000 .2437514 .2564206
------------------------------------------------------------------------------------
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 = -47826.733
Iteration 1: log pseudolikelihood = -47768.626
Iteration 2: log pseudolikelihood = -47768.329
Iteration 3: log pseudolikelihood = -47768.329
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47768.329 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084488 .0252116 3.49 0.000 1.036183 1.135044
_rcs1 | 2.561288 .031619 76.19 0.000 2.500059 2.624016
_rcs2 | 1.131979 .0116568 12.04 0.000 1.109361 1.155058
_rcs_tr_outcome1 | .9433086 .0193437 -2.85 0.004 .9061475 .9819937
_rcs_tr_outcome2 | .9876477 .0164342 -0.75 0.455 .9559569 1.020389
_cons | .2498717 .0032435 -106.84 0.000 .2435947 .2563104
------------------------------------------------------------------------------------
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 = -47800.846
Iteration 1: log pseudolikelihood = -47766.559
Iteration 2: log pseudolikelihood = -47766.463
Iteration 3: log pseudolikelihood = -47766.463
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47766.463 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084679 .0252027 3.50 0.000 1.036391 1.135218
_rcs1 | 2.561139 .0316051 76.21 0.000 2.499937 2.623839
_rcs2 | 1.131875 .0116507 12.03 0.000 1.109269 1.154942
_rcs_tr_outcome1 | .9427344 .0190119 -2.92 0.003 .9061986 .9807432
_rcs_tr_outcome2 | .9792468 .0161005 -1.28 0.202 .9481935 1.011317
_rcs_tr_outcome3 | 1.011099 .0082735 1.35 0.177 .9950123 1.027445
_cons | .2498745 .0032434 -106.84 0.000 .2435976 .256313
------------------------------------------------------------------------------------
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 = -47799.976
Iteration 1: log pseudolikelihood = -47765.982
Iteration 2: log pseudolikelihood = -47765.886
Iteration 3: log pseudolikelihood = -47765.886
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47765.886 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084855 .0252063 3.51 0.000 1.03656 1.135401
_rcs1 | 2.561288 .031619 76.19 0.000 2.500059 2.624016
_rcs2 | 1.131979 .0116568 12.04 0.000 1.109361 1.155058
_rcs_tr_outcome1 | .9428307 .0190046 -2.92 0.003 .9063085 .9808246
_rcs_tr_outcome2 | .9787581 .0161889 -1.30 0.194 .9475371 1.011008
_rcs_tr_outcome3 | 1.009597 .0086188 1.12 0.263 .9928445 1.026631
_rcs_tr_outcome4 | 1.004826 .0056848 0.85 0.395 .9937455 1.01603
_cons | .2498717 .0032435 -106.84 0.000 .2435947 .2563104
------------------------------------------------------------------------------------
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 = -47796.403
Iteration 1: log pseudolikelihood = -47763.667
Iteration 2: log pseudolikelihood = -47763.573
Iteration 3: log pseudolikelihood = -47763.573
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47763.573 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085008 .0252089 3.51 0.000 1.036707 1.135559
_rcs1 | 2.561264 .0316169 76.19 0.000 2.500039 2.623987
_rcs2 | 1.131962 .011656 12.04 0.000 1.109345 1.155039
_rcs_tr_outcome1 | .9426834 .0188873 -2.95 0.003 .9063824 .9804383
_rcs_tr_outcome2 | .975949 .0155809 -1.52 0.127 .9458839 1.00697
_rcs_tr_outcome3 | 1.012751 .0089585 1.43 0.152 .9953438 1.030462
_rcs_tr_outcome4 | 1.00065 .0059136 0.11 0.912 .9891263 1.012308
_rcs_tr_outcome5 | 1.007014 .0041736 1.69 0.092 .9988666 1.015227
_cons | .2498722 .0032435 -106.84 0.000 .2435952 .2563109
------------------------------------------------------------------------------------
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 = -47797.733
Iteration 1: log pseudolikelihood = -47764.147
Iteration 2: log pseudolikelihood = -47764.031
Iteration 3: log pseudolikelihood = -47764.031
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47764.031 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084803 .0252069 3.50 0.000 1.036506 1.13535
_rcs1 | 2.561288 .031619 76.19 0.000 2.500059 2.624016
_rcs2 | 1.131979 .0116568 12.04 0.000 1.109361 1.155058
_rcs_tr_outcome1 | .9427061 .0188829 -2.95 0.003 .9064133 .980452
_rcs_tr_outcome2 | .9756525 .0154195 -1.56 0.119 .9458941 1.006347
_rcs_tr_outcome3 | 1.013387 .0092238 1.46 0.144 .9954692 1.031628
_rcs_tr_outcome4 | .9997701 .0062224 -0.04 0.971 .9876485 1.01204
_rcs_tr_outcome5 | 1.00657 .0043626 1.51 0.131 .9980559 1.015157
_rcs_tr_outcome6 | 1.000619 .0034285 0.18 0.857 .9939216 1.007361
_cons | .2498717 .0032435 -106.84 0.000 .2435947 .2563104
------------------------------------------------------------------------------------
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 = -47796.468
Iteration 1: log pseudolikelihood = -47760.503
Iteration 2: log pseudolikelihood = -47760.344
Iteration 3: log pseudolikelihood = -47760.344
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47760.344 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08482 .0252086 3.50 0.000 1.03652 1.13537
_rcs1 | 2.561298 .0316199 76.18 0.000 2.500068 2.624027
_rcs2 | 1.131986 .0116571 12.04 0.000 1.109367 1.155065
_rcs_tr_outcome1 | .942574 .0189192 -2.95 0.003 .906213 .980394
_rcs_tr_outcome2 | .9768094 .0157437 -1.46 0.145 .9464346 1.008159
_rcs_tr_outcome3 | 1.009932 .0095828 1.04 0.298 .9913236 1.02889
_rcs_tr_outcome4 | 1.003217 .0064411 0.50 0.617 .9906713 1.015921
_rcs_tr_outcome5 | 1.002912 .0045395 0.64 0.521 .9940544 1.011849
_rcs_tr_outcome6 | 1.005413 .0035747 1.52 0.129 .9984312 1.012444
_rcs_tr_outcome7 | .9962951 .0029902 -1.24 0.216 .9904515 1.002173
_cons | .2498715 .0032435 -106.84 0.000 .2435945 .2563103
------------------------------------------------------------------------------------
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 = -47755.696
Iteration 1: log pseudolikelihood = -47747.684
Iteration 2: log pseudolikelihood = -47747.677
Iteration 3: log pseudolikelihood = -47747.677
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47747.677 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083437 .0250906 3.46 0.001 1.03536 1.133747
_rcs1 | 2.543053 .0273047 86.93 0.000 2.490096 2.597137
_rcs2 | 1.106913 .0088657 12.68 0.000 1.089672 1.124427
_rcs3 | 1.030056 .0046925 6.50 0.000 1.0209 1.039294
_rcs_tr_outcome1 | .9541056 .0167737 -2.67 0.008 .9217897 .9875545
_cons | .2499412 .0032281 -107.36 0.000 .2436937 .2563488
------------------------------------------------------------------------------------
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 = -47755.868
Iteration 1: log pseudolikelihood = -47747.215
Iteration 2: log pseudolikelihood = -47747.205
Iteration 3: log pseudolikelihood = -47747.205
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47747.205 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084586 .0251746 3.50 0.000 1.036351 1.135067
_rcs1 | 2.549818 .029745 80.24 0.000 2.49218 2.608789
_rcs2 | 1.111581 .0113237 10.38 0.000 1.089607 1.133998
_rcs3 | 1.030245 .004661 6.59 0.000 1.02115 1.039421
_rcs_tr_outcome1 | .9473299 .0184623 -2.78 0.005 .9118269 .9842153
_rcs_tr_outcome2 | .9888184 .01479 -0.75 0.452 .9602514 1.018235
_cons | .2498124 .0032351 -107.11 0.000 .2435515 .2562343
------------------------------------------------------------------------------------
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 = -47755.979
Iteration 1: log pseudolikelihood = -47744.279
Iteration 2: log pseudolikelihood = -47744.235
Iteration 3: log pseudolikelihood = -47744.235
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47744.235 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085483 .0251971 3.53 0.000 1.037204 1.136009
_rcs1 | 2.548429 .0291263 81.85 0.000 2.491977 2.60616
_rcs2 | 1.106062 .0112547 9.91 0.000 1.084222 1.128342
_rcs3 | 1.036998 .0056293 6.69 0.000 1.026023 1.04809
_rcs_tr_outcome1 | .9475172 .0186115 -2.74 0.006 .9117326 .9847063
_rcs_tr_outcome2 | 1.00228 .0164664 0.14 0.890 .9705205 1.035079
_rcs_tr_outcome3 | .981361 .0096177 -1.92 0.055 .9626906 1.000394
_cons | .2497026 .0032318 -107.20 0.000 .243448 .2561179
------------------------------------------------------------------------------------
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 = -47756.379
Iteration 1: log pseudolikelihood = -47744.813
Iteration 2: log pseudolikelihood = -47744.768
Iteration 3: log pseudolikelihood = -47744.768
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47744.768 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085411 .0251939 3.53 0.000 1.037138 1.13593
_rcs1 | 2.548569 .0291991 81.66 0.000 2.491978 2.606446
_rcs2 | 1.106684 .0113158 9.91 0.000 1.084726 1.129086
_rcs3 | 1.036251 .0056245 6.56 0.000 1.025286 1.047334
_rcs_tr_outcome1 | .9476948 .01862 -2.73 0.006 .9118939 .9849012
_rcs_tr_outcome2 | 1.002223 .0166828 0.13 0.894 .9700532 1.03546
_rcs_tr_outcome3 | .9823091 .0098163 -1.79 0.074 .9632567 1.001738
_rcs_tr_outcome4 | .9979299 .0057624 -0.36 0.720 .9866995 1.009288
_cons | .2497162 .003232 -107.20 0.000 .2434613 .2561318
------------------------------------------------------------------------------------
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 = -47751.049
Iteration 1: log pseudolikelihood = -47741.288
Iteration 2: log pseudolikelihood = -47741.25
Iteration 3: log pseudolikelihood = -47741.25
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47741.25 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085769 .0252016 3.55 0.000 1.037482 1.136304
_rcs1 | 2.548457 .0291193 81.87 0.000 2.492018 2.606173
_rcs2 | 1.105999 .0112463 9.91 0.000 1.084175 1.128263
_rcs3 | 1.037114 .0056281 6.72 0.000 1.026142 1.048204
_rcs_tr_outcome1 | .9473867 .0184585 -2.77 0.006 .9118907 .9842645
_rcs_tr_outcome2 | 1.000173 .0159991 0.01 0.991 .9693018 1.032028
_rcs_tr_outcome3 | .9868122 .0099061 -1.32 0.186 .9675864 1.00642
_rcs_tr_outcome4 | .9895178 .0061396 -1.70 0.089 .9775572 1.001625
_rcs_tr_outcome5 | 1.005972 .0041704 1.44 0.151 .9978308 1.014179
_cons | .2496995 .0032318 -107.20 0.000 .2434449 .2561148
------------------------------------------------------------------------------------
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 = -47752.65
Iteration 1: log pseudolikelihood = -47742.043
Iteration 2: log pseudolikelihood = -47741.982
Iteration 3: log pseudolikelihood = -47741.982
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47741.982 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085538 .0251986 3.54 0.000 1.037256 1.136067
_rcs1 | 2.548429 .0291263 81.85 0.000 2.491977 2.60616
_rcs2 | 1.106062 .0112547 9.91 0.000 1.084222 1.128342
_rcs3 | 1.036998 .0056293 6.69 0.000 1.026023 1.04809
_rcs_tr_outcome1 | .9474628 .0184556 -2.77 0.006 .9119722 .9843346
_rcs_tr_outcome2 | 1.000172 .0158661 0.01 0.991 .9695536 1.031758
_rcs_tr_outcome3 | .9891426 .0100031 -1.08 0.280 .9697299 1.008944
_rcs_tr_outcome4 | .986636 .0065514 -2.03 0.043 .9738786 .9995605
_rcs_tr_outcome5 | 1.003167 .0043772 0.72 0.469 .9946243 1.011783
_rcs_tr_outcome6 | 1.000619 .0034285 0.18 0.857 .9939216 1.007361
_cons | .2497026 .0032318 -107.20 0.000 .243448 .2561179
------------------------------------------------------------------------------------
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 = -47751.419
Iteration 1: log pseudolikelihood = -47738.457
