. 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)
Date created: 23:37:17 7 Apr 2023.
Get the folder
C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)
Fecha: 7 Apr 2023, considerando un SO Windows para el usuario: CISS Fondecyt
Path data= ;
Tiempo: 7 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_feb_2023_match_SENDA_miss_pris.dta
=============================================================================
=============================================================================
We open the files
. use "fiscalia_mariel_feb_2023_match_SENDA_miss_pris.dta", clear
.
. *b) select 10% of the data
. /*
> set seed 2125
> sample 10
> */
.
.
. fs mariel_ags_*.do
mariel_ags_b.do mariel_ags_b_m1.do mariel_ags_b_m2.do mariel_ags_b_m3.do
. di "`r(dofile)'"
.
. *tostring tr_modality, gen(tr_modality_str)
.
. cap noi encode tr_modality_str, gen(newtr_modality)
variable tr_modality_str not found
. cap confirm variable newtr_modality
. if !_rc {
. cap noi drop tr_modality
. cap noi rename newtr_modality tr_modality
. }
.
. cap noi encode condicion_ocupacional_cor, gen(newcondicion_ocupacional_cor)
not possible with numeric variable
. cap confirm variable newcondicion_ocupacional_cor
. if !_rc {
. cap noi drop condicion_ocupacional_cor
. cap noi rename newcondicion_ocupacional_cor condicion_ocupacional_cor
. }
.
. cap noi encode tipo_centro, gen(newtipo_centro)
variable tipo_centro not found
. cap confirm variable newtipo_centro
. if !_rc {
. cap noi drop tipo_centro
. cap noi rename newtipo_centro tipo_centro
. }
.
. cap noi encode sus_ini_mod_mvv, gen(newsus_ini_mod_mvv)
. cap confirm variable newsus_ini_mod_mvv
. if !_rc {
. cap noi drop sus_ini_mod_mvv
. cap noi rename newsus_ini_mod_mvv sus_ini_mod_mvv
. }
.
. cap noi encode dg_trs_cons_sus_or, gen(newdg_trs_cons_sus_or)
. cap confirm variable newdg_trs_cons_sus_or
. if !_rc {
. cap noi drop dg_trs_cons_sus_or
. cap noi rename newdg_trs_cons_sus_or dg_trs_cons_sus_or
. }
.
. cap noi encode con_quien_vive_joel, gen(newcon_quien_vive_joel)
. cap confirm variable newcon_quien_vive_joel
. if !_rc {
. cap noi drop con_quien_vive_joel
. cap noi rename newcon_quien_vive_joel con_quien_vive_joel
. }
.
.
. *order and encode
. cap noi decode freq_cons_sus_prin, gen(str_freq_cons_sus_prin)
. cap confirm variable str_freq_cons_sus_prin
. if !_rc {
. cap noi drop freq_cons_sus_prin
. label def freq_cons_sus_prin2 1 "Less than 1 day a week" 2 "1 day a week or more" 3 "2 to 3 days a week" 4 "4 to 6 days a week" 5 "Daily"
. encode str_freq_cons_sus_prin, gen(freq_cons_sus_prin) label (freq_cons_sus_prin2)
. }
. cap noi decode dg_trs_cons_sus_or, gen(str_dg_trs_cons_sus_or)
. cap confirm variable str_dg_trs_cons_sus_or
. if !_rc {
. cap noi drop dg_trs_cons_sus_or
. cap label def dg_trs_cons_sus_or2 1 "Hazardous consumption" 2 "Drug dependence"
. encode str_dg_trs_cons_sus_or, gen(dg_trs_cons_sus_or) label (dg_trs_cons_sus_or2)
. }
.
.
. cap noi encode escolaridad_rec, gen(esc_rec)
not possible with numeric variable
. cap noi encode sex, generate(sex_enc)
. cap noi encode sus_principal_mod, gen(sus_prin_mod)
not possible with numeric variable
. cap noi encode freq_cons_sus_prin, gen(fr_sus_prin)
not possible with numeric variable
. cap noi encode compromiso_biopsicosocial, gen(comp_biosoc)
variable compromiso_biopsicosocial not found
. cap noi encode tenencia_de_la_vivienda_mod, gen(ten_viv)
not possible with numeric variable
. *encode dg_cie_10_rec, generate(dg_cie_10_mental_h) *already numeric
. cap noi encode dg_trs_cons_sus_or, gen(sud_severity_icd10)
not possible with numeric variable
. cap noi encode macrozona, gen(macrozone)
not possible with numeric variable
.
. /*
> *2023-02-28, not done in R
> cap noi recode numero_de_hijos_mod (0=0 "No children") (1/10=1 "Children"), gen(newnumero_de_hijos_mod)
> cap confirm variable newnumero_de_hijos_mod
> if !_rc {
> drop numero_de_hijos_mod
> cap noi rename newnumero_de_hijos_mod numero_de_hijos_mod
> }
> */
.
. *same for condemnatory sentence
. mkspline2 rc_x = edad_al_ing_1, cubic nknots(4) displayknots
| knot1 knot2 knot3 knot4
-------------+--------------------------------------------
edad_al_in~1 | 21.18685 29.99178 38.92615 56.32477
.
. *not necessary: 2023-02-28
. *gen motivodeegreso_mod_imp_rec3 = 1
. *replace motivodeegreso_mod_imp_rec3 = 2 if strpos(motivodeegreso_mod_imp_rec,"Early")>0
. *replace motivodeegreso_mod_imp_rec3 = 3 if strpos(motivodeegreso_mod_imp_rec,"Late")>0
.
. *encode policonsumo, generate(policon) *already numeric
. // Generate a restricted cubic spline variable for a variable "x" with 4 knots
. *https://chat.openai.com/chat/4a9396cd-2caa-4a2e-b5f4-ed2c2d0779b3
. *https://www.stata.com/meeting/nordic-and-baltic15/abstracts/materials/sweden15_oskarsson.pdf
. *mkspline xspline = edad_al_ing_1, cubic nknots(4)
. *gen rcs_x = xspline1*xspline2 xspline3 xspline4
.
. *https://www.statalist.org/forums/forum/general-stata-discussion/general/1638622-comparing-cox-proportional-hazard-linear-and-non-linear-restricted-
> cubic-spline-models-using-likelihood-ratio-test
.
=============================================================================
=============================================================================
Reset-time
. *if missing offender_d (status) , means that there was a record and the time is the time of offense
.
. *set the indicator
. gen event=0
. replace event=1 if !missing(offender_d)
(5,144 real changes made)
. *replace event=1 if !missing(sex)
.
. *correct time to event if _st=0
. gen diff= age_offending_imp-edad_al_egres_imp
. gen diffc= cond(diff<0.001, 0.001, diff)
. drop diff
. rename diffc diff
. lab var diff "Time to offense leading to condemnatory sentence"
.
. *age time
. *stset age_offending_imp, fail(event ==1) enter(edad_al_egres_imp)
. *reset time
. stset diff, failure(event ==1)
failure event: event == 1
obs. time interval: (0, diff]
exit on or before: failure
------------------------------------------------------------------------------
70,863 total observations
0 exclusions
------------------------------------------------------------------------------
70,863 observations remaining, representing
5,144 failures in single-record/single-failure data
302,812.79 total analysis time at risk and under observation
at risk from t = 0
earliest observed entry t = 0
last observed exit t = 10.75828
.
. stdescribe, weight
failure _d: event == 1
analysis time _t: diff
|-------------- per subject --------------|
Category total mean min median max
------------------------------------------------------------------------------
no. of subjects 70863
no. of records 70863 1 1 1 1
(first) entry time 0 0 0 0
(final) exit time 4.273214 .001 3.964384 10.75828
subjects with gap 0
time on gap if gap 0
time at risk 302812.79 4.273214 .001 3.964384 10.75828
failures 5144 .0725908 0 0 1
------------------------------------------------------------------------------
We calculate the incidence rate.
. stsum, by (motivodeegreso_mod_imp_rec)
failure _d: event == 1
analysis time _t: diff
| Incidence Number of |------ Survival time -----|
motivo~c | Time at risk rate subjects 25% 50% 75%
---------+---------------------------------------------------------------------
Treatmen | 76,638.2951 .0086641 19277 . . .
Treatmen | 65,879.5092 .0259717 15797 . . .
Treatmen | 160,294.984 .0172744 35789 . . .
---------+---------------------------------------------------------------------
Total | 302,812.789 .0169874 70863 . . .
. *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
(35,789 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
(35,789 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
(19,277 real changes made)
. replace motivodeegreso_mod_imp_rec_earl = 0 if motivodeegreso_mod_imp_rec==3
(35,789 real changes made)
.
. gen motivodeegreso_mod_imp_rec_late = 1
. replace motivodeegreso_mod_imp_rec_late = 0 if motivodeegreso_mod_imp_rec==1
(19,277 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 (imprisonment)") ///
> subtitle("Nelson-Aalen Cum Hazards w/ Confidence Intervals 95%") ///
> risktable(, size(*.5) order(1 "Tr Completion" 2 "Early Disch" 3 "Late Disch")) ///
> ytitle("Cum. Hazards") ylabel(#8) ///
> xtitle("Years since tr. outcome") xlabel(#8) ///
> note("Source: nDP, SENDA's SUD Treatments & POs Office Data period 2010-2019 ") ///
> legend(rows(3)) ///
> legend(cols(4)) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> legend(order(1 "95CI Tr Completion" 2 "Tr Completion" 3 "95CI Early Tr Disch" 4 "Early Tr Disch " 5 "95CI Late Tr Disch" 6 "Late Tr Disch" )size(*.5
> )region(lstyle(none)) region(c(none)) nobox)
failure _d: event == 1
analysis time _t: diff
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\tto_2023_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\tto_2023_pris_m1.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"
.
. // 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 = -55523.635
Iteration 1: log likelihood = -53064.211
Iteration 2: log likelihood = -52545.345
Iteration 3: log likelihood = -52543.043
Iteration 4: log likelihood = -52543.041
Refining estimates:
Iteration 0: log likelihood = -52543.041
Cox regression -- Breslow method for ties
No. of subjects = 70,863 Number of obs = 70,863
No. of failures = 5,144
Time at risk = 302812.7888
LR chi2(51) = 5961.19
Log likelihood = -52543.041 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.884404 .0981311 12.17 0.000 1.70156 2.086895
Treatment non-completion (Late) | 1.578257 .0701245 10.27 0.000 1.44663 1.72186
|
tr_modality |
Residential | 1.150163 .0429181 3.75 0.000 1.069048 1.237434
|
sex_enc |
Women | .5899026 .0255316 -12.19 0.000 .5419252 .6421275
edad_ini_cons | .973513 .0040329 -6.48 0.000 .9656407 .9814496
|
escolaridad_rec |
2-Completed high school or less | .8859481 .0274859 -3.90 0.000 .8336819 .9414912
1-More than high school | .6580041 .036147 -7.62 0.000 .590838 .7328057
|
sus_principal_mod |
Cocaine hydrochloride | 1.196657 .0709668 3.03 0.002 1.065344 1.344156
Cocaine paste | 1.71875 .0824318 11.29 0.000 1.564548 1.88815
Marijuana | 1.142536 .0793249 1.92 0.055 .9971767 1.309085
Other | 1.35583 .1840874 2.24 0.025 1.039043 1.7692
|
freq_cons_sus_prin |
1 day a week or more | .9738254 .0966081 -0.27 0.789 .801748 1.182835
2 to 3 days a week | .9910605 .0795741 -0.11 0.911 .8467507 1.159965
4 to 6 days a week | 1.034083 .0860077 0.40 0.687 .8785338 1.217173
Daily | 1.083616 .0862068 1.01 0.313 .9271678 1.266463
|
condicion_ocupacional_corr |
Inactive | 1.084016 .0669211 1.31 0.191 .960478 1.223445
Looking for a job for the first time | 1.146334 .2805966 0.56 0.577 .7095061 1.852106
No activity | 1.233706 .0806776 3.21 0.001 1.085295 1.402412
Not seeking for work | 1.322057 .1358087 2.72 0.007 1.080962 1.616926
Unemployed | 1.20921 .0419433 5.48 0.000 1.129735 1.294276
|
1.policonsumo | 1.00362 .0430418 0.08 0.933 .9227079 1.091627
1.num_hijos_mod_joel_bin | 1.137312 .0394767 3.71 0.000 1.062513 1.217378
|
tenencia_de_la_vivienda_mod |
Others | 1.048896 .1362236 0.37 0.713 .8131746 1.352947
Owner/Transferred dwellings/Pays Dividends | .9794562 .1106882 -0.18 0.854 .7848575 1.222304
Renting | .9893639 .1124914 -0.09 0.925 .7917241 1.236341
Stays temporarily with a relative | .9709575 .1099212 -0.26 0.795 .7777437 1.212171
|
macrozona |
North | 1.420945 .0526803 9.48 0.000 1.321356 1.528041
South | 1.552346 .0870059 7.85 0.000 1.39085 1.732594
|
n_off_vio |
1 | 1.457764 .0501976 10.95 0.000 1.362625 1.559546
|
n_off_acq |
1 | 2.796119 .0870609 33.02 0.000 2.630585 2.972069
|
n_off_sud |
1 | 1.376311 .0456138 9.64 0.000 1.289752 1.46868
|
n_off_oth |
1 | 1.701012 .0564014 16.02 0.000 1.593982 1.815228
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.100792 .0471597 2.24 0.025 1.012135 1.197215
With psychiatric comorbidity | 1.085812 .0366507 2.44 0.015 1.016302 1.160075
|
dg_trs_cons_sus_or |
Drug dependence | 1.033287 .0387763 0.87 0.383 .9600141 1.112152
|
clas_r |
Mixta | .9379319 .0520943 -1.15 0.249 .8411901 1.0458
Rural | .863297 .0539399 -2.35 0.019 .7637936 .9757632
|
porc_pobr | 1.66451 .3595064 2.36 0.018 1.090036 2.541743
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.098158 .0720589 1.43 0.154 .9656296 1.248875
Cocaine paste | 1.268234 .0730058 4.13 0.000 1.132922 1.419707
Marijuana | 1.153607 .0378324 4.36 0.000 1.08179 1.230192
Other | 1.379569 .1165374 3.81 0.000 1.169067 1.627975
|
ano_nac_corr | .8455368 .0067619 -20.98 0.000 .832387 .8588943
|
con_quien_vive_joel |
Family of origin | .863208 .0473362 -2.68 0.007 .7752427 .9611547
Others | 1.075051 .0686417 1.13 0.257 .9485929 1.218366
With couple/children | .9458194 .0519347 -1.01 0.310 .8493154 1.053289
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.082794 .032835 2.62 0.009 1.020314 1.1491
One or more | .840642 .0632671 -2.31 0.021 .7253528 .9742556
|
rc_x1 | .8434646 .0086551 -16.59 0.000 .8266703 .8606001
rc_x2 | .8821128 .0305598 -3.62 0.000 .824205 .9440891
rc_x3 | 1.294844 .1193905 2.80 0.005 1.080769 1.551322
-------------------------------------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 70,863 -55523.64 -52543.04 51 105188.1 105655.7
-----------------------------------------------------------------------------
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 = -55523.635
Iteration 1: log likelihood = -53050.534
Iteration 2: log likelihood = -52560.229
Iteration 3: log likelihood = -52558.832
Iteration 4: log likelihood = -52558.832
Refining estimates:
Iteration 0: log likelihood = -52558.832
Cox regression -- Breslow method for ties
No. of subjects = 70,863 Number of obs = 70,863
No. of failures = 5,144
Time at risk = 302812.7888
LR chi2(49) = 5929.61
Log likelihood = -52558.832 Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------------------------------
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------------------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec |
Treatment non-completion (Early) | 1.886179 .0982619 12.18 0.000 1.703096 2.088945
Treatment non-completion (Late) | 1.580301 .0702164 10.30 0.000 1.448501 1.724093
|
tr_modality |
Residential | 1.143671 .0426495 3.60 0.000 1.063062 1.230393
|
sex_enc |
Women | .5888584 .0254551 -12.25 0.000 .5410224 .6409238
edad_ini_cons | .9733961 .0039936 -6.57 0.000 .9656002 .9812549
|
escolaridad_rec |
2-Completed high school or less | .9002364 .0278138 -3.40 0.001 .8473401 .9564348
1-More than high school | .6794858 .0371275 -7.07 0.000 .6104783 .7562939
|
sus_principal_mod |
Cocaine hydrochloride | 1.222019 .0724536 3.38 0.001 1.087954 1.372606
Cocaine paste | 1.768974 .0846252 11.92 0.000 1.61065 1.942861
Marijuana | 1.145128 .0796271 1.95 0.051 .9992298 1.31233
Other | 1.35767 .1845741 2.25 0.025 1.040098 1.772207
|
freq_cons_sus_prin |
1 day a week or more | .9706511 .0962954 -0.30 0.764 .7991311 1.178985
2 to 3 days a week | .9897477 .0794768 -0.13 0.898 .8456156 1.158447
4 to 6 days a week | 1.029478 .0856337 0.35 0.727 .874607 1.211774
Daily | 1.080416 .0859617 0.97 0.331 .9244139 1.262745
|
condicion_ocupacional_corr |
Inactive | 1.060497 .0652407 0.95 0.340 .9400358 1.196395
Looking for a job for the first time | 1.096626 .2683173 0.38 0.706 .6788758 1.77144
No activity | 1.211994 .0791813 2.94 0.003 1.066327 1.377561
Not seeking for work | 1.30488 .1340026 2.59 0.010 1.066983 1.595818
Unemployed | 1.202885 .0417213 5.33 0.000 1.12383 1.287501
|
1.policonsumo | 1.013918 .0434855 0.32 0.747 .9321719 1.102833
1.num_hijos_mod_joel_bin | 1.167331 .0402325 4.49 0.000 1.091081 1.24891
|
tenencia_de_la_vivienda_mod |
Others | 1.043175 .1355065 0.33 0.745 .8087003 1.345633
Owner/Transferred dwellings/Pays Dividends | .9685326 .1094391 -0.28 0.777 .7761273 1.208636
Renting | .9904664 .1126103 -0.08 0.933 .7926165 1.237703
Stays temporarily with a relative | .9698048 .1097813 -0.27 0.787 .7768352 1.210709
|
macrozona |
North | 1.408468 .0521732 9.25 0.000 1.309834 1.514529
South | 1.555748 .0871593 7.89 0.000 1.393964 1.736309
|
n_off_vio |
1 | 1.454508 .0500971 10.88 0.000 1.35956 1.556086
|
n_off_acq |
1 | 2.796169 .0871489 32.99 0.000 2.630473 2.972303
|
n_off_sud |
1 | 1.384685 .0458561 9.83 0.000 1.297664 1.477543
|
n_off_oth |
1 | 1.704443 .0564911 16.09 0.000 1.597242 1.818839
|
dg_cie_10_rec |
Diagnosis unknown (under study) | 1.101732 .0472025 2.26 0.024 1.012995 1.198243
With psychiatric comorbidity | 1.090338 .0367894 2.56 0.010 1.020564 1.164882
|
dg_trs_cons_sus_or |
Drug dependence | 1.03841 .0389459 1.00 0.315 .9648153 1.117618
|
clas_r |
Mixta | .9419758 .0523031 -1.08 0.282 .8448446 1.050274
Rural | .8671207 .0541775 -2.28 0.022 .7671789 .980082
|
porc_pobr | 1.65026 .3561016 2.32 0.020 1.081125 2.519005
|
sus_ini_mod_mvv |
Cocaine hydrochloride | 1.106079 .0725794 1.54 0.124 .9725935 1.257884
Cocaine paste | 1.274831 .0733736 4.22 0.000 1.138836 1.427066
Marijuana | 1.147762 .0376558 4.20 0.000 1.07628 1.22399
Other | 1.387549 .1172585 3.88 0.000 1.175751 1.6375
|
ano_nac_corr | .8453248 .0067566 -21.02 0.000 .8321853 .8586718
|
con_quien_vive_joel |
Family of origin | .8599531 .0472136 -2.75 0.006 .772221 .9576524
Others | 1.073964 .0685973 1.12 0.264 .9475909 1.21719
With couple/children | .9522297 .0522638 -0.89 0.372 .8551117 1.060378
|
fis_comorbidity_icd_10 |
Diagnosis unknown (under study) | 1.081475 .0327956 2.58 0.010 1.019069 1.147702
One or more | .8298684 .062449 -2.48 0.013 .716069 .9617531
|
rc_x1 | .8183393 .0066577 -24.64 0.000 .805394 .8314927
-------------------------------------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 70,863 -55523.64 -52558.83 49 105215.7 105664.9
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.
. scalar ll_2= e(ll)
. estimates store linear_term
.
. lrtest full_spline linear_term
Likelihood-ratio test LR chi2(2) = 31.58
(Assumption: linear_term nested in full_spline) Prob > chi2 = 0.0000
.
. scalar ll_diff= round(`=scalar(ll_1)'-`=scalar(ll_2)',.01)
. di "Log-likelihood difference (spline - linear): `=scalar(ll_diff)'"
Log-likelihood difference (spline - linear): 15.79
.
. * 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.
Log-likelihood difference (spline - linear): 15.79
Nevetheless, we chose the model with spline terms due to linearity over a better fit.
=============================================================================
=============================================================================
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 | 60,462 85.32 85.32
Residential | 10,401 14.68 100.00
------------+-----------------------------------
Total | 70,863 100.00
. 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 | 20,338 28.70 28.70
2-Completed high school or less | 39,170 55.28 83.98
1-More than high school | 11,355 16.02 100.00
-----------------------------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab sus_principal_mod, gen(sus_prin)
Primary Substance |
(admission to |
treatment) | Freq. Percent Cum.
----------------------+-----------------------------------
Alcohol | 23,864 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 | 70,863 100.00
. 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 | 3,496 4.93 4.93
1 day a week or more | 4,831 6.82 11.75
2 to 3 days a week | 20,197 28.50 40.25
4 to 6 days a week | 11,667 16.46 56.72
Daily | 30,672 43.28 100.00
-----------------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab condicion_ocupacional_cor, gen(cond_ocu)
Corrected Occupational Status (f) | Freq. Percent Cum.
-------------------------------------+-----------------------------------
Employed | 35,368 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 | 70,863 100.00
. cap noi tab num_hijos_mod_joel_bin, gen(num_hij)
Number of |
Children |
(dichotomiz |
ed) | Freq. Percent Cum.
------------+-----------------------------------
0 | 16,538 23.34 23.34
1 | 54,325 76.66 100.00
------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab tenencia_de_la_vivienda_mod, gen(tenviv)
Housing Situation (Tenure Status) | Freq. Percent Cum.
----------------------------------------+-----------------------------------
Illegal Settlement | 775 1.09 1.09
Others | 2,031 2.87 3.96
Owner/Transferred dwellings/Pays Divide | 26,522 37.43 41.39
Renting | 13,652 19.27 60.65
Stays temporarily with a relative | 27,883 39.35 100.00
----------------------------------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab macrozona, gen(mzone)
Macro |
Administrat |
ive Zone in |
Chile | Freq. Percent Cum.
------------+-----------------------------------
Center | 53,698 75.78 75.78
North | 10,486 14.80 90.57
South | 6,679 9.43 100.00
------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab clas_r, gen(rural)
Socioeconom |
ic |
Classificat |
ion | Freq. Percent Cum.
------------+-----------------------------------
Urbana | 58,278 82.24 82.24
Mixta | 6,835 9.65 91.89
Rural | 5,750 8.11 100.00
------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab sus_ini_mod_mvv, gen(susini)
sus_ini_mod_mvv | Freq. Percent Cum.
----------------------+-----------------------------------
Alcohol | 42,210 59.57 59.57
Cocaine hydrochloride | 4,015 5.67 65.23
Cocaine paste | 3,315 4.68 69.91
Marijuana | 19,670 27.76 97.67
Other | 1,653 2.33 100.00
----------------------+-----------------------------------
Total | 70,863 100.00
. cap noi tab con_quien_vive_joel, gen(cohab)
con_quien_vive_joel | Freq. Percent Cum.
---------------------+-----------------------------------
Alone | 6,689 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 | 70,863 100.00
. 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)
dg_trs_cons_sus_or | Freq. Percent Cum.
----------------------+-----------------------------------
Hazardous consumption | 19,697 27.80 27.80
Drug dependence | 51,166 72.20 100.00
----------------------+-----------------------------------
Total | 70,863 100.00
.
. /*
> *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
> }
> */
.
. 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"
.
. *REALLY NEEDS DUMMY VARS
. global covs_3b_pre_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_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 dep2 rural2 rural3 porc_pobr susini2 susini3 susini4 susini5 ano_nac_corr
> cohab2 cohab3 cohab4 fis_com2 rc_x1 rc_x2 rc_x3"
.