Iteration 2: log pseudolikelihood = -47738.354
Iteration 3: log pseudolikelihood = -47738.354
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.354 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085545 .0252 3.54 0.000 1.03726 1.136077
_rcs1 | 2.548432 .0291291 81.84 0.000 2.491975 2.606169
_rcs2 | 1.106086 .0112572 9.91 0.000 1.084241 1.128371
_rcs3 | 1.036967 .0056294 6.69 0.000 1.025992 1.048059
_rcs_tr_outcome1 | .9473441 .0184939 -2.77 0.006 .9117815 .9842939
_rcs_tr_outcome2 | 1.001778 .0162357 0.11 0.913 .9704565 1.03411
_rcs_tr_outcome3 | .9874349 .0101912 -1.23 0.221 .9676612 1.007613
_rcs_tr_outcome4 | .9887517 .0068436 -1.63 0.102 .9754292 1.002256
_rcs_tr_outcome5 | .9975684 .0045947 -0.53 0.597 .9886035 1.006615
_rcs_tr_outcome6 | 1.004426 .0035725 1.24 0.214 .9974482 1.011452
_rcs_tr_outcome7 | .9963797 .0029912 -1.21 0.227 .9905344 1.00226
_cons | .2497032 .0032318 -107.20 0.000 .2434486 .2561185
------------------------------------------------------------------------------------
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 = -47755.652
Iteration 1: log pseudolikelihood = -47746.104
Iteration 2: log pseudolikelihood = -47746.091
Iteration 3: log pseudolikelihood = -47746.091
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47746.091 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083501 .0250942 3.46 0.001 1.035416 1.133818
_rcs1 | 2.542562 .0272241 87.15 0.000 2.48976 2.596484
_rcs2 | 1.104772 .0089388 12.31 0.000 1.08739 1.122431
_rcs3 | 1.034095 .0052036 6.66 0.000 1.023946 1.044344
_rcs4 | 1.005958 .0031521 1.90 0.058 .9997985 1.012155
_rcs_tr_outcome1 | .954308 .0167572 -2.66 0.008 .9220232 .9877233
_cons | .2499321 .0032268 -107.40 0.000 .243687 .2563373
------------------------------------------------------------------------------------
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 = -47755.774
Iteration 1: log pseudolikelihood = -47745.625
Iteration 2: log pseudolikelihood = -47745.61
Iteration 3: log pseudolikelihood = -47745.61
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47745.61 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084666 .025176 3.50 0.000 1.036427 1.13515
_rcs1 | 2.549389 .0296528 80.46 0.000 2.491928 2.608174
_rcs2 | 1.109489 .0114134 10.10 0.000 1.087343 1.132086
_rcs3 | 1.034386 .0051632 6.77 0.000 1.024316 1.044555
_rcs4 | 1.006035 .0031483 1.92 0.055 .9998831 1.012224
_rcs_tr_outcome1 | .9474769 .0183985 -2.78 0.005 .9120941 .9842323
_rcs_tr_outcome2 | .988726 .0146968 -0.76 0.446 .9603363 1.017955
_cons | .2498022 .0032338 -107.15 0.000 .2435438 .2562215
------------------------------------------------------------------------------------
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 = -47755.797
Iteration 1: log pseudolikelihood = -47742.224
Iteration 2: log pseudolikelihood = -47742.19
Iteration 3: log pseudolikelihood = -47742.19
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47742.19 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085712 .0252032 3.54 0.000 1.037421 1.13625
_rcs1 | 2.548006 .0289684 82.27 0.000 2.491857 2.60542
_rcs2 | 1.103311 .0113006 9.60 0.000 1.081383 1.125683
_rcs3 | 1.04141 .0061475 6.87 0.000 1.02943 1.053529
_rcs4 | 1.007526 .0031343 2.41 0.016 1.001402 1.013688
_rcs_tr_outcome1 | .9475541 .0185457 -2.75 0.006 .9118936 .9846091
_rcs_tr_outcome2 | 1.003005 .0163284 0.18 0.854 .9715073 1.035524
_rcs_tr_outcome3 | .9801326 .0095352 -2.06 0.039 .9616211 .9990005
_cons | .2496774 .0032305 -107.24 0.000 .2434253 .2560901
------------------------------------------------------------------------------------
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 = -47755.705
Iteration 1: log pseudolikelihood = -47742.697
Iteration 2: log pseudolikelihood = -47742.654
Iteration 3: log pseudolikelihood = -47742.654
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47742.654 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085666 .0251962 3.54 0.000 1.037388 1.13619
_rcs1 | 2.548055 .0289802 82.24 0.000 2.491883 2.605493
_rcs2 | 1.103435 .0113567 9.56 0.000 1.081399 1.125919
_rcs3 | 1.041427 .0064819 6.52 0.000 1.0288 1.054209
_rcs4 | 1.00674 .0037268 1.81 0.070 .9994617 1.01407
_rcs_tr_outcome1 | .947727 .0185532 -2.74 0.006 .9120522 .9847972
_rcs_tr_outcome2 | 1.00357 .0166213 0.22 0.830 .9715164 1.036682
_rcs_tr_outcome3 | .9803285 .0103138 -1.89 0.059 .9603208 1.000753
_rcs_tr_outcome4 | .9980992 .0067457 -0.28 0.778 .984965 1.011409
_cons | .2496851 .00323 -107.26 0.000 .243434 .2560968
------------------------------------------------------------------------------------
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 = -47752.117
Iteration 1: log pseudolikelihood = -47740.148
Iteration 2: log pseudolikelihood = -47740.109
Iteration 3: log pseudolikelihood = -47740.109
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47740.109 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085838 .0251999 3.55 0.000 1.037553 1.136369
_rcs1 | 2.548079 .028999 82.19 0.000 2.491871 2.605554
_rcs2 | 1.103556 .0113842 9.55 0.000 1.081468 1.126096
_rcs3 | 1.041214 .0064688 6.50 0.000 1.028612 1.05397
_rcs4 | 1.007174 .0037071 1.94 0.052 .9999338 1.014466
_rcs_tr_outcome1 | .9475274 .0184372 -2.77 0.006 .9120716 .9843616
_rcs_tr_outcome2 | 1.001231 .016081 0.08 0.939 .9702041 1.033251
_rcs_tr_outcome3 | .985194 .0106362 -1.38 0.167 .9645664 1.006263
_rcs_tr_outcome4 | .99012 .006755 -1.46 0.146 .9769685 1.003449
_rcs_tr_outcome5 | 1.004921 .0043943 1.12 0.262 .9963451 1.01357
_cons | .2496843 .0032302 -107.25 0.000 .2434328 .2560963
------------------------------------------------------------------------------------
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 = -47753.106
Iteration 1: log pseudolikelihood = -47740.588
Iteration 2: log pseudolikelihood = -47740.528
Iteration 3: log pseudolikelihood = -47740.528
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47740.528 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085654 .0251984 3.54 0.000 1.037372 1.136183
_rcs1 | 2.548031 .0289603 82.29 0.000 2.491897 2.605429
_rcs2 | 1.103266 .0113334 9.57 0.000 1.081275 1.125704
_rcs3 | 1.041631 .0064773 6.56 0.000 1.029013 1.054404
_rcs4 | 1.006739 .0037236 1.82 0.069 .9994669 1.014063
_rcs_tr_outcome1 | .9475636 .0184239 -2.77 0.006 .9121328 .9843706
_rcs_tr_outcome2 | 1.001649 .0159432 0.10 0.918 .9708835 1.03339
_rcs_tr_outcome3 | .9868386 .0107617 -1.21 0.224 .9659699 1.008158
_rcs_tr_outcome4 | .9874967 .00677 -1.84 0.066 .9743166 1.000855
_rcs_tr_outcome5 | 1.002713 .0048878 0.56 0.578 .9931782 1.012338
_rcs_tr_outcome6 | 1.00002 .0034394 0.01 0.995 .9933013 1.006783
_cons | .2496813 .00323 -107.26 0.000 .2434302 .2560929
------------------------------------------------------------------------------------
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 = -47752.098
Iteration 1: log pseudolikelihood = -47737.179
Iteration 2: log pseudolikelihood = -47737.072
Iteration 3: log pseudolikelihood = -47737.072
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47737.072 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085642 .0251989 3.54 0.000 1.03736 1.136172
_rcs1 | 2.548029 .028958 82.30 0.000 2.4919 2.605422
_rcs2 | 1.103266 .0113261 9.57 0.000 1.08129 1.12569
_rcs3 | 1.041666 .0064793 6.56 0.000 1.029044 1.054443
_rcs4 | 1.006498 .0037236 1.75 0.080 .9992259 1.013822
_rcs_tr_outcome1 | .9474718 .0184607 -2.77 0.006 .9119716 .9843538
_rcs_tr_outcome2 | 1.003309 .0163165 0.20 0.839 .9718337 1.035804
_rcs_tr_outcome3 | .9850524 .0109317 -1.36 0.175 .963858 1.006713
_rcs_tr_outcome4 | .9894469 .0068754 -1.53 0.127 .9760627 1.003015
_rcs_tr_outcome5 | .9977828 .0051641 -0.43 0.668 .9877124 1.007956
_rcs_tr_outcome6 | 1.003769 .0037021 1.02 0.308 .9965387 1.011051
_rcs_tr_outcome7 | .9961486 .0029902 -1.29 0.199 .990305 1.002027
_cons | .2496832 .0032299 -107.26 0.000 .2434323 .2560947
------------------------------------------------------------------------------------
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 = -47749.298
Iteration 1: log pseudolikelihood = -47742.984
Iteration 2: log pseudolikelihood = -47742.978
Iteration 3: log pseudolikelihood = -47742.978
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47742.978 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083794 .0251012 3.47 0.001 1.035696 1.134125
_rcs1 | 2.542121 .0270616 87.64 0.000 2.489631 2.595718
_rcs2 | 1.10237 .0086971 12.35 0.000 1.085455 1.119549
_rcs3 | 1.038133 .0055193 7.04 0.000 1.027371 1.049007
_rcs4 | 1.006914 .0033598 2.06 0.039 1.00035 1.01352
_rcs5 | 1.005592 .0022496 2.49 0.013 1.001193 1.010011
_rcs_tr_outcome1 | .9545863 .0167579 -2.65 0.008 .9223001 .9880028
_cons | .2498998 .0032255 -107.43 0.000 .2436571 .2563024
------------------------------------------------------------------------------------
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 = -47749.434
Iteration 1: log pseudolikelihood = -47742.461
Iteration 2: log pseudolikelihood = -47742.454
Iteration 3: log pseudolikelihood = -47742.454
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47742.454 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085017 .0251775 3.52 0.000 1.036776 1.135504
_rcs1 | 2.549238 .0294509 81.00 0.000 2.492164 2.60762
_rcs2 | 1.107269 .0112102 10.06 0.000 1.085514 1.12946
_rcs3 | 1.038532 .0054665 7.18 0.000 1.027873 1.049302
_rcs4 | 1.00701 .0033577 2.10 0.036 1.00045 1.013612
_rcs5 | 1.005646 .0022441 2.52 0.012 1.001257 1.010054
_rcs_tr_outcome1 | .9474671 .0182804 -2.80 0.005 .9123071 .9839821
_rcs_tr_outcome2 | .988259 .0144787 -0.81 0.420 .9602848 1.017048
_cons | .2497638 .0032321 -107.20 0.000 .2435086 .2561797
------------------------------------------------------------------------------------
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 = -47749.49
Iteration 1: log pseudolikelihood = -47739.248
Iteration 2: log pseudolikelihood = -47739.217
Iteration 3: log pseudolikelihood = -47739.217
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47739.217 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085993 .0252025 3.55 0.000 1.037703 1.136529
_rcs1 | 2.547902 .0287917 82.77 0.000 2.492092 2.604962
_rcs2 | 1.101256 .0110632 9.60 0.000 1.079785 1.123154
_rcs3 | 1.044892 .0063033 7.28 0.000 1.032611 1.05732
_rcs4 | 1.009298 .0034135 2.74 0.006 1.00263 1.01601
_rcs5 | 1.005968 .0022348 2.68 0.007 1.001597 1.010357
_rcs_tr_outcome1 | .9475413 .0184125 -2.77 0.006 .9121319 .9843252
_rcs_tr_outcome2 | 1.001817 .0158469 0.11 0.909 .971234 1.033363
_rcs_tr_outcome3 | .9809023 .0093726 -2.02 0.044 .9627034 .9994453
_cons | .249646 .0032292 -107.28 0.000 .2433965 .256056
------------------------------------------------------------------------------------
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 = -47749.387
Iteration 1: log pseudolikelihood = -47739.348
Iteration 2: log pseudolikelihood = -47739.318
Iteration 3: log pseudolikelihood = -47739.318
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47739.318 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08606 .0252024 3.56 0.000 1.037771 1.136597
_rcs1 | 2.547992 .028757 82.87 0.000 2.492248 2.604983
_rcs2 | 1.101011 .0110653 9.57 0.000 1.079535 1.122913