. forvalues i=1/10 {
2. forvalues j=1/7 {
3. qui noi stpm2 $covs_3b_pre_dum , 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 = -21965.629
Iteration 1: log likelihood = -21857.582
Iteration 2: log likelihood = -21856.23
Iteration 3: log likelihood = -21856.23
Log likelihood = -21856.23 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.950563 .106277 12.26 0.000 1.753001 2.170391
mot_egr_late | 1.636171 .0771238 10.45 0.000 1.491783 1.794533
tr_mod2 | 1.150696 .0429146 3.76 0.000 1.069585 1.237958
sex_dum2 | .5902908 .0254664 -12.22 0.000 .5424296 .642375
edad_ini_cons | .9734306 .0040323 -6.50 0.000 .9655594 .9813659
esc1 | 1.52148 .0835681 7.64 0.000 1.366197 1.694411
esc2 | 1.345989 .0694404 5.76 0.000 1.216543 1.489209
sus_prin2 | 1.1919 .0706573 2.96 0.003 1.061157 1.338752
sus_prin3 | 1.713585 .0820949 11.24 0.000 1.560005 1.882284
sus_prin4 | 1.137395 .0789364 1.86 0.064 .9927429 1.303123
sus_prin5 | 1.348288 .1830323 2.20 0.028 1.03331 1.759279
fr_cons_sus_prin2 | .9776434 .0969828 -0.23 0.820 .8048979 1.187463
fr_cons_sus_prin3 | .995821 .07994 -0.05 0.958 .8508455 1.165499
fr_cons_sus_prin4 | 1.037803 .0862871 0.45 0.655 .8817446 1.221483
fr_cons_sus_prin5 | 1.089828 .0866548 1.08 0.279 .9325605 1.273618
cond_ocu2 | 1.089886 .0672217 1.40 0.163 .9657859 1.229932
cond_ocu3 | 1.143457 .2799164 0.55 0.584 .7076961 1.847534
cond_ocu4 | 1.248072 .0815059 3.39 0.001 1.098124 1.418494
cond_ocu5 | 1.33165 .1367129 2.79 0.005 1.088935 1.628464
cond_ocu6 | 1.211939 .0420103 5.55 0.000 1.132335 1.29714
policonsumo | 1.003811 .0429753 0.09 0.929 .923018 1.091676
num_hij2 | 1.136752 .0394375 3.69 0.000 1.062025 1.216737
tenviv1 | 1.015093 .11468 0.13 0.895 .8134696 1.266689
tenviv2 | 1.064257 .0799823 0.83 0.407 .9184931 1.233153
tenviv4 | 1.010397 .0419846 0.25 0.803 .9313702 1.096129
tenviv5 | .9908889 .0331282 -0.27 0.784 .9280405 1.057994
mzone2 | 1.417196 .0525047 9.41 0.000 1.317936 1.523931
mzone3 | 1.550308 .0868333 7.83 0.000 1.389126 1.730191
n_off_vio | 1.465396 .0505191 11.08 0.000 1.369651 1.567834
n_off_acq | 2.821031 .08799 33.25 0.000 2.65374 2.998869
n_off_sud | 1.381611 .0458381 9.74 0.000 1.294628 1.474437
n_off_oth | 1.709345 .0567741 16.14 0.000 1.601614 1.824322
psy_com2 | 1.047826 .0402307 1.22 0.224 .9718686 1.129719
dep2 | 1.03261 .0387376 0.86 0.392 .9594097 1.111395
rural2 | .937512 .0520534 -1.16 0.245 .8408445 1.045293
rural3 | .865654 .0540473 -2.31 0.021 .7659481 .9783389
porc_pobr | 1.658529 .3583667 2.34 0.019 1.085924 2.533066
susini2 | 1.091978 .0716106 1.34 0.180 .9602694 1.241751
susini3 | 1.275938 .0734499 4.23 0.000 1.139803 1.428332
susini4 | 1.159131 .0380104 4.50 0.000 1.086975 1.236076
susini5 | 1.382419 .1167894 3.83 0.000 1.171463 1.631364
ano_nac_corr | .8650581 .0068213 -18.38 0.000 .8517915 .8785314
cohab2 | .8621904 .0472842 -2.70 0.007 .7743221 .9600297
cohab3 | 1.074369 .0685906 1.12 0.261 .9480041 1.217577
cohab4 | .9445028 .05187 -1.04 0.298 .8481197 1.051839
fis_com2 | 1.116894 .0327426 3.77 0.000 1.054529 1.182948
rc_x1 | .8630748 .0087815 -14.47 0.000 .8460338 .8804589
rc_x2 | .8805635 .030503 -3.67 0.000 .822763 .9424246
rc_x3 | 1.299427 .1197976 2.84 0.004 1.084619 1.556777
_rcs1 | 2.134952 .0577588 28.03 0.000 2.024696 2.251213
_rcs_mot_egr_early1 | .912469 .0279029 -3.00 0.003 .859387 .9688297
_rcs_mot_egr_late1 | .9274713 .0272644 -2.56 0.010 .8755444 .982478
_cons | 8.7e+123 1.4e+125 17.99 0.000 2.7e+110 2.8e+137
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21852.046
Iteration 1: log likelihood = -21797.019
Iteration 2: log likelihood = -21796.399
Iteration 3: log likelihood = -21796.398
Log likelihood = -21796.398 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997332 .1091216 12.66 0.000 1.79451 2.223077
mot_egr_late | 1.665028 .0786375 10.80 0.000 1.51782 1.826513
tr_mod2 | 1.150718 .0429096 3.76 0.000 1.069617 1.237969
sex_dum2 | .5915382 .0255216 -12.17 0.000 .5435734 .6437354
edad_ini_cons | .9735105 .0040319 -6.48 0.000 .9656402 .981445
esc1 | 1.51923 .083452 7.61 0.000 1.364164 1.691923
esc2 | 1.345578 .0694209 5.75 0.000 1.216169 1.488758
sus_prin2 | 1.189316 .0704993 2.92 0.003 1.058864 1.335839
sus_prin3 | 1.709726 .0819016 11.20 0.000 1.556508 1.878028
sus_prin4 | 1.137321 .0789302 1.85 0.064 .9926809 1.303037
sus_prin5 | 1.344518 .1825209 2.18 0.029 1.03042 1.75436
fr_cons_sus_prin2 | .9776179 .0969791 -0.23 0.819 .8048787 1.187429
fr_cons_sus_prin3 | .9963056 .0799764 -0.05 0.963 .8512637 1.16606
fr_cons_sus_prin4 | 1.037674 .0862724 0.44 0.656 .8816416 1.221322
fr_cons_sus_prin5 | 1.089343 .0866094 1.08 0.282 .9321569 1.273035
cond_ocu2 | 1.090237 .067242 1.40 0.161 .9660994 1.230326
cond_ocu3 | 1.135709 .2780153 0.52 0.603 .7029063 1.835001
cond_ocu4 | 1.247362 .0814558 3.38 0.001 1.097506 1.41768
cond_ocu5 | 1.329753 .1365102 2.78 0.006 1.087397 1.626125
cond_ocu6 | 1.210923 .0419754 5.52 0.000 1.131385 1.296052
policonsumo | 1.005162 .0430297 0.12 0.904 .9242666 1.093138
num_hij2 | 1.136752 .039441 3.69 0.000 1.062019 1.216744
tenviv1 | 1.017072 .1148944 0.15 0.881 .8150706 1.269137
tenviv2 | 1.063673 .0799422 0.82 0.411 .9179831 1.232485
tenviv4 | 1.010965 .0420083 0.26 0.793 .9318933 1.096745
tenviv5 | .9915952 .0331532 -0.25 0.801 .9286994 1.058751
mzone2 | 1.414257 .0523954 9.36 0.000 1.315204 1.52077
mzone3 | 1.547024 .0866244 7.79 0.000 1.386228 1.726472
n_off_vio | 1.462566 .0504213 11.03 0.000 1.367007 1.564805
n_off_acq | 2.807603 .087584 33.09 0.000 2.641084 2.984621
n_off_sud | 1.380855 .0458078 9.73 0.000 1.29393 1.47362
n_off_oth | 1.705225 .0566383 16.07 0.000 1.597752 1.819927
psy_com2 | 1.048481 .0402855 1.23 0.218 .9724226 1.130488
dep2 | 1.032405 .038733 0.85 0.395 .9592141 1.111181
rural2 | .9366369 .0520016 -1.18 0.238 .8400652 1.04431
rural3 | .8641285 .0539617 -2.34 0.019 .7645817 .976636
porc_pobr | 1.685856 .3642192 2.42 0.016 1.103884 2.574646
susini2 | 1.090385 .0715087 1.32 0.187 .9588645 1.239946
susini3 | 1.27519 .0733998 4.22 0.000 1.139147 1.42748
susini4 | 1.159131 .0380114 4.50 0.000 1.086974 1.236078
susini5 | 1.381468 .1166993 3.83 0.000 1.170673 1.630219
ano_nac_corr | .8538917 .0067806 -19.89 0.000 .8407048 .8672855
cohab2 | .8626539 .0473069 -2.69 0.007 .774743 .96054
cohab3 | 1.07536 .0686616 1.14 0.255 .9488659 1.218717
cohab4 | .9445471 .0518713 -1.04 0.299 .8481615 1.051886
fis_com2 | 1.117116 .0327564 3.78 0.000 1.054724 1.183198
rc_x1 | .8520486 .008704 -15.67 0.000 .8351587 .8692801
rc_x2 | .8808563 .0305119 -3.66 0.000 .823039 .9427352
rc_x3 | 1.297764 .1196432 2.83 0.005 1.083233 1.554783
_rcs1 | 2.118576 .0569156 27.95 0.000 2.00991 2.233118
_rcs_mot_egr_early1 | .9198429 .0280935 -2.74 0.006 .8663962 .9765865
_rcs_mot_egr_early2 | 1.073895 .0124832 6.13 0.000 1.049705 1.098643
_rcs_mot_egr_late1 | .9464898 .0278979 -1.87 0.062 .8933603 1.002779
_rcs_mot_egr_late2 | 1.089382 .0109883 8.49 0.000 1.068056 1.111133
_cons | 2.0e+135 3.2e+136 19.50 0.000 5.0e+121 7.9e+148
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21793.551
Iteration 1: log likelihood = -21782.719
Iteration 2: log likelihood = -21782.69
Iteration 3: log likelihood = -21782.69
Log likelihood = -21782.69 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.007833 .1097704 12.75 0.000 1.803813 2.234929
mot_egr_late | 1.668288 .0788422 10.83 0.000 1.520701 1.830199
tr_mod2 | 1.150732 .0429066 3.77 0.000 1.069636 1.237976
sex_dum2 | .5919365 .0255374 -12.15 0.000 .5439419 .6441659
edad_ini_cons | .9735006 .0040321 -6.48 0.000 .9656299 .9814354
esc1 | 1.518331 .0834071 7.60 0.000 1.363349 1.690931
esc2 | 1.345021 .0693935 5.75 0.000 1.215663 1.488144
sus_prin2 | 1.190625 .0705834 2.94 0.003 1.060019 1.337323
sus_prin3 | 1.710856 .0819617 11.21 0.000 1.557525 1.879281
sus_prin4 | 1.139093 .0790585 1.88 0.061 .9942181 1.305078
sus_prin5 | 1.346688 .1828233 2.19 0.028 1.032072 1.757212
fr_cons_sus_prin2 | .9774567 .0969629 -0.23 0.818 .8047464 1.187233
fr_cons_sus_prin3 | .9960303 .0799547 -0.05 0.960 .8510279 1.165739
fr_cons_sus_prin4 | 1.03766 .0862706 0.44 0.657 .8816301 1.221303
fr_cons_sus_prin5 | 1.089169 .0865957 1.07 0.283 .9320076 1.272832
cond_ocu2 | 1.089823 .067214 1.39 0.163 .9657369 1.229853
cond_ocu3 | 1.134473 .2777116 0.52 0.606 .7021426 1.833001
cond_ocu4 | 1.245556 .0813401 3.36 0.001 1.095913 1.415632
cond_ocu5 | 1.330339 .1365679 2.78 0.005 1.08788 1.626836
cond_ocu6 | 1.211229 .041985 5.53 0.000 1.131672 1.296377
policonsumo | 1.006087 .0430719 0.14 0.887 .9251124 1.094149
num_hij2 | 1.136566 .0394366 3.69 0.000 1.061842 1.216549
tenviv1 | 1.018209 .1150171 0.16 0.873 .81599 1.270541
tenviv2 | 1.064827 .0800313 0.84 0.403 .9189744 1.233827
tenviv4 | 1.011667 .0420369 0.28 0.780 .9325416 1.097505
tenviv5 | .9923362 .0331798 -0.23 0.818 .9293902 1.059546
mzone2 | 1.414949 .0524259 9.37 0.000 1.315838 1.521525
mzone3 | 1.546382 .0865957 7.78 0.000 1.38564 1.72577
n_off_vio | 1.462295 .0503948 11.03 0.000 1.366785 1.564479
n_off_acq | 2.803476 .0874321 33.05 0.000 2.637244 2.980185
n_off_sud | 1.379845 .0457668 9.71 0.000 1.292997 1.472526
n_off_oth | 1.703978 .056578 16.05 0.000 1.596619 1.818557
psy_com2 | 1.048159 .0402891 1.22 0.221 .9720948 1.130175
dep2 | 1.032723 .0387454 0.86 0.391 .9595081 1.111524
rural2 | .9361356 .0519748 -1.19 0.235 .8396138 1.043753
rural3 | .8636956 .0539417 -2.35 0.019 .7641864 .9761625
porc_pobr | 1.713388 .3701304 2.49 0.013 1.12196 2.616583
susini2 | 1.091788 .0716053 1.34 0.181 .9600903 1.241551
susini3 | 1.273263 .0732905 4.20 0.000 1.137423 1.425326
susini4 | 1.158265 .0379846 4.48 0.000 1.086158 1.235158
susini5 | 1.380669 .1166353 3.82 0.000 1.169991 1.629284
ano_nac_corr | .8504647 .0067821 -20.31 0.000 .8372755 .8638617
cohab2 | .8631277 .0473318 -2.68 0.007 .7751706 .9610651
cohab3 | 1.076097 .0687085 1.15 0.251 .949516 1.219552
cohab4 | .9449231 .0518926 -1.03 0.302 .8484979 1.052306
fis_com2 | 1.116232 .0327341 3.75 0.000 1.053884 1.182269
rc_x1 | .8486619 .0086907 -16.02 0.000 .8317982 .8658674
rc_x2 | .8808286 .0305122 -3.66 0.000 .8230107 .9427083
rc_x3 | 1.29782 .1196528 2.83 0.005 1.083272 1.554859
_rcs1 | 2.113312 .0566461 27.92 0.000 2.005154 2.227305
_rcs_mot_egr_early1 | .9243681 .0282506 -2.57 0.010 .8706237 .9814303
_rcs_mot_egr_early2 | 1.068692 .0116303 6.10 0.000 1.046139 1.091732
_rcs_mot_egr_early3 | 1.033725 .00859 3.99 0.000 1.017026 1.050699
_rcs_mot_egr_late1 | .9508421 .0280123 -1.71 0.087 .897494 1.007361
_rcs_mot_egr_late2 | 1.080366 .0104725 7.97 0.000 1.060034 1.101088
_rcs_mot_egr_late3 | 1.032885 .0071458 4.68 0.000 1.018974 1.046986
_cons | 6.5e+138 1.0e+140 19.92 0.000 1.4e+125 3.0e+152
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21801.356
Iteration 1: log likelihood = -21779.277
Iteration 2: log likelihood = -21779.039
Iteration 3: log likelihood = -21779.039
Log likelihood = -21779.039 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.009779 .1098938 12.77 0.000 1.805531 2.237132
mot_egr_late | 1.668947 .0788858 10.84 0.000 1.521279 1.830949
tr_mod2 | 1.150739 .0429065 3.77 0.000 1.069643 1.237983
sex_dum2 | .5920986 .0255442 -12.15 0.000 .5440911 .644342
edad_ini_cons | .9734945 .0040321 -6.49 0.000 .9656237 .9814295
esc1 | 1.518019 .0833912 7.60 0.000 1.363067 1.690586
esc2 | 1.344832 .0693841 5.74 0.000 1.215491 1.487936
sus_prin2 | 1.191521 .0706406 2.96 0.003 1.060809 1.338339
sus_prin3 | 1.711909 .082018 11.22 0.000 1.558474 1.880451
sus_prin4 | 1.140023 .0791266 1.89 0.059 .9950239 1.306152
sus_prin5 | 1.348214 .1830369 2.20 0.028 1.033232 1.75922
fr_cons_sus_prin2 | .9774678 .096964 -0.23 0.818 .8047555 1.187247
fr_cons_sus_prin3 | .9960034 .0799527 -0.05 0.960 .8510045 1.165708
fr_cons_sus_prin4 | 1.037571 .0862633 0.44 0.657 .881555 1.221199
fr_cons_sus_prin5 | 1.089101 .0865903 1.07 0.283 .9319495 1.272753
cond_ocu2 | 1.089436 .0671896 1.39 0.165 .9653943 1.229415
cond_ocu3 | 1.136226 .2781395 0.52 0.602 .7032298 1.83583
cond_ocu4 | 1.244745 .0812853 3.35 0.001 1.095203 1.414706
cond_ocu5 | 1.330932 .1366296 2.78 0.005 1.088364 1.627563
cond_ocu6 | 1.211368 .04199 5.53 0.000 1.131802 1.296527
policonsumo | 1.006363 .0430842 0.15 0.882 .925365 1.09445
num_hij2 | 1.136533 .0394352 3.69 0.000 1.061811 1.216514
tenviv1 | 1.018923 .1151001 0.17 0.868 .8165587 1.271438
tenviv2 | 1.065597 .0800919 0.85 0.398 .9196345 1.234726
tenviv4 | 1.012057 .0420535 0.29 0.773 .9329008 1.09793
tenviv5 | .9927535 .033194 -0.22 0.828 .9297804 1.059992
mzone2 | 1.415315 .0524424 9.37 0.000 1.316173 1.521924
mzone3 | 1.546865 .0866294 7.79 0.000 1.386061 1.726324
n_off_vio | 1.462211 .0503853 11.03 0.000 1.366719 1.564375
n_off_acq | 2.801937 .0873724 33.04 0.000 2.635818 2.978525
n_off_sud | 1.379318 .0457459 9.70 0.000 1.29251 1.471957
n_off_oth | 1.70361 .0565577 16.05 0.000 1.596289 1.818147
psy_com2 | 1.048364 .0402999 1.23 0.219 .9722796 1.130402
dep2 | 1.032781 .0387478 0.86 0.390 .9595614 1.111587
rural2 | .9362216 .0519799 -1.19 0.235 .8396903 1.04385
rural3 | .863886 .0539548 -2.34 0.019 .7643528 .9763804
porc_pobr | 1.71529 .370524 2.50 0.012 1.123227 2.619436
susini2 | 1.093082 .0716939 1.36 0.175 .9612212 1.243031
susini3 | 1.272842 .0732676 4.19 0.000 1.137045 1.424858
susini4 | 1.157752 .0379688 4.47 0.000 1.085676 1.234613
susini5 | 1.37996 .1165788 3.81 0.000 1.169384 1.628456
ano_nac_corr | .8497876 .006782 -20.39 0.000 .8365985 .8631846
cohab2 | .8630964 .0473305 -2.68 0.007 .7751417 .9610313
cohab3 | 1.075903 .0686966 1.15 0.252 .9493444 1.219333
cohab4 | .9448776 .0518908 -1.03 0.302 .8484559 1.052257
fis_com2 | 1.115643 .0327172 3.73 0.000 1.053326 1.181646
rc_x1 | .8479978 .0086877 -16.09 0.000 .83114 .8651974
rc_x2 | .8807599 .0305097 -3.67 0.000 .8229467 .9426345
rc_x3 | 1.298099 .1196781 2.83 0.005 1.083506 1.555193
_rcs1 | 2.112224 .05659 27.91 0.000 2.004172 2.226103
_rcs_mot_egr_early1 | .9247182 .0282543 -2.56 0.010 .8709663 .9817873
_rcs_mot_egr_early2 | 1.067437 .0116095 6.00 0.000 1.044924 1.090435
_rcs_mot_egr_early3 | 1.03605 .008815 4.16 0.000 1.018916 1.053472
_rcs_mot_egr_early4 | 1.009136 .0062022 1.48 0.139 .9970525 1.021365
_rcs_mot_egr_late1 | .9509718 .0280023 -1.71 0.088 .897642 1.00747
_rcs_mot_egr_late2 | 1.080186 .0107371 7.76 0.000 1.059345 1.101437
_rcs_mot_egr_late3 | 1.030285 .0074622 4.12 0.000 1.015762 1.045015
_rcs_mot_egr_late4 | 1.017734 .0049983 3.58 0.000 1.007984 1.027578
_cons | 3.2e+139 5.2e+140 20.00 0.000 6.9e+125 1.5e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21787.91
Iteration 1: log likelihood = -21775.445
Iteration 2: log likelihood = -21775.391
Iteration 3: log likelihood = -21775.391
Log likelihood = -21775.391 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.011595 .1100025 12.78 0.000 1.807146 2.239174
mot_egr_late | 1.669191 .0789065 10.84 0.000 1.521486 1.831236
tr_mod2 | 1.150707 .0429038 3.76 0.000 1.069616 1.237946
sex_dum2 | .5922814 .0255517 -12.14 0.000 .5442599 .6445401
edad_ini_cons | .9734822 .0040322 -6.49 0.000 .9656112 .9814174
esc1 | 1.517841 .083382 7.60 0.000 1.362905 1.690389
esc2 | 1.344645 .0693745 5.74 0.000 1.215323 1.487729
sus_prin2 | 1.192126 .0706788 2.96 0.003 1.061344 1.339024
sus_prin3 | 1.712569 .0820525 11.23 0.000 1.559069 1.881182
sus_prin4 | 1.140732 .0791774 1.90 0.058 .9956402 1.306968
sus_prin5 | 1.348725 .1831101 2.20 0.028 1.033618 1.759897
fr_cons_sus_prin2 | .97745 .0969623 -0.23 0.818 .8047409 1.187225
fr_cons_sus_prin3 | .9958742 .0799424 -0.05 0.959 .850894 1.165557
fr_cons_sus_prin4 | 1.037483 .086256 0.44 0.658 .8814799 1.221096
fr_cons_sus_prin5 | 1.088966 .0865803 1.07 0.284 .9318329 1.272597
cond_ocu2 | 1.088974 .0671608 1.38 0.167 .9649859 1.228893
cond_ocu3 | 1.137679 .2784931 0.53 0.598 .7041313 1.838171
cond_ocu4 | 1.244013 .0812336 3.34 0.001 1.094566 1.413866
cond_ocu5 | 1.331905 .1367288 2.79 0.005 1.089161 1.628751
cond_ocu6 | 1.211604 .0419977 5.54 0.000 1.132024 1.296779
policonsumo | 1.006415 .0430857 0.15 0.881 .9254145 1.094506
num_hij2 | 1.136543 .039436 3.69 0.000 1.061819 1.216525
tenviv1 | 1.019343 .1151486 0.17 0.865 .816894 1.271965
tenviv2 | 1.066313 .0801488 0.85 0.393 .920248 1.235563
tenviv4 | 1.012543 .0420744 0.30 0.764 .9333479 1.098459
tenviv5 | .9930552 .0332039 -0.21 0.835 .9300633 1.060313
mzone2 | 1.415462 .0524494 9.38 0.000 1.316307 1.522086
mzone3 | 1.547081 .0866459 7.79 0.000 1.386247 1.726575
n_off_vio | 1.462046 .0503731 11.02 0.000 1.366576 1.564185
n_off_acq | 2.800415 .0873135 33.03 0.000 2.634408 2.976883
n_off_sud | 1.378784 .0457246 9.69 0.000 1.292015 1.471379
n_off_oth | 1.703251 .056537 16.04 0.000 1.595968 1.817746
psy_com2 | 1.048063 .0402927 1.22 0.222 .9719932 1.130087
dep2 | 1.032712 .038746 0.86 0.391 .9594965 1.111515
rural2 | .9362059 .0519788 -1.19 0.235 .8396765 1.043832
rural3 | .8642015 .0539757 -2.34 0.019 .76463 .9767395
porc_pobr | 1.718134 .3711072 2.51 0.012 1.12513 2.623686
susini2 | 1.09418 .0717689 1.37 0.170 .9621816 1.244286
susini3 | 1.272665 .0732581 4.19 0.000 1.136885 1.424661
susini4 | 1.157202 .0379513 4.45 0.000 1.085159 1.234028
susini5 | 1.379744 .1165619 3.81 0.000 1.169198 1.628203
ano_nac_corr | .8493236 .0067802 -20.46 0.000 .8361381 .8627171
cohab2 | .8630943 .0473304 -2.68 0.007 .7751398 .9610289
cohab3 | 1.075768 .0686881 1.14 0.253 .9492252 1.219181
cohab4 | .9447517 .0518834 -1.03 0.301 .8483436 1.052116
fis_com2 | 1.115315 .032707 3.72 0.000 1.053018 1.181297
rc_x1 | .8475388 .0086843 -16.14 0.000 .8306876 .8647318
rc_x2 | .8806872 .0305076 -3.67 0.000 .8228781 .9425576
rc_x3 | 1.298412 .1197092 2.83 0.005 1.083763 1.555573
_rcs1 | 2.111445 .0565491 27.91 0.000 2.00347 2.22524
_rcs_mot_egr_early1 | .9253276 .0282737 -2.54 0.011 .8715388 .9824361
_rcs_mot_egr_early2 | 1.066609 .0115402 5.96 0.000 1.044228 1.089469
_rcs_mot_egr_early3 | 1.037982 .0088978 4.35 0.000 1.020688 1.055569
_rcs_mot_egr_early4 | 1.011944 .0064005 1.88 0.060 .9994767 1.024567
_rcs_mot_egr_early5 | 1.009088 .0046025 1.98 0.047 1.000108 1.018149
_rcs_mot_egr_late1 | .9512328 .0280029 -1.70 0.089 .8979014 1.007732
_rcs_mot_egr_late2 | 1.079007 .0107455 7.64 0.000 1.05815 1.100274
_rcs_mot_egr_late3 | 1.030865 .0076941 4.07 0.000 1.015895 1.046056
_rcs_mot_egr_late4 | 1.018963 .0052173 3.67 0.000 1.008789 1.029241
_rcs_mot_egr_late5 | 1.011933 .0036636 3.28 0.001 1.004778 1.019139
_cons | 9.7e+139 1.6e+141 20.07 0.000 2.1e+126 4.6e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21789.296
Iteration 1: log likelihood = -21771.783
Iteration 2: log likelihood = -21771.645
Iteration 3: log likelihood = -21771.645
Log likelihood = -21771.645 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.01232 .1100484 12.79 0.000 1.807786 2.239994
mot_egr_late | 1.669372 .0789197 10.84 0.000 1.521642 1.831444
tr_mod2 | 1.150745 .0429035 3.77 0.000 1.069655 1.237983
sex_dum2 | .5924388 .0255581 -12.13 0.000 .5444051 .6447106
edad_ini_cons | .9734705 .0040323 -6.49 0.000 .9655993 .9814058
esc1 | 1.517798 .0833801 7.60 0.000 1.362867 1.690343
esc2 | 1.344558 .0693698 5.74 0.000 1.215244 1.487632
sus_prin2 | 1.192512 .0707035 2.97 0.003 1.061684 1.339461
sus_prin3 | 1.713035 .0820761 11.23 0.000 1.559491 1.881696
sus_prin4 | 1.141113 .0792048 1.90 0.057 .9959712 1.307407
sus_prin5 | 1.348946 .1831398 2.20 0.027 1.033787 1.760184
fr_cons_sus_prin2 | .9774618 .0969635 -0.23 0.818 .8047504 1.18724
fr_cons_sus_prin3 | .9957724 .0799341 -0.05 0.958 .8508072 1.165438
fr_cons_sus_prin4 | 1.037507 .086258 0.44 0.658 .8814998 1.221123
fr_cons_sus_prin5 | 1.088854 .0865722 1.07 0.284 .9317357 1.272468
cond_ocu2 | 1.088626 .0671393 1.38 0.169 .9646779 1.228501
cond_ocu3 | 1.138831 .2787737 0.53 0.595 .7048455 1.840027
cond_ocu4 | 1.243579 .0812009 3.34 0.001 1.094191 1.413363
cond_ocu5 | 1.332484 .136788 2.80 0.005 1.089635 1.629459
cond_ocu6 | 1.211799 .0420036 5.54 0.000 1.132208 1.296985
policonsumo | 1.00635 .0430824 0.15 0.882 .925356 1.094434
num_hij2 | 1.136536 .0394362 3.69 0.000 1.061812 1.216519
tenviv1 | 1.019339 .115149 0.17 0.865 .8168895 1.271962
tenviv2 | 1.066946 .0801979 0.86 0.389 .920791 1.236299
tenviv4 | 1.01286 .0420875 0.31 0.758 .9336401 1.098803
tenviv5 | .9932654 .0332107 -0.20 0.840 .9302605 1.060537
mzone2 | 1.415535 .0524535 9.38 0.000 1.316372 1.522167
mzone3 | 1.547298 .08666 7.79 0.000 1.386438 1.726822
n_off_vio | 1.461936 .0503643 11.02 0.000 1.366482 1.564057
n_off_acq | 2.799523 .0872759 33.02 0.000 2.633586 2.975914
n_off_sud | 1.378446 .0457108 9.68 0.000 1.291704 1.471013
n_off_oth | 1.703063 .0565239 16.04 0.000 1.595805 1.817531
psy_com2 | 1.047849 .0402875 1.22 0.224 .9717886 1.129862
dep2 | 1.032658 .0387445 0.86 0.392 .9594446 1.111457
rural2 | .9361294 .0519737 -1.19 0.235 .8396096 1.043745
rural3 | .8642837 .0539817 -2.34 0.020 .7647011 .9768344
porc_pobr | 1.720246 .3715494 2.51 0.012 1.12653 2.626869
susini2 | 1.09499 .0718237 1.38 0.167 .962891 1.245211
susini3 | 1.272647 .0732575 4.19 0.000 1.136868 1.424642
susini4 | 1.156807 .0379384 4.44 0.000 1.084788 1.233606
susini5 | 1.37941 .1165333 3.81 0.000 1.168916 1.627808
ano_nac_corr | .8491254 .0067794 -20.48 0.000 .8359415 .8625173
cohab2 | .8631753 .0473346 -2.68 0.007 .775213 .9611186
cohab3 | 1.075715 .0686846 1.14 0.253 .9491781 1.21912
cohab4 | .9446956 .0518798 -1.04 0.300 .8482942 1.052052
fis_com2 | 1.115232 .0327034 3.72 0.000 1.052941 1.181207
rc_x1 | .8473497 .0086829 -16.16 0.000 .8305013 .8645399
rc_x2 | .8806076 .0305048 -3.67 0.000 .8228038 .9424723
rc_x3 | 1.298732 .1197399 2.84 0.005 1.084029 1.555959
_rcs1 | 2.111083 .05653 27.90 0.000 2.003144 2.224839
_rcs_mot_egr_early1 | .92523 .0282653 -2.54 0.011 .8714569 .9823212
_rcs_mot_egr_early2 | 1.0657 .0115533 5.87 0.000 1.043295 1.088586
_rcs_mot_egr_early3 | 1.037192 .0090311 4.19 0.000 1.019641 1.055044
_rcs_mot_egr_early4 | 1.015404 .0063643 2.44 0.015 1.003006 1.027955
_rcs_mot_egr_early5 | 1.007607 .0047442 1.61 0.108 .9983513 1.016948
_rcs_mot_egr_early6 | 1.010309 .0037672 2.75 0.006 1.002952 1.017719
_rcs_mot_egr_late1 | .9512197 .0279976 -1.70 0.089 .8978982 1.007708
_rcs_mot_egr_late2 | 1.078943 .0108436 7.56 0.000 1.057897 1.100406
_rcs_mot_egr_late3 | 1.028974 .0079236 3.71 0.000 1.013561 1.044622
_rcs_mot_egr_late4 | 1.02081 .0053267 3.95 0.000 1.010423 1.031304
_rcs_mot_egr_late5 | 1.012414 .003833 3.26 0.001 1.004929 1.019955
_rcs_mot_egr_late6 | 1.009615 .0029759 3.25 0.001 1.003799 1.015465
_cons | 1.6e+140 2.5e+141 20.09 0.000 3.3e+126 7.3e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21786.722
Iteration 1: log likelihood = -21771.387
Iteration 2: log likelihood = -21771.287
Iteration 3: log likelihood = -21771.287
Log likelihood = -21771.287 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 2.012581 .1100658 12.79 0.000 1.808015 2.240292
mot_egr_late | 1.669453 .0789256 10.84 0.000 1.521712 1.831537
tr_mod2 | 1.150729 .042903 3.77 0.000 1.06964 1.237966
sex_dum2 | .5924907 .0255602 -12.13 0.000 .5444531 .6447668
edad_ini_cons | .9734659 .0040324 -6.49 0.000 .9655946 .9814013
esc1 | 1.517848 .0833825 7.60 0.000 1.362912 1.690397
esc2 | 1.344571 .0693704 5.74 0.000 1.215256 1.487646
sus_prin2 | 1.192636 .0707116 2.97 0.003 1.061794 1.339602
sus_prin3 | 1.713254 .0820878 11.24 0.000 1.559688 1.88194
sus_prin4 | 1.141309 .0792188 1.90 0.057 .9961411 1.307632
sus_prin5 | 1.349185 .183173 2.21 0.027 1.033969 1.760497
fr_cons_sus_prin2 | .9774646 .0969638 -0.23 0.818 .8047527 1.187243
fr_cons_sus_prin3 | .9957292 .0799307 -0.05 0.957 .8507702 1.165387
fr_cons_sus_prin4 | 1.037483 .0862562 0.44 0.658 .8814799 1.221096
fr_cons_sus_prin5 | 1.088766 .0865656 1.07 0.285 .9316594 1.272365
cond_ocu2 | 1.088554 .0671348 1.38 0.169 .9646136 1.228418
cond_ocu3 | 1.139281 .2788839 0.53 0.594 .7051246 1.840755
cond_ocu4 | 1.243336 .0811837 3.34 0.001 1.09398 1.413083
cond_ocu5 | 1.332689 .1368087 2.80 0.005 1.089803 1.629709
cond_ocu6 | 1.211916 .0420077 5.55 0.000 1.132317 1.29711
policonsumo | 1.006264 .0430786 0.15 0.884 .9252765 1.09434
num_hij2 | 1.136548 .0394369 3.69 0.000 1.061822 1.216531
tenviv1 | 1.019419 .1151579 0.17 0.865 .8169535 1.272061
tenviv2 | 1.067191 .0802171 0.87 0.387 .9210011 1.236585
tenviv4 | 1.012956 .0420914 0.31 0.757 .9337284 1.098906
tenviv5 | .9933274 .0332127 -0.20 0.841 .9303187 1.060603
mzone2 | 1.415558 .052455 9.38 0.000 1.316393 1.522194
mzone3 | 1.547484 .0866716 7.80 0.000 1.386602 1.727032
n_off_vio | 1.461832 .0503591 11.02 0.000 1.366389 1.563943
n_off_acq | 2.799227 .0872627 33.02 0.000 2.633316 2.975592
n_off_sud | 1.378306 .0457048 9.68 0.000 1.291575 1.470861
n_off_oth | 1.702948 .0565178 16.04 0.000 1.595701 1.817403
psy_com2 | 1.047826 .0402886 1.22 0.224 .9717641 1.129842
dep2 | 1.03261 .0387428 0.86 0.392 .9594006 1.111407
rural2 | .9361566 .0519749 -1.19 0.235 .8396346 1.043775
rural3 | .8643234 .0539844 -2.33 0.020 .7647359 .9768796
porc_pobr | 1.719799 .3714434 2.51 0.012 1.12625 2.626158
susini2 | 1.095364 .0718494 1.39 0.165 .9632178 1.245639
susini3 | 1.272449 .0732468 4.19 0.000 1.13669 1.424422
susini4 | 1.15664 .0379331 4.44 0.000 1.084631 1.233429
susini5 | 1.379223 .1165179 3.81 0.000 1.168757 1.627589
ano_nac_corr | .8490302 .006779 -20.50 0.000 .8358471 .8624212
cohab2 | .8631923 .0473354 -2.68 0.007 .7752285 .9611372
cohab3 | 1.075758 .0686873 1.14 0.253 .9492161 1.219169
cohab4 | .9446716 .0518785 -1.04 0.300 .8482726 1.052026
fis_com2 | 1.115159 .032701 3.72 0.000 1.052873 1.181129
rc_x1 | .8472628 .0086822 -16.17 0.000 .8304157 .8644517
rc_x2 | .8805562 .0305031 -3.67 0.000 .8227556 .9424174
rc_x3 | 1.298928 .1197584 2.84 0.005 1.084191 1.556195
_rcs1 | 2.110926 .0565217 27.90 0.000 2.003002 2.224665
_rcs_mot_egr_early1 | .925391 .028271 -2.54 0.011 .8716072 .9824936
_rcs_mot_egr_early2 | 1.064878 .0114923 5.82 0.000 1.04259 1.087642
_rcs_mot_egr_early3 | 1.038542 .0090202 4.35 0.000 1.021012 1.056373
_rcs_mot_egr_early4 | 1.015726 .0064992 2.44 0.015 1.003067 1.028544
_rcs_mot_egr_early5 | 1.008445 .0048375 1.75 0.080 .9990077 1.017971
_rcs_mot_egr_early6 | 1.009744 .0039463 2.48 0.013 1.002039 1.017508
_rcs_mot_egr_early7 | 1.007087 .0032291 2.20 0.028 1.000778 1.013436
_rcs_mot_egr_late1 | .9512524 .0279968 -1.70 0.090 .8979324 1.007739
_rcs_mot_egr_late2 | 1.078517 .0109223 7.46 0.000 1.057321 1.100139
_rcs_mot_egr_late3 | 1.028471 .0081248 3.55 0.000 1.012669 1.044519
_rcs_mot_egr_late4 | 1.021926 .0055091 4.02 0.000 1.011185 1.032781
_rcs_mot_egr_late5 | 1.012323 .0039001 3.18 0.001 1.004708 1.019996
_rcs_mot_egr_late6 | 1.011585 .0031047 3.75 0.000 1.005518 1.017688
_rcs_mot_egr_late7 | 1.006408 .0025303 2.54 0.011 1.001461 1.01138
_cons | 1.9e+140 3.1e+141 20.11 0.000 4.1e+126 9.2e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21842.551
Iteration 1: log likelihood = -21791.556
Iteration 2: log likelihood = -21791.032
Iteration 3: log likelihood = -21791.032
Log likelihood = -21791.032 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.985226 .1081844 12.58 0.000 1.78412 2.209002
mot_egr_late | 1.651674 .0778136 10.65 0.000 1.505992 1.811449
tr_mod2 | 1.151929 .0429524 3.79 0.000 1.070747 1.239267
sex_dum2 | .5914888 .0255193 -12.17 0.000 .5435284 .6436813
edad_ini_cons | .973481 .0040323 -6.49 0.000 .9656098 .9814163
esc1 | 1.518805 .0834267 7.61 0.000 1.363786 1.691445
esc2 | 1.345495 .0694156 5.75 0.000 1.216095 1.488663
sus_prin2 | 1.190233 .070559 2.94 0.003 1.059672 1.336881
sus_prin3 | 1.710706 .0819608 11.21 0.000 1.557377 1.87913
sus_prin4 | 1.137775 .0789662 1.86 0.063 .9930695 1.303567
sus_prin5 | 1.347139 .182869 2.20 0.028 1.03244 1.757761
fr_cons_sus_prin2 | .9775016 .0969666 -0.23 0.819 .8047846 1.187286
fr_cons_sus_prin3 | .9963836 .0799815 -0.05 0.964 .8513322 1.166149
fr_cons_sus_prin4 | 1.037951 .0862956 0.45 0.654 .8818762 1.221648
fr_cons_sus_prin5 | 1.089509 .0866231 1.08 0.281 .9322983 1.27323
cond_ocu2 | 1.090152 .0672389 1.40 0.162 .96602 1.230234
cond_ocu3 | 1.138029 .2785843 0.53 0.597 .7043408 1.838753
cond_ocu4 | 1.246466 .0814001 3.37 0.001 1.096713 1.416667
cond_ocu5 | 1.328426 .1363773 2.77 0.006 1.086306 1.62451
cond_ocu6 | 1.210796 .0419726 5.52 0.000 1.131264 1.29592
policonsumo | 1.005691 .0430572 0.13 0.895 .9247446 1.093724
num_hij2 | 1.136581 .0394341 3.69 0.000 1.061861 1.216558
tenviv1 | 1.016527 .1148387 0.15 0.885 .8146239 1.268471
tenviv2 | 1.06365 .0799426 0.82 0.412 .9179592 1.232463
tenviv4 | 1.010397 .0419848 0.25 0.803 .9313698 1.096129
tenviv5 | .9911849 .033139 -0.26 0.791 .9283159 1.058312
mzone2 | 1.414294 .0524001 9.36 0.000 1.315232 1.520817
mzone3 | 1.545341 .0865361 7.77 0.000 1.38471 1.724606
n_off_vio | 1.462592 .050416 11.03 0.000 1.367043 1.56482
n_off_acq | 2.806596 .0875311 33.09 0.000 2.640176 2.983506
n_off_sud | 1.38044 .0457917 9.72 0.000 1.293546 1.473172
n_off_oth | 1.704934 .0566204 16.07 0.000 1.597495 1.819599
psy_com2 | 1.048453 .0402744 1.23 0.218 .9724152 1.130437
dep2 | 1.032431 .0387345 0.85 0.395 .9592373 1.111211
rural2 | .9369791 .0520234 -1.17 0.241 .8403672 1.044698
rural3 | .8643759 .0539731 -2.33 0.020 .7648075 .9769068
porc_pobr | 1.68267 .3635599 2.41 0.016 1.10176 2.569867
susini2 | 1.09088 .0715403 1.33 0.185 .959301 1.240507
susini3 | 1.274666 .0733709 4.22 0.000 1.138677 1.426896
susini4 | 1.159013 .038007 4.50 0.000 1.086864 1.235951
susini5 | 1.380986 .1166544 3.82 0.000 1.170272 1.629641
ano_nac_corr | .8523983 .0067784 -20.08 0.000 .839216 .8657877
cohab2 | .8629079 .0473195 -2.69 0.007 .7749735 .9608199
cohab3 | 1.076005 .0687019 1.15 0.251 .9494362 1.219446
cohab4 | .9448529 .0518877 -1.03 0.302 .8484367 1.052226
fis_com2 | 1.1164 .0327315 3.76 0.000 1.054055 1.182431
rc_x1 | .8505655 .008696 -15.83 0.000 .8336913 .8677813
rc_x2 | .8809934 .0305158 -3.66 0.000 .8231686 .9428803
rc_x3 | 1.296924 .119562 2.82 0.005 1.082538 1.553767
_rcs1 | 2.176123 .0588964 28.73 0.000 2.063696 2.294675
_rcs2 | 1.082878 .0079312 10.87 0.000 1.067444 1.098535
_rcs_mot_egr_early1 | .8968416 .0273186 -3.57 0.000 .8448652 .9520157
_rcs_mot_egr_late1 | .9187534 .0268804 -2.90 0.004 .8675509 .9729779
_cons | 6.8e+136 1.1e+138 19.69 0.000 1.6e+123 2.8e+150
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21842.975
Iteration 1: log likelihood = -21791.206
Iteration 2: log likelihood = -21790.583
Iteration 3: log likelihood = -21790.582
Log likelihood = -21790.582 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.983413 .108138 12.56 0.000 1.782398 2.207099
mot_egr_late | 1.652433 .0778667 10.66 0.000 1.506653 1.812319
tr_mod2 | 1.151673 .0429446 3.79 0.000 1.070505 1.238995
sex_dum2 | .5914879 .0255193 -12.17 0.000 .5435273 .6436805
edad_ini_cons | .9734843 .0040323 -6.49 0.000 .9656131 .9814196
esc1 | 1.51879 .0834257 7.61 0.000 1.363773 1.691428
esc2 | 1.345466 .0694142 5.75 0.000 1.216069 1.488632
sus_prin2 | 1.190342 .0705666 2.94 0.003 1.059767 1.337006
sus_prin3 | 1.710887 .0819699 11.21 0.000 1.557542 1.87933
sus_prin4 | 1.137943 .0789788 1.86 0.063 .9932145 1.303761
sus_prin5 | 1.347704 .1829496 2.20 0.028 1.032868 1.758508
fr_cons_sus_prin2 | .977544 .0969709 -0.23 0.819 .8048193 1.187338
fr_cons_sus_prin3 | .9964735 .0799887 -0.04 0.965 .851409 1.166254
fr_cons_sus_prin4 | 1.03798 .0862977 0.45 0.654 .8819015 1.221681
fr_cons_sus_prin5 | 1.089582 .0866278 1.08 0.281 .9323622 1.273313
cond_ocu2 | 1.089982 .0672289 1.40 0.162 .965869 1.230044
cond_ocu3 | 1.138353 .2786649 0.53 0.597 .7045403 1.839282
cond_ocu4 | 1.246736 .0814159 3.38 0.001 1.096953 1.41697
cond_ocu5 | 1.328928 .1364309 2.77 0.006 1.086713 1.625129
cond_ocu6 | 1.21078 .0419719 5.52 0.000 1.131249 1.295903
policonsumo | 1.005745 .04306 0.13 0.894 .9247934 1.093784
num_hij2 | 1.136592 .0394344 3.69 0.000 1.061872 1.216571
tenviv1 | 1.016583 .1148462 0.15 0.884 .8146674 1.268544
tenviv2 | 1.063466 .0799293 0.82 0.413 .9177995 1.232251
tenviv4 | 1.010523 .0419905 0.25 0.801 .9314855 1.096267
tenviv5 | .9913205 .0331437 -0.26 0.794 .9284427 1.058457
mzone2 | 1.414503 .0524078 9.36 0.000 1.315426 1.521042
mzone3 | 1.545635 .0865522 7.78 0.000 1.384974 1.724933
n_off_vio | 1.462661 .0504185 11.03 0.000 1.367107 1.564894
n_off_acq | 2.806725 .0875324 33.09 0.000 2.640303 2.983637
n_off_sud | 1.380378 .0457887 9.72 0.000 1.293489 1.473103
n_off_oth | 1.705043 .0566233 16.07 0.000 1.597598 1.819714
psy_com2 | 1.04916 .040308 1.25 0.212 .9730586 1.131212
dep2 | 1.032425 .0387346 0.85 0.395 .9592302 1.111204
rural2 | .9369102 .0520199 -1.17 0.241 .8403049 1.044622
rural3 | .8641508 .0539606 -2.34 0.019 .7646056 .9766559
porc_pobr | 1.680279 .3630687 2.40 0.016 1.100162 2.566291
susini2 | 1.091067 .071554 1.33 0.184 .9594625 1.240722
susini3 | 1.274845 .0733815 4.22 0.000 1.138836 1.427097
susini4 | 1.158896 .0380035 4.50 0.000 1.086754 1.235827
susini5 | 1.380834 .1166425 3.82 0.000 1.170141 1.629463
ano_nac_corr | .852377 .0067796 -20.08 0.000 .8391923 .8657688
cohab2 | .8626275 .0473048 -2.69 0.007 .7747205 .9605092
cohab3 | 1.075613 .0686781 1.14 0.254 .9490879 1.219004
cohab4 | .9446023 .0518739 -1.04 0.299 .8482117 1.051947
fis_com2 | 1.11625 .0327284 3.75 0.000 1.053912 1.182276
rc_x1 | .8505304 .0086968 -15.83 0.000 .8336546 .8677479
rc_x2 | .8810471 .0305175 -3.66 0.000 .823219 .9429373
rc_x3 | 1.296742 .1195452 2.82 0.005 1.082387 1.553549
_rcs1 | 2.172791 .0630308 26.75 0.000 2.0527 2.299909
_rcs2 | 1.079392 .0254554 3.24 0.001 1.030636 1.130454
_rcs_mot_egr_early1 | .8963375 .0291235 -3.37 0.001 .8410361 .9552753
_rcs_mot_egr_early2 | .995277 .0260808 -0.18 0.857 .9454501 1.04773
_rcs_mot_egr_late1 | .9223576 .0290435 -2.57 0.010 .8671543 .9810751
_rcs_mot_egr_late2 | 1.009649 .025802 0.38 0.707 .9603236 1.061508
_cons | 7.1e+136 1.1e+138 19.69 0.000 1.7e+123 3.0e+150
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21784.974
Iteration 1: log likelihood = -21777.02
Iteration 2: log likelihood = -21776.981
Iteration 3: log likelihood = -21776.981
Log likelihood = -21776.981 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993216 .1087483 12.64 0.000 1.791074 2.218172
mot_egr_late | 1.655197 .0780497 10.69 0.000 1.509079 1.815464
tr_mod2 | 1.151683 .0429416 3.79 0.000 1.070521 1.238999
sex_dum2 | .5918803 .0255349 -12.16 0.000 .5438903 .6441047
edad_ini_cons | .9734749 .0040325 -6.49 0.000 .9656034 .9814106
esc1 | 1.517907 .0833816 7.60 0.000 1.362972 1.690454
esc2 | 1.344925 .0693876 5.74 0.000 1.215578 1.488036
sus_prin2 | 1.191582 .0706462 2.96 0.003 1.06086 1.338412
sus_prin3 | 1.711946 .0820261 11.22 0.000 1.558496 1.880505
sus_prin4 | 1.139653 .0791025 1.88 0.060 .9946986 1.305732
sus_prin5 | 1.349747 .1832342 2.21 0.027 1.034423 1.761193
fr_cons_sus_prin2 | .9773915 .0969556 -0.23 0.818 .8046941 1.187152
fr_cons_sus_prin3 | .996211 .079968 -0.05 0.962 .8511841 1.165948
fr_cons_sus_prin4 | 1.037975 .0862966 0.45 0.654 .8818981 1.221674
fr_cons_sus_prin5 | 1.089411 .0866144 1.08 0.281 .9322159 1.273114
cond_ocu2 | 1.089598 .0672028 1.39 0.164 .965533 1.229605
cond_ocu3 | 1.13702 .2783376 0.52 0.600 .7037167 1.837125
cond_ocu4 | 1.245007 .0813052 3.36 0.001 1.095428 1.41501
cond_ocu5 | 1.329458 .136483 2.77 0.006 1.087151 1.625771
cond_ocu6 | 1.211071 .0419811 5.52 0.000 1.131522 1.296212
policonsumo | 1.006639 .0431007 0.15 0.877 .9256105 1.09476
num_hij2 | 1.136404 .0394299 3.69 0.000 1.061692 1.216373
tenviv1 | 1.017658 .114962 0.15 0.877 .8155374 1.269871
tenviv2 | 1.064575 .0800149 0.83 0.405 .9187527 1.233541
tenviv4 | 1.011209 .0420183 0.27 0.788 .9321194 1.09701
tenviv5 | .9920457 .0331698 -0.24 0.811 .9291185 1.059235
mzone2 | 1.415164 .052437 9.37 0.000 1.316033 1.521763
mzone3 | 1.544996 .0865233 7.77 0.000 1.384389 1.724236
n_off_vio | 1.462387 .0503924 11.03 0.000 1.366881 1.564566
n_off_acq | 2.802693 .0873844 33.05 0.000 2.636551 2.979305
n_off_sud | 1.37941 .0457492 9.70 0.000 1.292595 1.472055
n_off_oth | 1.703832 .0565648 16.05 0.000 1.596497 1.818383
psy_com2 | 1.048848 .040312 1.24 0.215 .9727406 1.13091
dep2 | 1.032737 .0387468 0.86 0.391 .9595199 1.111542
rural2 | .9364145 .0519933 -1.18 0.237 .8398586 1.044071
rural3 | .863702 .0539396 -2.35 0.019 .7641964 .9761643
porc_pobr | 1.707367 .368887 2.48 0.013 1.117943 2.607559
susini2 | 1.09241 .0716465 1.35 0.178 .9606361 1.242259
susini3 | 1.272949 .0732739 4.19 0.000 1.13714 1.424978
susini4 | 1.158059 .0379775 4.47 0.000 1.085966 1.234938
susini5 | 1.380068 .116581 3.81 0.000 1.169487 1.628566
ano_nac_corr | .849004 .0067803 -20.50 0.000 .8358183 .8623977
cohab2 | .8631021 .0473297 -2.68 0.007 .7751488 .9610352
cohab3 | 1.076351 .0687253 1.15 0.249 .9497399 1.219842
cohab4 | .9449802 .0518953 -1.03 0.303 .8485499 1.052369
fis_com2 | 1.115403 .0327071 3.72 0.000 1.053106 1.181386
rc_x1 | .8471987 .0086832 -16.18 0.000 .8303497 .8643896
rc_x2 | .8810157 .0305177 -3.66 0.000 .8231874 .9429064
rc_x3 | 1.296812 .119556 2.82 0.005 1.082438 1.553643
_rcs1 | 2.166229 .0626033 26.75 0.000 2.046939 2.29247
_rcs2 | 1.078123 .0252495 3.21 0.001 1.029754 1.128765
_rcs_mot_egr_early1 | .9013338 .029251 -3.20 0.001 .8457882 .9605273
_rcs_mot_egr_early2 | .9917009 .0254892 -0.32 0.746 .9429804 1.042939
_rcs_mot_egr_early3 | 1.028979 .0086749 3.39 0.001 1.012116 1.046123
_rcs_mot_egr_late1 | .9272438 .0291243 -2.40 0.016 .8718828 .9861199
_rcs_mot_egr_late2 | 1.002451 .0253079 0.10 0.923 .9540554 1.053301
_rcs_mot_egr_late3 | 1.028285 .0072547 3.95 0.000 1.014164 1.042603
_cons | 2.1e+140 3.3e+141 20.11 0.000 4.4e+126 9.9e+153
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21792.243
Iteration 1: log likelihood = -21773.207
Iteration 2: log likelihood = -21772.967
Iteration 3: log likelihood = -21772.967
Log likelihood = -21772.967 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.995113 .1088587 12.66 0.000 1.792766 2.220298
mot_egr_late | 1.655726 .0780781 10.69 0.000 1.509554 1.816051
tr_mod2 | 1.151705 .0429419 3.79 0.000 1.070542 1.239021
sex_dum2 | .5920534 .0255422 -12.15 0.000 .5440497 .6442927
edad_ini_cons | .9734676 .0040326 -6.49 0.000 .9655959 .9814035
esc1 | 1.517563 .0833639 7.59 0.000 1.362661 1.690073
esc2 | 1.344709 .0693768 5.74 0.000 1.215382 1.487797
sus_prin2 | 1.192579 .07071 2.97 0.003 1.06174 1.339542
sus_prin3 | 1.713109 .0820883 11.23 0.000 1.559543 1.881797
sus_prin4 | 1.14067 .079177 1.90 0.058 .995579 1.306905
sus_prin5 | 1.35149 .1834775 2.22 0.027 1.035748 1.763484
fr_cons_sus_prin2 | .9773964 .0969561 -0.23 0.818 .8046981 1.187158
fr_cons_sus_prin3 | .996174 .0799652 -0.05 0.962 .8511522 1.165905
fr_cons_sus_prin4 | 1.037883 .086289 0.45 0.655 .88182 1.221565
fr_cons_sus_prin5 | 1.089342 .086609 1.08 0.282 .9321566 1.273033
cond_ocu2 | 1.089169 .0671759 1.38 0.166 .9651534 1.22912
cond_ocu3 | 1.138946 .2788074 0.53 0.595 .7049101 1.840231
cond_ocu4 | 1.244097 .0812439 3.34 0.001 1.094631 1.413971
cond_ocu5 | 1.330103 .13655 2.78 0.005 1.087677 1.626563
cond_ocu6 | 1.211222 .0419865 5.53 0.000 1.131664 1.296374
policonsumo | 1.006952 .0431148 0.16 0.871 .9258975 1.095103
num_hij2 | 1.136369 .0394285 3.68 0.000 1.061659 1.216335
tenviv1 | 1.018412 .1150497 0.16 0.872 .8161381 1.270819
tenviv2 | 1.0654 .0800799 0.84 0.399 .9194604 1.234505
tenviv4 | 1.011614 .0420355 0.28 0.781 .9324916 1.09745
tenviv5 | .9924786 .0331845 -0.23 0.821 .9295235 1.059698
mzone2 | 1.415568 .0524552 9.38 0.000 1.316402 1.522204
mzone3 | 1.545452 .086556 7.77 0.000 1.384785 1.72476
n_off_vio | 1.462303 .0503821 11.03 0.000 1.366816 1.56446
n_off_acq | 2.801009 .0873185 33.04 0.000 2.634991 2.977486
n_off_sud | 1.378823 .0457259 9.69 0.000 1.292052 1.471421
n_off_oth | 1.703419 .0565421 16.05 0.000 1.596127 1.817924
psy_com2 | 1.049058 .040323 1.25 0.213 .9729304 1.131143
dep2 | 1.032799 .0387495 0.86 0.390 .9595768 1.111609
rural2 | .9365099 .0519991 -1.18 0.237 .8399433 1.044179
rural3 | .8639169 .0539543 -2.34 0.019 .7643844 .9764098
porc_pobr | 1.709377 .3693015 2.48 0.013 1.119284 2.61057
susini2 | 1.093814 .0717426 1.37 0.172 .9618635 1.243865
susini3 | 1.272468 .0732477 4.19 0.000 1.136708 1.424443
susini4 | 1.157496 .0379601 4.46 0.000 1.085436 1.23434
susini5 | 1.379296 .1165194 3.81 0.000 1.168827 1.627664
ano_nac_corr | .848234 .0067804 -20.59 0.000 .8350483 .8616279
cohab2 | .8630692 .0473283 -2.69 0.007 .7751186 .9609994
cohab3 | 1.076153 .0687129 1.15 0.250 .949564 1.219617
cohab4 | .9449309 .0518933 -1.03 0.302 .8485045 1.052315
fis_com2 | 1.114749 .0326883 3.70 0.000 1.052488 1.180694
rc_x1 | .8464412 .0086799 -16.26 0.000 .8295987 .8636256
rc_x2 | .8809507 .0305153 -3.66 0.000 .8231268 .9428366
rc_x3 | 1.297073 .1195795 2.82 0.005 1.082657 1.553955
_rcs1 | 2.16774 .0628104 26.70 0.000 2.048064 2.294408
_rcs2 | 1.081077 .0254882 3.31 0.001 1.032258 1.132205
_rcs_mot_egr_early1 | .9005181 .0292942 -3.22 0.001 .8448946 .9598036
_rcs_mot_egr_early2 | .9880919 .0254707 -0.46 0.642 .9394104 1.039296
_rcs_mot_egr_early3 | 1.027748 .009091 3.09 0.002 1.010083 1.045721
_rcs_mot_egr_early4 | 1.009224 .0061949 1.50 0.135 .9971553 1.02144
_rcs_mot_egr_late1 | .9261364 .0291626 -2.44 0.015 .8707067 .9850948
_rcs_mot_egr_late2 | .9998817 .0253975 -0.00 0.996 .9513222 1.05092
_rcs_mot_egr_late3 | 1.022027 .0078078 2.85 0.004 1.006838 1.037445
_rcs_mot_egr_late4 | 1.017829 .0049932 3.60 0.000 1.00809 1.027663
_cons | 1.3e+141 2.1e+142 20.20 0.000 2.6e+127 6.3e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21778.933
Iteration 1: log likelihood = -21769.244
Iteration 2: log likelihood = -21769.174
Iteration 3: log likelihood = -21769.174
Log likelihood = -21769.174 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996818 .1089575 12.67 0.000 1.794288 2.222208
mot_egr_late | 1.655868 .0780906 10.69 0.000 1.509673 1.816219
tr_mod2 | 1.151684 .0429396 3.79 0.000 1.070525 1.238995
sex_dum2 | .5922375 .0255497 -12.14 0.000 .5442196 .6444921
edad_ini_cons | .9734549 .0040327 -6.49 0.000 .965583 .9813909
esc1 | 1.517378 .0833544 7.59 0.000 1.362494 1.689869
esc2 | 1.34452 .069367 5.74 0.000 1.215211 1.487588
sus_prin2 | 1.193199 .0707491 2.98 0.003 1.062288 1.340244
sus_prin3 | 1.713788 .0821239 11.24 0.000 1.560155 1.882548
sus_prin4 | 1.14139 .0792286 1.91 0.057 .9962051 1.307735
sus_prin5 | 1.352038 .1835557 2.22 0.026 1.036163 1.764209
fr_cons_sus_prin2 | .9773793 .0969544 -0.23 0.818 .8046841 1.187137
fr_cons_sus_prin3 | .9960462 .079955 -0.05 0.961 .851043 1.165756
fr_cons_sus_prin4 | 1.037799 .086282 0.45 0.655 .8817482 1.221466
fr_cons_sus_prin5 | 1.089209 .0865991 1.07 0.282 .9320414 1.272879
cond_ocu2 | 1.088701 .0671467 1.38 0.168 .9647393 1.228591
cond_ocu3 | 1.140453 .2791744 0.54 0.591 .7058454 1.84266
cond_ocu4 | 1.243355 .0811915 3.34 0.001 1.093986 1.41312
cond_ocu5 | 1.331068 .1366485 2.79 0.005 1.088467 1.627741
cond_ocu6 | 1.211458 .0419942 5.53 0.000 1.131885 1.296626
policonsumo | 1.007009 .0431165 0.16 0.870 .9259509 1.095163
num_hij2 | 1.136376 .0394291 3.68 0.000 1.061665 1.216343
tenviv1 | 1.018822 .1150971 0.17 0.869 .8164648 1.271333
tenviv2 | 1.066123 .0801373 0.85 0.394 .9200783 1.235348
tenviv4 | 1.012099 .0420564 0.29 0.772 .9329372 1.097977
tenviv5 | .9927791 .0331944 -0.22 0.828 .9298052 1.060018
mzone2 | 1.415716 .0524623 9.38 0.000 1.316538 1.522367
mzone3 | 1.545657 .0865719 7.77 0.000 1.384961 1.724998
n_off_vio | 1.462136 .0503697 11.03 0.000 1.366672 1.564268