_rcs3 | 1.045498 .0068008 6.84 0.000 1.032254 1.058913
_rcs4 | 1.008613 .0038323 2.26 0.024 1.00113 1.016152
_rcs5 | 1.005986 .0023063 2.60 0.009 1.001476 1.010517
_rcs_tr_outcome1 | .9475568 .0184082 -2.77 0.006 .9121556 .9843318
_rcs_tr_outcome2 | 1.002964 .0160525 0.18 0.853 .9719899 1.034925
_rcs_tr_outcome3 | .9802305 .0101825 -1.92 0.055 .9604749 1.000392
_rcs_tr_outcome4 | .9976441 .006663 -0.35 0.724 .98467 1.010789
_cons | .2496404 .0032286 -107.30 0.000 .243392 .2560492
------------------------------------------------------------------------------------
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 = -47749.258
Iteration 1: log pseudolikelihood = -47738.941
Iteration 2: log pseudolikelihood = -47738.901
Iteration 3: log pseudolikelihood = -47738.901
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.901 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085937 .0251991 3.55 0.000 1.037654 1.136466
_rcs1 | 2.547852 .0288271 82.66 0.000 2.491974 2.604983
_rcs2 | 1.101507 .011231 9.48 0.000 1.079713 1.123741
_rcs3 | 1.044443 .0069568 6.53 0.000 1.030897 1.058168
_rcs4 | 1.009713 .0040442 2.41 0.016 1.001818 1.017671
_rcs5 | 1.005242 .0026106 2.01 0.044 1.000139 1.010372
_rcs_tr_outcome1 | .9476416 .0183996 -2.77 0.006 .9122566 .9843992
_rcs_tr_outcome2 | 1.002137 .0159698 0.13 0.893 .971321 1.033932
_rcs_tr_outcome3 | .9832069 .0108245 -1.54 0.124 .9622185 1.004653
_rcs_tr_outcome4 | .9921586 .0070824 -1.10 0.270 .978374 1.006137
_rcs_tr_outcome5 | 1.001634 .0048981 0.33 0.738 .9920798 1.01128
_cons | .249658 .0032285 -107.31 0.000 .2434097 .2560667
------------------------------------------------------------------------------------
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 = -47749.34
Iteration 1: log pseudolikelihood = -47738.54
Iteration 2: log pseudolikelihood = -47738.487
Iteration 3: log pseudolikelihood = -47738.487
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.487 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08588 .025204 3.55 0.000 1.037588 1.13642
_rcs1 | 2.547952 .0287764 82.81 0.000 2.492171 2.604981
_rcs2 | 1.101132 .0111276 9.53 0.000 1.079537 1.123159
_rcs3 | 1.045159 .0069291 6.66 0.000 1.031666 1.058828
_rcs4 | 1.009086 .0040108 2.28 0.023 1.001255 1.016978
_rcs5 | 1.006041 .0025826 2.35 0.019 1.000992 1.011116
_rcs_tr_outcome1 | .9474869 .0183908 -2.78 0.005 .9121186 .9842266
_rcs_tr_outcome2 | 1.002893 .015856 0.18 0.855 .9722924 1.034457
_rcs_tr_outcome3 | .9838952 .0110942 -1.44 0.150 .9623894 1.005882
_rcs_tr_outcome4 | .9907031 .007226 -1.28 0.200 .9766413 1.004967
_rcs_tr_outcome5 | .999599 .0049901 -0.08 0.936 .9898664 1.009427
_rcs_tr_outcome6 | .9976815 .0036904 -0.63 0.530 .9904747 1.004941
_cons | .2496416 .0032284 -107.31 0.000 .2433936 .2560501
------------------------------------------------------------------------------------
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 = -47749.354
Iteration 1: log pseudolikelihood = -47735.618
Iteration 2: log pseudolikelihood = -47735.506
Iteration 3: log pseudolikelihood = -47735.506
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47735.506 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085785 .0252015 3.55 0.000 1.037497 1.13632
_rcs1 | 2.54784 .0287915 82.76 0.000 2.49203 2.6049
_rcs2 | 1.101231 .0111703 9.51 0.000 1.079554 1.123344
_rcs3 | 1.044894 .0069421 6.61 0.000 1.031376 1.058589
_rcs4 | 1.00939 .004033 2.34 0.019 1.001516 1.017326
_rcs5 | 1.005279 .0026042 2.03 0.042 1.000188 1.010396
_rcs_tr_outcome1 | .9475156 .018431 -2.77 0.006 .9120714 .9843373
_rcs_tr_outcome2 | 1.004458 .0162533 0.27 0.783 .9731021 1.036824
_rcs_tr_outcome3 | .9823705 .0113236 -1.54 0.123 .9604255 1.004817
_rcs_tr_outcome4 | .9919454 .0072085 -1.11 0.266 .9779171 1.006175
_rcs_tr_outcome5 | .9966262 .0050732 -0.66 0.507 .9867323 1.006619
_rcs_tr_outcome6 | 1.001015 .0041142 0.25 0.805 .9929841 1.009112
_rcs_tr_outcome7 | .9953245 .0030312 -1.54 0.124 .9894012 1.001283
_cons | .2496531 .0032284 -107.31 0.000 .2434049 .2560616
------------------------------------------------------------------------------------
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 = -47750.242
Iteration 1: log pseudolikelihood = -47743.874
Iteration 2: log pseudolikelihood = -47743.869
Iteration 3: log pseudolikelihood = -47743.869
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47743.869 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083628 .0250995 3.47 0.001 1.035533 1.133955
_rcs1 | 2.541821 .0270973 87.51 0.000 2.489262 2.59549
_rcs2 | 1.102116 .0088464 12.11 0.000 1.084913 1.119592
_rcs3 | 1.038271 .0057578 6.77 0.000 1.027047 1.049617
_rcs4 | 1.010132 .0035431 2.87 0.004 1.003211 1.0171
_rcs5 | 1.005104 .0023898 2.14 0.032 1.000431 1.009799
_rcs6 | 1.00327 .0017887 1.83 0.067 .9997704 1.006782
_rcs_tr_outcome1 | .9549422 .0167635 -2.63 0.009 .9226452 .9883698
_cons | .2499189 .0032261 -107.42 0.000 .2436751 .2563227
------------------------------------------------------------------------------------
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 = -47750.378
Iteration 1: log pseudolikelihood = -47743.34
Iteration 2: log pseudolikelihood = -47743.332
Iteration 3: log pseudolikelihood = -47743.332
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47743.332 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084865 .0251768 3.51 0.000 1.036625 1.13535
_rcs1 | 2.549018 .0295107 80.82 0.000 2.491829 2.607519
_rcs2 | 1.10707 .0113871 9.89 0.000 1.084975 1.129614
_rcs3 | 1.038737 .0056963 6.93 0.000 1.027632 1.049962
_rcs4 | 1.010255 .0035439 2.91 0.004 1.003333 1.017225
_rcs5 | 1.005165 .002383 2.17 0.030 1.000505 1.009846
_rcs6 | 1.003317 .0017859 1.86 0.063 .9998228 1.006824
_rcs_tr_outcome1 | .9477385 .0183149 -2.78 0.005 .9125134 .9843235
_rcs_tr_outcome2 | .9881141 .0145451 -0.81 0.417 .9600136 1.017037
_cons | .2497814 .0032328 -107.18 0.000 .243525 .2561986
------------------------------------------------------------------------------------
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 = -47750.438
Iteration 1: log pseudolikelihood = -47740.114
Iteration 2: log pseudolikelihood = -47740.084
Iteration 3: log pseudolikelihood = -47740.084
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47740.084 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085845 .0252018 3.55 0.000 1.037557 1.13638
_rcs1 | 2.547631 .0288454 82.59 0.000 2.491718 2.604799
_rcs2 | 1.100889 .0112538 9.40 0.000 1.079051 1.123168
_rcs3 | 1.044764 .0064622 7.08 0.000 1.032175 1.057507
_rcs4 | 1.013061 .0036601 3.59 0.000 1.005912 1.02026
_rcs5 | 1.005939 .0023736 2.51 0.012 1.001298 1.010602
_rcs6 | 1.003417 .0017803 1.92 0.055 .9999337 1.006912
_rcs_tr_outcome1 | .9478561 .0184497 -2.75 0.006 .9123765 .9847155
_rcs_tr_outcome2 | 1.001834 .01597 0.11 0.909 .971017 1.033629
_rcs_tr_outcome3 | .9807992 .009416 -2.02 0.043 .9625167 .9994289
_cons | .2496635 .0032298 -107.27 0.000 .2434129 .2560747
------------------------------------------------------------------------------------
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 = -47750.35
Iteration 1: log pseudolikelihood = -47740.265
Iteration 2: log pseudolikelihood = -47740.236
Iteration 3: log pseudolikelihood = -47740.236
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47740.236 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085908 .0251998 3.55 0.000 1.037623 1.136439
_rcs1 | 2.547709 .0288062 82.71 0.000 2.491871 2.604799
_rcs2 | 1.100605 .0112535 9.38 0.000 1.078768 1.122884
_rcs3 | 1.045541 .0069793 6.67 0.000 1.031951 1.059311
_rcs4 | 1.012307 .0038318 3.23 0.001 1.004825 1.019845
_rcs5 | 1.005637 .0026165 2.16 0.031 1.000522 1.010779
_rcs6 | 1.003475 .0017754 1.96 0.050 1.000002 1.006961
_rcs_tr_outcome1 | .9479299 .0184472 -2.75 0.006 .9124549 .9847842
_rcs_tr_outcome2 | 1.003004 .0162075 0.19 0.853 .9717356 1.035279
_rcs_tr_outcome3 | .9799692 .0102234 -1.94 0.052 .9601351 1.000213
_rcs_tr_outcome4 | .9981861 .0066712 -0.27 0.786 .9851959 1.011347
_cons | .2496595 .0032291 -107.29 0.000 .2434101 .2560693
------------------------------------------------------------------------------------
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 = -47750.113
Iteration 1: log pseudolikelihood = -47739.464
Iteration 2: log pseudolikelihood = -47739.421
Iteration 3: log pseudolikelihood = -47739.421
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47739.421 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085796 .0251982 3.55 0.000 1.037515 1.136324
_rcs1 | 2.547683 .0289308 82.35 0.000 2.491606 2.605022
_rcs2 | 1.101475 .0115507 9.22 0.000 1.079067 1.124348
_rcs3 | 1.04372 .0072507 6.16 0.000 1.029605 1.058028
_rcs4 | 1.013981 .0041538 3.39 0.001 1.005873 1.022156
_rcs5 | 1.005137 .0026889 1.92 0.055 .9998806 1.010421
_rcs6 | 1.002799 .0018647 1.50 0.133 .999151 1.00646
_rcs_tr_outcome1 | .9478839 .018429 -2.75 0.006 .9124432 .9847012
_rcs_tr_outcome2 | 1.00146 .0161422 0.09 0.928 .9703161 1.033603
_rcs_tr_outcome3 | .9840963 .010855 -1.45 0.146 .9630493 1.005603
_rcs_tr_outcome4 | .9912527 .0070214 -1.24 0.215 .977586 1.00511
_rcs_tr_outcome5 | 1.00286 .0048157 0.59 0.552 .9934656 1.012343
_cons | .2496801 .0032294 -107.28 0.000 .2434301 .2560906
------------------------------------------------------------------------------------
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 = -47750.054
Iteration 1: log pseudolikelihood = -47737.918
Iteration 2: log pseudolikelihood = -47737.851
Iteration 3: log pseudolikelihood = -47737.851
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47737.851 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085718 .0251984 3.54 0.000 1.037437 1.136247
_rcs1 | 2.54777 .0289673 82.26 0.000 2.491623 2.605182
_rcs2 | 1.101675 .0116583 9.15 0.000 1.07906 1.124763
_rcs3 | 1.043328 .0073484 6.02 0.000 1.029025 1.057831
_rcs4 | 1.014631 .0042782 3.44 0.001 1.006281 1.023051
_rcs5 | 1.004578 .0027996 1.64 0.101 .9991057 1.01008
_rcs6 | 1.004897 .0020311 2.42 0.016 1.000924 1.008886
_rcs_tr_outcome1 | .9477079 .0184273 -2.76 0.006 .9122706 .9845218
_rcs_tr_outcome2 | 1.001453 .0160692 0.09 0.928 .9704478 1.033448
_rcs_tr_outcome3 | .9867873 .0112814 -1.16 0.245 .9649221 1.009148
_rcs_tr_outcome4 | .9876491 .0074202 -1.65 0.098 .9732123 1.0023
_rcs_tr_outcome5 | 1.001983 .0051628 0.38 0.701 .9919154 1.012154
_rcs_tr_outcome6 | .9957427 .0039625 -1.07 0.284 .9880066 1.003539
_cons | .2496611 .0032294 -107.28 0.000 .2434112 .2560715
------------------------------------------------------------------------------------
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 = -47748.444
Iteration 1: log pseudolikelihood = -47734.273
Iteration 2: log pseudolikelihood = -47734.166
Iteration 3: log pseudolikelihood = -47734.166
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47734.166 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085746 .0252004 3.54 0.000 1.037461 1.136278
_rcs1 | 2.547706 .0289072 82.42 0.000 2.491674 2.604998
_rcs2 | 1.101268 .0115453 9.20 0.000 1.078871 1.124131
_rcs3 | 1.044052 .0073135 6.15 0.000 1.029816 1.058486
_rcs4 | 1.014032 .0042453 3.33 0.001 1.005745 1.022387
_rcs5 | 1.004787 .0027785 1.73 0.084 .9993556 1.010247
_rcs6 | 1.004779 .002008 2.39 0.017 1.000851 1.008722