n_off_acq | 2.799464 .0872585 33.03 0.000 2.63356 2.97582
n_off_sud | 1.378278 .0457043 9.68 0.000 1.291548 1.470832
n_off_oth | 1.703056 .056521 16.04 0.000 1.595803 1.817517
psy_com2 | 1.048765 .040316 1.24 0.215 .9726508 1.130836
dep2 | 1.032731 .0387477 0.86 0.391 .9595118 1.111537
rural2 | .9364983 .0519983 -1.18 0.237 .8399332 1.044165
rural3 | .8642345 .0539752 -2.34 0.019 .7646635 .9767713
porc_pobr | 1.712103 .36986 2.49 0.013 1.121109 2.614642
susini2 | 1.094933 .0718191 1.38 0.167 .9628428 1.245144
susini3 | 1.272286 .073238 4.18 0.000 1.136543 1.42424
susini4 | 1.156938 .0379424 4.45 0.000 1.084912 1.233746
susini5 | 1.379066 .1165014 3.80 0.000 1.16863 1.627396
ano_nac_corr | .8477508 .0067783 -20.66 0.000 .8345691 .8611407
cohab2 | .863067 .0473282 -2.69 0.007 .7751166 .960997
cohab3 | 1.076019 .0687046 1.15 0.251 .9494457 1.219466
cohab4 | .9448045 .0518859 -1.03 0.301 .8483918 1.052174
fis_com2 | 1.11441 .0326776 3.69 0.000 1.052169 1.180333
rc_x1 | .845963 .0086763 -16.31 0.000 .8291275 .8631403
rc_x2 | .8808795 .0305132 -3.66 0.000 .8230596 .9427612
rc_x3 | 1.297377 .1196099 2.82 0.005 1.082906 1.554324
_rcs1 | 2.167924 .0628601 26.69 0.000 2.048156 2.294696
_rcs2 | 1.082148 .0255483 3.34 0.001 1.033216 1.133399
_rcs_mot_egr_early1 | .9006804 .0293257 -3.21 0.001 .8449987 .9600313
_rcs_mot_egr_early2 | .9866275 .0253645 -0.52 0.601 .9381456 1.037615
_rcs_mot_egr_early3 | 1.027152 .0093743 2.94 0.003 1.008942 1.04569
_rcs_mot_egr_early4 | 1.01111 .006391 1.75 0.080 .9986617 1.023714
_rcs_mot_egr_early5 | 1.009245 .0045972 2.02 0.043 1.000274 1.018295
_rcs_mot_egr_late1 | .9259543 .0291767 -2.44 0.015 .870499 .9849424
_rcs_mot_egr_late2 | .9980897 .0253116 -0.08 0.940 .9496927 1.048953
_rcs_mot_egr_late3 | 1.020104 .00826 2.46 0.014 1.004042 1.036422
_rcs_mot_egr_late4 | 1.018131 .0052128 3.51 0.000 1.007965 1.028399
_rcs_mot_egr_late5 | 1.012091 .0036601 3.32 0.001 1.004943 1.01929
_cons | 4.1e+141 6.6e+142 20.27 0.000 8.3e+127 2.0e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21780.365
Iteration 1: log likelihood = -21765.676
Iteration 2: log likelihood = -21765.526
Iteration 3: log likelihood = -21765.526
Log likelihood = -21765.526 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.9975 .1090033 12.68 0.000 1.794886 2.222986
mot_egr_late | 1.656032 .0781052 10.70 0.000 1.509811 1.816414
tr_mod2 | 1.151714 .042939 3.79 0.000 1.070557 1.239024
sex_dum2 | .5923939 .0255561 -12.14 0.000 .544364 .6446616
edad_ini_cons | .9734434 .0040327 -6.50 0.000 .9655714 .9813796
esc1 | 1.517341 .0833528 7.59 0.000 1.36246 1.689829
esc2 | 1.344434 .0693624 5.74 0.000 1.215134 1.487493
sus_prin2 | 1.193574 .070773 2.98 0.003 1.062618 1.340668
sus_prin3 | 1.714242 .0821468 11.25 0.000 1.560566 1.88305
sus_prin4 | 1.141764 .0792555 1.91 0.056 .9965295 1.308165
sus_prin5 | 1.352234 .1835821 2.22 0.026 1.036313 1.764463
fr_cons_sus_prin2 | .9773912 .0969557 -0.23 0.818 .8046937 1.187152
fr_cons_sus_prin3 | .9959434 .0799467 -0.05 0.960 .8509552 1.165635
fr_cons_sus_prin4 | 1.037819 .0862838 0.45 0.655 .8817655 1.221491
fr_cons_sus_prin5 | 1.089095 .0865908 1.07 0.283 .9319423 1.272747
cond_ocu2 | 1.088359 .0671256 1.37 0.170 .9644366 1.228205
cond_ocu3 | 1.141579 .2794487 0.54 0.589 .7065437 1.844475
cond_ocu4 | 1.24293 .0811594 3.33 0.001 1.093619 1.412627
cond_ocu5 | 1.331648 .1367077 2.79 0.005 1.088942 1.628449
cond_ocu6 | 1.211653 .042 5.54 0.000 1.132069 1.296833
policonsumo | 1.006939 .043113 0.16 0.872 .9258879 1.095086
num_hij2 | 1.13637 .0394294 3.68 0.000 1.061659 1.216338
tenviv1 | 1.018821 .1150978 0.17 0.869 .816463 1.271334
tenviv2 | 1.06675 .0801861 0.86 0.390 .9206174 1.236079
tenviv4 | 1.012416 .0420695 0.30 0.767 .9332295 1.098321
tenviv5 | .9929887 .0332012 -0.21 0.833 .930002 1.060241
mzone2 | 1.415788 .0524663 9.38 0.000 1.316601 1.522446
mzone3 | 1.545885 .0865866 7.78 0.000 1.385162 1.725257
n_off_vio | 1.462026 .0503611 11.03 0.000 1.366579 1.56414
n_off_acq | 2.79859 .0872217 33.02 0.000 2.632756 2.974871
n_off_sud | 1.377947 .0456907 9.67 0.000 1.291243 1.470473
n_off_oth | 1.702871 .0565082 16.04 0.000 1.595642 1.817306
psy_com2 | 1.048548 .0403108 1.23 0.218 .9724434 1.130608
dep2 | 1.032676 .0387462 0.86 0.391 .9594603 1.11148
rural2 | .9364225 .0519932 -1.18 0.237 .8398668 1.044079
rural3 | .8643163 .0539812 -2.33 0.020 .7647343 .9768658
porc_pobr | 1.714201 .3702989 2.49 0.013 1.1225 2.617803
susini2 | 1.095734 .0718733 1.39 0.163 .9635442 1.246059
susini3 | 1.272268 .0732373 4.18 0.000 1.136527 1.424222
susini4 | 1.156548 .0379297 4.43 0.000 1.084546 1.23333
susini5 | 1.378739 .1164734 3.80 0.000 1.168353 1.627009
ano_nac_corr | .8475673 .0067776 -20.68 0.000 .8343869 .8609558
cohab2 | .8631488 .0473324 -2.68 0.007 .7751905 .9610875
cohab3 | 1.075965 .0687011 1.15 0.252 .9493985 1.219405
cohab4 | .9447498 .0518823 -1.03 0.301 .8483436 1.052112
fis_com2 | 1.114334 .0326743 3.69 0.000 1.052099 1.18025
rc_x1 | .8457885 .008675 -16.33 0.000 .8289556 .8629633
rc_x2 | .8807987 .0305104 -3.66 0.000 .8229842 .9426747
rc_x3 | 1.297704 .1196411 2.83 0.005 1.083177 1.554718
_rcs1 | 2.166844 .0627708 26.69 0.000 2.047243 2.293432
_rcs2 | 1.081385 .0254942 3.32 0.001 1.032554 1.132525
_rcs_mot_egr_early1 | .9009073 .0293074 -3.21 0.001 .8452587 .9602196
_rcs_mot_egr_early2 | .9866605 .0252935 -0.52 0.600 .938311 1.037501
_rcs_mot_egr_early3 | 1.025221 .0096186 2.65 0.008 1.006541 1.044248
_rcs_mot_egr_early4 | 1.013608 .0063702 2.15 0.032 1.001199 1.02617
_rcs_mot_egr_early5 | 1.007654 .0047374 1.62 0.105 .9984111 1.016982
_rcs_mot_egr_early6 | 1.010325 .0037609 2.76 0.006 1.00298 1.017723
_rcs_mot_egr_late1 | .9262636 .0291609 -2.43 0.015 .8708369 .985218
_rcs_mot_egr_late2 | .9989078 .0252874 -0.04 0.966 .9505549 1.04972
_rcs_mot_egr_late3 | 1.017099 .0086028 2.00 0.045 1.000376 1.0341
_rcs_mot_egr_late4 | 1.019007 .0053411 3.59 0.000 1.008592 1.02953
_rcs_mot_egr_late5 | 1.012469 .0038285 3.28 0.001 1.004993 1.02
_rcs_mot_egr_late6 | 1.009641 .0029716 3.26 0.001 1.003833 1.015482
_cons | 6.3e+141 1.0e+143 20.29 0.000 1.3e+128 3.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21777.852
Iteration 1: log likelihood = -21765.249
Iteration 2: log likelihood = -21765.131
Iteration 3: log likelihood = -21765.131
Log likelihood = -21765.131 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997738 .1090184 12.68 0.000 1.795096 2.223256
mot_egr_late | 1.65609 .0781091 10.70 0.000 1.509862 1.81648
tr_mod2 | 1.151701 .0429386 3.79 0.000 1.070544 1.23901
sex_dum2 | .5924454 .0255582 -12.13 0.000 .5444116 .6447174
edad_ini_cons | .9734388 .0040328 -6.50 0.000 .9655666 .9813751
esc1 | 1.517389 .0833552 7.59 0.000 1.362504 1.689882
esc2 | 1.344447 .069363 5.74 0.000 1.215145 1.487507
sus_prin2 | 1.193701 .0707814 2.99 0.003 1.06273 1.340813
sus_prin3 | 1.714464 .0821588 11.25 0.000 1.560767 1.883297
sus_prin4 | 1.141961 .0792696 1.91 0.056 .9967006 1.308391
sus_prin5 | 1.352482 .1836165 2.22 0.026 1.036502 1.764789
fr_cons_sus_prin2 | .9773945 .096956 -0.23 0.818 .8046964 1.187156
fr_cons_sus_prin3 | .9959011 .0799433 -0.05 0.959 .8509191 1.165586
fr_cons_sus_prin4 | 1.037797 .0862821 0.45 0.655 .8817469 1.221465
fr_cons_sus_prin5 | 1.089008 .0865843 1.07 0.284 .9318672 1.272647
cond_ocu2 | 1.088286 .067121 1.37 0.170 .9643719 1.228123
cond_ocu3 | 1.142039 .2795613 0.54 0.587 .706829 1.845219
cond_ocu4 | 1.242686 .0811422 3.33 0.001 1.093406 1.412347
cond_ocu5 | 1.331849 .136728 2.79 0.005 1.089106 1.628694
cond_ocu6 | 1.211769 .0420041 5.54 0.000 1.132177 1.296957
policonsumo | 1.006854 .0431092 0.16 0.873 .9258097 1.094993
num_hij2 | 1.136381 .03943 3.68 0.000 1.061669 1.216351
tenviv1 | 1.0189 .1151066 0.17 0.868 .8165259 1.271431
tenviv2 | 1.066995 .0802052 0.86 0.388 .920827 1.236364
tenviv4 | 1.01251 .0420733 0.30 0.765 .9333165 1.098423
tenviv5 | .9930495 .0332032 -0.21 0.835 .930059 1.060306
mzone2 | 1.415812 .0524679 9.38 0.000 1.316622 1.522474
mzone3 | 1.546066 .0865979 7.78 0.000 1.385322 1.725462
n_off_vio | 1.461923 .0503559 11.02 0.000 1.366486 1.564027
n_off_acq | 2.798292 .0872083 33.02 0.000 2.632483 2.974546
n_off_sud | 1.377805 .0456847 9.67 0.000 1.291112 1.470319
n_off_oth | 1.702755 .056502 16.04 0.000 1.595537 1.817177
psy_com2 | 1.048527 .0403119 1.23 0.218 .972421 1.13059
dep2 | 1.032629 .0387445 0.86 0.392 .9594165 1.111429
rural2 | .9364505 .0519944 -1.18 0.237 .8398925 1.044109
rural3 | .8643566 .0539839 -2.33 0.020 .7647697 .9769116
porc_pobr | 1.71372 .3701859 2.49 0.013 1.122197 2.61704
susini2 | 1.096111 .0718991 1.40 0.162 .9638734 1.24649
susini3 | 1.272069 .0732266 4.18 0.000 1.136348 1.424
susini4 | 1.15638 .0379243 4.43 0.000 1.084388 1.233151
susini5 | 1.37855 .1164577 3.80 0.000 1.168192 1.626787
ano_nac_corr | .8474678 .0067771 -20.70 0.000 .8342884 .8608554
cohab2 | .8631654 .0473332 -2.68 0.007 .7752056 .9611057
cohab3 | 1.076008 .0687037 1.15 0.251 .9494363 1.219453
cohab4 | .9447255 .051881 -1.04 0.300 .8483218 1.052085
fis_com2 | 1.114259 .0326718 3.69 0.000 1.052028 1.18017
rc_x1 | .8456973 .0086743 -16.34 0.000 .8288658 .8628707
rc_x2 | .880748 .0305087 -3.67 0.000 .8229366 .9426205
rc_x3 | 1.297896 .1196593 2.83 0.005 1.083337 1.554949
_rcs1 | 2.166934 .062786 26.69 0.000 2.047304 2.293554
_rcs2 | 1.081657 .0255102 3.33 0.001 1.032796 1.13283
_rcs_mot_egr_early1 | .9009532 .0293162 -3.21 0.001 .8452883 .9602837
_rcs_mot_egr_early2 | .9859513 .0251818 -0.55 0.580 .9378109 1.036563
_rcs_mot_egr_early3 | 1.024754 .0098047 2.56 0.011 1.005716 1.044152
_rcs_mot_egr_early4 | 1.013089 .0065258 2.02 0.044 1.000379 1.02596
_rcs_mot_egr_early5 | 1.008246 .0048303 1.71 0.086 .9988232 1.017758
_rcs_mot_egr_early6 | 1.009801 .00394 2.50 0.012 1.002108 1.017553
_rcs_mot_egr_early7 | 1.007085 .0032231 2.21 0.027 1.000788 1.013422
_rcs_mot_egr_late1 | .926184 .0291636 -2.44 0.015 .8707525 .9851442
_rcs_mot_egr_late2 | .9985651 .0252348 -0.06 0.955 .9503107 1.04927
_rcs_mot_egr_late3 | 1.01482 .0089937 1.66 0.097 .9973449 1.032601
_rcs_mot_egr_late4 | 1.019275 .0055493 3.51 0.000 1.008457 1.03021
_rcs_mot_egr_late5 | 1.012129 .0038953 3.13 0.002 1.004523 1.019792
_rcs_mot_egr_late6 | 1.011651 .0031004 3.78 0.000 1.005593 1.017746
_rcs_mot_egr_late7 | 1.006414 .0025262 2.55 0.011 1.001475 1.011378
_cons | 8.0e+141 1.3e+143 20.31 0.000 1.6e+128 4.0e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21781.703
Iteration 1: log likelihood = -21773.518
Iteration 2: log likelihood = -21773.475
Iteration 3: log likelihood = -21773.475
Log likelihood = -21773.475 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993529 .1086635 12.66 0.000 1.791534 2.218298
mot_egr_late | 1.653293 .0778932 10.67 0.000 1.507462 1.813232
tr_mod2 | 1.152168 .0429583 3.80 0.000 1.070974 1.239517
sex_dum2 | .591895 .025535 -12.16 0.000 .5439048 .6441195
edad_ini_cons | .9734502 .0040327 -6.50 0.000 .9655782 .9813864
esc1 | 1.517643 .0833662 7.59 0.000 1.362737 1.690158
esc2 | 1.3448 .0693804 5.74 0.000 1.215466 1.487896
sus_prin2 | 1.192529 .0707069 2.97 0.003 1.061696 1.339486
sus_prin3 | 1.713039 .0820871 11.23 0.000 1.559475 1.881724
sus_prin4 | 1.140279 .079149 1.89 0.059 .9952389 1.306455
sus_prin5 | 1.351712 .1835003 2.22 0.026 1.035929 1.763755
fr_cons_sus_prin2 | .9774067 .0969565 -0.23 0.818 .8047076 1.187169
fr_cons_sus_prin3 | .996186 .0799651 -0.05 0.962 .8511643 1.165917
fr_cons_sus_prin4 | 1.038179 .0863135 0.45 0.652 .8820716 1.221914
fr_cons_sus_prin5 | 1.089458 .0866187 1.08 0.281 .9322548 1.27317
cond_ocu2 | 1.089397 .0671911 1.39 0.165 .9653534 1.229379
cond_ocu3 | 1.138617 .2787262 0.53 0.596 .7047079 1.839698
cond_ocu4 | 1.24384 .081231 3.34 0.001 1.094399 1.413688
cond_ocu5 | 1.32926 .1364645 2.77 0.006 1.086986 1.625533
cond_ocu6 | 1.211064 .0419823 5.52 0.000 1.131513 1.296207
policonsumo | 1.006989 .0431179 0.16 0.871 .9259288 1.095147
num_hij2 | 1.136264 .0394243 3.68 0.000 1.061563 1.216222
tenviv1 | 1.017438 .1149412 0.15 0.878 .8153549 1.269607
tenviv2 | 1.065248 .080067 0.84 0.400 .9193315 1.234325
tenviv4 | 1.011025 .0420103 0.26 0.792 .9319502 1.09681
tenviv5 | .9919276 .0331652 -0.24 0.808 .9290092 1.059107
mzone2 | 1.415405 .0524485 9.38 0.000 1.316252 1.522027
mzone3 | 1.544209 .0864803 7.76 0.000 1.383682 1.72336
n_off_vio | 1.462295 .0503827 11.03 0.000 1.366807 1.564454
n_off_acq | 2.801657 .087336 33.05 0.000 2.635606 2.97817
n_off_sud | 1.378926 .04573 9.69 0.000 1.292148 1.471532
n_off_oth | 1.703534 .0565466 16.05 0.000 1.596233 1.818048
psy_com2 | 1.048778 .0402975 1.24 0.215 .972697 1.13081
dep2 | 1.032802 .0387498 0.86 0.390 .9595787 1.111612
rural2 | .9367132 .0520101 -1.18 0.239 .8401261 1.044405
rural3 | .863939 .0539522 -2.34 0.019 .76441 .9764272
porc_pobr | 1.706498 .3686555 2.47 0.013 1.11743 2.6061
susini2 | 1.093285 .0717045 1.36 0.174 .9614047 1.243256
susini3 | 1.272439 .0732448 4.19 0.000 1.136683 1.424407
susini4 | 1.157743 .0379667 4.47 0.000 1.085671 1.2346
susini5 | 1.379862 .1165636 3.81 0.000 1.169313 1.628324
ano_nac_corr | .8481991 .0067776 -20.60 0.000 .8350189 .8615875
cohab2 | .8633364 .0473409 -2.68 0.007 .7753621 .9612924
cohab3 | 1.076619 .068741 1.16 0.248 .9499785 1.220142
cohab4 | .9451859 .0519057 -1.03 0.305 .8487362 1.052596
fis_com2 | 1.114694 .0326829 3.70 0.000 1.052443 1.180628
rc_x1 | .8463984 .0086775 -16.27 0.000 .8295606 .8635779
rc_x2 | .881055 .0305185 -3.66 0.000 .8232251 .9429473
rc_x3 | 1.296514 .1195259 2.82 0.005 1.082193 1.553279
_rcs1 | 2.182114 .0589407 28.89 0.000 2.069597 2.300748
_rcs2 | 1.074676 .0074221 10.43 0.000 1.060226 1.089321
_rcs3 | 1.035255 .0052332 6.85 0.000 1.025049 1.045563
_rcs_mot_egr_early1 | .8959997 .0272148 -3.62 0.000 .8442163 .9509594
_rcs_mot_egr_late1 | .9188774 .0268091 -2.90 0.004 .8678067 .9729536
_cons | 1.4e+141 2.3e+142 20.21 0.000 2.9e+127 6.8e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21782.456
Iteration 1: log likelihood = -21773.266
Iteration 2: log likelihood = -21773.213
Iteration 3: log likelihood = -21773.213
Log likelihood = -21773.213 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.992387 .1086396 12.64 0.000 1.790441 2.217112
mot_egr_late | 1.653717 .0779246 10.68 0.000 1.507828 1.813721
tr_mod2 | 1.151976 .0429526 3.79 0.000 1.070793 1.239314
sex_dum2 | .591896 .0255351 -12.16 0.000 .5439056 .6441208
edad_ini_cons | .9734534 .0040327 -6.49 0.000 .9655814 .9813896
esc1 | 1.517643 .0833661 7.59 0.000 1.362737 1.690158
esc2 | 1.344786 .0693798 5.74 0.000 1.215453 1.487881
sus_prin2 | 1.192564 .0707096 2.97 0.003 1.061725 1.339526
sus_prin3 | 1.713118 .082091 11.23 0.000 1.559547 1.881811
sus_prin4 | 1.140364 .0791555 1.89 0.058 .9953128 1.306555
sus_prin5 | 1.351995 .1835419 2.22 0.026 1.036142 1.764132
fr_cons_sus_prin2 | .977437 .0969596 -0.23 0.818 .8047323 1.187206
fr_cons_sus_prin3 | .9962465 .07997 -0.05 0.963 .851216 1.165987
fr_cons_sus_prin4 | 1.038191 .0863143 0.45 0.652 .8820826 1.221928
fr_cons_sus_prin5 | 1.089501 .0866215 1.08 0.281 .9322931 1.273219
cond_ocu2 | 1.089294 .067185 1.39 0.166 .9652613 1.229264
cond_ocu3 | 1.13876 .2787623 0.53 0.596 .7047949 1.839932
cond_ocu4 | 1.244049 .0812436 3.34 0.001 1.094584 1.413923
cond_ocu5 | 1.329608 .1365016 2.77 0.006 1.087268 1.625963
cond_ocu6 | 1.211053 .0419818 5.52 0.000 1.131503 1.296196
policonsumo | 1.007009 .043119 0.16 0.870 .9259467 1.095169
num_hij2 | 1.136278 .0394247 3.68 0.000 1.061575 1.216237
tenviv1 | 1.017481 .1149466 0.15 0.878 .8153886 1.269662
tenviv2 | 1.065116 .0800574 0.84 0.401 .919217 1.234173
tenviv4 | 1.011118 .0420145 0.27 0.790 .9320352 1.096911
tenviv5 | .992023 .0331685 -0.24 0.811 .9290982 1.059209
mzone2 | 1.415527 .0524529 9.38 0.000 1.316365 1.522158
mzone3 | 1.544434 .0864926 7.76 0.000 1.383884 1.72361
n_off_vio | 1.462334 .0503844 11.03 0.000 1.366843 1.564496
n_off_acq | 2.801759 .0873383 33.05 0.000 2.635704 2.978276
n_off_sud | 1.378902 .0457287 9.69 0.000 1.292126 1.471506
n_off_oth | 1.703609 .056549 16.05 0.000 1.596304 1.818128
psy_com2 | 1.049237 .0403207 1.25 0.211 .9731127 1.131316
dep2 | 1.032795 .0387498 0.86 0.390 .9595722 1.111605
rural2 | .9366603 .0520074 -1.18 0.239 .8400783 1.044346
rural3 | .8637846 .0539437 -2.34 0.019 .7642713 .9762551
porc_pobr | 1.704937 .3683398 2.47 0.014 1.11638 2.603781
susini2 | 1.093376 .0717115 1.36 0.173 .9614828 1.243362
susini3 | 1.272575 .073253 4.19 0.000 1.136805 1.424561
susini4 | 1.157676 .0379647 4.46 0.000 1.085608 1.234529
susini5 | 1.37977 .1165564 3.81 0.000 1.169233 1.628216
ano_nac_corr | .8482083 .0067787 -20.60 0.000 .8350259 .8615989
cohab2 | .8631477 .0473312 -2.68 0.007 .7751915 .9610838
cohab3 | 1.076351 .068725 1.15 0.249 .9497404 1.219841
cohab4 | .9450157 .0518965 -1.03 0.303 .8485832 1.052407
fis_com2 | 1.114623 .0326819 3.70 0.000 1.052373 1.180555
rc_x1 | .8463985 .0086783 -16.26 0.000 .8295591 .8635798
rc_x2 | .8810881 .0305196 -3.65 0.000 .8232562 .9429826
rc_x3 | 1.296408 .1195163 2.82 0.005 1.082105 1.553154
_rcs1 | 2.177553 .0629336 26.93 0.000 2.057634 2.304461
_rcs2 | 1.070248 .0238432 3.05 0.002 1.024522 1.118015
_rcs3 | 1.034866 .0053764 6.60 0.000 1.024381 1.045457
_rcs_mot_egr_early1 | .896524 .0290247 -3.37 0.001 .8414039 .955255
_rcs_mot_egr_early2 | .9988697 .0246557 -0.05 0.963 .9516957 1.048382
_rcs_mot_egr_late1 | .9224769 .0289352 -2.57 0.010 .8674731 .9809684
_rcs_mot_egr_late2 | 1.008833 .0242824 0.37 0.715 .9623459 1.057567
_cons | 1.4e+141 2.2e+142 20.21 0.000 2.8e+127 6.7e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21781.761
Iteration 1: log likelihood = -21772.77
Iteration 2: log likelihood = -21772.708
Iteration 3: log likelihood = -21772.708
Log likelihood = -21772.708 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994201 .1087591 12.66 0.000 1.792034 2.219174
mot_egr_late | 1.655458 .0780287 10.69 0.000 1.509376 1.815678
tr_mod2 | 1.152062 .0429563 3.80 0.000 1.070872 1.239408
sex_dum2 | .5918779 .0255343 -12.16 0.000 .543889 .6441009
edad_ini_cons | .9734461 .0040328 -6.50 0.000 .965574 .9813824
esc1 | 1.517618 .0833642 7.59 0.000 1.362715 1.690129
esc2 | 1.344767 .0693785 5.74 0.000 1.215437 1.487859
sus_prin2 | 1.192791 .0707248 2.97 0.003 1.061924 1.339785
sus_prin3 | 1.7134 .0821083 11.24 0.000 1.559797 1.882129
sus_prin4 | 1.140464 .0791634 1.89 0.058 .9953981 1.306671
sus_prin5 | 1.352527 .1836172 2.22 0.026 1.036545 1.764834
fr_cons_sus_prin2 | .977463 .0969621 -0.23 0.818 .8047538 1.187237
fr_cons_sus_prin3 | .9962484 .0799702 -0.05 0.963 .8512175 1.16599
fr_cons_sus_prin4 | 1.038257 .0863198 0.45 0.652 .8821378 1.222005
fr_cons_sus_prin5 | 1.089522 .0866234 1.08 0.281 .9323103 1.273244
cond_ocu2 | 1.089226 .0671814 1.39 0.166 .9652005 1.229189
cond_ocu3 | 1.139291 .2788951 0.53 0.594 .7051196 1.840798
cond_ocu4 | 1.243757 .0812259 3.34 0.001 1.094325 1.413594
cond_ocu5 | 1.329578 .1365005 2.77 0.006 1.087241 1.625931
cond_ocu6 | 1.211024 .0419815 5.52 0.000 1.131475 1.296166
policonsumo | 1.007064 .0431218 0.16 0.869 .9259958 1.095229
num_hij2 | 1.13623 .0394227 3.68 0.000 1.061531 1.216185
tenviv1 | 1.01732 .1149305 0.15 0.879 .8152563 1.269466
tenviv2 | 1.065288 .080071 0.84 0.400 .9193642 1.234373
tenviv4 | 1.011027 .042011 0.26 0.792 .9319505 1.096813
tenviv5 | .9919423 .0331661 -0.24 0.809 .9290221 1.059124
mzone2 | 1.415572 .0524554 9.38 0.000 1.316406 1.522208
mzone3 | 1.544224 .0864818 7.76 0.000 1.383694 1.723377
n_off_vio | 1.462308 .0503824 11.03 0.000 1.36682 1.564466
n_off_acq | 2.801655 .0873315 33.05 0.000 2.635612 2.978159
n_off_sud | 1.378782 .0457242 9.69 0.000 1.292015 1.471377
n_off_oth | 1.703585 .0565468 16.05 0.000 1.596283 1.818099
psy_com2 | 1.049306 .0403283 1.25 0.210 .9731675 1.131401
dep2 | 1.032794 .03875 0.86 0.390 .9595711 1.111605
rural2 | .9367893 .0520146 -1.18 0.240 .8401939 1.04449
rural3 | .8638749 .053949 -2.34 0.019 .7643519 .9763565
porc_pobr | 1.702683 .3679058 2.46 0.014 1.114837 2.600498
susini2 | 1.093604 .0717273 1.36 0.172 .9616817 1.243623
susini3 | 1.272582 .0732535 4.19 0.000 1.136811 1.424569
susini4 | 1.15762 .0379628 4.46 0.000 1.085555 1.234469
susini5 | 1.379828 .1165626 3.81 0.000 1.16928 1.628288
ano_nac_corr | .8481871 .0067786 -20.60 0.000 .8350047 .8615775
cohab2 | .863175 .0473332 -2.68 0.007 .7752151 .9611154
cohab3 | 1.07634 .0687254 1.15 0.249 .9497285 1.219831
cohab4 | .945034 .0518977 -1.03 0.303 .8485992 1.052428
fis_com2 | 1.11443 .0326765 3.70 0.000 1.052191 1.180351
rc_x1 | .8463734 .0086781 -16.27 0.000 .8295344 .8635542
rc_x2 | .8811139 .0305203 -3.65 0.000 .8232807 .9430098
rc_x3 | 1.296266 .1195025 2.81 0.005 1.081987 1.552981
_rcs1 | 2.184624 .0638396 26.74 0.000 2.063016 2.3134
_rcs2 | 1.062578 .0234112 2.75 0.006 1.017669 1.109468
_rcs3 | 1.049606 .0165602 3.07 0.002 1.017645 1.08257
_rcs_mot_egr_early1 | .8934283 .0292654 -3.44 0.001 .8378717 .9526686
_rcs_mot_egr_early2 | 1.006194 .024673 0.25 0.801 .9589791 1.055733
_rcs_mot_egr_early3 | .985161 .017533 -0.84 0.401 .9513895 1.020131
_rcs_mot_egr_late1 | .9190858 .0291609 -2.66 0.008 .8636723 .9780548
_rcs_mot_egr_late2 | 1.01717 .0244328 0.71 0.478 .9703925 1.066203
_rcs_mot_egr_late3 | .984397 .0169211 -0.91 0.360 .9517846 1.018127
_cons | 1.4e+141 2.3e+142 20.21 0.000 3.0e+127 7.1e+154
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21791.574
Iteration 1: log likelihood = -21770.715
Iteration 2: log likelihood = -21770.432
Iteration 3: log likelihood = -21770.432
Log likelihood = -21770.432 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993994 .1087479 12.65 0.000 1.791848 2.218944
mot_egr_late | 1.654633 .0779882 10.68 0.000 1.508627 1.81477