_rcs_tr_outcome1 | .9475675 .0184458 -2.77 0.006 .9120954 .9844193
_rcs_tr_outcome2 | 1.003468 .0163807 0.21 0.832 .9718708 1.036093
_rcs_tr_outcome3 | .9844142 .0115721 -1.34 0.181 .9619925 1.007358
_rcs_tr_outcome4 | .9897519 .0075614 -1.35 0.178 .9750422 1.004683
_rcs_tr_outcome5 | .9979937 .0052233 -0.38 0.701 .9878085 1.008284
_rcs_tr_outcome6 | 1.000401 .0040783 0.10 0.922 .9924394 1.008426
_rcs_tr_outcome7 | .9932803 .0032747 -2.05 0.041 .9868826 .9997195
_cons | .2496575 .0032292 -107.29 0.000 .243408 .2560675
------------------------------------------------------------------------------------
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 = -47749.642
Iteration 1: log pseudolikelihood = -47742.24
Iteration 2: log pseudolikelihood = -47742.233
Iteration 3: log pseudolikelihood = -47742.233
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47742.233 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083887 .0251101 3.48 0.001 1.035773 1.134237
_rcs1 | 2.542586 .0272043 87.22 0.000 2.489822 2.596469
_rcs2 | 1.102177 .009129 11.75 0.000 1.084429 1.120216
_rcs3 | 1.037852 .0059899 6.44 0.000 1.026179 1.049659
_rcs4 | 1.013908 .0036453 3.84 0.000 1.006789 1.021078
_rcs5 | 1.004106 .0024907 1.65 0.099 .9992364 1.009
_rcs6 | 1.005035 .001904 2.65 0.008 1.001311 1.008774
_rcs7 | 1.000215 .0015648 0.14 0.891 .9971532 1.003287
_rcs_tr_outcome1 | .9543137 .016757 -2.66 0.008 .9220293 .9877284
_cons | .2499028 .0032267 -107.40 0.000 .2436579 .2563077
------------------------------------------------------------------------------------
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 = -47749.776
Iteration 1: log pseudolikelihood = -47741.724
Iteration 2: log pseudolikelihood = -47741.714
Iteration 3: log pseudolikelihood = -47741.714
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47741.714 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085101 .0251903 3.52 0.000 1.036836 1.135614
_rcs1 | 2.549669 .0296552 80.47 0.000 2.492204 2.60846
_rcs2 | 1.107048 .0116892 9.63 0.000 1.084373 1.130197
_rcs3 | 1.038391 .005916 6.61 0.000 1.02686 1.050051
_rcs4 | 1.014055 .0036509 3.88 0.000 1.006924 1.021236
_rcs5 | 1.004168 .0024834 1.68 0.093 .9993119 1.009047
_rcs6 | 1.005088 .0018998 2.69 0.007 1.001371 1.008819
_rcs7 | 1.000255 .0015619 0.16 0.870 .9971983 1.003321
_rcs_tr_outcome1 | .947233 .0183723 -2.79 0.005 .9118999 .9839352
_rcs_tr_outcome2 | .9882943 .014656 -0.79 0.427 .9599825 1.017441
_cons | .2497678 .0032336 -107.15 0.000 .2435098 .2561866
------------------------------------------------------------------------------------
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 = -47749.829
Iteration 1: log pseudolikelihood = -47738.488
Iteration 2: log pseudolikelihood = -47738.455
Iteration 3: log pseudolikelihood = -47738.455
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.455 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086078 .0252141 3.56 0.000 1.037766 1.136638
_rcs1 | 2.548243 .0289842 82.24 0.000 2.492064 2.605689
_rcs2 | 1.100707 .0115884 9.11 0.000 1.078227 1.123655
_rcs3 | 1.044061 .0066153 6.81 0.000 1.031175 1.057107
_rcs4 | 1.017225 .0038165 4.55 0.000 1.009772 1.024733
_rcs5 | 1.005313 .0024926 2.14 0.033 1.00044 1.010211
_rcs6 | 1.005402 .001888 2.87 0.004 1.001708 1.009109
_rcs7 | 1.000313 .0015566 0.20 0.841 .9972665 1.003368
_rcs_tr_outcome1 | .9473919 .0185059 -2.77 0.006 .9118065 .9843661
_rcs_tr_outcome2 | 1.002121 .0161869 0.13 0.896 .9708925 1.034355
_rcs_tr_outcome3 | .9807494 .0094574 -2.02 0.044 .9623873 .9994618
_cons | .2496503 .0032305 -107.24 0.000 .2433983 .2560628
------------------------------------------------------------------------------------
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 = -47749.735
Iteration 1: log pseudolikelihood = -47738.634
Iteration 2: log pseudolikelihood = -47738.603
Iteration 3: log pseudolikelihood = -47738.603
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.603 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086134 .0252119 3.56 0.000 1.037827 1.13669
_rcs1 | 2.54831 .0289513 82.34 0.000 2.492194 2.60569
_rcs2 | 1.100444 .0116237 9.06 0.000 1.077896 1.123464
_rcs3 | 1.044748 .0071509 6.40 0.000 1.030826 1.058858
_rcs4 | 1.016723 .0038662 4.36 0.000 1.009174 1.024329
_rcs5 | 1.004948 .0028091 1.77 0.077 .9994574 1.010469
_rcs6 | 1.005414 .0019268 2.82 0.005 1.001644 1.009198
_rcs7 | 1.000345 .0015542 0.22 0.824 .9973032 1.003396
_rcs_tr_outcome1 | .9474424 .0185051 -2.76 0.006 .9118585 .9844148
_rcs_tr_outcome2 | 1.003244 .0164739 0.20 0.844 .9714702 1.036058
_rcs_tr_outcome3 | .9801312 .0103393 -1.90 0.057 .9600746 1.000607
_rcs_tr_outcome4 | .9977218 .0067394 -0.34 0.736 .9845998 1.011019
_cons | .249646 .0032298 -107.26 0.000 .2433952 .2560574
------------------------------------------------------------------------------------
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 = -47749.608
Iteration 1: log pseudolikelihood = -47738.156
Iteration 2: log pseudolikelihood = -47738.109
Iteration 3: log pseudolikelihood = -47738.109
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.109 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085972 .0252067 3.55 0.000 1.037675 1.136517
_rcs1 | 2.54824 .0290651 82.01 0.000 2.491906 2.605848
_rcs2 | 1.101303 .0119176 8.92 0.000 1.078191 1.124911
_rcs3 | 1.043095 .007452 5.91 0.000 1.028591 1.057803
_rcs4 | 1.017818 .0040909 4.39 0.000 1.009832 1.025868
_rcs5 | 1.005186 .00276 1.88 0.060 .9997909 1.01061
_rcs6 | 1.004717 .0021472 2.20 0.028 1.000517 1.008934
_rcs7 | 1.000185 .0015498 0.12 0.905 .9971516 1.003227
_rcs_tr_outcome1 | .9474612 .0184869 -2.77 0.006 .9119116 .9843966
_rcs_tr_outcome2 | 1.001691 .0164522 0.10 0.918 .9699583 1.034461
_rcs_tr_outcome3 | .9840885 .0109833 -1.44 0.151 .9627954 1.005853
_rcs_tr_outcome4 | .9915948 .0070263 -1.19 0.234 .9779188 1.005462
_rcs_tr_outcome5 | 1.002 .0048896 0.41 0.682 .9924625 1.01163
_cons | .2496682 .00323 -107.26 0.000 .243417 .2560799
------------------------------------------------------------------------------------
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 = -47749.796
Iteration 1: log pseudolikelihood = -47736.687
Iteration 2: log pseudolikelihood = -47736.621
Iteration 3: log pseudolikelihood = -47736.621
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47736.621 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085897 .0252102 3.55 0.000 1.037593 1.136449
_rcs1 | 2.548511 .0291714 81.73 0.000 2.491972 2.606332
_rcs2 | 1.101929 .0121757 8.78 0.000 1.078321 1.126053
_rcs3 | 1.042085 .0076431 5.62 0.000 1.027212 1.057173
_rcs4 | 1.019169 .0043 4.50 0.000 1.010776 1.027632
_rcs5 | 1.004096 .0028603 1.43 0.151 .9985052 1.009717
_rcs6 | 1.005744 .0021133 2.73 0.006 1.00161 1.009894
_rcs7 | 1.001233 .0016553 0.75 0.456 .997994 1.004483
_rcs_tr_outcome1 | .9471813 .0184984 -2.78 0.005 .9116103 .9841403
_rcs_tr_outcome2 | 1.001061 .0165008 0.06 0.949 .969237 1.03393
_rcs_tr_outcome3 | .9870782 .0114193 -1.12 0.261 .9649487 1.009715
_rcs_tr_outcome4 | .9875791 .0072906 -1.69 0.090 .9733928 1.001972
_rcs_tr_outcome5 | 1.001728 .0051673 0.33 0.738 .9916512 1.011907
_rcs_tr_outcome6 | .9965285 .0038901 -0.89 0.373 .9889331 1.004182
_cons | .2496544 .0032303 -107.25 0.000 .2434027 .2560667
------------------------------------------------------------------------------------
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 = -47749.918
Iteration 1: log pseudolikelihood = -47734.775
Iteration 2: log pseudolikelihood = -47734.666
Iteration 3: log pseudolikelihood = -47734.665
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47734.665 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085722 .0252006 3.54 0.000 1.037436 1.136255
_rcs1 | 2.54792 .0290447 82.05 0.000 2.491624 2.605487
_rcs2 | 1.101182 .0119617 8.87 0.000 1.077985 1.124878
_rcs3 | 1.043339 .0076213 5.81 0.000 1.028508 1.058384
_rcs4 | 1.018022 .0043816 4.15 0.000 1.00947 1.026646
_rcs5 | 1.004873 .0029262 1.67 0.095 .9991544 1.010625
_rcs6 | 1.005189 .0021805 2.39 0.017 1.000924 1.009472
_rcs7 | 1.002499 .0017676 1.42 0.157 .9990406 1.005969
_rcs_tr_outcome1 | .947526 .0184806 -2.76 0.006 .9119883 .9844485
_rcs_tr_outcome2 | 1.002749 .0166012 0.17 0.868 .9707339 1.035821
_rcs_tr_outcome3 | .9857104 .0117092 -1.21 0.226 .9630259 1.008929
_rcs_tr_outcome4 | .9887655 .0076357 -1.46 0.143 .9739126 1.003845
_rcs_tr_outcome5 | .9983563 .0053738 -0.31 0.760 .9878791 1.008945
_rcs_tr_outcome6 | 1.000184 .0041654 0.04 0.965 .9920537 1.008382
_rcs_tr_outcome7 | .9938246 .0034619 -1.78 0.075 .9870626 1.000633
_cons | .2496636 .0032298 -107.26 0.000 .2434129 .2560748
------------------------------------------------------------------------------------
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 = -47748.981
Iteration 1: log pseudolikelihood = -47740.903
Iteration 2: log pseudolikelihood = -47740.893
Iteration 3: log pseudolikelihood = -47740.893
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47740.893 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083866 .0251112 3.48 0.001 1.03575 1.134218
_rcs1 | 2.542631 .0272464 87.09 0.000 2.489786 2.596598
_rcs2 | 1.102193 .0093136 11.51 0.000 1.084089 1.120599
_rcs3 | 1.037062 .0061295 6.16 0.000 1.025118 1.049146
_rcs4 | 1.016725 .003659 4.61 0.000 1.009579 1.023922
_rcs5 | 1.004064 .0025432 1.60 0.109 .9990916 1.009061
_rcs6 | 1.004911 .0019859 2.48 0.013 1.001026 1.008811
_rcs7 | 1.002992 .0016574 1.81 0.071 .9997492 1.006246
_rcs8 | .999002 .0014114 -0.71 0.480 .9962395 1.001772
_rcs_tr_outcome1 | .9542987 .0167567 -2.66 0.008 .9220148 .9877129
_cons | .2499093 .0032269 -107.39 0.000 .2436641 .2563147
------------------------------------------------------------------------------------
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 = -47749.111
Iteration 1: log pseudolikelihood = -47740.387
Iteration 2: log pseudolikelihood = -47740.373
Iteration 3: log pseudolikelihood = -47740.373
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47740.373 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085083 .0251942 3.52 0.000 1.03681 1.135603
_rcs1 | 2.549727 .0297285 80.28 0.000 2.492121 2.608665
_rcs2 | 1.10707 .0118931 9.47 0.000 1.084003 1.130627
_rcs3 | 1.037633 .0060484 6.34 0.000 1.025846 1.049556
_rcs4 | 1.016904 .0036702 4.64 0.000 1.009736 1.024123
_rcs5 | 1.004124 .0025361 1.63 0.103 .9991652 1.009107
_rcs6 | 1.004967 .0019813 2.51 0.012 1.001091 1.008858
_rcs7 | 1.003038 .0016533 1.84 0.066 .9998024 1.006283
_rcs8 | .9990349 .0014091 -0.68 0.494 .9962769 1.0018
_rcs_tr_outcome1 | .947206 .0184107 -2.79 0.005 .9118003 .9839865
_rcs_tr_outcome2 | .9882759 .014717 -0.79 0.428 .9598479 1.017546
_cons | .2497741 .0032339 -107.14 0.000 .2435154 .2561936
------------------------------------------------------------------------------------
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 = -47749.164
Iteration 1: log pseudolikelihood = -47737.214
Iteration 2: log pseudolikelihood = -47737.18
Iteration 3: log pseudolikelihood = -47737.18
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47737.18 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08604 .025217 3.55 0.000 1.037723 1.136606