tr_mod2 | 1.151998 .0429537 3.79 0.000 1.070813 1.239339
sex_dum2 | .5920072 .0255399 -12.15 0.000 .5440078 .6442416
edad_ini_cons | .9734475 .0040328 -6.50 0.000 .9655754 .9813839
esc1 | 1.517391 .0833531 7.59 0.000 1.362509 1.689879
esc2 | 1.344631 .0693721 5.74 0.000 1.215313 1.48771
sus_prin2 | 1.193389 .0707629 2.98 0.003 1.062452 1.340462
sus_prin3 | 1.714107 .0821453 11.24 0.000 1.560435 1.882912
sus_prin4 | 1.141144 .0792131 1.90 0.057 .9959872 1.307455
sus_prin5 | 1.353507 .1837552 2.23 0.026 1.037288 1.766125
fr_cons_sus_prin2 | .9774521 .0969612 -0.23 0.818 .8047446 1.187225
fr_cons_sus_prin3 | .9962251 .0799687 -0.05 0.962 .8511969 1.165963
fr_cons_sus_prin4 | 1.03812 .0863085 0.45 0.653 .8820213 1.221844
fr_cons_sus_prin5 | 1.089452 .0866178 1.08 0.281 .9322504 1.273162
cond_ocu2 | 1.088986 .0671659 1.38 0.167 .9649891 1.228916
cond_ocu3 | 1.140375 .2791593 0.54 0.592 .7057927 1.842547
cond_ocu4 | 1.243308 .0811949 3.33 0.001 1.093932 1.41308
cond_ocu5 | 1.329993 .1365432 2.78 0.005 1.08758 1.626438
cond_ocu6 | 1.211143 .0419854 5.53 0.000 1.131587 1.296293
policonsumo | 1.007251 .04313 0.17 0.866 .9261673 1.095432
num_hij2 | 1.136236 .0394229 3.68 0.000 1.061537 1.216191
tenviv1 | 1.018052 .115014 0.16 0.874 .8158414 1.270381
tenviv2 | 1.065812 .0801121 0.85 0.396 .9198134 1.234984
tenviv4 | 1.011369 .0420256 0.27 0.786 .9322657 1.097185
tenviv5 | .9923359 .0331797 -0.23 0.818 .9293899 1.059545
mzone2 | 1.415867 .0524685 9.38 0.000 1.316676 1.52253
mzone3 | 1.544849 .0865228 7.77 0.000 1.384243 1.724088
n_off_vio | 1.462284 .0503772 11.03 0.000 1.366806 1.564431
n_off_acq | 2.800594 .0872922 33.04 0.000 2.634626 2.977018
n_off_sud | 1.378462 .0457115 9.68 0.000 1.291719 1.471031
n_off_oth | 1.703325 .0565332 16.05 0.000 1.596049 1.817811
psy_com2 | 1.04948 .0403378 1.26 0.209 .9733236 1.131594
dep2 | 1.032859 .0387524 0.86 0.389 .9596314 1.111675
rural2 | .9367953 .0520153 -1.18 0.240 .8401986 1.044498
rural3 | .8639789 .0539568 -2.34 0.019 .7644415 .976477
porc_pobr | 1.705112 .3684138 2.47 0.014 1.116449 2.604157
susini2 | 1.094516 .0717901 1.38 0.169 .9624787 1.244667
susini3 | 1.272212 .0732333 4.18 0.000 1.136478 1.424157
susini4 | 1.157271 .0379524 4.45 0.000 1.085226 1.234099
susini5 | 1.379149 .1165082 3.81 0.000 1.168701 1.627494
ano_nac_corr | .8477443 .0067794 -20.65 0.000 .8345606 .8611363
cohab2 | .8631261 .0473311 -2.68 0.007 .7751702 .961062
cohab3 | 1.07617 .0687147 1.15 0.250 .9495777 1.219638
cohab4 | .9449985 .0518967 -1.03 0.303 .8485658 1.05239
fis_com2 | 1.114072 .0326672 3.68 0.000 1.051851 1.179974
rc_x1 | .8459445 .0086768 -16.31 0.000 .8291081 .8631227
rc_x2 | .8810347 .0305175 -3.66 0.000 .8232067 .942925
rc_x3 | 1.296617 .1195345 2.82 0.005 1.08228 1.553401
_rcs1 | 2.176878 .0632546 26.77 0.000 2.056365 2.304453
_rcs2 | 1.065772 .0240704 2.82 0.005 1.019624 1.114009
_rcs3 | 1.039626 .0159037 2.54 0.011 1.008918 1.071268
_rcs_mot_egr_early1 | .8965947 .0292313 -3.35 0.001 .8410946 .955757
_rcs_mot_egr_early2 | 1.003356 .0251742 0.13 0.894 .9552088 1.05393
_rcs_mot_egr_early3 | .9949928 .0170701 -0.29 0.770 .9620924 1.029018
_rcs_mot_egr_early4 | 1.0009 .0070189 0.13 0.898 .987237 1.014752
_rcs_mot_egr_late1 | .9222255 .0291028 -2.57 0.010 .8669133 .9810667
_rcs_mot_egr_late2 | 1.015608 .025126 0.63 0.531 .9675369 1.066067
_rcs_mot_egr_late3 | .9893874 .0164136 -0.64 0.520 .9577348 1.022086
_rcs_mot_egr_late4 | 1.009536 .0059928 1.60 0.110 .9978581 1.02135
_cons | 4.1e+141 6.7e+142 20.27 0.000 8.3e+127 2.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21776.225
Iteration 1: log likelihood = -21765.405
Iteration 2: log likelihood = -21765.318
Iteration 3: log likelihood = -21765.318
Log likelihood = -21765.318 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997668 .1089716 12.69 0.000 1.795109 2.223084
mot_egr_late | 1.656154 .0780808 10.70 0.000 1.509976 1.816483
tr_mod2 | 1.152032 .0429534 3.80 0.000 1.070847 1.239371
sex_dum2 | .5922149 .0255483 -12.14 0.000 .5441998 .6444665
edad_ini_cons | .9734278 .004033 -6.50 0.000 .9655553 .9813644
esc1 | 1.517123 .0833388 7.59 0.000 1.362267 1.689581
esc2 | 1.344386 .0693593 5.74 0.000 1.215091 1.487438
sus_prin2 | 1.194293 .0708202 2.99 0.003 1.063251 1.341486
sus_prin3 | 1.715114 .082199 11.26 0.000 1.561342 1.88403
sus_prin4 | 1.142103 .0792822 1.91 0.056 .99682 1.30856
sus_prin5 | 1.354572 .1839048 2.24 0.025 1.038097 1.767528
fr_cons_sus_prin2 | .9774577 .0969617 -0.23 0.818 .8047494 1.187231
fr_cons_sus_prin3 | .9960994 .0799585 -0.05 0.961 .8510898 1.165816
fr_cons_sus_prin4 | 1.038086 .0863057 0.45 0.653 .8819933 1.221805
fr_cons_sus_prin5 | 1.089328 .0866086 1.08 0.282 .932143 1.273018
cond_ocu2 | 1.088384 .0671287 1.37 0.170 .9644559 1.228237
cond_ocu3 | 1.142463 .2796682 0.54 0.586 .707087 1.845913
cond_ocu4 | 1.242245 .0811214 3.32 0.001 1.093004 1.411863
cond_ocu5 | 1.331131 .1366602 2.79 0.005 1.08851 1.627831
cond_ocu6 | 1.211394 .041994 5.53 0.000 1.131821 1.296561
policonsumo | 1.007392 .0431357 0.17 0.863 .9262978 1.095585
num_hij2 | 1.136201 .0394218 3.68 0.000 1.061504 1.216154
tenviv1 | 1.018428 .1150592 0.16 0.872 .8161387 1.270857
tenviv2 | 1.066763 .0801875 0.86 0.390 .9206271 1.236095
tenviv4 | 1.011894 .0420481 0.28 0.776 .9327474 1.097755
tenviv5 | .9926531 .03319 -0.22 0.825 .9296876 1.059883
mzone2 | 1.416084 .0524789 9.39 0.000 1.316874 1.522768
mzone3 | 1.544923 .0865321 7.77 0.000 1.384301 1.724182
n_off_vio | 1.462062 .0503609 11.03 0.000 1.366615 1.564176
n_off_acq | 2.798629 .0872141 33.02 0.000 2.632808 2.974894
n_off_sud | 1.377727 .0456823 9.66 0.000 1.291039 1.470236
n_off_oth | 1.702875 .0565063 16.04 0.000 1.595649 1.817306
psy_com2 | 1.049219 .0403321 1.25 0.211 .9730744 1.131323
dep2 | 1.032788 .0387508 0.86 0.390 .9595635 1.111601
rural2 | .9368713 .0520193 -1.17 0.240 .8402671 1.044582
rural3 | .8643857 .0539832 -2.33 0.020 .7647998 .9769389
porc_pobr | 1.707136 .3688211 2.48 0.013 1.117813 2.607159
susini2 | 1.095994 .0718908 1.40 0.162 .9637718 1.246356
susini3 | 1.271974 .0732205 4.18 0.000 1.136264 1.423893
susini4 | 1.15656 .0379297 4.44 0.000 1.084558 1.233342
susini5 | 1.378916 .1164904 3.80 0.000 1.168499 1.627223
ano_nac_corr | .8470538 .0067765 -20.75 0.000 .8338756 .8604402
cohab2 | .8631389 .0473318 -2.68 0.007 .7751817 .9610763
cohab3 | 1.076014 .0687052 1.15 0.251 .9494395 1.219463
cohab4 | .944867 .0518888 -1.03 0.302 .8484488 1.052242
fis_com2 | 1.113505 .0326491 3.67 0.000 1.051318 1.17937
rc_x1 | .845257 .0086716 -16.39 0.000 .8284307 .862425
rc_x2 | .8809782 .0305158 -3.66 0.000 .8231534 .9428652
rc_x3 | 1.296836 .1195568 2.82 0.005 1.08246 1.553668
_rcs1 | 2.182866 .0637338 26.74 0.000 2.061457 2.311425
_rcs2 | 1.062362 .0233448 2.75 0.006 1.017578 1.109116
_rcs3 | 1.049923 .0164743 3.10 0.002 1.018125 1.082714
_rcs_mot_egr_early1 | .8942936 .0292828 -3.41 0.001 .8387033 .9535685
_rcs_mot_egr_early2 | 1.007682 .0247443 0.31 0.755 .9603326 1.057366
_rcs_mot_egr_early3 | .9882245 .016502 -0.71 0.478 .9564046 1.021103
_rcs_mot_egr_early4 | .9940239 .0085759 -0.69 0.487 .9773568 1.010975
_rcs_mot_egr_early5 | 1.008014 .0045997 1.75 0.080 .999039 1.01707
_rcs_mot_egr_late1 | .9194097 .0291463 -2.65 0.008 .8640226 .9783474
_rcs_mot_egr_late2 | 1.019444 .0246737 0.80 0.426 .9722139 1.068969
_rcs_mot_egr_late3 | .9813818 .0158814 -1.16 0.246 .9507434 1.013008
_rcs_mot_egr_late4 | 1.000978 .0077828 0.13 0.900 .9858393 1.016348
_rcs_mot_egr_late5 | 1.01085 .0036678 2.97 0.003 1.003686 1.018064
_cons | 2.1e+142 3.4e+143 20.36 0.000 4.3e+128 1.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21777.503
Iteration 1: log likelihood = -21761.691
Iteration 2: log likelihood = -21761.522
Iteration 3: log likelihood = -21761.522
Log likelihood = -21761.522 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.998368 .109015 12.69 0.000 1.795729 2.223874
mot_egr_late | 1.656283 .0780903 10.70 0.000 1.510088 1.816632
tr_mod2 | 1.152082 .0429535 3.80 0.000 1.070897 1.239421
sex_dum2 | .5923819 .0255551 -12.14 0.000 .5443539 .6446474
edad_ini_cons | .9734156 .0040331 -6.50 0.000 .965543 .9813525
esc1 | 1.517081 .0833369 7.59 0.000 1.362229 1.689536
esc2 | 1.344301 .0693547 5.73 0.000 1.215015 1.487344
sus_prin2 | 1.194694 .0708459 3.00 0.003 1.063604 1.341941
sus_prin3 | 1.715605 .082224 11.26 0.000 1.561787 1.884573
sus_prin4 | 1.142499 .0793108 1.92 0.055 .9971642 1.309017
sus_prin5 | 1.354825 .1839391 2.24 0.025 1.038291 1.767858
fr_cons_sus_prin2 | .9774705 .096963 -0.23 0.818 .8047598 1.187247
fr_cons_sus_prin3 | .9959907 .0799497 -0.05 0.960 .850997 1.165689
fr_cons_sus_prin4 | 1.038106 .0863074 0.45 0.653 .8820094 1.221828
fr_cons_sus_prin5 | 1.089206 .0865998 1.07 0.282 .9320374 1.272878
cond_ocu2 | 1.088024 .0671064 1.37 0.171 .9641365 1.22783
cond_ocu3 | 1.143741 .2799798 0.55 0.583 .7078797 1.847974
cond_ocu4 | 1.241773 .0810861 3.32 0.001 1.092597 1.411317
cond_ocu5 | 1.331715 .1367198 2.79 0.005 1.088988 1.628544
cond_ocu6 | 1.211594 .042 5.54 0.000 1.132009 1.296773
policonsumo | 1.007325 .0431323 0.17 0.865 .9262376 1.095511
num_hij2 | 1.136195 .0394221 3.68 0.000 1.061498 1.216149
tenviv1 | 1.018435 .1150608 0.16 0.872 .8161432 1.270868
tenviv2 | 1.067419 .0802386 0.87 0.385 .9211909 1.23686
tenviv4 | 1.012219 .0420616 0.29 0.770 .9330478 1.098109
tenviv5 | .9928694 .033197 -0.21 0.831 .9298906 1.060114
mzone2 | 1.41616 .0524832 9.39 0.000 1.316942 1.522853
mzone3 | 1.545138 .0865461 7.77 0.000 1.38449 1.724427
n_off_vio | 1.461946 .0503517 11.03 0.000 1.366516 1.564041
n_off_acq | 2.797681 .0871743 33.02 0.000 2.631935 2.973865
n_off_sud | 1.377372 .0456677 9.66 0.000 1.290711 1.469851
n_off_oth | 1.702664 .0564924 16.04 0.000 1.595465 1.817067
psy_com2 | 1.049007 .0403271 1.24 0.213 .9728712 1.1311
dep2 | 1.032729 .0387492 0.86 0.391 .959507 1.111538
rural2 | .9367973 .0520143 -1.18 0.240 .8402024 1.044497
rural3 | .8644717 .0539895 -2.33 0.020 .7648743 .9770381
porc_pobr | 1.709202 .3692529 2.48 0.013 1.119183 2.61027
susini2 | 1.096852 .0719489 1.41 0.159 .9645232 1.247335
susini3 | 1.271949 .0732195 4.18 0.000 1.136241 1.423866
susini4 | 1.156149 .0379163 4.42 0.000 1.084173 1.232904
susini5 | 1.378549 .1164591 3.80 0.000 1.168189 1.626789
ano_nac_corr | .8468317 .0067757 -20.78 0.000 .8336551 .8602165
cohab2 | .8632232 .0473361 -2.68 0.007 .775258 .9611694
cohab3 | 1.075957 .0687015 1.15 0.252 .9493896 1.219398
cohab4 | .9448071 .0518849 -1.03 0.301 .848396 1.052174
fis_com2 | 1.113414 .0326453 3.66 0.000 1.051234 1.179272
rc_x1 | .8450452 .00867 -16.41 0.000 .8282219 .8622101
rc_x2 | .8808926 .0305128 -3.66 0.000 .8230734 .9427734
rc_x3 | 1.297178 .1195894 2.82 0.005 1.082743 1.55408
_rcs1 | 2.182986 .063767 26.73 0.000 2.061516 2.311614
_rcs2 | 1.062973 .0234098 2.77 0.006 1.018067 1.10986
_rcs3 | 1.04988 .0165477 3.09 0.002 1.017943 1.082819
_rcs_mot_egr_early1 | .8939957 .0292832 -3.42 0.001 .8384052 .9532721
_rcs_mot_egr_early2 | 1.007075 .0248046 0.29 0.775 .9596138 1.056884
_rcs_mot_egr_early3 | .9885873 .0160005 -0.71 0.478 .9577192 1.02045
_rcs_mot_egr_early4 | .993109 .0094877 -0.72 0.469 .9746865 1.01188
_rcs_mot_egr_early5 | 1.002645 .0049805 0.53 0.595 .9929307 1.012454
_rcs_mot_egr_early6 | 1.010333 .0037579 2.76 0.006 1.002995 1.017726
_rcs_mot_egr_late1 | .9191796 .0291509 -2.66 0.008 .8637843 .9781274
_rcs_mot_egr_late2 | 1.019571 .0247717 0.80 0.425 .9721575 1.069297
_rcs_mot_egr_late3 | .9807565 .0153663 -1.24 0.215 .9510967 1.011341
_rcs_mot_egr_late4 | .9984015 .0088881 -0.18 0.857 .9811323 1.015975
_rcs_mot_egr_late5 | 1.007441 .0041389 1.80 0.071 .9993611 1.015586
_rcs_mot_egr_late6 | 1.009655 .0029694 3.27 0.001 1.003852 1.015492
_cons | 3.6e+142 5.8e+143 20.39 0.000 7.2e+128 1.8e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.855
Iteration 1: log likelihood = -21761.11
Iteration 2: log likelihood = -21760.974
Iteration 3: log likelihood = -21760.974
Log likelihood = -21760.974 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.998852 .1090466 12.70 0.000 1.796155 2.224425
mot_egr_late | 1.656515 .0781049 10.70 0.000 1.510293 1.816895
tr_mod2 | 1.152071 .0429532 3.80 0.000 1.070887 1.23941
sex_dum2 | .5924347 .0255572 -12.14 0.000 .5444027 .6447045
edad_ini_cons | .9734104 .0040331 -6.50 0.000 .9655377 .9813474
esc1 | 1.517126 .0833391 7.59 0.000 1.36227 1.689585
esc2 | 1.34431 .069355 5.74 0.000 1.215023 1.487353
sus_prin2 | 1.194847 .0708559 3.00 0.003 1.063738 1.342114
sus_prin3 | 1.715861 .0822378 11.27 0.000 1.562017 1.884857
sus_prin4 | 1.142715 .0793263 1.92 0.055 .9973515 1.309265
sus_prin5 | 1.35512 .1839799 2.24 0.025 1.038515 1.768244
fr_cons_sus_prin2 | .9774758 .0969635 -0.23 0.818 .8047641 1.187253
fr_cons_sus_prin3 | .9959496 .0799464 -0.05 0.960 .8509618 1.16564
fr_cons_sus_prin4 | 1.038089 .0863061 0.45 0.653 .8819949 1.221808
fr_cons_sus_prin5 | 1.089119 .0865933 1.07 0.283 .9319618 1.272777
cond_ocu2 | 1.087945 .0671015 1.37 0.172 .9640664 1.22774
cond_ocu3 | 1.144252 .2801048 0.55 0.582 .7081962 1.848799
cond_ocu4 | 1.241502 .0810671 3.31 0.001 1.092361 1.411006
cond_ocu5 | 1.331928 .1367415 2.79 0.005 1.089162 1.628804
cond_ocu6 | 1.211713 .0420042 5.54 0.000 1.132121 1.296901
policonsumo | 1.007242 .0431286 0.17 0.866 .9261619 1.095421
num_hij2 | 1.136205 .0394227 3.68 0.000 1.061506 1.21616
tenviv1 | 1.018509 .1150692 0.16 0.871 .8162025 1.270961
tenviv2 | 1.067689 .0802598 0.87 0.384 .9214225 1.237174
tenviv4 | 1.012313 .0420654 0.29 0.768 .9331341 1.09821
tenviv5 | .9929295 .033199 -0.21 0.832 .929947 1.060178
mzone2 | 1.41619 .0524851 9.39 0.000 1.316969 1.522887
mzone3 | 1.54532 .0865574 7.77 0.000 1.384651 1.724632
n_off_vio | 1.461836 .0503461 11.02 0.000 1.366416 1.56392
n_off_acq | 2.797358 .0871597 33.01 0.000 2.63164 2.973512
n_off_sud | 1.377215 .0456611 9.65 0.000 1.290566 1.469681
n_off_oth | 1.702542 .0564857 16.04 0.000 1.595355 1.81693
psy_com2 | 1.048993 .0403285 1.24 0.213 .9728553 1.13109
dep2 | 1.032681 .0387475 0.86 0.391 .9594628 1.111487
rural2 | .9368326 .0520159 -1.18 0.240 .8402347 1.044536
rural3 | .8645174 .0539925 -2.33 0.020 .7649145 .97709
porc_pobr | 1.708573 .3691082 2.48 0.013 1.118783 2.609283
susini2 | 1.097262 .071977 1.41 0.157 .9648821 1.247805
susini3 | 1.271733 .0732079 4.18 0.000 1.136047 1.423626
susini4 | 1.15597 .0379105 4.42 0.000 1.084004 1.232713
susini5 | 1.378352 .1164428 3.80 0.000 1.168022 1.626558
ano_nac_corr | .8467188 .0067751 -20.79 0.000 .8335434 .8601024
cohab2 | .863243 .0473371 -2.68 0.007 .775276 .9611913
cohab3 | 1.076 .0687042 1.15 0.251 .9494277 1.219447
cohab4 | .9447845 .0518837 -1.03 0.301 .8483757 1.052149
fis_com2 | 1.113321 .0326422 3.66 0.000 1.051147 1.179172
rc_x1 | .8449406 .0086692 -16.42 0.000 .8281191 .8621039
rc_x2 | .8808417 .0305111 -3.66 0.000 .8230258 .9427191
rc_x3 | 1.297367 .1196072 2.82 0.005 1.082901 1.554308
_rcs1 | 2.183614 .0638145 26.72 0.000 2.062054 2.312339
_rcs2 | 1.062623 .0233273 2.77 0.006 1.017872 1.109342
_rcs3 | 1.050939 .0165452 3.16 0.002 1.019007 1.083873
_rcs_mot_egr_early1 | .8937787 .0292915 -3.43 0.001 .8381735 .9530729
_rcs_mot_egr_early2 | 1.007825 .0247461 0.32 0.751 .9604724 1.057513
_rcs_mot_egr_early3 | .9897714 .0154199 -0.66 0.509 .9600058 1.02046
_rcs_mot_egr_early4 | .9898718 .0102039 -0.99 0.323 .9700732 1.010075
_rcs_mot_egr_early5 | .9999751 .0054971 -0.00 0.996 .9892588 1.010807
_rcs_mot_egr_early6 | 1.008368 .0039524 2.13 0.033 1.000651 1.016144
_rcs_mot_egr_early7 | 1.007234 .0032222 2.25 0.024 1.000938 1.013569
_rcs_mot_egr_late1 | .9188694 .0291527 -2.67 0.008 .8634715 .9778215
_rcs_mot_egr_late2 | 1.020703 .0247728 0.84 0.398 .9732859 1.07043
_rcs_mot_egr_late3 | .9802241 .0148415 -1.32 0.187 .9515627 1.009749
_rcs_mot_egr_late4 | .9959306 .0096733 -0.42 0.675 .9771507 1.015072
_rcs_mot_egr_late5 | 1.003819 .0047203 0.81 0.418 .9946098 1.013113
_rcs_mot_egr_late6 | 1.010216 .0031225 3.29 0.001 1.004115 1.016355
_rcs_mot_egr_late7 | 1.006564 .0025258 2.61 0.009 1.001626 1.011527
_cons | 4.8e+142 7.6e+143 20.41 0.000 9.4e+128 2.4e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21790.134
Iteration 1: log likelihood = -21769.037
Iteration 2: log likelihood = -21768.782
Iteration 3: log likelihood = -21768.782
Log likelihood = -21768.782 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993958 .1086859 12.66 0.000 1.791922 2.218773
mot_egr_late | 1.652238 .0778417 10.66 0.000 1.506503 1.812071
tr_mod2 | 1.152047 .0429533 3.80 0.000 1.070862 1.239386
sex_dum2 | .5920882 .0255428 -12.15 0.000 .5440833 .6443286
edad_ini_cons | .9734352 .0040329 -6.50 0.000 .9655628 .9813718
esc1 | 1.517275 .0833466 7.59 0.000 1.362406 1.68975
esc2 | 1.344387 .0693592 5.74 0.000 1.215092 1.487439
sus_prin2 | 1.193866 .0707929 2.99 0.003 1.062874 1.341003
sus_prin3 | 1.714551 .0821692 11.25 0.000 1.560835 1.883406
sus_prin4 | 1.141512 .0792391 1.91 0.057 .9963077 1.307878
sus_prin5 | 1.353683 .1837765 2.23 0.026 1.037427 1.766349
fr_cons_sus_prin2 | .9773686 .0969526 -0.23 0.817 .8046764 1.187122
fr_cons_sus_prin3 | .9959841 .0799491 -0.05 0.960 .8509915 1.165681
fr_cons_sus_prin4 | 1.038103 .0863075 0.45 0.653 .882007 1.221825
fr_cons_sus_prin5 | 1.089336 .0866099 1.08 0.282 .9321486 1.273029
cond_ocu2 | 1.088771 .0671523 1.38 0.168 .9647988 1.228672
cond_ocu3 | 1.140326 .2791431 0.54 0.592 .7057669 1.842454
cond_ocu4 | 1.242339 .0811321 3.32 0.001 1.09308 1.411981
cond_ocu5 | 1.330771 .1366206 2.78 0.005 1.08822 1.627384
cond_ocu6 | 1.211329 .0419917 5.53 0.000 1.131761 1.296492
policonsumo | 1.007332 .0431334 0.17 0.865 .9262427 1.095521
num_hij2 | 1.136269 .0394244 3.68 0.000 1.061568 1.216228
tenviv1 | 1.018138 .1150215 0.16 0.874 .8159139 1.270483
tenviv2 | 1.066419 .0801587 0.86 0.392 .920336 1.23569
tenviv4 | 1.011442 .042028 0.27 0.784 .9323335 1.097263
tenviv5 | .9922987 .0331778 -0.23 0.817 .9293563 1.059504
mzone2 | 1.415957 .0524723 9.39 0.000 1.316759 1.522628
mzone3 | 1.544291 .0864923 7.76 0.000 1.383743 1.723468
n_off_vio | 1.462211 .0503692 11.03 0.000 1.366748 1.564342
n_off_acq | 2.799605 .087253 33.03 0.000 2.63371 2.975949
n_off_sud | 1.378086 .0456974 9.67 0.000 1.291369 1.470626
n_off_oth | 1.702955 .0565145 16.04 0.000 1.595714 1.817403
psy_com2 | 1.048694 .0402977 1.24 0.216 .9726129 1.130726
dep2 | 1.032846 .038752 0.86 0.389 .9596189 1.111661
rural2 | .9368096 .0520157 -1.18 0.240 .8402122 1.044513
rural3 | .8642968 .0539762 -2.34 0.020 .7647236 .9768352
porc_pobr | 1.709729 .369308 2.48 0.013 1.119604 2.610899
susini2 | 1.094965 .0718203 1.38 0.167 .9628723 1.245179
susini3 | 1.27174 .0732063 4.18 0.000 1.136056 1.423629
susini4 | 1.156917 .0379411 4.44 0.000 1.084894 1.233722
susini5 | 1.379248 .1165154 3.81 0.000 1.168786 1.627607
ano_nac_corr | .8473511 .0067772 -20.71 0.000 .8341717 .8607388
cohab2 | .8633409 .0473406 -2.68 0.007 .7753671 .9612962
cohab3 | 1.076356 .0687235 1.15 0.249 .9497473 1.219842
cohab4 | .9450834 .0518994 -1.03 0.304 .8486455 1.05248
fis_com2 | 1.113859 .0326583 3.68 0.000 1.051655 1.179743
rc_x1 | .8455493 .0086735 -16.36 0.000 .8287194 .8627211
rc_x2 | .880994 .0305165 -3.66 0.000 .8231678 .9428824
rc_x3 | 1.296789 .1195515 2.82 0.005 1.082422 1.553609
_rcs1 | 2.179023 .0588019 28.86 0.000 2.066768 2.297375
_rcs2 | 1.073701 .0075346 10.13 0.000 1.059034 1.08857
_rcs3 | 1.034044 .0054165 6.39 0.000 1.023483 1.044715
_rcs4 | 1.016514 .0036877 4.51 0.000 1.009312 1.023768
_rcs_mot_egr_early1 | .8971604 .0272223 -3.58 0.000 .8453611 .9521336
_rcs_mot_egr_late1 | .9197571 .0268092 -2.87 0.004 .8686848 .9738321
_cons | 1.1e+142 1.7e+143 20.32 0.000 2.1e+128 5.3e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21790.803
Iteration 1: log likelihood = -21768.762
Iteration 2: log likelihood = -21768.483
Iteration 3: log likelihood = -21768.483
Log likelihood = -21768.483 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.9929 .1086738 12.65 0.000 1.79089 2.217695
mot_egr_late | 1.652736 .0778829 10.66 0.000 1.506925 1.812655
tr_mod2 | 1.151831 .0429467 3.79 0.000 1.070659 1.239157
sex_dum2 | .5920938 .0255432 -12.15 0.000 .5440882 .644335
edad_ini_cons | .9734393 .0040329 -6.50 0.000 .9655669 .9813758
esc1 | 1.517278 .0833467 7.59 0.000 1.362408 1.689752
esc2 | 1.344371 .0693585 5.74 0.000 1.215078 1.487422
sus_prin2 | 1.193883 .0707945 2.99 0.003 1.062888 1.341022
sus_prin3 | 1.714609 .0821718 11.25 0.000 1.560887 1.883469
sus_prin4 | 1.141593 .0792451 1.91 0.056 .9963777 1.307972
sus_prin5 | 1.353908 .1838102 2.23 0.026 1.037594 1.76665
fr_cons_sus_prin2 | .9774003 .0969559 -0.23 0.818 .8047023 1.187161
fr_cons_sus_prin3 | .9960424 .0799539 -0.05 0.961 .8510411 1.165749
fr_cons_sus_prin4 | 1.038109 .0863078 0.45 0.653 .8820126 1.221832
fr_cons_sus_prin5 | 1.089373 .0866122 1.08 0.282 .932182 1.273071
cond_ocu2 | 1.088669 .0671462 1.38 0.168 .9647079 1.228558
cond_ocu3 | 1.140407 .2791641 0.54 0.591 .7058161 1.842589
cond_ocu4 | 1.242557 .0811452 3.33 0.001 1.093273 1.412226
cond_ocu5 | 1.331151 .136661 2.79 0.005 1.088529 1.627852
cond_ocu6 | 1.211321 .0419913 5.53 0.000 1.131753 1.296483
policonsumo | 1.007346 .0431341 0.17 0.864 .9262551 1.095536
num_hij2 | 1.136287 .039425 3.68 0.000 1.061584 1.216246
tenviv1 | 1.018203 .1150292 0.16 0.873 .8159651 1.270565
tenviv2 | 1.066291 .0801493 0.85 0.393 .9202251 1.235542
tenviv4 | 1.011549 .0420328 0.28 0.782 .9324315 1.097379
tenviv5 | .9924056 .0331816 -0.23 0.820 .9294561 1.059619
mzone2 | 1.416075 .0524765 9.39 0.000 1.316869 1.522754
mzone3 | 1.544544 .0865061 7.76 0.000 1.38397 1.723749
n_off_vio | 1.462244 .0503708 11.03 0.000 1.366778 1.564378