_rcs1 | 2.548268 .029058 82.03 0.000 2.491947 2.605862
_rcs2 | 1.100695 .0118156 8.94 0.000 1.077779 1.124099
_rcs3 | 1.042974 .0067008 6.55 0.000 1.029923 1.05619
_rcs4 | 1.020236 .0038656 5.29 0.000 1.012688 1.02784
_rcs5 | 1.005575 .0025747 2.17 0.030 1.000542 1.010634
_rcs6 | 1.005511 .0019677 2.81 0.005 1.001662 1.009375
_rcs7 | 1.003187 .0016456 1.94 0.052 .9999672 1.006418
_rcs8 | .9990988 .0014031 -0.64 0.521 .9963525 1.001853
_rcs_tr_outcome1 | .9474059 .018542 -2.76 0.006 .9117524 .9844536
_rcs_tr_outcome2 | 1.002037 .0162975 0.13 0.900 .9705984 1.034494
_rcs_tr_outcome3 | .9809043 .0094875 -1.99 0.046 .9624843 .9996769
_cons | .2496585 .0032308 -107.23 0.000 .243406 .2560717
------------------------------------------------------------------------------------
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 = -47749.085
Iteration 1: log pseudolikelihood = -47737.296
Iteration 2: log pseudolikelihood = -47737.264
Iteration 3: log pseudolikelihood = -47737.264
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47737.264 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086129 .0252154 3.56 0.000 1.037816 1.136692
_rcs1 | 2.548343 .0290115 82.17 0.000 2.492111 2.605844
_rcs2 | 1.100307 .0118382 8.88 0.000 1.077347 1.123756
_rcs3 | 1.043852 .0072337 6.19 0.000 1.02977 1.058127
_rcs4 | 1.019856 .0038627 5.19 0.000 1.012313 1.027455
_rcs5 | 1.005026 .0028657 1.76 0.079 .9994246 1.010658
_rcs6 | 1.005356 .0020976 2.56 0.010 1.001253 1.009476
_rcs7 | 1.003207 .0016447 1.95 0.051 .9999884 1.006435
_rcs8 | .9991053 .0014024 -0.64 0.524 .9963604 1.001858
_rcs_tr_outcome1 | .9474688 .0185477 -2.76 0.006 .9118046 .984528
_rcs_tr_outcome2 | 1.003434 .0166356 0.21 0.836 .9713531 1.036575
_rcs_tr_outcome3 | .9798164 .0104075 -1.92 0.055 .9596289 1.000429
_rcs_tr_outcome4 | .9981773 .0067393 -0.27 0.787 .9850556 1.011474
_cons | .2496522 .00323 -107.26 0.000 .243401 .2560638
------------------------------------------------------------------------------------
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 = -47748.95
Iteration 1: log pseudolikelihood = -47736.915
Iteration 2: log pseudolikelihood = -47736.869
Iteration 3: log pseudolikelihood = -47736.869
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47736.869 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08595 .0252094 3.55 0.000 1.037647 1.1365
_rcs1 | 2.548278 .0291304 81.83 0.000 2.491819 2.606017
_rcs2 | 1.101223 .0121428 8.74 0.000 1.077679 1.125281
_rcs3 | 1.042147 .0075433 5.70 0.000 1.027467 1.057037
_rcs4 | 1.020651 .0040017 5.21 0.000 1.012838 1.028524
_rcs5 | 1.005721 .002876 2.00 0.046 1.0001 1.011374
_rcs6 | 1.004931 .0022501 2.20 0.028 1.00053 1.009351
_rcs7 | 1.002797 .0017256 1.62 0.105 .9994207 1.006185
_rcs8 | .9990738 .0013958 -0.66 0.507 .9963417 1.001813
_rcs_tr_outcome1 | .9474613 .0185245 -2.76 0.006 .9118409 .9844733
_rcs_tr_outcome2 | 1.001739 .0166166 0.10 0.917 .9696949 1.034842
_rcs_tr_outcome3 | .983944 .0110628 -1.44 0.150 .9624985 1.005867
_rcs_tr_outcome4 | .9918577 .0070392 -1.15 0.249 .9781567 1.005751
_rcs_tr_outcome5 | 1.001886 .0049331 0.38 0.702 .9922633 1.011601
_cons | .2496743 .0032302 -107.25 0.000 .2434229 .2560863
------------------------------------------------------------------------------------
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 = -47748.915
Iteration 1: log pseudolikelihood = -47734.545
Iteration 2: log pseudolikelihood = -47734.455
Iteration 3: log pseudolikelihood = -47734.455
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47734.455 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085974 .0252151 3.55 0.000 1.037661 1.136537
_rcs1 | 2.548811 .0292805 81.44 0.000 2.492063 2.606851
_rcs2 | 1.102034 .0124732 8.58 0.000 1.077856 1.126754
_rcs3 | 1.04075 .0077726 5.35 0.000 1.025627 1.056096
_rcs4 | 1.022378 .0042197 5.36 0.000 1.01414 1.030682
_rcs5 | 1.004853 .0028422 1.71 0.087 .9992978 1.010439
_rcs6 | 1.004764 .0022495 2.12 0.034 1.000365 1.009183
_rcs7 | 1.004495 .0018402 2.45 0.014 1.000895 1.008109
_rcs8 | .9996121 .0014101 -0.28 0.783 .9968522 1.00238
_rcs_tr_outcome1 | .946891 .0185334 -2.79 0.005 .9112542 .9839215
_rcs_tr_outcome2 | 1.000742 .0166518 0.04 0.964 .9686316 1.033917
_rcs_tr_outcome3 | .987671 .0115093 -1.06 0.287 .9653688 1.010488
_rcs_tr_outcome4 | .9868737 .0073574 -1.77 0.076 .9725584 1.0014
_rcs_tr_outcome5 | 1.002484 .0052004 0.48 0.632 .992343 1.012728
_rcs_tr_outcome6 | .9955281 .0039154 -1.14 0.254 .9878836 1.003232
_cons | .2496494 .0032307 -107.23 0.000 .243397 .2560624
------------------------------------------------------------------------------------
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 = -47748.767
Iteration 1: log pseudolikelihood = -47733.329
Iteration 2: log pseudolikelihood = -47733.224
Iteration 3: log pseudolikelihood = -47733.224
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47733.224 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085772 .0252051 3.54 0.000 1.037478 1.136315
_rcs1 | 2.548194 .029165 81.73 0.000 2.491668 2.606002
_rcs2 | 1.101346 .0123019 8.64 0.000 1.077497 1.125723
_rcs3 | 1.041972 .0077959 5.50 0.000 1.026803 1.057364
_rcs4 | 1.021361 .0043456 4.97 0.000 1.012879 1.029914
_rcs5 | 1.005406 .0029523 1.84 0.066 .9996363 1.011209
_rcs6 | 1.00459 .002244 2.05 0.040 1.000201 1.008998
_rcs7 | 1.004532 .0018161 2.50 0.012 1.000979 1.008097
_rcs8 | 1.000759 .0015096 0.50 0.615 .9978043 1.003722
_rcs_tr_outcome1 | .9473157 .0185171 -2.77 0.006 .9117093 .9843127
_rcs_tr_outcome2 | 1.00223 .0167823 0.13 0.894 .9698711 1.035668
_rcs_tr_outcome3 | .9865196 .0117751 -1.14 0.256 .9637087 1.00987
_rcs_tr_outcome4 | .98771 .0075925 -1.61 0.108 .9729404 1.002704
_rcs_tr_outcome5 | .9991962 .0053311 -0.15 0.880 .9888018 1.0097
_rcs_tr_outcome6 | .9998839 .0041484 -0.03 0.978 .9917862 1.008048
_rcs_tr_outcome7 | .9939133 .0033907 -1.79 0.074 .9872898 1.000581
_cons | .2496623 .0032301 -107.25 0.000 .2434109 .2560742
------------------------------------------------------------------------------------
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 = -47750.612
Iteration 1: log pseudolikelihood = -47740.492
Iteration 2: log pseudolikelihood = -47740.475
Iteration 3: log pseudolikelihood = -47740.475
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47740.475 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083916 .0251176 3.48 0.001 1.035788 1.134281
_rcs1 | 2.542952 .0273234 86.86 0.000 2.489959 2.597073
_rcs2 | 1.102397 .009559 11.24 0.000 1.08382 1.121293
_rcs3 | 1.036177 .0062688 5.87 0.000 1.023963 1.048537
_rcs4 | 1.019354 .0036549 5.35 0.000 1.012216 1.026543
_rcs5 | 1.004053 .0026165 1.55 0.121 .9989373 1.009194
_rcs6 | 1.004889 .0020162 2.43 0.015 1.000945 1.008848
_rcs7 | 1.00405 .0017004 2.39 0.017 1.000723 1.007388
_rcs8 | 1.000963 .0015138 0.64 0.524 .9980006 1.003935
_rcs9 | .9991087 .0013156 -0.68 0.498 .9965335 1.001691
_rcs_tr_outcome1 | .9542024 .016764 -2.67 0.008 .9219048 .9876315
_cons | .2499058 .0032272 -107.38 0.000 .24366 .2563117
------------------------------------------------------------------------------------
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 = -47750.736
Iteration 1: log pseudolikelihood = -47739.961
Iteration 2: log pseudolikelihood = -47739.937
Iteration 3: log pseudolikelihood = -47739.937
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47739.937 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085153 .0252036 3.52 0.000 1.036862 1.135692
_rcs1 | 2.550172 .0298524 79.97 0.000 2.492329 2.609358
_rcs2 | 1.107358 .0121602 9.29 0.000 1.083779 1.13145
_rcs3 | 1.036792 .0061798 6.06 0.000 1.02475 1.048975
_rcs4 | 1.019563 .0036717 5.38 0.000 1.012392 1.026785
_rcs5 | 1.004115 .0026094 1.58 0.114 .9990139 1.009243
_rcs6 | 1.004948 .0020115 2.47 0.014 1.001014 1.008899
_rcs7 | 1.004096 .0016959 2.42 0.016 1.000777 1.007425
_rcs8 | 1.001008 .0015104 0.67 0.505 .9980515 1.003972
_rcs9 | .9991352 .0013131 -0.66 0.510 .9965648 1.001712
_rcs_tr_outcome1 | .9469911 .0184532 -2.80 0.005 .9115056 .9838582
_rcs_tr_outcome2 | .9880753 .0147829 -0.80 0.423 .959522 1.017478
_cons | .2497683 .0032344 -107.12 0.000 .2435087 .2561888
------------------------------------------------------------------------------------
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 = -47750.783
Iteration 1: log pseudolikelihood = -47736.798
Iteration 2: log pseudolikelihood = -47736.755
Iteration 3: log pseudolikelihood = -47736.755
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47736.755 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086113 .0252257 3.56 0.000 1.03778 1.136697
_rcs1 | 2.548705 .0291787 81.72 0.000 2.492152 2.60654
_rcs2 | 1.100908 .0121065 8.74 0.000 1.077434 1.124894
_rcs3 | 1.041896 .0067931 6.29 0.000 1.028667 1.055296
_rcs4 | 1.022988 .0038892 5.98 0.000 1.015393 1.030639
_rcs5 | 1.005818 .0026775 2.18 0.029 1.000584 1.01108
_rcs6 | 1.005695 .0020035 2.85 0.004 1.001776 1.00963
_rcs7 | 1.004387 .001685 2.61 0.009 1.00109 1.007695
_rcs8 | 1.001095 .001503 0.73 0.466 .9981538 1.004045
_rcs9 | .9992194 .0013066 -0.60 0.550 .9966618 1.001784
_rcs_tr_outcome1 | .9471957 .018582 -2.77 0.006 .911467 .984325
_rcs_tr_outcome2 | 1.001838 .0164287 0.11 0.911 .9701503 1.034561
_rcs_tr_outcome3 | .9809088 .0095223 -1.99 0.047 .9624218 .9997509
_cons | .2496525 .0032312 -107.22 0.000 .2433991 .2560665
------------------------------------------------------------------------------------
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 = -47750.705
Iteration 1: log pseudolikelihood = -47736.847
Iteration 2: log pseudolikelihood = -47736.804
Iteration 3: log pseudolikelihood = -47736.804
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47736.804 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08621 .0252243 3.56 0.000 1.03788 1.136792
_rcs1 | 2.548765 .0291258 81.87 0.000 2.492314 2.606495
_rcs2 | 1.100456 .0121344 8.68 0.000 1.076928 1.124498
_rcs3 | 1.042824 .0073231 5.97 0.000 1.028569 1.057276
_rcs4 | 1.022755 .0038753 5.94 0.000 1.015187 1.030378
_rcs5 | 1.005239 .0029046 1.81 0.071 .9995622 1.010948
_rcs6 | 1.005422 .0022037 2.47 0.014 1.001112 1.00975
_rcs7 | 1.00434 .0017195 2.53 0.011 1.000976 1.007716
_rcs8 | 1.001121 .0014985 0.75 0.454 .9981883 1.004063
_rcs9 | .9992078 .0013057 -0.61 0.544 .9966519 1.00177
_rcs_tr_outcome1 | .9472736 .0185914 -2.76 0.006 .9115271 .984422
_rcs_tr_outcome2 | 1.00337 .0168123 0.20 0.841 .9709535 1.036868
_rcs_tr_outcome3 | .9796479 .0104845 -1.92 0.055 .9593127 1.000414
_rcs_tr_outcome4 | .9982538 .0067569 -0.26 0.796 .985098 1.011585
_cons | .2496455 .0032304 -107.24 0.000 .2433936 .2560579
------------------------------------------------------------------------------------
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 = -47750.572
Iteration 1: log pseudolikelihood = -47736.526
Iteration 2: log pseudolikelihood = -47736.472
Iteration 3: log pseudolikelihood = -47736.472
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47736.472 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086042 .0252182 3.55 0.000 1.037723 1.136611
_rcs1 | 2.548687 .0292317 81.57 0.000 2.492033 2.606629
_rcs2 | 1.101285 .0124263 8.55 0.000 1.077197 1.125911