n_off_acq | 2.799691 .0872551 33.03 0.000 2.633792 2.976039
n_off_sud | 1.378066 .0456962 9.67 0.000 1.291351 1.470603
n_off_oth | 1.703025 .0565168 16.04 0.000 1.59578 1.817478
psy_com2 | 1.049149 .0403208 1.25 0.212 .9730244 1.131228
dep2 | 1.032839 .0387519 0.86 0.389 .9596125 1.111654
rural2 | .936749 .0520124 -1.18 0.239 .8401577 1.044445
rural3 | .8641368 .0539674 -2.34 0.019 .76458 .976657
porc_pobr | 1.708336 .3690269 2.48 0.013 1.118666 2.608831
susini2 | 1.095048 .0718267 1.38 0.166 .9629437 1.245276
susini3 | 1.271879 .0732146 4.18 0.000 1.13618 1.423786
susini4 | 1.15685 .0379391 4.44 0.000 1.08483 1.233651
susini5 | 1.379157 .1165086 3.81 0.000 1.168708 1.627503
ano_nac_corr | .8473647 .0067783 -20.71 0.000 .8341831 .8607545
cohab2 | .8631495 .0473307 -2.68 0.007 .7751941 .9610845
cohab3 | 1.076078 .0687069 1.15 0.251 .9495001 1.21953
cohab4 | .9449082 .0518898 -1.03 0.302 .848488 1.052285
fis_com2 | 1.113801 .0326577 3.68 0.000 1.051597 1.179684
rc_x1 | .8455541 .0086743 -16.35 0.000 .8287224 .8627275
rc_x2 | .8810237 .0305175 -3.66 0.000 .8231957 .942914
rc_x3 | 1.296702 .1195437 2.82 0.005 1.08235 1.553506
_rcs1 | 2.172325 .0626115 26.92 0.000 2.05301 2.298573
_rcs2 | 1.067156 .0236496 2.93 0.003 1.021796 1.11453
_rcs3 | 1.033253 .0058251 5.80 0.000 1.021899 1.044734
_rcs4 | 1.016507 .0036873 4.51 0.000 1.009306 1.02376
_rcs_mot_egr_early1 | .8985586 .0290185 -3.31 0.001 .843446 .9572725
_rcs_mot_egr_early2 | 1.000896 .0246569 0.04 0.971 .9537172 1.050408
_rcs_mot_egr_late1 | .9244049 .0289207 -2.51 0.012 .8694243 .9828624
_rcs_mot_egr_late2 | 1.011208 .0242783 0.46 0.642 .9647255 1.05993
_cons | 1.0e+142 1.6e+143 20.32 0.000 2.0e+128 5.1e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21790.757
Iteration 1: log likelihood = -21767.777
Iteration 2: log likelihood = -21767.441
Iteration 3: log likelihood = -21767.441
Log likelihood = -21767.441 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996402 .1088971 12.67 0.000 1.793981 2.221663
mot_egr_late | 1.655984 .0780669 10.70 0.000 1.509832 1.816284
tr_mod2 | 1.151984 .0429528 3.79 0.000 1.0708 1.239322
sex_dum2 | .5920829 .0255425 -12.15 0.000 .5440786 .6443227
edad_ini_cons | .9734269 .004033 -6.50 0.000 .9655543 .9813636
esc1 | 1.517217 .0833426 7.59 0.000 1.362355 1.689683
esc2 | 1.344321 .0693555 5.74 0.000 1.215033 1.487365
sus_prin2 | 1.194269 .0708198 2.99 0.003 1.063228 1.341462
sus_prin3 | 1.715079 .0821996 11.26 0.000 1.561306 1.883997
sus_prin4 | 1.141797 .0792606 1.91 0.056 .9965532 1.308209
sus_prin5 | 1.354721 .1839239 2.24 0.025 1.038213 1.76772
fr_cons_sus_prin2 | .9774348 .0969591 -0.23 0.818 .804731 1.187203
fr_cons_sus_prin3 | .9960315 .0799529 -0.05 0.960 .851032 1.165736
fr_cons_sus_prin4 | 1.038203 .0863157 0.45 0.652 .8820921 1.221943
fr_cons_sus_prin5 | 1.089391 .0866141 1.08 0.282 .9321967 1.273093
cond_ocu2 | 1.088523 .0671381 1.38 0.169 .9645778 1.228396
cond_ocu3 | 1.141364 .2794011 0.54 0.589 .7064043 1.844143
cond_ocu4 | 1.242036 .0811126 3.32 0.001 1.092812 1.411637
cond_ocu5 | 1.331177 .1366661 2.79 0.005 1.088546 1.627889
cond_ocu6 | 1.211297 .0419913 5.53 0.000 1.131729 1.296459
policonsumo | 1.007428 .0431382 0.17 0.863 .9263294 1.095627
num_hij2 | 1.136205 .0394216 3.68 0.000 1.061509 1.216158
tenviv1 | 1.017972 .1150063 0.16 0.875 .8157753 1.270285
tenviv2 | 1.066601 .0801739 0.86 0.391 .9204907 1.235905
tenviv4 | 1.011443 .0420288 0.27 0.784 .9323333 1.097265
tenviv5 | .9922943 .0331779 -0.23 0.817 .9293516 1.0595
mzone2 | 1.416135 .0524799 9.39 0.000 1.316923 1.522821
mzone3 | 1.544188 .0864873 7.76 0.000 1.383649 1.723354
n_off_vio | 1.462189 .0503666 11.03 0.000 1.366731 1.564315
n_off_acq | 2.799391 .0872394 33.03 0.000 2.633522 2.975707
n_off_sud | 1.377835 .0456874 9.67 0.000 1.291137 1.470355
n_off_oth | 1.70296 .0565117 16.04 0.000 1.595725 1.817403
psy_com2 | 1.049203 .040327 1.25 0.211 .973067 1.131295
dep2 | 1.032826 .0387519 0.86 0.389 .9595991 1.111641
rural2 | .936957 .052024 -1.17 0.241 .8403441 1.044677
rural3 | .8643173 .0539781 -2.33 0.020 .7647408 .9768597
porc_pobr | 1.705121 .3683867 2.47 0.014 1.116492 2.604084
susini2 | 1.095486 .0718567 1.39 0.164 .9633272 1.245777
susini3 | 1.27189 .0732154 4.18 0.000 1.136189 1.423797
susini4 | 1.156721 .0379347 4.44 0.000 1.084709 1.233513
susini5 | 1.379259 .1165185 3.81 0.000 1.168792 1.627626
ano_nac_corr | .8472875 .0067778 -20.72 0.000 .8341069 .8606764
cohab2 | .8632007 .047334 -2.68 0.007 .7752393 .9611425
cohab3 | 1.076069 .0687075 1.15 0.251 .94949 1.219522
cohab4 | .9449345 .0518912 -1.03 0.302 .8485116 1.052315
fis_com2 | 1.113481 .0326477 3.67 0.000 1.051296 1.179343
rc_x1 | .8454719 .0086735 -16.36 0.000 .8286419 .8626437
rc_x2 | .8810561 .0305183 -3.66 0.000 .8232265 .942948
rc_x3 | 1.296512 .1195251 2.82 0.005 1.082192 1.553276
_rcs1 | 2.187279 .0640248 26.74 0.000 2.065325 2.316435
_rcs2 | 1.058618 .0232069 2.60 0.009 1.014097 1.105094
_rcs3 | 1.053901 .0156999 3.52 0.000 1.023574 1.085126
_rcs4 | 1.02094 .0048462 4.37 0.000 1.011486 1.030483
_rcs_mot_egr_early1 | .8921717 .0292544 -3.48 0.001 .8366376 .9513919
_rcs_mot_egr_early2 | 1.008034 .024585 0.33 0.743 .9609817 1.05739
_rcs_mot_egr_early3 | .9792859 .0167059 -1.23 0.220 .9470843 1.012582
_rcs_mot_egr_late1 | .9172385 .0291403 -2.72 0.007 .8618664 .9761681
_rcs_mot_egr_late2 | 1.019831 .0243429 0.82 0.411 .9732181 1.068675
_rcs_mot_egr_late3 | .9772939 .0160341 -1.40 0.162 .9463676 1.009231
_cons | 1.2e+142 2.0e+143 20.33 0.000 2.5e+128 6.2e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21789.874
Iteration 1: log likelihood = -21766.795
Iteration 2: log likelihood = -21766.344
Iteration 3: log likelihood = -21766.344
Log likelihood = -21766.344 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994632 .1087918 12.66 0.000 1.792406 2.219674
mot_egr_late | 1.654776 .0780019 10.69 0.000 1.508745 1.814942
tr_mod2 | 1.1521 .0429587 3.80 0.000 1.070906 1.239451
sex_dum2 | .5920592 .0255417 -12.15 0.000 .5440564 .6442973
edad_ini_cons | .9734286 .004033 -6.50 0.000 .965556 .9813653
esc1 | 1.517147 .0833386 7.59 0.000 1.362292 1.689605
esc2 | 1.344383 .0693587 5.74 0.000 1.21509 1.487434
sus_prin2 | 1.194334 .0708242 2.99 0.003 1.063284 1.341536
sus_prin3 | 1.715332 .0822138 11.26 0.000 1.561533 1.884279
sus_prin4 | 1.141844 .079264 1.91 0.056 .9965949 1.308264
sus_prin5 | 1.354744 .1839286 2.24 0.025 1.038228 1.767753
fr_cons_sus_prin2 | .9775372 .0969693 -0.23 0.819 .8048152 1.187327
fr_cons_sus_prin3 | .9961934 .0799658 -0.05 0.962 .8511705 1.165925
fr_cons_sus_prin4 | 1.038314 .0863249 0.45 0.651 .8821863 1.222073
fr_cons_sus_prin5 | 1.089413 .0866154 1.08 0.281 .9322154 1.273117
cond_ocu2 | 1.088621 .0671443 1.38 0.169 .9646637 1.228506
cond_ocu3 | 1.141746 .2794953 0.54 0.588 .7066402 1.844763
cond_ocu4 | 1.242022 .0811122 3.32 0.001 1.092799 1.411622
cond_ocu5 | 1.3309 .1366406 2.78 0.005 1.088314 1.627558
cond_ocu6 | 1.211165 .0419875 5.53 0.000 1.131604 1.296319
policonsumo | 1.007394 .0431368 0.17 0.863 .926298 1.09559
num_hij2 | 1.136117 .0394178 3.68 0.000 1.061428 1.216062
tenviv1 | 1.017844 .114995 0.16 0.876 .8156676 1.270132
tenviv2 | 1.066736 .0801836 0.86 0.390 .9206073 1.236059
tenviv4 | 1.011325 .042024 0.27 0.786 .9322243 1.097137
tenviv5 | .9923091 .0331786 -0.23 0.817 .9293651 1.059516
mzone2 | 1.416134 .0524806 9.39 0.000 1.316921 1.522822
mzone3 | 1.544381 .0865 7.76 0.000 1.383818 1.723574
n_off_vio | 1.462114 .050365 11.03 0.000 1.366659 1.564236
n_off_acq | 2.799429 .0872405 33.03 0.000 2.633558 2.975747
n_off_sud | 1.377889 .0456883 9.67 0.000 1.291189 1.47041
n_off_oth | 1.703074 .0565162 16.04 0.000 1.595829 1.817525
psy_com2 | 1.04974 .040348 1.26 0.207 .973565 1.131876
dep2 | 1.032847 .0387527 0.86 0.389 .9596191 1.111664
rural2 | .9372055 .0520375 -1.17 0.243 .8405676 1.044954
rural3 | .8642485 .0539733 -2.34 0.019 .7646807 .9767808
porc_pobr | 1.700222 .3673657 2.46 0.014 1.113236 2.596714
susini2 | 1.09575 .0718739 1.39 0.163 .963559 1.246076
susini3 | 1.271979 .0732206 4.18 0.000 1.136269 1.423898
susini4 | 1.156745 .0379349 4.44 0.000 1.084733 1.233538
susini5 | 1.379171 .1165123 3.81 0.000 1.168715 1.627524
ano_nac_corr | .84732 .0067779 -20.71 0.000 .834139 .8607091
cohab2 | .8632115 .0473359 -2.68 0.007 .7752467 .9611573
cohab3 | 1.075965 .0687023 1.15 0.252 .9493962 1.219408
cohab4 | .9449925 .0518956 -1.03 0.303 .8485617 1.052382
fis_com2 | 1.113231 .0326403 3.66 0.000 1.051061 1.179079
rc_x1 | .8455241 .0086736 -16.36 0.000 .8286938 .8626961
rc_x2 | .8809886 .0305149 -3.66 0.000 .8231653 .9428736
rc_x3 | 1.296724 .1195411 2.82 0.005 1.082375 1.553522
_rcs1 | 2.18319 .0636297 26.79 0.000 2.061973 2.311533
_rcs2 | 1.062441 .024715 2.60 0.009 1.015088 1.112003
_rcs3 | 1.038737 .0173837 2.27 0.023 1.005218 1.073373
_rcs4 | 1.033573 .0115422 2.96 0.003 1.011196 1.056445
_rcs_mot_egr_early1 | .8938248 .0292197 -3.43 0.001 .8383513 .9529689
_rcs_mot_egr_early2 | 1.005162 .0257659 0.20 0.841 .9559093 1.056952
_rcs_mot_egr_early3 | .9977142 .0187058 -0.12 0.903 .9617169 1.035059
_rcs_mot_egr_early4 | .9764995 .0124361 -1.87 0.062 .9524269 1.00118
_rcs_mot_egr_late1 | .9192805 .0290991 -2.66 0.008 .8639806 .9781201
_rcs_mot_egr_late2 | 1.017153 .0256873 0.67 0.501 .9680321 1.068766
_rcs_mot_egr_late3 | .9921635 .0180688 -0.43 0.666 .9573739 1.028217
_rcs_mot_egr_late4 | .9848318 .0120043 -1.25 0.210 .9615826 1.008643
_cons | 1.1e+142 1.8e+143 20.32 0.000 2.3e+128 5.7e+155
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21776.089
Iteration 1: log likelihood = -21764.985
Iteration 2: log likelihood = -21764.889
Iteration 3: log likelihood = -21764.889
Log likelihood = -21764.889 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996003 .1088834 12.67 0.000 1.793608 2.221237
mot_egr_late | 1.654788 .07802 10.68 0.000 1.508724 1.814993
tr_mod2 | 1.152066 .0429552 3.80 0.000 1.070878 1.239409
sex_dum2 | .5922071 .0255479 -12.14 0.000 .5441927 .6444579
edad_ini_cons | .9734251 .004033 -6.50 0.000 .9655526 .9813619
esc1 | 1.517071 .0833356 7.59 0.000 1.362221 1.689522
esc2 | 1.344332 .0693564 5.74 0.000 1.215043 1.487378
sus_prin2 | 1.194474 .0708323 3.00 0.003 1.063409 1.341692
sus_prin3 | 1.715364 .0822136 11.26 0.000 1.561565 1.884311
sus_prin4 | 1.142215 .0792905 1.92 0.055 .9969168 1.308689
sus_prin5 | 1.354824 .1839403 2.24 0.025 1.038288 1.76786
fr_cons_sus_prin2 | .9774707 .0969629 -0.23 0.818 .8047602 1.187247
fr_cons_sus_prin3 | .9961007 .0799585 -0.05 0.961 .851091 1.165817
fr_cons_sus_prin4 | 1.038137 .0863101 0.45 0.653 .8820364 1.221865
fr_cons_sus_prin5 | 1.089331 .086609 1.08 0.282 .9321454 1.273022
cond_ocu2 | 1.088339 .0671262 1.37 0.170 .9644155 1.228186
cond_ocu3 | 1.142672 .2797199 0.54 0.586 .7072164 1.846253
cond_ocu4 | 1.241987 .0811057 3.32 0.001 1.092775 1.411572
cond_ocu5 | 1.331301 .1366789 2.79 0.005 1.088647 1.628042
cond_ocu6 | 1.211378 .0419939 5.53 0.000 1.131805 1.296545
policonsumo | 1.007426 .0431374 0.17 0.863 .9263289 1.095623
num_hij2 | 1.136173 .0394206 3.68 0.000 1.061479 1.216124
tenviv1 | 1.018371 .1150536 0.16 0.872 .8160916 1.270788
tenviv2 | 1.066892 .0801975 0.86 0.389 .9207386 1.236246
tenviv4 | 1.011825 .0420454 0.28 0.777 .9326844 1.097682
tenviv5 | .9926123 .0331887 -0.22 0.824 .9296493 1.05984
mzone2 | 1.416141 .0524814 9.39 0.000 1.316927 1.522831
mzone3 | 1.54476 .0865241 7.76 0.000 1.384153 1.724003
n_off_vio | 1.462051 .0503595 11.03 0.000 1.366606 1.564162
n_off_acq | 2.798507 .0872075 33.02 0.000 2.632698 2.974758
n_off_sud | 1.37764 .0456787 9.66 0.000 1.290959 1.470142
n_off_oth | 1.702857 .0565044 16.04 0.000 1.595634 1.817284
psy_com2 | 1.049314 .0403356 1.25 0.210 .9731624 1.131424
dep2 | 1.03279 .038751 0.86 0.390 .9595652 1.111603
rural2 | .9369886 .0520261 -1.17 0.241 .840372 1.044713
rural3 | .8644345 .0539861 -2.33 0.020 .7648433 .9769937
porc_pobr | 1.705543 .3684855 2.47 0.013 1.116758 2.604751
susini2 | 1.096193 .0719045 1.40 0.161 .9639456 1.246583
susini3 | 1.27192 .0732176 4.18 0.000 1.136216 1.423833
susini4 | 1.156482 .0379271 4.43 0.000 1.084485 1.233259
susini5 | 1.378934 .1164924 3.80 0.000 1.168514 1.627245
ano_nac_corr | .8469905 .0067764 -20.76 0.000 .8338126 .8603768
cohab2 | .8631516 .0473325 -2.68 0.007 .7751931 .9610905
cohab3 | 1.07597 .0687024 1.15 0.251 .9494006 1.219413
cohab4 | .9448764 .0518892 -1.03 0.302 .8484575 1.052252
fis_com2 | 1.113313 .0326436 3.66 0.000 1.051137 1.179168
rc_x1 | .8451943 .0086712 -16.39 0.000 .8283688 .8623616
rc_x2 | .8809696 .0305153 -3.66 0.000 .8231458 .9428554
rc_x3 | 1.296853 .1195572 2.82 0.005 1.082476 1.553686
_rcs1 | 2.181498 .0637555 26.69 0.000 2.060051 2.310104
_rcs2 | 1.060983 .0236339 2.66 0.008 1.015657 1.10833
_rcs3 | 1.049066 .0172289 2.92 0.004 1.015835 1.083383
_rcs4 | 1.016086 .009962 1.63 0.104 .9967476 1.0358
_rcs_mot_egr_early1 | .8949995 .0293364 -3.38 0.001 .8393093 .954385
_rcs_mot_egr_early2 | 1.00704 .0249469 0.28 0.777 .9593132 1.057142
_rcs_mot_egr_early3 | .9905852 .0182321 -0.51 0.607 .9554879 1.026972
_rcs_mot_egr_early4 | .9904703 .0112046 -0.85 0.397 .9687514 1.012676
_rcs_mot_egr_early5 | 1.004084 .0059236 0.69 0.490 .9925409 1.015762
_rcs_mot_egr_late1 | .9200743 .0292002 -2.62 0.009 .8645866 .9791231
_rcs_mot_egr_late2 | 1.018711 .0248833 0.76 0.448 .9710895 1.068667
_rcs_mot_egr_late3 | .9837786 .0177403 -0.91 0.364 .9496156 1.019171
_rcs_mot_egr_late4 | .9973637 .0107092 -0.25 0.806 .9765934 1.018576
_rcs_mot_egr_late5 | 1.00697 .005186 1.35 0.177 .9968566 1.017186
_cons | 2.5e+142 4.0e+143 20.37 0.000 4.9e+128 1.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21778.105
Iteration 1: log likelihood = -21759.515
Iteration 2: log likelihood = -21759.204
Iteration 3: log likelihood = -21759.204
Log likelihood = -21759.204 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996491 .1088993 12.68 0.000 1.794065 2.221756
mot_egr_late | 1.654604 .0779993 10.68 0.000 1.508578 1.814765
tr_mod2 | 1.1521 .0429555 3.80 0.000 1.070911 1.239444
sex_dum2 | .5923795 .0255548 -12.14 0.000 .544352 .6446445
edad_ini_cons | .9734065 .0040332 -6.51 0.000 .9655337 .9813436
esc1 | 1.516928 .0833277 7.59 0.000 1.362093 1.689363
esc2 | 1.344115 .0693448 5.73 0.000 1.214848 1.487138
sus_prin2 | 1.195253 .0708824 3.01 0.003 1.064096 1.342576
sus_prin3 | 1.716357 .0822664 11.27 0.000 1.56246 1.885412
sus_prin4 | 1.14286 .0793369 1.92 0.054 .9974776 1.309433
sus_prin5 | 1.355367 .1840165 2.24 0.025 1.0387 1.768575
fr_cons_sus_prin2 | .9775258 .0969683 -0.23 0.819 .8048056 1.187314
fr_cons_sus_prin3 | .9959769 .0799484 -0.05 0.960 .8509855 1.165672
fr_cons_sus_prin4 | 1.038243 .0863191 0.45 0.652 .882126 1.22199
fr_cons_sus_prin5 | 1.08919 .0865991 1.07 0.283 .9320226 1.27286
cond_ocu2 | 1.087853 .0670965 1.37 0.172 .9639841 1.227639
cond_ocu3 | 1.144216 .2800967 0.55 0.582 .7081727 1.848743
cond_ocu4 | 1.240977 .081036 3.31 0.001 1.091893 1.410416
cond_ocu5 | 1.332379 .1367914 2.80 0.005 1.089525 1.629364
cond_ocu6 | 1.211571 .0420002 5.54 0.000 1.131986 1.296751
policonsumo | 1.007393 .0431355 0.17 0.863 .9262998 1.095586
num_hij2 | 1.136128 .039419 3.68 0.000 1.061436 1.216075
tenviv1 | 1.018262 .1150439 0.16 0.873 .8160003 1.270659
tenviv2 | 1.067978 .0802813 0.87 0.382 .9216718 1.237508
tenviv4 | 1.012103 .042057 0.29 0.772 .9329403 1.097983
tenviv5 | .9928015 .0331947 -0.22 0.829 .929827 1.060041
mzone2 | 1.416335 .0524907 9.39 0.000 1.317103 1.523044
mzone3 | 1.544801 .0865296 7.76 0.000 1.384184 1.724055
n_off_vio | 1.461873 .0503459 11.03 0.000 1.366453 1.563956
n_off_acq | 2.797146 .0871497 33.01 0.000 2.631446 2.973279
n_off_sud | 1.377071 .0456555 9.65 0.000 1.290433 1.469526
n_off_oth | 1.702562 .0564846 16.04 0.000 1.595377 1.816948
psy_com2 | 1.049211 .0403347 1.25 0.211 .9730607 1.13132
dep2 | 1.032737 .0387498 0.86 0.391 .9595144 1.111548
rural2 | .9370915 .0520302 -1.17 0.242 .840467 1.044824
rural3 | .864637 .0539995 -2.33 0.020 .765021 .9772243
porc_pobr | 1.70515 .3683881 2.47 0.014 1.116517 2.604113
susini2 | 1.097533 .0719953 1.42 0.156 .9651189 1.248113
susini3 | 1.271829 .0732129 4.18 0.000 1.136134 1.423732
susini4 | 1.155852 .0379063 4.42 0.000 1.083894 1.232587
susini5 | 1.378649 .1164692 3.80 0.000 1.168272 1.626911
ano_nac_corr | .8466993 .0067754 -20.80 0.000 .8335233 .8600836
cohab2 | .8632731 .0473391 -2.68 0.007 .7753024 .9612256
cohab3 | 1.075767 .0686897 1.14 0.253 .9492213 1.219183
cohab4 | .9448084 .0518846 -1.03 0.301 .848398 1.052175
fis_com2 | 1.11287 .0326282 3.65 0.000 1.050723 1.178693
rc_x1 | .8449153 .0086691 -16.42 0.000 .8280938 .8620785
rc_x2 | .8808478 .0305105 -3.66 0.000 .8230331 .9427238
rc_x3 | 1.297318 .1195994 2.82 0.005 1.082865 1.554242
_rcs1 | 2.18116 .0635115 26.78 0.000 2.060165 2.309261
_rcs2 | 1.063339 .0247961 2.63 0.008 1.015834 1.113067
_rcs3 | 1.037796 .0172601 2.23 0.026 1.004512 1.072182
_rcs4 | 1.032597 .0113391 2.92 0.003 1.01061 1.055062
_rcs_mot_egr_early1 | .8946956 .0292292 -3.41 0.001 .8392029 .9538577
_rcs_mot_egr_early2 | 1.004009 .0259297 0.15 0.877 .9544526 1.056139
_rcs_mot_egr_early3 | 1.004471 .0184026 0.24 0.808 .9690425 1.041195
_rcs_mot_egr_early4 | .9818927 .0107573 -1.67 0.095 .9610335 1.003205
_rcs_mot_egr_early5 | .9880518 .0081886 -1.45 0.147 .9721321 1.004232
_rcs_mot_egr_early6 | 1.007507 .0038607 1.95 0.051 .9999689 1.015103
_rcs_mot_egr_late1 | .9199898 .0290943 -2.64 0.008 .8646974 .9788179
_rcs_mot_egr_late2 | 1.016417 .0259367 0.64 0.523 .966832 1.068544
_rcs_mot_egr_late3 | .9966545 .017909 -0.19 0.852 .9621644 1.032381
_rcs_mot_egr_late4 | .9870928 .0102692 -1.25 0.212 .9671694 1.007427
_rcs_mot_egr_late5 | .9927719 .0077479 -0.93 0.353 .9777017 1.008074
_rcs_mot_egr_late6 | 1.006873 .0030986 2.23 0.026 1.000818 1.012964
_cons | 5.0e+142 8.0e+143 20.41 0.000 9.8e+128 2.5e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21775.55
Iteration 1: log likelihood = -21759.301
Iteration 2: log likelihood = -21759.042
Iteration 3: log likelihood = -21759.042
Log likelihood = -21759.042 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996662 .1089116 12.68 0.000 1.794214 2.221953
mot_egr_late | 1.654649 .0780032 10.68 0.000 1.508615 1.814818
tr_mod2 | 1.152095 .0429553 3.80 0.000 1.070907 1.239439
sex_dum2 | .5924469 .0255575 -12.14 0.000 .5444143 .6447174
edad_ini_cons | .9734015 .0040332 -6.51 0.000 .9655285 .9813386
esc1 | 1.516986 .0833306 7.59 0.000 1.362146 1.689428
esc2 | 1.344141 .069346 5.73 0.000 1.214871 1.487166
sus_prin2 | 1.195383 .0708909 3.01 0.003 1.06421 1.342723
sus_prin3 | 1.716587 .0822787 11.27 0.000 1.562667 1.885668
sus_prin4 | 1.143074 .0793523 1.93 0.054 .9976634 1.309679
sus_prin5 | 1.355638 .1840538 2.24 0.025 1.038907 1.76893
fr_cons_sus_prin2 | .9775231 .096968 -0.23 0.819 .8048034 1.18731
fr_cons_sus_prin3 | .9959219 .079944 -0.05 0.959 .8509385 1.165608
fr_cons_sus_prin4 | 1.038208 .0863163 0.45 0.652 .8820961 1.221949
fr_cons_sus_prin5 | 1.089083 .086591 1.07 0.283 .9319301 1.272736
cond_ocu2 | 1.087763 .0670908 1.36 0.173 .9639044 1.227537
cond_ocu3 | 1.144762 .2802301 0.55 0.581 .7085113 1.849625
cond_ocu4 | 1.240726 .081018 3.30 0.001 1.091675 1.410128
cond_ocu5 | 1.332584 .136812 2.80 0.005 1.089694 1.629614
cond_ocu6 | 1.211703 .0420047 5.54 0.000 1.13211 1.296892
policonsumo | 1.007296 .0431311 0.17 0.865 .9262102 1.09548
num_hij2 | 1.136141 .0394198 3.68 0.000 1.061448 1.21609
tenviv1 | 1.01834 .1150525 0.16 0.872 .8160627 1.270755
tenviv2 | 1.068247 .0803025 0.88 0.380 .9219024 1.237823
tenviv4 | 1.012223 .0420619 0.29 0.770 .9330509 1.098113
tenviv5 | .9928802 .0331973 -0.21 0.831 .9299008 1.060125
mzone2 | 1.416356 .0524923 9.39 0.000 1.317121 1.523068
mzone3 | 1.545014 .0865428 7.77 0.000 1.384373 1.724296
n_off_vio | 1.461758 .0503401 11.02 0.000 1.366349 1.563828
n_off_acq | 2.796817 .0871349 33.01 0.000 2.631145 2.97292
n_off_sud | 1.376929 .0456493 9.65 0.000 1.290302 1.469371
n_off_oth | 1.702431 .0564776 16.04 0.000 1.595259 1.816803
psy_com2 | 1.04918 .0403357 1.25 0.212 .9730283 1.131291
dep2 | 1.032682 .0387478 0.86 0.391 .9594628 1.111488
rural2 | .93712 .0520314 -1.17 0.242 .8404932 1.044855
rural3 | .8646691 .0540018 -2.33 0.020 .7650491 .9772611
porc_pobr | 1.704956 .3683354 2.47 0.014 1.116403 2.603785
susini2 | 1.09794 .0720232 1.42 0.154 .9654748 1.248579
susini3 | 1.271612 .0732013 4.17 0.000 1.135938 1.423491
susini4 | 1.155675 .0379007 4.41 0.000 1.083728 1.232399
susini5 | 1.378425 .1164504 3.80 0.000 1.168081 1.626647
ano_nac_corr | .8465814 .0067749 -20.81 0.000 .8334065 .8599647
cohab2 | .8632983 .0473402 -2.68 0.007 .7753254 .9612532
cohab3 | 1.075832 .0686937 1.14 0.252 .9492788 1.219257
cohab4 | .9447856 .0518833 -1.03 0.301 .8483776 1.052149
fis_com2 | 1.112809 .0326261 3.65 0.000 1.050665 1.178628
rc_x1 | .8448068 .0086683 -16.44 0.000 .8279869 .8619683
rc_x2 | .8807931 .0305087 -3.66 0.000 .8229818 .9426655
rc_x3 | 1.297525 .1196191 2.83 0.005 1.083037 1.554491
_rcs1 | 2.181018 .0635272 26.77 0.000 2.059995 2.309152
_rcs2 | 1.062978 .0246672 2.63 0.008 1.015714 1.112441
_rcs3 | 1.039288 .0173765 2.30 0.021 1.005782 1.073909
_rcs4 | 1.031207 .0114146 2.78 0.005 1.009076 1.053824
_rcs_mot_egr_early1 | .8949034 .0292452 -3.40 0.001 .839381 .9540984
_rcs_mot_egr_early2 | 1.004387 .0258705 0.17 0.865 .9549408 1.056394
_rcs_mot_egr_early3 | 1.005273 .0180966 0.29 0.770 .9704226 1.041375
_rcs_mot_egr_early4 | .9838708 .0104234 -1.53 0.125 .963652 1.004514
_rcs_mot_egr_early5 | .9858853 .0090172 -1.55 0.120 .9683694 1.003718
_rcs_mot_egr_early6 | 1.001445 .0049429 0.29 0.770 .9918043 1.01118