_rcs3 | 1.041299 .00765 5.51 0.000 1.026413 1.056401
_rcs4 | 1.023264 .0039499 5.96 0.000 1.015551 1.031035
_rcs5 | 1.006018 .0029974 2.01 0.044 1.00016 1.01191
_rcs6 | 1.005314 .0022302 2.39 0.017 1.000953 1.009695
_rcs7 | 1.003871 .0018863 2.06 0.040 1.000181 1.007575
_rcs8 | 1.00092 .001508 0.61 0.541 .9979691 1.003881
_rcs9 | .9992 .0013023 -0.61 0.539 .9966509 1.001756
_rcs_tr_outcome1 | .9472845 .0185713 -2.76 0.006 .9115758 .984392
_rcs_tr_outcome2 | 1.001898 .0168429 0.11 0.910 .969424 1.035459
_rcs_tr_outcome3 | .9833934 .0111969 -1.47 0.141 .9616909 1.005586
_rcs_tr_outcome4 | .9922646 .007077 -1.09 0.276 .9784903 1.006233
_rcs_tr_outcome5 | 1.001761 .0049206 0.36 0.720 .9921635 1.011452
_cons | .2496663 .0032304 -107.24 0.000 .2434144 .2560787
------------------------------------------------------------------------------------
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 = -47750.583
Iteration 1: log pseudolikelihood = -47734.794
Iteration 2: log pseudolikelihood = -47734.684
Iteration 3: log pseudolikelihood = -47734.684
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47734.684 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085967 .0252196 3.55 0.000 1.037645 1.136538
_rcs1 | 2.549028 .0293585 81.24 0.000 2.492131 2.607224
_rcs2 | 1.102018 .0127259 8.41 0.000 1.077356 1.127245
_rcs3 | 1.040058 .0078809 5.18 0.000 1.024725 1.055619
_rcs4 | 1.024571 .0041259 6.03 0.000 1.016516 1.03269
_rcs5 | 1.005778 .0029372 1.97 0.048 1.000038 1.011552
_rcs6 | 1.004577 .0023307 1.97 0.049 1.000019 1.009155
_rcs7 | 1.004958 .0018785 2.65 0.008 1.001283 1.008647
_rcs8 | 1.002065 .0016055 1.29 0.198 .9989231 1.005217
_rcs9 | .9994186 .0012992 -0.45 0.655 .9968754 1.001968
_rcs_tr_outcome1 | .9468988 .0185735 -2.78 0.005 .9111862 .9840111
_rcs_tr_outcome2 | 1.000935 .0168925 0.06 0.956 .9683674 1.034597
_rcs_tr_outcome3 | .9869161 .0116555 -1.12 0.265 .9643342 1.010027
_rcs_tr_outcome4 | .987785 .0073788 -1.65 0.100 .9734281 1.002354
_rcs_tr_outcome5 | 1.001884 .0051982 0.36 0.717 .991747 1.012124
_rcs_tr_outcome6 | .9959569 .0039547 -1.02 0.308 .988236 1.003738
_cons | .2496506 .0032309 -107.23 0.000 .2433979 .256064
------------------------------------------------------------------------------------
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 = -47750.399
Iteration 1: log pseudolikelihood = -47732.771
Iteration 2: log pseudolikelihood = -47732.668
Iteration 3: log pseudolikelihood = -47732.668
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47732.668 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085818 .0252121 3.55 0.000 1.037511 1.136375
_rcs1 | 2.548534 .0292783 81.43 0.000 2.491791 2.606569
_rcs2 | 1.101474 .0126263 8.43 0.000 1.077003 1.126502
_rcs3 | 1.040998 .0079452 5.26 0.000 1.025542 1.056687
_rcs4 | 1.023866 .0042728 5.65 0.000 1.015525 1.032274
_rcs5 | 1.00605 .0029785 2.04 0.042 1.000229 1.011904
_rcs6 | 1.004829 .0022697 2.13 0.033 1.000391 1.009288
_rcs7 | 1.004369 .0019195 2.28 0.023 1.000614 1.008139
_rcs8 | 1.003068 .0016716 1.84 0.066 .9997974 1.00635
_rcs9 | 1.000186 .0013271 0.14 0.889 .9975882 1.00279
_rcs_tr_outcome1 | .9471847 .0185686 -2.77 0.006 .9114813 .9842867
_rcs_tr_outcome2 | 1.002319 .0170958 0.14 0.892 .9693658 1.036393
_rcs_tr_outcome3 | .9860134 .0119476 -1.16 0.245 .9628723 1.009711
_rcs_tr_outcome4 | .9882577 .007573 -1.54 0.123 .9735258 1.003212
_rcs_tr_outcome5 | .9988101 .0053608 -0.22 0.824 .9883582 1.009372
_rcs_tr_outcome6 | 1.000086 .0041795 0.02 0.984 .9919277 1.008311
_rcs_tr_outcome7 | .9937091 .0034204 -1.83 0.067 .9870278 1.000436
_cons | .2496597 .0032305 -107.24 0.000 .2434078 .2560723
------------------------------------------------------------------------------------
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 = -47751.738
Iteration 1: log pseudolikelihood = -47738.917
Iteration 2: log pseudolikelihood = -47738.888
Iteration 3: log pseudolikelihood = -47738.888
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.888 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.084065 .025125 3.48 0.000 1.035923 1.134445
_rcs1 | 2.543421 .0273884 86.69 0.000 2.490304 2.597672
_rcs2 | 1.102452 .0097418 11.04 0.000 1.083523 1.121712
_rcs3 | 1.03559 .0063791 5.68 0.000 1.023162 1.048168
_rcs4 | 1.021303 .0036689 5.87 0.000 1.014137 1.028519
_rcs5 | 1.004518 .0026879 1.68 0.092 .9992641 1.0098
_rcs6 | 1.004789 .0020273 2.37 0.018 1.000823 1.00877
_rcs7 | 1.003987 .0017334 2.30 0.021 1.000595 1.00739
_rcs8 | 1.003277 .0015119 2.17 0.030 1.000319 1.006245
_rcs9 | .9996242 .0014316 -0.26 0.793 .9968222 1.002434
_rcs10 | .9997861 .0012499 -0.17 0.864 .9973393 1.002239
_rcs_tr_outcome1 | .9539927 .0167653 -2.68 0.007 .9216927 .9874246
_cons | .2498899 .0032272 -107.38 0.000 .2436442 .2562958
------------------------------------------------------------------------------------
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 = -47751.852
Iteration 1: log pseudolikelihood = -47738.378
Iteration 2: log pseudolikelihood = -47738.34
Iteration 3: log pseudolikelihood = -47738.34
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47738.34 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085313 .0252128 3.52 0.000 1.037005 1.135872
_rcs1 | 2.550716 .0299516 79.74 0.000 2.492682 2.610101
_rcs2 | 1.107462 .0123615 9.14 0.000 1.083497 1.131957
_rcs3 | 1.036241 .0062834 5.87 0.000 1.023998 1.04863
_rcs4 | 1.021539 .0036903 5.90 0.000 1.014332 1.028797
_rcs5 | 1.004586 .0026808 1.71 0.086 .9993457 1.009854
_rcs6 | 1.004851 .0020226 2.40 0.016 1.000894 1.008823
_rcs7 | 1.004033 .0017289 2.34 0.019 1.00065 1.007427
_rcs8 | 1.003323 .001508 2.21 0.027 1.000371 1.006283
_rcs9 | .9996653 .0014283 -0.23 0.815 .9968699 1.002469
_rcs10 | .9998084 .0012474 -0.15 0.878 .9973665 1.002256
_rcs_tr_outcome1 | .9467151 .018475 -2.81 0.005 .9111886 .9836268
_rcs_tr_outcome2 | .9879596 .0148209 -0.81 0.419 .959334 1.017439
_cons | .2497511 .0032346 -107.12 0.000 .2434912 .2561719
------------------------------------------------------------------------------------
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 = -47751.894
Iteration 1: log pseudolikelihood = -47735.194
Iteration 2: log pseudolikelihood = -47735.135
Iteration 3: log pseudolikelihood = -47735.135
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47735.135 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086277 .0252343 3.56 0.000 1.037928 1.136878
_rcs1 | 2.54923 .0292713 81.50 0.000 2.4925 2.607251
_rcs2 | 1.100925 .0123181 8.59 0.000 1.077045 1.125335
_rcs3 | 1.041145 .006858 6.12 0.000 1.02779 1.054674
_rcs4 | 1.025049 .0039251 6.46 0.000 1.017385 1.032771
_rcs5 | 1.006477 .0027737 2.34 0.019 1.001055 1.011928
_rcs6 | 1.005796 .0020262 2.87 0.004 1.001833 1.009775
_rcs7 | 1.004477 .0017175 2.61 0.009 1.001116 1.007849
_rcs8 | 1.003488 .0014997 2.33 0.020 1.000553 1.006431
_rcs9 | .9997499 .0014202 -0.18 0.860 .9969703 1.002537
_rcs10 | .9998966 .0012407 -0.08 0.934 .9974677 1.002331
_rcs_tr_outcome1 | .9469313 .0186026 -2.78 0.006 .911164 .9841027
_rcs_tr_outcome2 | 1.001785 .0165043 0.11 0.914 .9699538 1.034661
_rcs_tr_outcome3 | .9808343 .0095394 -1.99 0.047 .9623146 .9997105
_cons | .2496349 .0032312 -107.21 0.000 .2433815 .2560491
------------------------------------------------------------------------------------
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 = -47751.812
Iteration 1: log pseudolikelihood = -47735.242
Iteration 2: log pseudolikelihood = -47735.182
Iteration 3: log pseudolikelihood = -47735.182
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47735.182 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086375 .0252329 3.57 0.000 1.038028 1.136974
_rcs1 | 2.549297 .0292183 81.65 0.000 2.492668 2.607212
_rcs2 | 1.100468 .0123563 8.53 0.000 1.076515 1.124954
_rcs3 | 1.042066 .0073827 5.82 0.000 1.027696 1.056636
_rcs4 | 1.024925 .0039193 6.44 0.000 1.017272 1.032636
_rcs5 | 1.005932 .00293 2.03 0.042 1.000205 1.011691
_rcs6 | 1.005444 .0022575 2.42 0.016 1.001029 1.009878
_rcs7 | 1.004352 .0018122 2.41 0.016 1.000807 1.007911
_rcs8 | 1.003495 .0015033 2.33 0.020 1.000553 1.006446
_rcs9 | .9997624 .0014173 -0.17 0.867 .9969885 1.002544
_rcs10 | .999882 .00124 -0.10 0.924 .9974546 1.002315
_rcs_tr_outcome1 | .9470033 .0186127 -2.77 0.006 .9112167 .9841953
_rcs_tr_outcome2 | 1.003316 .0169109 0.20 0.844 .9707125 1.037014
_rcs_tr_outcome3 | .9795776 .0105264 -1.92 0.055 .9591621 1.000428
_rcs_tr_outcome4 | .9982132 .0067536 -0.26 0.792 .9850637 1.011538
_cons | .2496277 .0032305 -107.24 0.000 .2433758 .2560403
------------------------------------------------------------------------------------
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 = -47751.678
Iteration 1: log pseudolikelihood = -47734.944
Iteration 2: log pseudolikelihood = -47734.877
Iteration 3: log pseudolikelihood = -47734.877
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47734.877 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.08621 .025227 3.56 0.000 1.037875 1.136797
_rcs1 | 2.549212 .0293203 81.36 0.000 2.492388 2.607331
_rcs2 | 1.101272 .0126479 8.40 0.000 1.07676 1.126343
_rcs3 | 1.040598 .0077178 5.37 0.000 1.02558 1.055835
_rcs4 | 1.02525 .0039473 6.48 0.000 1.017543 1.033016
_rcs5 | 1.006729 .003073 2.20 0.028 1.000724 1.01277
_rcs6 | 1.005631 .0022256 2.54 0.011 1.001279 1.010003
_rcs7 | 1.004004 .0019575 2.05 0.040 1.000175 1.007848
_rcs8 | 1.003155 .0015919 1.99 0.047 1.00004 1.00628
_rcs9 | .9996722 .0014125 -0.23 0.817 .9969076 1.002445
_rcs10 | .9998739 .0012377 -0.10 0.919 .9974511 1.002303
_rcs_tr_outcome1 | .9470116 .0185941 -2.77 0.006 .9112602 .9841657
_rcs_tr_outcome2 | 1.001936 .0169672 0.11 0.909 .9692267 1.035749
_rcs_tr_outcome3 | .9832039 .0112632 -1.48 0.139 .9613744 1.005529
_rcs_tr_outcome4 | .9922998 .0070836 -1.08 0.279 .9785128 1.006281
_rcs_tr_outcome5 | 1.001595 .0049308 0.32 0.746 .9919768 1.011306
_cons | .2496477 .0032304 -107.24 0.000 .2433958 .2560602
------------------------------------------------------------------------------------
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 = -47751.634
Iteration 1: log pseudolikelihood = -47733.167
Iteration 2: log pseudolikelihood = -47733.049
Iteration 3: log pseudolikelihood = -47733.049
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47733.049 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.086144 .0252282 3.56 0.000 1.037807 1.136734
_rcs1 | 2.549532 .02944 81.05 0.000 2.492479 2.607892
_rcs2 | 1.101956 .0129425 8.27 0.000 1.076879 1.127617
_rcs3 | 1.03939 .0079639 5.04 0.000 1.023898 1.055117
_rcs4 | 1.026429 .0040854 6.55 0.000 1.018453 1.034467
_rcs5 | 1.006877 .0030528 2.26 0.024 1.000912 1.012879
_rcs6 | 1.004682 .0023151 2.03 0.043 1.000155 1.00923
_rcs7 | 1.004262 .0019205 2.22 0.026 1.000505 1.008034
_rcs8 | 1.00447 .0016725 2.68 0.007 1.001197 1.007753
_rcs9 | 1.000429 .0014553 0.29 0.768 .9975803 1.003285
_rcs10 | .9999859 .0012347 -0.01 0.991 .9975688 1.002409
_rcs_tr_outcome1 | .9466381 .0185963 -2.79 0.005 .9108828 .983797
_rcs_tr_outcome2 | 1.00107 .0170333 0.06 0.950 .9682364 1.035018
_rcs_tr_outcome3 | .9866 .0117645 -1.13 0.258 .9638094 1.00993
_rcs_tr_outcome4 | .9878681 .0074263 -1.62 0.104 .9734194 1.002531