_rcs_mot_egr_early7 | 1.006504 .0032213 2.03 0.043 1.00021 1.012838
_rcs_mot_egr_late1 | .9199927 .0291033 -2.64 0.008 .8646836 .9788395
_rcs_mot_egr_late2 | 1.017208 .0259358 0.67 0.503 .9676236 1.069332
_rcs_mot_egr_late3 | .9955516 .0175987 -0.25 0.801 .9616496 1.030649
_rcs_mot_egr_late4 | .989871 .00992 -1.02 0.310 .9706178 1.009506
_rcs_mot_egr_late5 | .9896894 .0086004 -1.19 0.233 .9729755 1.00669
_rcs_mot_egr_late6 | 1.003294 .0043167 0.76 0.445 .9948693 1.011791
_rcs_mot_egr_late7 | 1.005835 .0025281 2.31 0.021 1.000892 1.010802
_cons | 6.6e+142 1.1e+144 20.42 0.000 1.3e+129 3.4e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21773.459
Iteration 1: log likelihood = -21763.689
Iteration 2: log likelihood = -21763.63
Iteration 3: log likelihood = -21763.63
Log likelihood = -21763.63 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994102 .1086908 12.66 0.000 1.792057 2.218928
mot_egr_late | 1.651296 .077793 10.65 0.000 1.505652 1.811028
tr_mod2 | 1.152051 .0429522 3.80 0.000 1.070869 1.239388
sex_dum2 | .5923059 .0255517 -12.14 0.000 .5442842 .6445645
edad_ini_cons | .9734192 .004033 -6.50 0.000 .9655466 .981356
esc1 | 1.517013 .0833324 7.59 0.000 1.362169 1.689458
esc2 | 1.344104 .0693444 5.73 0.000 1.214838 1.487126
sus_prin2 | 1.194752 .0708493 3.00 0.003 1.063655 1.342005
sus_prin3 | 1.715622 .0822262 11.26 0.000 1.561799 1.884594
sus_prin4 | 1.142472 .0793078 1.92 0.055 .9971417 1.308983
sus_prin5 | 1.354482 .1838897 2.23 0.025 1.038032 1.767404
fr_cons_sus_prin2 | .9773744 .0969531 -0.23 0.818 .8046813 1.187129
fr_cons_sus_prin3 | .9958577 .0799389 -0.05 0.959 .8508836 1.165533
fr_cons_sus_prin4 | 1.03809 .0863065 0.45 0.653 .8819957 1.22181
fr_cons_sus_prin5 | 1.089192 .0865993 1.07 0.283 .9320242 1.272863
cond_ocu2 | 1.08814 .0671133 1.37 0.171 .9642396 1.22796
cond_ocu3 | 1.142493 .2796717 0.54 0.586 .7071106 1.84595
cond_ocu4 | 1.241276 .0810581 3.31 0.001 1.092152 1.410762
cond_ocu5 | 1.332155 .1367619 2.79 0.005 1.089353 1.629075
cond_ocu6 | 1.211583 .0420001 5.54 0.000 1.131998 1.296762
policonsumo | 1.007385 .0431348 0.17 0.864 .9262924 1.095576
num_hij2 | 1.136238 .0394233 3.68 0.000 1.061538 1.216194
tenviv1 | 1.018512 .115066 0.16 0.871 .8162104 1.270956
tenviv2 | 1.067376 .080234 0.87 0.386 .9211556 1.236806
tenviv4 | 1.011932 .042049 0.29 0.775 .9327847 1.097796
tenviv5 | .9926197 .0331882 -0.22 0.825 .9296575 1.059846
mzone2 | 1.416165 .0524821 9.39 0.000 1.316949 1.522856
mzone3 | 1.544396 .0865047 7.76 0.000 1.383825 1.723599
n_off_vio | 1.462003 .0503532 11.03 0.000 1.36657 1.564101
n_off_acq | 2.797747 .0871775 33.02 0.000 2.631995 2.973937
n_off_sud | 1.377378 .0456686 9.66 0.000 1.290716 1.469859
n_off_oth | 1.702587 .0564902 16.04 0.000 1.595392 1.816985
psy_com2 | 1.048537 .0402954 1.23 0.217 .9724609 1.130565
dep2 | 1.032758 .0387498 0.86 0.390 .9595351 1.111569
rural2 | .9369758 .0520248 -1.17 0.241 .8403616 1.044698
rural3 | .8647198 .0540038 -2.33 0.020 .7650958 .977316
porc_pobr | 1.709887 .3693125 2.48 0.013 1.119746 2.611052
susini2 | 1.096545 .0719278 1.41 0.160 .9642551 1.246984
susini3 | 1.271521 .0731949 4.17 0.000 1.135859 1.423386
susini4 | 1.156171 .0379171 4.42 0.000 1.084193 1.232927
susini5 | 1.378934 .1164905 3.80 0.000 1.168518 1.627241
ano_nac_corr | .8467815 .0067747 -20.79 0.000 .833607 .8601643
cohab2 | .8633183 .0473393 -2.68 0.007 .775347 .961271
cohab3 | 1.076089 .0687066 1.15 0.251 .9495113 1.21954
cohab4 | .9449197 .0518895 -1.03 0.302 .8485001 1.052296
fis_com2 | 1.113255 .0326388 3.66 0.000 1.051088 1.1791
rc_x1 | .8449858 .0086692 -16.42 0.000 .8281642 .8621491
rc_x2 | .8808934 .030513 -3.66 0.000 .8230739 .9427746
rc_x3 | 1.29721 .1195913 2.82 0.005 1.082772 1.554116
_rcs1 | 2.177527 .0587084 28.86 0.000 2.065448 2.295688
_rcs2 | 1.072413 .0074958 10.00 0.000 1.057822 1.087205
_rcs3 | 1.035318 .0055303 6.50 0.000 1.024535 1.046214
_rcs4 | 1.017659 .003821 4.66 0.000 1.010197 1.025175
_rcs5 | 1.012385 .0027141 4.59 0.000 1.00708 1.017719
_rcs_mot_egr_early1 | .8980823 .0272228 -3.55 0.000 .8462807 .9530548
_rcs_mot_egr_late1 | .9204822 .0268054 -2.85 0.004 .8694159 .974548
_cons | 4.1e+142 6.6e+143 20.40 0.000 8.1e+128 2.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.248
Iteration 1: log likelihood = -21763.406
Iteration 2: log likelihood = -21763.334
Iteration 3: log likelihood = -21763.334
Log likelihood = -21763.334 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993113 .1086829 12.65 0.000 1.791087 2.217928
mot_egr_late | 1.651782 .0778349 10.65 0.000 1.506061 1.811602
tr_mod2 | 1.151836 .0429456 3.79 0.000 1.070666 1.23916
sex_dum2 | .5923121 .0255521 -12.14 0.000 .5442897 .6445714
edad_ini_cons | .9734233 .004033 -6.50 0.000 .9655508 .9813601
esc1 | 1.517016 .0833327 7.59 0.000 1.362173 1.689462
esc2 | 1.34409 .0693438 5.73 0.000 1.214824 1.48711
sus_prin2 | 1.19476 .0708503 3.00 0.003 1.063662 1.342016
sus_prin3 | 1.715668 .0822282 11.26 0.000 1.561842 1.884645
sus_prin4 | 1.142546 .0793133 1.92 0.055 .9972059 1.309068
sus_prin5 | 1.35468 .1839198 2.24 0.025 1.038179 1.76767
fr_cons_sus_prin2 | .9774056 .0969563 -0.23 0.818 .8047067 1.187167
fr_cons_sus_prin3 | .9959135 .0799435 -0.05 0.959 .8509312 1.165598
fr_cons_sus_prin4 | 1.038094 .0863066 0.45 0.653 .8819994 1.221814
fr_cons_sus_prin5 | 1.089227 .0866014 1.07 0.282 .9320552 1.272902
cond_ocu2 | 1.088043 .0671076 1.37 0.171 .9641537 1.227852
cond_ocu3 | 1.14255 .2796867 0.54 0.586 .7071447 1.846045
cond_ocu4 | 1.241491 .081071 3.31 0.001 1.092343 1.411004
cond_ocu5 | 1.332525 .1368011 2.80 0.005 1.089653 1.62953
cond_ocu6 | 1.211575 .0419997 5.54 0.000 1.131991 1.296754
policonsumo | 1.007396 .0431354 0.17 0.863 .9263022 1.095588
num_hij2 | 1.136256 .039424 3.68 0.000 1.061555 1.216213
tenviv1 | 1.018578 .1150738 0.16 0.871 .8162625 1.271039
tenviv2 | 1.067251 .0802249 0.87 0.387 .9210477 1.236662
tenviv4 | 1.012039 .0420537 0.29 0.773 .932882 1.097912
tenviv5 | .9927253 .0331919 -0.22 0.827 .9297561 1.059959
mzone2 | 1.416276 .0524861 9.39 0.000 1.317052 1.522975
mzone3 | 1.544647 .0865184 7.76 0.000 1.384051 1.723878
n_off_vio | 1.462033 .0503547 11.03 0.000 1.366597 1.564134
n_off_acq | 2.797829 .0871797 33.02 0.000 2.632073 2.974024
n_off_sud | 1.377361 .0456676 9.66 0.000 1.2907 1.46984
n_off_oth | 1.702654 .0564924 16.04 0.000 1.595454 1.817056
psy_com2 | 1.048974 .0403176 1.24 0.214 .9728561 1.131048
dep2 | 1.032752 .0387498 0.86 0.390 .9595288 1.111562
rural2 | .9369146 .0520214 -1.17 0.241 .8403065 1.04463
rural3 | .864563 .0539952 -2.33 0.020 .764955 .9771414
porc_pobr | 1.708592 .3690517 2.48 0.013 1.118874 2.609131
susini2 | 1.09662 .0719336 1.41 0.160 .9643192 1.247071
susini3 | 1.271657 .0732029 4.17 0.000 1.13598 1.423539
susini4 | 1.156107 .0379152 4.42 0.000 1.084133 1.23286
susini5 | 1.378848 .1164839 3.80 0.000 1.168443 1.627141
ano_nac_corr | .8467972 .0067758 -20.78 0.000 .8336205 .8601822
cohab2 | .8631328 .0473297 -2.68 0.007 .7751793 .9610658
cohab3 | 1.075819 .0686904 1.14 0.252 .9492711 1.219236
cohab4 | .9447491 .0518802 -1.03 0.301 .8483468 1.052106
fis_com2 | 1.113204 .0326385 3.66 0.000 1.051037 1.179048
rc_x1 | .8449931 .0086701 -16.41 0.000 .8281697 .8621582
rc_x2 | .8809212 .0305139 -3.66 0.000 .8231 .9428042
rc_x3 | 1.297132 .1195843 2.82 0.005 1.082706 1.554023
_rcs1 | 2.170417 .062481 26.92 0.000 2.051347 2.296398
_rcs2 | 1.065503 .0234552 2.88 0.004 1.020509 1.11248
_rcs3 | 1.034281 .0061853 5.64 0.000 1.022228 1.046475
_rcs4 | 1.017569 .0038292 4.63 0.000 1.010091 1.025102
_rcs5 | 1.012383 .002714 4.59 0.000 1.007077 1.017716
_rcs_mot_egr_early1 | .8996731 .0290211 -3.28 0.001 .8445535 .9583901
_rcs_mot_egr_early2 | 1.001443 .0245812 0.06 0.953 .9544053 1.050799
_rcs_mot_egr_late1 | .9253077 .0289162 -2.48 0.013 .8703337 .9837541
_rcs_mot_egr_late2 | 1.011543 .024204 0.48 0.631 .9651989 1.060112
_cons | 4.0e+142 6.4e+143 20.39 0.000 7.8e+128 2.0e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21773.829
Iteration 1: log likelihood = -21763.019
Iteration 2: log likelihood = -21762.913
Iteration 3: log likelihood = -21762.913
Log likelihood = -21762.913 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994881 .1087944 12.66 0.000 1.792648 2.219927
mot_egr_late | 1.653577 .0779376 10.67 0.000 1.507665 1.81361
tr_mod2 | 1.151947 .0429501 3.79 0.000 1.070768 1.239279
sex_dum2 | .592293 .0255512 -12.14 0.000 .5442724 .6445505
edad_ini_cons | .9734156 .0040331 -6.50 0.000 .9655429 .9813525
esc1 | 1.516991 .0833307 7.59 0.000 1.362151 1.689432
esc2 | 1.344065 .0693423 5.73 0.000 1.214802 1.487083
sus_prin2 | 1.194978 .0708648 3.00 0.003 1.063854 1.342265
sus_prin3 | 1.715927 .0822441 11.27 0.000 1.562071 1.884937
sus_prin4 | 1.14263 .0793201 1.92 0.055 .9972777 1.309167
sus_prin5 | 1.355195 .1839922 2.24 0.025 1.03857 1.768348
fr_cons_sus_prin2 | .9774171 .0969574 -0.23 0.818 .8047164 1.187181
fr_cons_sus_prin3 | .995897 .0799422 -0.05 0.959 .850917 1.165579
fr_cons_sus_prin4 | 1.038149 .0863113 0.45 0.652 .8820456 1.221879
fr_cons_sus_prin5 | 1.089239 .0866027 1.08 0.282 .9320647 1.272917
cond_ocu2 | 1.087984 .0671045 1.37 0.172 .9641002 1.227786
cond_ocu3 | 1.143038 .2798087 0.55 0.585 .7074431 1.846841
cond_ocu4 | 1.241182 .0810523 3.31 0.001 1.092068 1.410656
cond_ocu5 | 1.33244 .1367941 2.80 0.005 1.089581 1.62943
cond_ocu6 | 1.211551 .0419995 5.54 0.000 1.131968 1.29673
policonsumo | 1.007449 .0431382 0.17 0.862 .9263506 1.095648
num_hij2 | 1.136201 .0394217 3.68 0.000 1.061504 1.216153
tenviv1 | 1.01841 .1150567 0.16 0.872 .816125 1.270834
tenviv2 | 1.06741 .0802375 0.87 0.385 .9211832 1.236848
tenviv4 | 1.011934 .0420496 0.29 0.775 .9327852 1.097799
tenviv5 | .9926282 .0331889 -0.22 0.825 .9296647 1.059856
mzone2 | 1.416313 .0524882 9.39 0.000 1.317086 1.523017
mzone3 | 1.544382 .0865043 7.76 0.000 1.383812 1.723584
n_off_vio | 1.462017 .0503529 11.03 0.000 1.366584 1.564114
n_off_acq | 2.797731 .0871731 33.02 0.000 2.631987 2.973912
n_off_sud | 1.37724 .0456631 9.65 0.000 1.290588 1.46971
n_off_oth | 1.702622 .0564899 16.04 0.000 1.595427 1.817019
psy_com2 | 1.04899 .0403222 1.24 0.213 .9728636 1.131073
dep2 | 1.03275 .03875 0.86 0.390 .9595272 1.111561
rural2 | .9370376 .0520284 -1.17 0.242 .8404166 1.044767
rural3 | .8646662 .0540013 -2.33 0.020 .7650469 .9772572
porc_pobr | 1.706777 .3687075 2.47 0.013 1.117623 2.606502
susini2 | 1.096831 .0719483 1.41 0.159 .9645041 1.247314
susini3 | 1.271652 .0732028 4.17 0.000 1.135975 1.423534
susini4 | 1.156065 .0379137 4.42 0.000 1.084093 1.232815
susini5 | 1.378932 .1164921 3.80 0.000 1.168513 1.627243
ano_nac_corr | .8467723 .0067757 -20.79 0.000 .8335957 .8601571
cohab2 | .8631804 .0473327 -2.68 0.007 .7752214 .9611196
cohab3 | 1.075846 .068693 1.14 0.252 .9492937 1.219269
cohab4 | .9447865 .0518823 -1.03 0.301 .8483802 1.052148
fis_com2 | 1.113029 .0326333 3.65 0.000 1.050872 1.178863
rc_x1 | .8449649 .0086699 -16.42 0.000 .828142 .8621295
rc_x2 | .8809465 .0305146 -3.66 0.000 .8231239 .942831
rc_x3 | 1.296987 .1195704 2.82 0.005 1.082586 1.553849
_rcs1 | 2.180017 .0635727 26.72 0.000 2.058911 2.308247
_rcs2 | 1.060506 .0237937 2.62 0.009 1.014882 1.108181
_rcs3 | 1.046539 .0149429 3.19 0.001 1.017657 1.07624
_rcs4 | 1.022457 .0067049 3.39 0.001 1.009399 1.035683
_rcs5 | 1.012631 .002729 4.66 0.000 1.007297 1.017994
_rcs_mot_egr_early1 | .8956418 .0292785 -3.37 0.001 .8400566 .9549049
_rcs_mot_egr_early2 | 1.004971 .0248891 0.20 0.841 .9573544 1.054956
_rcs_mot_egr_early3 | .987274 .0169831 -0.74 0.457 .9545426 1.021128
_rcs_mot_egr_late1 | .9206266 .0291493 -2.61 0.009 .8652316 .9795681
_rcs_mot_egr_late2 | 1.016378 .0246748 0.67 0.503 .9691492 1.065909
_rcs_mot_egr_late3 | .9854063 .0163726 -0.88 0.376 .9538333 1.018024
_cons | 4.2e+142 6.8e+143 20.40 0.000 8.3e+128 2.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.071
Iteration 1: log likelihood = -21759.471
Iteration 2: log likelihood = -21759.299
Iteration 3: log likelihood = -21759.299
Log likelihood = -21759.299 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.998095 .1090092 12.69 0.000 1.795467 2.22359
mot_egr_late | 1.656238 .0780912 10.70 0.000 1.510041 1.816589
tr_mod2 | 1.152111 .0429578 3.80 0.000 1.070918 1.23946
sex_dum2 | .5923245 .0255525 -12.14 0.000 .5443014 .6445847
edad_ini_cons | .9734064 .0040332 -6.51 0.000 .9655335 .9813435
esc1 | 1.516864 .0833231 7.58 0.000 1.362037 1.68929
esc2 | 1.344044 .0693408 5.73 0.000 1.214784 1.487058
sus_prin2 | 1.195388 .0708913 3.01 0.003 1.064215 1.34273
sus_prin3 | 1.716658 .0822842 11.27 0.000 1.562728 1.885751
sus_prin4 | 1.142967 .079344 1.92 0.054 .9975712 1.309555
sus_prin5 | 1.355505 .1840376 2.24 0.025 1.038802 1.768761
fr_cons_sus_prin2 | .9775746 .0969729 -0.23 0.819 .8048462 1.187372
fr_cons_sus_prin3 | .9960206 .0799517 -0.05 0.960 .8510232 1.165723
fr_cons_sus_prin4 | 1.038351 .086328 0.45 0.651 .8822175 1.222117
fr_cons_sus_prin5 | 1.089207 .0866003 1.07 0.282 .9320378 1.27288
cond_ocu2 | 1.087859 .0670974 1.37 0.172 .9639888 1.227647
cond_ocu3 | 1.144162 .2800842 0.55 0.582 .7081382 1.848658
cond_ocu4 | 1.240626 .0810156 3.30 0.001 1.09158 1.410023
cond_ocu5 | 1.332512 .1368058 2.80 0.005 1.089633 1.629528
cond_ocu6 | 1.211442 .0419967 5.53 0.000 1.131864 1.296615
policonsumo | 1.007368 .0431344 0.17 0.864 .926276 1.095558
num_hij2 | 1.136061 .0394158 3.68 0.000 1.061375 1.216001
tenviv1 | 1.018066 .115023 0.16 0.874 .8158411 1.270417
tenviv2 | 1.06808 .0802885 0.88 0.381 .9217607 1.237626
tenviv4 | 1.011849 .0420462 0.28 0.777 .9327067 1.097707
tenviv5 | .9926549 .0331897 -0.22 0.825 .9296899 1.059884
mzone2 | 1.416338 .0524907 9.39 0.000 1.317106 1.523047
mzone3 | 1.544446 .0865104 7.76 0.000 1.383865 1.723661
n_off_vio | 1.46179 .0503431 11.02 0.000 1.366376 1.563867
n_off_acq | 2.797139 .0871479 33.01 0.000 2.631443 2.973269
n_off_sud | 1.377031 .0456532 9.65 0.000 1.290397 1.46948
n_off_oth | 1.702618 .0564863 16.04 0.000 1.59543 1.817008
psy_com2 | 1.049547 .0403443 1.26 0.208 .9733786 1.131675
dep2 | 1.032745 .0387503 0.86 0.391 .9595208 1.111556
rural2 | .9374703 .0520511 -1.16 0.245 .840807 1.045246
rural3 | .8647734 .0540074 -2.33 0.020 .7651429 .977377
porc_pobr | 1.699299 .3671297 2.45 0.014 1.112679 2.595195
susini2 | 1.097728 .072008 1.42 0.155 .9652913 1.248336
susini3 | 1.271715 .0732065 4.18 0.000 1.136031 1.423604
susini4 | 1.155814 .0379046 4.42 0.000 1.083859 1.232545
susini5 | 1.37883 .1164851 3.80 0.000 1.168423 1.627126
ano_nac_corr | .8467049 .0067751 -20.80 0.000 .8335295 .8600885
cohab2 | .8632417 .0473373 -2.68 0.007 .7752742 .9611905
cohab3 | 1.075633 .0686812 1.14 0.254 .9491028 1.219032
cohab4 | .9448136 .0518845 -1.03 0.301 .8484033 1.05218
fis_com2 | 1.112478 .0326151 3.64 0.000 1.050355 1.178274
rc_x1 | .8449194 .0086689 -16.42 0.000 .8280984 .8620821
rc_x2 | .880844 .0305096 -3.66 0.000 .8230308 .9427181
rc_x3 | 1.297317 .1195963 2.82 0.005 1.082869 1.554233
_rcs1 | 2.186186 .0636997 26.84 0.000 2.064835 2.314669
_rcs2 | 1.061308 .0251022 2.52 0.012 1.013231 1.111665
_rcs3 | 1.03152 .0167122 1.92 0.055 .9992796 1.064801
_rcs4 | 1.040997 .0099879 4.19 0.000 1.021604 1.060758
_rcs5 | 1.021386 .0044904 4.81 0.000 1.012623 1.030225
_rcs_mot_egr_early1 | .8924861 .0291787 -3.48 0.001 .8370905 .9515474
_rcs_mot_egr_early2 | 1.005821 .0260176 0.22 0.822 .9560985 1.058129
_rcs_mot_egr_early3 | 1.002218 .018293 0.12 0.903 .9669983 1.038721
_rcs_mot_egr_early4 | .9688743 .010992 -2.79 0.005 .9475681 .9906596
_rcs_mot_egr_late1 | .9177114 .0290475 -2.71 0.007 .8625093 .9764465
_rcs_mot_egr_late2 | 1.017136 .0258924 0.67 0.504 .9676331 1.069172
_rcs_mot_egr_late3 | .9965999 .0175554 -0.19 0.847 .9627791 1.031609
_rcs_mot_egr_late4 | .9766444 .0104957 -2.20 0.028 .9562884 .9974338
_cons | 4.9e+142 7.9e+143 20.41 0.000 9.7e+128 2.5e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21773.928
Iteration 1: log likelihood = -21761.227
Iteration 2: log likelihood = -21761.049
Iteration 3: log likelihood = -21761.048
Log likelihood = -21761.048 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996326 .10889 12.67 0.000 1.793917 2.221572
mot_egr_late | 1.654911 .0780133 10.69 0.000 1.508859 1.815101
tr_mod2 | 1.152113 .0429584 3.80 0.000 1.070919 1.239462
sex_dum2 | .5922715 .0255504 -12.14 0.000 .5442524 .6445273
edad_ini_cons | .9734136 .0040331 -6.50 0.000 .9655408 .9813505
esc1 | 1.51689 .0833245 7.59 0.000 1.362061 1.689318
esc2 | 1.344073 .0693425 5.73 0.000 1.21481 1.487091
sus_prin2 | 1.195218 .0708806 3.01 0.003 1.064064 1.342537
sus_prin3 | 1.716455 .0822739 11.27 0.000 1.562544 1.885527
sus_prin4 | 1.14282 .0793336 1.92 0.054 .9974429 1.309385
sus_prin5 | 1.355453 .1840298 2.24 0.025 1.038764 1.768691
fr_cons_sus_prin2 | .9775338 .0969689 -0.23 0.819 .8048125 1.187323
fr_cons_sus_prin3 | .9960411 .0799535 -0.05 0.961 .8510404 1.165747
fr_cons_sus_prin4 | 1.038311 .0863248 0.45 0.651 .8821833 1.22207
fr_cons_sus_prin5 | 1.089247 .0866033 1.08 0.282 .932072 1.272926
cond_ocu2 | 1.087991 .0671054 1.37 0.172 .9641057 1.227795
cond_ocu3 | 1.143843 .2800067 0.55 0.583 .7079402 1.848145
cond_ocu4 | 1.240829 .0810306 3.30 0.001 1.091755 1.410258
cond_ocu5 | 1.332324 .136787 2.79 0.005 1.089478 1.6293
cond_ocu6 | 1.211394 .0419952 5.53 0.000 1.131819 1.296564
policonsumo | 1.007389 .0431356 0.17 0.863 .9262952 1.095582
num_hij2 | 1.136088 .0394167 3.68 0.000 1.061401 1.216031
tenviv1 | 1.018169 .1150334 0.16 0.873 .8159259 1.270543
tenviv2 | 1.067772 .0802645 0.87 0.383 .9214967 1.237267
tenviv4 | 1.011725 .0420411 0.28 0.779 .9325923 1.097573
tenviv5 | .9925834 .0331876 -0.22 0.824 .9296224 1.059809
mzone2 | 1.4163 .0524888 9.39 0.000 1.317071 1.523004
mzone3 | 1.544381 .0865073 7.76 0.000 1.383805 1.723589
n_off_vio | 1.46187 .0503476 11.03 0.000 1.366447 1.563956
n_off_acq | 2.797577 .0871651 33.02 0.000 2.631849 2.973742
n_off_sud | 1.3772 .0456599 9.65 0.000 1.290554 1.469664
n_off_oth | 1.702714 .0564921 16.04 0.000 1.595515 1.817116
psy_com2 | 1.049569 .0403459 1.26 0.208 .973398 1.131701
dep2 | 1.032765 .0387509 0.86 0.390 .9595398 1.111577
rural2 | .9374655 .0520518 -1.16 0.245 .8408009 1.045243
rural3 | .8647358 .0540048 -2.33 0.020 .76511 .9773341
porc_pobr | 1.699096 .3671003 2.45 0.014 1.112527 2.594928
susini2 | 1.09737 .0719841 1.42 0.157 .9649769 1.247927
susini3 | 1.271655 .0732032 4.17 0.000 1.135977 1.423538
susini4 | 1.155976 .0379101 4.42 0.000 1.084011 1.232718
susini5 | 1.378818 .1164837 3.80 0.000 1.168414 1.627111
ano_nac_corr | .8467618 .0067757 -20.79 0.000 .8335853 .8601466
cohab2 | .8632043 .0473352 -2.68 0.007 .7752407 .9611487
cohab3 | 1.075694 .0686846 1.14 0.253 .9491578 1.2191
cohab4 | .9448466 .0518865 -1.03 0.302 .8484325 1.052217
fis_com2 | 1.11256 .0326188 3.64 0.000 1.05043 1.178364
rc_x1 | .8449726 .0086696 -16.42 0.000 .8281501 .8621368
rc_x2 | .8808695 .0305106 -3.66 0.000 .8230545 .9427458
rc_x3 | 1.297233 .1195884 2.82 0.005 1.082799 1.554132
_rcs1 | 2.183572 .0636584 26.79 0.000 2.062301 2.311973
_rcs2 | 1.058999 .0242038 2.51 0.012 1.012607 1.107516
_rcs3 | 1.040859 .0176917 2.36 0.018 1.006755 1.076118
_rcs4 | 1.028639 .0118038 2.46 0.014 1.005762 1.052036
_rcs5 | 1.02424 .0085575 2.87 0.004 1.007604 1.04115
_rcs_mot_egr_early1 | .8939056 .0292361 -3.43 0.001 .838402 .9530837
_rcs_mot_egr_early2 | 1.007646 .0254366 0.30 0.763 .9590045 1.058755
_rcs_mot_egr_early3 | .9975363 .0189657 -0.13 0.897 .9610483 1.03541
_rcs_mot_egr_early4 | .9839185 .0128818 -1.24 0.216 .9589918 1.009493
_rcs_mot_egr_early5 | .9852776 .0093723 -1.56 0.119 .9670785 1.003819
_rcs_mot_egr_late1 | .9190097 .029098 -2.67 0.008 .8637122 .9778476
_rcs_mot_egr_late2 | 1.01934 .025372 0.77 0.442 .9708049 1.070301
_rcs_mot_egr_late3 | .9907024 .0183686 -0.50 0.614 .955347 1.027366
_rcs_mot_egr_late4 | .9907512 .0124407 -0.74 0.459 .9666655 1.015437
_rcs_mot_egr_late5 | .9880701 .0089921 -1.32 0.187 .9706022 1.005852
_cons | 4.3e+142 6.9e+143 20.40 0.000 8.5e+128 2.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21777.609
Iteration 1: log likelihood = -21758.412
Iteration 2: log likelihood = -21758.091
Iteration 3: log likelihood = -21758.091
Log likelihood = -21758.091 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.99589 .1088611 12.67 0.000 1.793535 2.221076
mot_egr_late | 1.654179 .0779741 10.68 0.000 1.5082 1.814287
tr_mod2 | 1.152125 .042957 3.80 0.000 1.070934 1.239472
sex_dum2 | .5923773 .0255547 -12.14 0.000 .54435 .644642
edad_ini_cons | .9734043 .0040332 -6.51 0.000 .9655313 .9813414
esc1 | 1.516855 .0833233 7.58 0.000 1.362029 1.689281
esc2 | 1.344025 .06934 5.73 0.000 1.214767 1.487038
sus_prin2 | 1.19544 .0708945 3.01 0.003 1.064261 1.342788
sus_prin3 | 1.716645 .0822824 11.27 0.000 1.562718 1.885733
sus_prin4 | 1.143029 .0793488 1.93 0.054 .9976248 1.309627
sus_prin5 | 1.355525 .1840387 2.24 0.025 1.03882 1.768783
fr_cons_sus_prin2 | .9775338 .096969 -0.23 0.819 .8048124 1.187323
fr_cons_sus_prin3 | .9959776 .0799483 -0.05 0.960 .8509863 1.165672
fr_cons_sus_prin4 | 1.038317 .0863252 0.45 0.651 .8821883 1.222076
fr_cons_sus_prin5 | 1.0892 .0865998 1.07 0.283 .9320308 1.272872
cond_ocu2 | 1.087754 .0670908 1.36 0.173 .9638962 1.227528
cond_ocu3 | 1.144435 .280151 0.55 0.582 .7083081 1.8491
cond_ocu4 | 1.240723 .08102 3.30 0.001 1.091669 1.410129
cond_ocu5 | 1.332623 .1368175 2.80 0.005 1.089724 1.629665
cond_ocu6 | 1.211542 .0419995 5.54 0.000 1.131959 1.296721
policonsumo | 1.007388 .0431353 0.17 0.864 .9262949 1.095581
num_hij2 | 1.13609 .0394173 3.68 0.000 1.061402 1.216034
tenviv1 | 1.018158 .1150329 0.16 0.873 .8159152 1.27053
tenviv2 | 1.068128 .0802925 0.88 0.381 .9218019 1.237683