_rcs_tr_outcome5 | 1.001862 .0052011 0.36 0.720 .9917196 1.012108
_rcs_tr_outcome6 | .9958748 .0039224 -1.05 0.294 .9882168 1.003592
_cons | .2496316 .0032308 -107.23 0.000 .243379 .2560449
------------------------------------------------------------------------------------
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 = -47751.708
Iteration 1: log pseudolikelihood = -47731.301
Iteration 2: log pseudolikelihood = -47731.192
Iteration 3: log pseudolikelihood = -47731.192
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -47731.192 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.085954 .0252205 3.55 0.000 1.037631 1.136528
_rcs1 | 2.548979 .0293421 81.28 0.000 2.492114 2.607143
_rcs2 | 1.10134 .0128157 8.30 0.000 1.076506 1.126747
_rcs3 | 1.040557 .0080391 5.15 0.000 1.024919 1.056433
_rcs4 | 1.025604 .0042168 6.15 0.000 1.017372 1.033902
_rcs5 | 1.00688 .003024 2.28 0.022 1.00097 1.012824
_rcs6 | 1.005343 .0023271 2.30 0.021 1.000793 1.009915
_rcs7 | 1.003684 .0019678 1.88 0.061 .9998344 1.007548
_rcs8 | 1.004466 .0016492 2.71 0.007 1.001239 1.007703
_rcs9 | 1.001576 .00155 1.02 0.309 .9985423 1.004618
_rcs10 | 1.000377 .0012367 0.30 0.761 .9979558 1.002804
_rcs_tr_outcome1 | .9469963 .0185925 -2.77 0.006 .9112479 .9841471
_rcs_tr_outcome2 | 1.002625 .0172866 0.15 0.879 .9693104 1.037085
_rcs_tr_outcome3 | .9851503 .0120951 -1.22 0.223 .9617273 1.009144
_rcs_tr_outcome4 | .9889386 .007606 -1.45 0.148 .9741429 1.003959
_rcs_tr_outcome5 | .9984148 .0053287 -0.30 0.766 .9880251 1.008914
_rcs_tr_outcome6 | 1.000258 .0041688 0.06 0.951 .99212 1.008462
_rcs_tr_outcome7 | .9937046 .0034392 -1.82 0.068 .9869866 1.000468
_cons | .2496445 .0032304 -107.24 0.000 .2433925 .256057
------------------------------------------------------------------------------------
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_pr
> in3 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 mzone
> 2 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_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 = -49344.272
Iteration 1: log pseudolikelihood = -49343.307
Iteration 2: log pseudolikelihood = -49343.307
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 43,782
Wald chi2(1) = 1.01
Log pseudolikelihood = -49343.307 Prob > chi2 = 0.3159
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.024735 .0249649 1.00 0.316 .9769545 1.074852
_cons | .1092272 .0013739 -176.05 0.000 .1065674 .1119534
------------------------------------------------------------------------------
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 = -49344.272
Iteration 1: log pseudolikelihood = -48032.475
Iteration 2: log pseudolikelihood = -48011.476
Iteration 3: log pseudolikelihood = -48011.47
Iteration 4: log pseudolikelihood = -48011.47
Fitting full model:
Iteration 0: log pseudolikelihood = -48011.47
Iteration 1: log pseudolikelihood = -48008.097
Iteration 2: log pseudolikelihood = -48008.096
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 43,782
Wald chi2(1) = 4.14
Log pseudolikelihood = -48008.096 Prob > chi2 = 0.0420
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.046811 .0235472 2.03 0.042 1.001662 1.093995
_cons | .1621256 .0022414 -131.60 0.000 .1577915 .1665787
-------------+----------------------------------------------------------------
/ln_p | -.3492258 .0079534 -43.91 0.000 -.364814 -.3336375
-------------+----------------------------------------------------------------
p | .7052339 .005609 .6943258 .7163134
1/p | 1.417969 .0112776 1.396037 1.440246
------------------------------------------------------------------------------
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 = -49343.712
Iteration 1: log pseudolikelihood = -47966.399
Iteration 2: log pseudolikelihood = -47883.222
Iteration 3: log pseudolikelihood = -47882.963
Iteration 4: log pseudolikelihood = -47882.963
Fitting full model:
Iteration 0: log pseudolikelihood = -47882.963
Iteration 1: log pseudolikelihood = -47877.207
Iteration 2: log pseudolikelihood = -47877.205
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 43,782
Wald chi2(1) = 7.42
Log pseudolikelihood = -47877.205 Prob > chi2 = 0.0064
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.06166 .0233134 2.72 0.006 1.016936 1.108351
_cons | .1874388 .0033327 -94.17 0.000 .1810193 .194086
-------------+----------------------------------------------------------------
/gamma | -.2752485 .0076733 -35.87 0.000 -.2902878 -.2602092
------------------------------------------------------------------------------
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 = -64388.447
Iteration 1: log pseudolikelihood = -48758.983
Iteration 2: log pseudolikelihood = -47862.731
Iteration 3: log pseudolikelihood = -47813.799
Iteration 4: log pseudolikelihood = -47813.642
Iteration 5: log pseudolikelihood = -47813.642
Fitting full model:
Iteration 0: log pseudolikelihood = -47813.642
Iteration 1: log pseudolikelihood = -47805.01
Iteration 2: log pseudolikelihood = -47805.004
Iteration 3: log pseudolikelihood = -47805.004
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 43,782
Wald chi2(1) = 11.30
Log pseudolikelihood = -47805.004 Prob > chi2 = 0.0008
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .8864864 .031772 -3.36 0.001 .8263512 .9509977
_cons | 9.713953 .2339931 94.38 0.000 9.265993 10.18357
-------------+----------------------------------------------------------------
/lnsigma | .837399 .0090971 92.05 0.000 .819569 .8552289
-------------+----------------------------------------------------------------
sigma | 2.31035 .0210174 2.269522 2.351913
------------------------------------------------------------------------------
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 = -48422.443
Iteration 1: log pseudolikelihood = -47836.979
Iteration 2: log pseudolikelihood = -47836.767
Iteration 3: log pseudolikelihood = -47836.767
Fitting full model:
Iteration 0: log pseudolikelihood = -47836.767
Iteration 1: log pseudolikelihood = -47831.251
Iteration 2: log pseudolikelihood = -47831.248
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 43,782
Wald chi2(1) = 6.99
Log pseudolikelihood = -47831.248 Prob > chi2 = 0.0082
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .9142361 .0310074 -2.64 0.008 .8554386 .977075
_cons | 8.410358 .173876 103.00 0.000 8.076379 8.758147
-------------+----------------------------------------------------------------
/lngamma | .2212346 .0083621 26.46 0.000 .2048452 .2376239
-------------+----------------------------------------------------------------
gamma | 1.247616 .0104327 1.227335 1.268232
------------------------------------------------------------------------------
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 colinear 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 | 18,462 . -48001.25 4 96010.51 96041.8
m3_stipw_n~2 | 18,462 . -47925.6 5 95861.2 95900.32
m3_stipw_n~3 | 18,462 . -47923.56 6 95859.11 95906.05
m3_stipw_n~4 | 18,462 . -47923.16 7 95860.31 95915.08
m3_stipw_n~5 | 18,462 . -47920.81 8 95857.62 95920.21
m3_stipw_n~6 | 18,462 . -47921.3 9 95860.6 95931.02
m3_stipw_n~7 | 18,462 . -47917.63 10 95855.26 95933.49
m3_stipw_n~1 | 18,462 . -47768.86 5 95547.71 95586.83
m3_stipw_n~2 | 18,462 . -47768.33 6 95548.66 95595.6
m3_stipw_n~3 | 18,462 . -47766.46 7 95546.93 95601.69
m3_stipw_n~4 | 18,462 . -47765.89 8 95547.77 95610.36
m3_stipw_n~5 | 18,462 . -47763.57 9 95545.15 95615.56
m3_stipw_n~6 | 18,462 . -47764.03 10 95548.06 95626.3
m3_stipw_n~7 | 18,462 . -47760.34 11 95542.69 95628.75
m3_stipw_n~1 | 18,462 . -47747.68 6 95507.35 95554.29
m3_stipw_n~2 | 18,462 . -47747.21 7 95508.41 95563.18
m3_stipw_n~3 | 18,462 . -47744.24 8 95504.47 95567.06
m3_stipw_n~4 | 18,462 . -47744.77 9 95507.54 95577.95
m3_stipw_n~5 | 18,462 . -47741.25 10 95502.5 95580.74
m3_stipw_n~6 | 18,462 . -47741.98 11 95505.96 95592.02
m3_stipw_n~7 | 18,462 . -47738.35 12 95500.71 95594.59
m3_stipw_n~1 | 18,462 . -47746.09 7 95506.18 95560.95
m3_stipw_n~2 | 18,462 . -47745.61 8 95507.22 95569.81
m3_stipw_n~3 | 18,462 . -47742.19 9 95502.38 95572.79
m3_stipw_n~4 | 18,462 . -47742.65 10 95505.31 95583.54
m3_stipw_n~5 | 18,462 . -47740.11 11 95502.22 95588.28
m3_stipw_n~6 | 18,462 . -47740.53 12 95505.06 95598.94
m3_stipw_n~7 | 18,462 . -47737.07 13 95500.14 95601.85
m3_stipw_n~1 | 18,462 . -47742.98 8 95501.96 95564.54
m3_stipw_n~2 | 18,462 . -47742.45 9 95502.91 95573.32
m3_stipw_n~3 | 18,462 . -47739.22 10 95498.43 95576.67
m3_stipw_n~4 | 18,462 . -47739.32 11 95500.64 95586.69
m3_stipw_n~5 | 18,462 . -47738.9 12 95501.8 95595.68
m3_stipw_n~6 | 18,462 . -47738.49 13 95502.97 95604.68
m3_stipw_n~7 | 18,462 . -47735.51 14 95499.01 95608.54
m3_stipw_n~1 | 18,462 . -47743.87 9 95505.74 95576.15
m3_stipw_n~2 | 18,462 . -47743.33 10 95506.66 95584.9
m3_stipw_n~3 | 18,462 . -47740.08 11 95502.17 95588.23
m3_stipw_n~4 | 18,462 . -47740.24 12 95504.47 95598.35
m3_stipw_n~5 | 18,462 . -47739.42 13 95504.84 95606.55
m3_stipw_n~6 | 18,462 . -47737.85 14 95503.7 95613.23
m3_stipw_n~7 | 18,462 . -47734.17 15 95498.33 95615.68
m3_stipw_n~1 | 18,462 . -47742.23 10 95504.47 95582.7
m3_stipw_n~2 | 18,462 . -47741.71 11 95505.43 95591.49
m3_stipw_n~3 | 18,462 . -47738.46 12 95500.91 95594.79
m3_stipw_n~4 | 18,462 . -47738.6 13 95503.21 95604.91
m3_stipw_n~5 | 18,462 . -47738.11 14 95504.22 95613.75
m3_stipw_n~6 | 18,462 . -47736.62 15 95503.24 95620.59
m3_stipw_n~7 | 18,462 . -47734.67 16 95501.33 95626.51
m3_stipw_n~1 | 18,462 . -47740.89 11 95503.79 95589.84
m3_stipw_n~2 | 18,462 . -47740.37 12 95504.75 95598.63
m3_stipw_n~3 | 18,462 . -47737.18 13 95500.36 95602.07
m3_stipw_n~4 | 18,462 . -47737.26 14 95502.53 95612.06
m3_stipw_n~5 | 18,462 . -47736.87 15 95503.74 95621.09
m3_stipw_n~6 | 18,462 . -47734.45 16 95500.91 95626.09
m3_stipw_n~7 | 18,462 . -47733.22 17 95500.45 95633.45
m3_stipw_n~1 | 18,462 . -47740.48 12 95504.95 95598.83
m3_stipw_n~2 | 18,462 . -47739.94 13 95505.87 95607.58
m3_stipw_n~3 | 18,462 . -47736.76 14 95501.51 95611.04
m3_stipw_n~4 | 18,462 . -47736.8 15 95503.61 95620.96
m3_stipw_n~5 | 18,462 . -47736.47 16 95504.94 95630.12
m3_stipw_n~6 | 18,462 . -47734.68 17 95503.37 95636.37
m3_stipw_n~7 | 18,462 . -47732.67 18 95501.34 95642.16
m3_stipw_n~1 | 18,462 . -47738.89 13 95503.78 95605.48
m3_stipw_n~2 | 18,462 . -47738.34 14 95504.68 95614.21
m3_stipw_n~3 | 18,462 . -47735.13 15 95500.27 95617.62
m3_stipw_n~4 | 18,462 . -47735.18 16 95502.36 95627.54
m3_stipw_n~5 | 18,462 . -47734.88 17 95503.75 95636.75
m3_stipw_n~6 | 18,462 . -47733.05 18 95502.1 95642.92
m3_stipw_n~7 | 18,462 . -47731.19 19 95500.38 95649.03
m3_stipw_n~p | 18,462 -49344.27 -49343.31 2 98690.61 98706.26
m3_stipw_n~i | 18,462 -48011.47 -48008.1 3 96022.19 96045.66
m3_stipw_n~m | 18,462 -47882.96 -47877.2 3 95760.41 95783.88
m3_stipw_n~n | 18,462 -47813.64 -47805 3 95616.01 95639.48
m3_stipw_n~g | 18,462 -47836.77 -47831.25 3 95668.5 95691.97
-----------------------------------------------------------------------------
. //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.csv", replace
(output written to testreg_aic_bic_mrl_23_4.csv)
. esttab matrix(stats_4) using "testreg_aic_bic_mrl_23_4.html", replace
(output written to testreg_aic_bic_mrl_23_4.html)
.
| stats_4 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m3_stipw_nostag_rp6_tvcdf7 | 18462 | . | -47734.17 | 15 | 95498.33 | 95615.68 |
| m3_stipw_nostag_rp5_tvcdf3 | 18462 | . | -47739.22 | 10 | 95498.43 | 95576.67 |
| m3_stipw_nostag_rp5_tvcdf7 | 18462 | . | -47735.51 | 14 | 95499.01 | 95608.54 |
| m3_stipw_nostag_rp4_tvcdf7 | 18462 | . | -47737.07 | 13 | 95500.14 | 95601.85 |
| m3_stipw_nostag_rp10_tvcdf3 | 18462 | . | -47735.13 | 15 | 95500.27 | 95617.62 |
| m3_stipw_nostag_rp8_tvcdf3 | 18462 | . | -47737.18 | 13 | 95500.36 | 95602.07 |
| m3_stipw_nostag_rp10_tvcdf7 | 18462 | . | -47731.19 | 19 | 95500.38 | 95649.03 |
| m3_stipw_nostag_rp8_tvcdf7 | 18462 | . | -47733.22 | 17 | 95500.45 | 95633.45 |
| m3_stipw_nostag_rp5_tvcdf4 | 18462 | . | -47739.32 | 11 | 95500.64 | 95586.69 |
| m3_stipw_nostag_rp3_tvcdf7 | 18462 | . | -47738.35 | 12 | 95500.71 | 95594.59 |
| m3_stipw_nostag_rp8_tvcdf6 | 18462 | . | -47734.45 | 16 | 95500.91 | 95626.09 |
| m3_stipw_nostag_rp7_tvcdf3 | 18462 | . | -47738.46 | 12 | 95500.91 | 95594.79 |
| m3_stipw_nostag_rp7_tvcdf7 | 18462 | . | -47734.67 | 16 | 95501.33 | 95626.51 |
| m3_stipw_nostag_rp9_tvcdf7 | 18462 | . | -47732.67 | 18 | 95501.34 | 95642.16 |
| m3_stipw_nostag_rp9_tvcdf3 | 18462 | . | -47736.76 | 14 | 95501.51 | 95611.04 |
| m3_stipw_nostag_rp5_tvcdf5 | 18462 | . | -47738.9 | 12 | 95501.8 | 95595.68 |
| m3_stipw_nostag_rp5_tvcdf1 | 18462 | . | -47742.98 | 8 | 95501.96 | 95564.54 |
| m3_stipw_nostag_rp10_tvcdf6 | 18462 | . | -47733.05 | 18 | 95502.1 | 95642.92 |
| m3_stipw_nostag_rp6_tvcdf3 | 18462 | . | -47740.08 | 11 | 95502.17 | 95588.23 |
| m3_stipw_nostag_rp4_tvcdf5 | 18462 | . | -47740.11 | 11 | 95502.22 | 95588.28 |
| m3_stipw_nostag_rp10_tvcdf4 | 18462 | . | -47735.18 | 16 | 95502.36 | 95627.54 |
| m3_stipw_nostag_rp4_tvcdf3 | 18462 | . | -47742.19 | 9 | 95502.38 | 95572.79 |
| m3_stipw_nostag_rp3_tvcdf5 | 18462 | . | -47741.25 | 10 | 95502.5 | 95580.74 |
| m3_stipw_nostag_rp8_tvcdf4 | 18462 | . | -47737.26 | 14 | 95502.53 | 95612.06 |
| m3_stipw_nostag_rp5_tvcdf2 | 18462 | . | -47742.45 | 9 | 95502.91 | 95573.32 |
| m3_stipw_nostag_rp5_tvcdf6 | 18462 | . | -47738.49 | 13 | 95502.97 | 95604.68 |
| m3_stipw_nostag_rp7_tvcdf4 | 18462 | . | -47738.6 | 13 | 95503.21 | 95604.91 |
| m3_stipw_nostag_rp7_tvcdf6 | 18462 | . | -47736.62 | 15 | 95503.24 | 95620.59 |
| m3_stipw_nostag_rp9_tvcdf6 | 18462 | . | -47734.68 | 17 | 95503.37 | 95636.37 |
| m3_stipw_nostag_rp9_tvcdf4 | 18462 | . | -47736.8 | 15 | 95503.61 | 95620.96 |
| m3_stipw_nostag_rp6_tvcdf6 | 18462 | . | -47737.85 | 14 | 95503.7 | 95613.23 |
| m3_stipw_nostag_rp8_tvcdf5 | 18462 | . | -47736.87 | 15 | 95503.74 | 95621.09 |
| m3_stipw_nostag_rp10_tvcdf5 | 18462 | . | -47734.88 | 17 | 95503.75 | 95636.75 |
| m3_stipw_nostag_rp10_tvcdf1 | 18462 | . | -47738.89 | 13 | 95503.78 | 95605.48 |
| m3_stipw_nostag_rp8_tvcdf1 | 18462 | . | -47740.89 | 11 | 95503.79 | 95589.84 |
| m3_stipw_nostag_rp7_tvcdf5 | 18462 | . | -47738.11 | 14 | 95504.22 | 95613.75 |
| m3_stipw_nostag_rp7_tvcdf1 | 18462 | . | -47742.23 | 10 | 95504.47 | 95582.7 |
| m3_stipw_nostag_rp3_tvcdf3 | 18462 | . | -47744.24 | 8 | 95504.47 | 95567.06 |
| m3_stipw_nostag_rp6_tvcdf4 | 18462 | . | -47740.24 | 12 | 95504.47 | 95598.35 |
| m3_stipw_nostag_rp10_tvcdf2 | 18462 | . | -47738.34 | 14 | 95504.68 | 95614.21 |
| m3_stipw_nostag_rp8_tvcdf2 | 18462 | . | -47740.37 | 12 | 95504.75 | 95598.63 |
| m3_stipw_nostag_rp6_tvcdf5 | 18462 | . | -47739.42 | 13 | 95504.84 | 95606.55 |
| m3_stipw_nostag_rp9_tvcdf5 | 18462 | . | -47736.47 | 16 | 95504.94 | 95630.12 |
| m3_stipw_nostag_rp9_tvcdf1 | 18462 | . | -47740.48 | 12 | 95504.95 | 95598.83 |
| m3_stipw_nostag_rp4_tvcdf6 | 18462 | . | -47740.53 | 12 | 95505.06 | 95598.94 |
| m3_stipw_nostag_rp4_tvcdf4 | 18462 | . | -47742.65 | 10 | 95505.31 | 95583.54 |
| m3_stipw_nostag_rp7_tvcdf2 | 18462 | . | -47741.71 | 11 | 95505.43 | 95591.49 |
| m3_stipw_nostag_rp6_tvcdf1 | 18462 | . | -47743.87 | 9 | 95505.74 | 95576.15 |
| m3_stipw_nostag_rp9_tvcdf2 | 18462 | . | -47739.94 | 13 | 95505.87 | 95607.58 |
| m3_stipw_nostag_rp3_tvcdf6 | 18462 | . | -47741.98 | 11 | 95505.96 | 95592.02 |
| m3_stipw_nostag_rp4_tvcdf1 | 18462 | . | -47746.09 | 7 | 95506.18 | 95560.95 |
| m3_stipw_nostag_rp6_tvcdf2 | 18462 | . | -47743.33 | 10 | 95506.66 | 95584.9 |
| m3_stipw_nostag_rp4_tvcdf2 | 18462 | . | -47745.61 | 8 | 95507.22 | 95569.81 |
| m3_stipw_nostag_rp3_tvcdf1 | 18462 | . | -47747.68 | 6 | 95507.35 | 95554.29 |
| m3_stipw_nostag_rp3_tvcdf4 | 18462 | . | -47744.77 | 9 | 95507.54 | 95577.95 |
| m3_stipw_nostag_rp3_tvcdf2 | 18462 | . | -47747.21 | 7 | 95508.41 | 95563.18 |
| m3_stipw_nostag_rp2_tvcdf7 | 18462 | . | -47760.34 | 11 | 95542.69 | 95628.75 |
| m3_stipw_nostag_rp2_tvcdf5 | 18462 | . | -47763.57 | 9 | 95545.15 | 95615.56 |
| m3_stipw_nostag_rp2_tvcdf3 | 18462 | . | -47766.46 | 7 | 95546.93 | 95601.69 |
| m3_stipw_nostag_rp2_tvcdf1 | 18462 | . | -47768.86 | 5 | 95547.71 | 95586.83 |
| m3_stipw_nostag_rp2_tvcdf4 | 18462 | . | -47765.89 | 8 | 95547.77 | 95610.36 |
| m3_stipw_nostag_rp2_tvcdf6 | 18462 | . | -47764.03 | 10 | 95548.06 | 95626.3 |
| m3_stipw_nostag_rp2_tvcdf2 | 18462 | . | -47768.33 | 6 | 95548.66 | 95595.6 |
| m3_stipw_nostag_logn | 18462 | -47813.64 | -47805 | 3 | 95616.01 | 95639.48 |
| m3_stipw_nostag_llog | 18462 | -47836.77 | -47831.25 | 3 | 95668.5 | 95691.97 |
| m3_stipw_nostag_gom | 18462 | -47882.96 | -47877.2 | 3 | 95760.41 | 95783.88 |
| m3_stipw_nostag_rp1_tvcdf7 | 18462 | . | -47917.63 | 10 | 95855.26 | 95933.49 |
| m3_stipw_nostag_rp1_tvcdf5 | 18462 | . | -47920.81 | 8 | 95857.62 | 95920.21 |
| m3_stipw_nostag_rp1_tvcdf3 | 18462 | . | -47923.56 | 6 | 95859.11 | 95906.05 |
| m3_stipw_nostag_rp1_tvcdf4 | 18462 | . | -47923.16 | 7 | 95860.31 | 95915.08 |
| m3_stipw_nostag_rp1_tvcdf6 | 18462 | . | -47921.3 | 9 | 95860.6 | 95931.02 |
| m3_stipw_nostag_rp1_tvcdf2 | 18462 | . | -47925.6 | 5 | 95861.2 | 95900.32 |
| m3_stipw_nostag_rp1_tvcdf1 | 18462 | . | -48001.25 | 4 | 96010.51 | 96041.8 |
| m3_stipw_nostag_wei | 18462 | -48011.47 | -48008.1 | 3 | 96022.19 | 96045.66 |
| m3_stipw_nostag_exp | 18462 | -49344.27 | -49343.31 | 2 | 98690.61 | 98706.26 |
.
. estimates replay m3_stipw_nostag_rp5_tvcdf1, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m3_stipw_nostag_rp5_tvcdf1
------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -47742.978 Number of obs = 43,782
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.083794 .0251012 3.47 0.001 1.035696 1.134125
_rcs1 | 2.542121 .0270616 87.64 0.000 2.489631 2.595718
_rcs2 | 1.10237 .0086971 12.35 0.000 1.085455 1.119549
_rcs3 | 1.038133 .0055193 7.04 0.000 1.027371 1.049007
_rcs4 | 1.006914 .0033598 2.06 0.039 1.00035 1.01352
_rcs5 | 1.005592 .0022496 2.49 0.013 1.001193 1.010011
_rcs_tr_outcome1 | .9545863 .0167579 -2.65 0.008 .9223001 .9880028
_cons | .2498998 .0032255 -107.43 0.000 .2436571 .2563024
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m3_stipw_nostag_rp5_tvcdf1 // m3_stipw_nostag_rp5_tvcdf1
(results m3_stipw_nostag_rp5_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)
.
. 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)
.
. * 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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_22_c.gph saved)
.
.
. 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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_c.gph saved)
Summary
. *list diff_comp_vs_early diff_comp_vs_early_lci diff_comp_vs_early_uci tt if !missing(tt)
. *frame results: save myresults, replace
.
. frame change default
. cap gen tt2= round(tt,.01)
.
. frame late: cap gen tt2= round(tt,.01)
. frame late: drop if missing(tt2)
(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 sp
> ecify 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(tt2)
(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(tt2)
(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(3) c
> ols(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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_s_abc.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) c
> ols(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.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_abc.gph saved)
Saved at= 20:36:33 5 Apr 2023
. frame late: cap qui save "mariel_feb_23_late.dta", all replace emptyok
. frame early: cap qui save "mariel_feb_23_early.dta", all replace emptyok
. frame early_late: cap qui save "mariel_feb_23_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_nostag_rp6_tvc_1_dum)
(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.sters saved)