tenviv4 | 1.012025 .0420538 0.29 0.774 .9328686 1.097899
tenviv5 | .9927657 .0331935 -0.22 0.828 .9297935 1.060003
mzone2 | 1.416362 .0524919 9.39 0.000 1.317127 1.523073
mzone3 | 1.544599 .0865203 7.76 0.000 1.384 1.723835
n_off_vio | 1.461835 .0503433 11.03 0.000 1.36642 1.563912
n_off_acq | 2.796985 .087141 33.01 0.000 2.631302 2.973101
n_off_sud | 1.376984 .0456515 9.65 0.000 1.290353 1.46943
n_off_oth | 1.702598 .0564838 16.04 0.000 1.595414 1.816982
psy_com2 | 1.049317 .0403386 1.25 0.210 .9731595 1.131434
dep2 | 1.03274 .0387503 0.86 0.391 .9595166 1.111552
rural2 | .9372895 .0520415 -1.17 0.243 .8406441 1.045046
rural3 | .8647542 .0540068 -2.33 0.020 .7651249 .9773566
porc_pobr | 1.702518 .3678284 2.46 0.014 1.114782 2.600121
susini2 | 1.097802 .0720134 1.42 0.155 .9653552 1.248421
susini3 | 1.27175 .0732087 4.18 0.000 1.136062 1.423644
susini4 | 1.155733 .0379023 4.41 0.000 1.083783 1.23246
susini5 | 1.378645 .1164688 3.80 0.000 1.168268 1.626906
ano_nac_corr | .8466382 .0067752 -20.80 0.000 .8334625 .8600221
cohab2 | .8632609 .0473383 -2.68 0.007 .7752915 .9612118
cohab3 | 1.075681 .0686841 1.14 0.253 .9491458 1.219086
cohab4 | .9448025 .0518839 -1.03 0.301 .8483933 1.052167
fis_com2 | 1.112618 .0326201 3.64 0.000 1.050486 1.178424
rc_x1 | .8448516 .0086687 -16.43 0.000 .8280309 .862014
rc_x2 | .8808414 .0305098 -3.66 0.000 .8230279 .9427161
rc_x3 | 1.297349 .1196009 2.82 0.005 1.082893 1.554275
_rcs1 | 2.181089 .0635089 26.78 0.000 2.0601 2.309185
_rcs2 | 1.060775 .0246559 2.54 0.011 1.013535 1.110218
_rcs3 | 1.037247 .017608 2.15 0.031 1.003304 1.072338
_rcs4 | 1.033001 .0114403 2.93 0.003 1.01082 1.055669
_rcs5 | 1.018382 .0071663 2.59 0.010 1.004433 1.032525
_rcs_mot_egr_early1 | .8948212 .0292342 -3.40 0.001 .8393192 .9539934
_rcs_mot_egr_early2 | 1.005616 .0258047 0.22 0.827 .956291 1.057486
_rcs_mot_egr_early3 | 1.001993 .0190437 0.10 0.917 .9653544 1.040022
_rcs_mot_egr_early4 | .9847016 .0120291 -1.26 0.207 .961405 1.008563
_rcs_mot_egr_early5 | .9842895 .0084957 -1.83 0.067 .9677782 1.001082
_rcs_mot_egr_early6 | 1.0017 .0052698 0.32 0.747 .991424 1.012082
_rcs_mot_egr_late1 | .9200146 .0290966 -2.64 0.008 .8647179 .9788474
_rcs_mot_egr_late2 | 1.018153 .0258158 0.71 0.478 .9687922 1.07003
_rcs_mot_egr_late3 | .9939444 .0185536 -0.33 0.745 .9582371 1.030982
_rcs_mot_egr_late4 | .9900196 .0116597 -0.85 0.394 .9674287 1.013138
_rcs_mot_egr_late5 | .9889742 .0080789 -1.36 0.175 .9732659 1.004936
_rcs_mot_egr_late6 | 1.00109 .0047222 0.23 0.817 .991877 1.010388
_cons | 5.8e+142 9.3e+143 20.42 0.000 1.1e+129 2.9e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.311
Iteration 1: log likelihood = -21758.103
Iteration 2: log likelihood = -21757.877
Iteration 3: log likelihood = -21757.877
Log likelihood = -21757.877 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996087 .1088735 12.67 0.000 1.793709 2.221299
mot_egr_late | 1.654197 .0779755 10.68 0.000 1.508215 1.814308
tr_mod2 | 1.152131 .0429571 3.80 0.000 1.070939 1.239478
sex_dum2 | .5924533 .0255578 -12.13 0.000 .5444202 .6447243
edad_ini_cons | .9733991 .0040332 -6.51 0.000 .9655261 .9813363
esc1 | 1.516906 .0833258 7.59 0.000 1.362075 1.689337
esc2 | 1.344046 .0693409 5.73 0.000 1.214785 1.48706
sus_prin2 | 1.195594 .0709046 3.01 0.003 1.064396 1.342964
sus_prin3 | 1.716907 .0822964 11.28 0.000 1.562954 1.886024
sus_prin4 | 1.143233 .0793635 1.93 0.054 .9978013 1.309861
sus_prin5 | 1.355792 .1840758 2.24 0.025 1.039023 1.769133
fr_cons_sus_prin2 | .977536 .0969692 -0.23 0.819 .8048142 1.187326
fr_cons_sus_prin3 | .9959112 .0799431 -0.05 0.959 .8509295 1.165595
fr_cons_sus_prin4 | 1.038277 .0863221 0.45 0.651 .8821545 1.22203
fr_cons_sus_prin5 | 1.089091 .0865919 1.07 0.283 .9319372 1.272747
cond_ocu2 | 1.087641 .0670836 1.36 0.173 .9637959 1.2274
cond_ocu3 | 1.14515 .2803254 0.55 0.580 .7087511 1.850253
cond_ocu4 | 1.240408 .0809979 3.30 0.001 1.091394 1.409768
cond_ocu5 | 1.33287 .1368423 2.80 0.005 1.089926 1.629965
cond_ocu6 | 1.211657 .0420034 5.54 0.000 1.132067 1.296844
policonsumo | 1.007302 .0431314 0.17 0.865 .926216 1.095486
num_hij2 | 1.136113 .0394184 3.68 0.000 1.061423 1.21606
tenviv1 | 1.018244 .1150424 0.16 0.873 .8159846 1.270637
tenviv2 | 1.068436 .0803166 0.88 0.379 .9220655 1.238041
tenviv4 | 1.012143 .0420586 0.29 0.771 .9329771 1.098026
tenviv5 | .9928486 .0331962 -0.21 0.830 .9298713 1.060091
mzone2 | 1.416404 .0524942 9.39 0.000 1.317165 1.52312
mzone3 | 1.54477 .0865309 7.76 0.000 1.38415 1.724027
n_off_vio | 1.46174 .050338 11.02 0.000 1.366336 1.563807
n_off_acq | 2.79661 .0871249 33.01 0.000 2.630958 2.972693
n_off_sud | 1.376836 .0456451 9.65 0.000 1.290218 1.469269
n_off_oth | 1.70244 .056476 16.04 0.000 1.59527 1.816808
psy_com2 | 1.049286 .0403393 1.25 0.211 .9731272 1.131404
dep2 | 1.032686 .0387484 0.86 0.391 .9594656 1.111493
rural2 | .9373114 .0520422 -1.17 0.244 .8406646 1.045069
rural3 | .8647828 .0540088 -2.33 0.020 .7651497 .9773896
porc_pobr | 1.702435 .3678005 2.46 0.014 1.11474 2.599964
susini2 | 1.098233 .072043 1.43 0.153 .9657321 1.248914
susini3 | 1.27158 .0731995 4.17 0.000 1.135909 1.423455
susini4 | 1.15555 .0378964 4.41 0.000 1.083611 1.232264
susini5 | 1.378391 .1164477 3.80 0.000 1.168052 1.626607
ano_nac_corr | .8465296 .0067748 -20.82 0.000 .8333549 .8599126
cohab2 | .8632802 .047339 -2.68 0.007 .7753095 .9612326
cohab3 | 1.075696 .0686847 1.14 0.253 .9491591 1.219102
cohab4 | .9447604 .0518814 -1.03 0.301 .8483559 1.05212
fis_com2 | 1.11255 .0326178 3.64 0.000 1.050423 1.178353
rc_x1 | .8447531 .008668 -16.44 0.000 .8279339 .861914
rc_x2 | .8807768 .0305077 -3.67 0.000 .8229674 .9426471
rc_x3 | 1.297599 .1196244 2.83 0.005 1.083101 1.554576
_rcs1 | 2.180842 .0634929 26.78 0.000 2.059882 2.308905
_rcs2 | 1.060903 .0246629 2.54 0.011 1.013649 1.11036
_rcs3 | 1.037235 .0175673 2.16 0.031 1.003369 1.072244
_rcs4 | 1.032657 .011679 2.84 0.004 1.010018 1.055803
_rcs5 | 1.018647 .0081433 2.31 0.021 1.00281 1.034733
_rcs_mot_egr_early1 | .8950071 .0292384 -3.40 0.001 .8394969 .9541878
_rcs_mot_egr_early2 | 1.005419 .0258407 0.21 0.833 .9560268 1.057363
_rcs_mot_egr_early3 | 1.00419 .0188381 0.22 0.824 .9679384 1.041799
_rcs_mot_egr_early4 | .9862652 .0118581 -1.15 0.250 .9632956 1.009783
_rcs_mot_egr_early5 | .9842652 .0084384 -1.85 0.064 .9678645 1.000944
_rcs_mot_egr_early6 | .9946624 .0074347 -0.72 0.474 .9801968 1.009341
_rcs_mot_egr_early7 | 1.003537 .0035652 0.99 0.320 .9965735 1.010549
_rcs_mot_egr_late1 | .920052 .0290935 -2.64 0.008 .8647608 .9788784
_rcs_mot_egr_late2 | 1.018281 .025921 0.71 0.477 .9687235 1.070374
_rcs_mot_egr_late3 | .9944705 .0184305 -0.30 0.765 .9589957 1.031258
_rcs_mot_egr_late4 | .9922599 .0114314 -0.67 0.500 .9701058 1.01492
_rcs_mot_egr_late5 | .9880322 .0079973 -1.49 0.137 .9724815 1.003831
_rcs_mot_egr_late6 | .9964964 .0070354 -0.50 0.619 .9828022 1.010381
_rcs_mot_egr_late7 | 1.002883 .0029554 0.98 0.329 .9971077 1.008693
_cons | 7.5e+142 1.2e+144 20.43 0.000 1.5e+129 3.8e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.145
Iteration 1: log likelihood = -21759.979
Iteration 2: log likelihood = -21759.872
Iteration 3: log likelihood = -21759.872
Log likelihood = -21759.872 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994852 .1087314 12.67 0.000 1.792731 2.219761
mot_egr_late | 1.651233 .0777884 10.65 0.000 1.505598 1.810956
tr_mod2 | 1.152116 .0429533 3.80 0.000 1.070932 1.239455
sex_dum2 | .5924607 .0255581 -12.13 0.000 .544427 .6447322
edad_ini_cons | .9734059 .0040331 -6.51 0.000 .9655331 .9813429
esc1 | 1.516986 .0833309 7.59 0.000 1.362145 1.689428
esc2 | 1.344025 .06934 5.73 0.000 1.214766 1.487037
sus_prin2 | 1.195219 .0708791 3.01 0.003 1.064068 1.342535
sus_prin3 | 1.716276 .0822599 11.27 0.000 1.56239 1.885318
sus_prin4 | 1.142999 .0793454 1.93 0.054 .9975999 1.309589
sus_prin5 | 1.354906 .1839488 2.24 0.025 1.038354 1.76796
fr_cons_sus_prin2 | .977386 .0969542 -0.23 0.818 .8046908 1.187143
fr_cons_sus_prin3 | .9957392 .0799293 -0.05 0.958 .8507824 1.165394
fr_cons_sus_prin4 | 1.038108 .086308 0.45 0.653 .882011 1.221831
fr_cons_sus_prin5 | 1.08905 .0865887 1.07 0.283 .9319019 1.272699
cond_ocu2 | 1.087743 .0670889 1.36 0.173 .9638878 1.227513
cond_ocu3 | 1.143944 .2800258 0.55 0.583 .70801 1.84829
cond_ocu4 | 1.240744 .0810192 3.30 0.001 1.091691 1.410148
cond_ocu5 | 1.332646 .1368113 2.80 0.005 1.089756 1.629673
cond_ocu6 | 1.211739 .0420048 5.54 0.000 1.132146 1.296928
policonsumo | 1.007253 .0431286 0.17 0.866 .9261719 1.095431
num_hij2 | 1.136224 .0394231 3.68 0.000 1.061525 1.21618
tenviv1 | 1.018513 .1150669 0.16 0.871 .8162099 1.270959
tenviv2 | 1.068074 .0802883 0.88 0.381 .9217552 1.23762
tenviv4 | 1.012196 .0420598 0.29 0.770 .933028 1.098082
tenviv5 | .9928354 .0331953 -0.22 0.830 .9298599 1.060076
mzone2 | 1.416263 .0524875 9.39 0.000 1.317037 1.522965
mzone3 | 1.544621 .0865199 7.76 0.000 1.384022 1.723855
n_off_vio | 1.461835 .0503418 11.03 0.000 1.366423 1.563909
n_off_acq | 2.796745 .0871343 33.01 0.000 2.631075 2.972847
n_off_sud | 1.376993 .0456524 9.65 0.000 1.290361 1.469441
n_off_oth | 1.702386 .0564758 16.04 0.000 1.595218 1.816754
psy_com2 | 1.048481 .0402961 1.23 0.218 .9724036 1.130511
dep2 | 1.032711 .0387488 0.86 0.391 .9594905 1.11152
rural2 | .9370524 .0520279 -1.17 0.242 .8404322 1.044781
rural3 | .8649187 .054017 -2.32 0.020 .7652705 .9775424
porc_pobr | 1.709119 .3691358 2.48 0.013 1.119256 2.609846
susini2 | 1.097617 .0720002 1.42 0.156 .9651941 1.248208
susini3 | 1.271345 .0731854 4.17 0.000 1.135701 1.423191
susini4 | 1.15569 .0379013 4.41 0.000 1.083742 1.232415
susini5 | 1.378443 .1164494 3.80 0.000 1.1681 1.626662
ano_nac_corr | .8465336 .0067735 -20.82 0.000 .8333613 .859914
cohab2 | .8633277 .0473393 -2.68 0.007 .7753563 .9612803
cohab3 | 1.07592 .0686957 1.15 0.252 .9493631 1.219349
cohab4 | .9448006 .0518821 -1.03 0.301 .8483947 1.052162
fis_com2 | 1.112992 .0326294 3.65 0.000 1.050843 1.178818
rc_x1 | .8447476 .0086673 -16.44 0.000 .8279298 .8619071
rc_x2 | .8807967 .0305094 -3.66 0.000 .822984 .9426706
rc_x3 | 1.297611 .1196287 2.83 0.005 1.083106 1.554598
_rcs1 | 2.177066 .0586595 28.87 0.000 2.065078 2.295126
_rcs2 | 1.071884 .0075279 9.88 0.000 1.057231 1.086741
_rcs3 | 1.033961 .0056547 6.11 0.000 1.022937 1.045104
_rcs4 | 1.019485 .0038677 5.09 0.000 1.011932 1.027094
_rcs5 | 1.012627 .0028211 4.50 0.000 1.007113 1.018171
_rcs6 | 1.01034 .0021956 4.73 0.000 1.006046 1.014653
_rcs_mot_egr_early1 | .8978523 .0271955 -3.56 0.000 .8461013 .9527685
_rcs_mot_egr_late1 | .9205885 .0267904 -2.84 0.004 .8695497 .974623
_cons | 7.4e+142 1.2e+144 20.43 0.000 1.5e+129 3.7e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.868
Iteration 1: log likelihood = -21759.707
Iteration 2: log likelihood = -21759.585
Iteration 3: log likelihood = -21759.585
Log likelihood = -21759.585 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993798 .1087157 12.66 0.000 1.79171 2.218679
mot_egr_late | 1.651669 .0778242 10.65 0.000 1.505968 1.811467
tr_mod2 | 1.151911 .0429471 3.79 0.000 1.070739 1.239238
sex_dum2 | .5924643 .0255583 -12.13 0.000 .5444302 .6447364
edad_ini_cons | .9734096 .0040331 -6.50 0.000 .9655368 .9813466
esc1 | 1.516986 .083331 7.59 0.000 1.362146 1.689428
esc2 | 1.344009 .0693393 5.73 0.000 1.214752 1.48702
sus_prin2 | 1.195245 .0708813 3.01 0.003 1.06409 1.342566
sus_prin3 | 1.716344 .0822631 11.27 0.000 1.562453 1.885393
sus_prin4 | 1.143082 .0793516 1.93 0.054 .9976719 1.309685
sus_prin5 | 1.355158 .1839862 2.24 0.025 1.038543 1.768297
fr_cons_sus_prin2 | .9774168 .0969574 -0.23 0.818 .804716 1.187181
fr_cons_sus_prin3 | .9957973 .079934 -0.05 0.958 .850832 1.165462
fr_cons_sus_prin4 | 1.038116 .0863084 0.45 0.653 .882018 1.22184
fr_cons_sus_prin5 | 1.08909 .0865912 1.07 0.283 .9319367 1.272743
cond_ocu2 | 1.087641 .0670829 1.36 0.173 .9637969 1.227398
cond_ocu3 | 1.144051 .2800531 0.55 0.582 .708075 1.848467
cond_ocu4 | 1.240952 .0810317 3.31 0.001 1.091876 1.410382
cond_ocu5 | 1.333011 .1368501 2.80 0.005 1.090052 1.630122
cond_ocu6 | 1.21173 .0420044 5.54 0.000 1.132138 1.296919
policonsumo | 1.007269 .0431294 0.17 0.866 .9261872 1.09545
num_hij2 | 1.136241 .0394237 3.68 0.000 1.061541 1.216198
tenviv1 | 1.018568 .1150735 0.16 0.871 .8162532 1.271028
tenviv2 | 1.067947 .080279 0.87 0.382 .921645 1.237473
tenviv4 | 1.012296 .0420643 0.29 0.769 .9331196 1.098191
tenviv5 | .9929371 .0331988 -0.21 0.832 .9299548 1.060185
mzone2 | 1.416383 .0524918 9.39 0.000 1.317148 1.523093
mzone3 | 1.54486 .086533 7.76 0.000 1.384237 1.724122
n_off_vio | 1.46187 .0503434 11.03 0.000 1.366455 1.563947
n_off_acq | 2.796835 .0871364 33.01 0.000 2.631161 2.972942
n_off_sud | 1.37697 .0456511 9.65 0.000 1.29034 1.469416
n_off_oth | 1.702455 .056478 16.04 0.000 1.595283 1.816828
psy_com2 | 1.048932 .0403187 1.24 0.214 .9728117 1.131008
dep2 | 1.032706 .0387488 0.86 0.391 .9594848 1.111514
rural2 | .936995 .0520249 -1.17 0.241 .8403805 1.044717
rural3 | .8647625 .0540084 -2.33 0.020 .7651303 .9773685
porc_pobr | 1.707685 .3688457 2.48 0.013 1.118292 2.607715
susini2 | 1.097704 .0720069 1.42 0.155 .9652687 1.248309
susini3 | 1.271479 .0731934 4.17 0.000 1.135819 1.423341
susini4 | 1.155622 .0378993 4.41 0.000 1.083677 1.232342
susini5 | 1.378347 .1164421 3.80 0.000 1.168017 1.626551
ano_nac_corr | .8465439 .0067746 -20.82 0.000 .8333695 .8599265
cohab2 | .8631387 .0473295 -2.68 0.007 .7751854 .9610712
cohab3 | 1.075649 .0686795 1.14 0.253 .949122 1.219044
cohab4 | .9446285 .0518727 -1.04 0.300 .8482399 1.05197
fis_com2 | 1.112929 .0326287 3.65 0.000 1.05078 1.178753
rc_x1 | .8447491 .0086681 -16.44 0.000 .8279296 .8619104
rc_x2 | .8808274 .0305104 -3.66 0.000 .8230128 .9427033
rc_x3 | 1.297519 .1196204 2.83 0.005 1.083029 1.554487
_rcs1 | 2.171257 .0625552 26.91 0.000 2.052049 2.297391
_rcs2 | 1.066298 .0235134 2.91 0.004 1.021194 1.113394
_rcs3 | 1.033003 .0064486 5.20 0.000 1.020441 1.04572
_rcs4 | 1.019337 .0039007 5.00 0.000 1.01172 1.027011
_rcs5 | 1.012613 .002821 4.50 0.000 1.007099 1.018157
_rcs6 | 1.010336 .0021959 4.73 0.000 1.006042 1.014649
_rcs_mot_egr_early1 | .8988299 .0290123 -3.30 0.001 .8437282 .9575302
_rcs_mot_egr_early2 | 1.000003 .0246103 0.00 1.000 .9529128 1.049421
_rcs_mot_egr_late1 | .9248465 .028921 -2.50 0.012 .8698644 .9833038
_rcs_mot_egr_late2 | 1.010236 .0242373 0.42 0.671 .9638317 1.058875
_cons | 7.2e+142 1.2e+144 20.43 0.000 1.4e+129 3.7e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.357
Iteration 1: log likelihood = -21759.242
Iteration 2: log likelihood = -21759.092
Iteration 3: log likelihood = -21759.092
Log likelihood = -21759.092 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.995817 .1088458 12.67 0.000 1.793489 2.22097
mot_egr_late | 1.653673 .077941 10.67 0.000 1.507755 1.813713
tr_mod2 | 1.152013 .0429512 3.80 0.000 1.070833 1.239348
sex_dum2 | .5924447 .0255574 -12.14 0.000 .5444124 .6447148
edad_ini_cons | .9734018 .0040332 -6.51 0.000 .9655289 .9813389
esc1 | 1.51696 .0833289 7.59 0.000 1.362123 1.689398
esc2 | 1.343985 .0693378 5.73 0.000 1.21473 1.486993
sus_prin2 | 1.195468 .070896 3.01 0.003 1.064286 1.34282
sus_prin3 | 1.716613 .0822795 11.27 0.000 1.562691 1.885695
sus_prin4 | 1.143172 .0793587 1.93 0.054 .9977487 1.30979
sus_prin5 | 1.355682 .1840598 2.24 0.025 1.038941 1.768987
fr_cons_sus_prin2 | .9774343 .0969591 -0.23 0.818 .8047305 1.187202
fr_cons_sus_prin3 | .9957875 .0799333 -0.05 0.958 .8508236 1.16545
fr_cons_sus_prin4 | 1.038177 .0863136 0.45 0.652 .8820698 1.221912
fr_cons_sus_prin5 | 1.089105 .0865927 1.07 0.283 .9319494 1.272762
cond_ocu2 | 1.087578 .0670796 1.36 0.173 .9637399 1.227328
cond_ocu3 | 1.144544 .2801764 0.55 0.581 .708377 1.849272
cond_ocu4 | 1.240645 .0810131 3.30 0.001 1.091604 1.410037
cond_ocu5 | 1.332943 .136845 2.80 0.005 1.089994 1.630043
cond_ocu6 | 1.211704 .0420041 5.54 0.000 1.132112 1.296892
policonsumo | 1.007323 .0431322 0.17 0.865 .9262353 1.095509
num_hij2 | 1.136185 .0394214 3.68 0.000 1.06149 1.216138
tenviv1 | 1.018393 .1150558 0.16 0.872 .81611 1.270815
tenviv2 | 1.068116 .0802925 0.88 0.381 .9217902 1.237671
tenviv4 | 1.012194 .0420603 0.29 0.771 .9330248 1.098081
tenviv5 | .992843 .0331959 -0.21 0.830 .9298663 1.060085
mzone2 | 1.41642 .0524939 9.39 0.000 1.317182 1.523135
mzone3 | 1.544608 .0865196 7.76 0.000 1.384009 1.723841
n_off_vio | 1.461847 .0503414 11.03 0.000 1.366436 1.563921
n_off_acq | 2.796732 .0871297 33.01 0.000 2.63107 2.972824
n_off_sud | 1.376848 .0456466 9.65 0.000 1.290227 1.469284
n_off_oth | 1.702427 .0564756 16.04 0.000 1.595258 1.816795
psy_com2 | 1.048965 .0403238 1.24 0.214 .9728357 1.131052
dep2 | 1.032705 .0387491 0.86 0.391 .9594841 1.111515
rural2 | .9371227 .052032 -1.17 0.242 .8404949 1.044859
rural3 | .8648641 .0540144 -2.32 0.020 .7652209 .9774824
porc_pobr | 1.705681 .3684607 2.47 0.013 1.116919 2.604798
susini2 | 1.097925 .0720221 1.42 0.154 .965462 1.248562
susini3 | 1.271481 .0731936 4.17 0.000 1.135821 1.423344
susini4 | 1.155574 .0378976 4.41 0.000 1.083633 1.232291
susini5 | 1.378431 .1164503 3.80 0.000 1.168087 1.626652
ano_nac_corr | .8465222 .0067745 -20.82 0.000 .8333479 .8599047
cohab2 | .8631814 .0473323 -2.68 0.007 .7752231 .9611196
cohab3 | 1.075664 .0686813 1.14 0.253 .9491338 1.219063
cohab4 | .9446606 .0518746 -1.04 0.300 .8482686 1.052006
fis_com2 | 1.112744 .0326232 3.64 0.000 1.050607 1.178557
rc_x1 | .8447237 .0086679 -16.44 0.000 .8279046 .8618845
rc_x2 | .8808545 .0305112 -3.66 0.000 .8230385 .942732
rc_x3 | 1.297367 .1196058 2.82 0.005 1.082903 1.554305
_rcs1 | 2.180356 .0635527 26.74 0.000 2.059287 2.308544
_rcs2 | 1.059381 .0237997 2.57 0.010 1.013747 1.10707
_rcs3 | 1.045253 .0142963 3.24 0.001 1.017606 1.073653
_rcs4 | 1.025834 .0078444 3.34 0.001 1.010574 1.041325
_rcs5 | 1.013987 .0031703 4.44 0.000 1.007792 1.02022
_rcs6 | 1.01037 .0021968 4.75 0.000 1.006074 1.014685
_rcs_mot_egr_early1 | .8949583 .0292435 -3.40 0.001 .8394389 .9541496
_rcs_mot_egr_early2 | 1.005241 .0249027 0.21 0.833 .957599 1.055254
_rcs_mot_egr_early3 | .9859903 .017 -0.82 0.413 .9532276 1.019879
_rcs_mot_egr_late1 | .9204187 .0291271 -2.62 0.009 .8650649 .9793145
_rcs_mot_egr_late2 | 1.016783 .0246897 0.69 0.493 .9695252 1.066344
_rcs_mot_egr_late3 | .9843906 .0163882 -0.95 0.345 .9527887 1.017041
_cons | 7.6e+142 1.2e+144 20.43 0.000 1.5e+129 3.9e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.477
Iteration 1: log likelihood = -21757.523
Iteration 2: log likelihood = -21757.316
Iteration 3: log likelihood = -21757.316
Log likelihood = -21757.316 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.99664 .1089039 12.68 0.000 1.794205 2.221915
mot_egr_late | 1.654442 .0779858 10.68 0.000 1.508441 1.814575
tr_mod2 | 1.152176 .0429589 3.80 0.000 1.070981 1.239527
sex_dum2 | .5924425 .0255573 -12.14 0.000 .5444103 .6447126
edad_ini_cons | .9733982 .0040332 -6.51 0.000 .9655252 .9813354
esc1 | 1.516871 .0833237 7.58 0.000 1.362044 1.689298
esc2 | 1.344016 .0693392 5.73 0.000 1.214759 1.487027
sus_prin2 | 1.195686 .0709103 3.01 0.003 1.064478 1.343067
sus_prin3 | 1.717052 .082304 11.28 0.000 1.563085 1.886185
sus_prin4 | 1.143337 .0793706 1.93 0.054 .9978927 1.309981
sus_prin5 | 1.355906 .1840926 2.24 0.025 1.039109 1.769286
fr_cons_sus_prin2 | .9775467 .0969702 -0.23 0.819 .8048232 1.187339
fr_cons_sus_prin3 | .9959167 .0799434 -0.05 0.959 .8509344 1.165601
fr_cons_sus_prin4 | 1.038307 .0863244 0.45 0.651 .8821803 1.222065
fr_cons_sus_prin5 | 1.089099 .0865922 1.07 0.283 .9319442 1.272755
cond_ocu2 | 1.087587 .0670805 1.36 0.173 .9637479 1.22734
cond_ocu3 | 1.145285 .2803581 0.55 0.579 .7088347 1.850469
cond_ocu4 | 1.240353 .0809944 3.30 0.001 1.091345 1.409705
cond_ocu5 | 1.332769 .1368305 2.80 0.005 1.089846 1.629838
cond_ocu6 | 1.21159 .0420011 5.54 0.000 1.132004 1.296772
policonsumo | 1.007291 .0431308 0.17 0.865 .9262065 1.095475
num_hij2 | 1.13607 .0394165 3.68 0.000 1.061383 1.216012
tenviv1 | 1.018197 .1150376 0.16 0.873 .8159468 1.27058
tenviv2 | 1.068453 .080318 0.88 0.378 .9220803 1.238061
tenviv4 | 1.012079 .0420556 0.29 0.773 .9329187 1.097956
tenviv5 | .992837 .0331957 -0.22 0.830 .9298606 1.060079
mzone2 | 1.41642 .052495 9.39 0.000 1.317179 1.523137
mzone3 | 1.544678 .0865255 7.76 0.000 1.384069 1.723925
n_off_vio | 1.461709 .0503363 11.02 0.000 1.366308 1.563772
n_off_acq | 2.796502 .0871196 33.01 0.000 2.630859 2.972574
n_off_sud | 1.376778 .0456426 9.64 0.000 1.290164 1.469206
n_off_oth | 1.702469 .056476 16.04 0.000 1.5953 1.816838
psy_com2 | 1.04944 .0403421 1.26 0.209 .9732758 1.131564
dep2 | 1.032707 .0387494 0.86 0.391 .9594847 1.111516
rural2 | .9374594 .0520502 -1.16 0.245 .8407978 1.045234
rural3 | .8649008 .0540159 -2.32 0.020 .7652546 .9775222
porc_pobr | 1.699981 .3672631 2.46 0.014 1.113143 2.596195
susini2 | 1.098426 .0720552 1.43 0.152 .9659018 1.249132
susini3 | 1.271533 .0731967 4.17 0.000 1.135867 1.423402
susini4 | 1.155503 .0378947 4.41 0.000 1.083568 1.232215
susini5 | 1.378379 .1164473 3.80 0.000 1.168041 1.626594
ano_nac_corr | .846493 .0067741 -20.82 0.000 .8333195 .8598747
cohab2 | .8632335 .0473363 -2.68 0.007 .7752679 .9611802
cohab3 | 1.075576 .0686771 1.14 0.254 .9490531 1.218966
cohab4 | .9447305 .0518794 -1.04 0.301 .8483297 1.052086
fis_com2 | 1.112377 .0326114 3.63 0.000 1.050262 1.178166
rc_x1 | .8447137 .0086673 -16.45 0.000 .8278957 .8618733
rc_x2 | .880785 .0305077 -3.66 0.000 .8229755 .9426552
rc_x3 | 1.297572 .1196209 2.83 0.005 1.08308 1.554541
_rcs1 | 2.182323 .0635508 26.80 0.000 2.061254 2.310504
_rcs2 | 1.059759 .0246192 2.50 0.012 1.012588 1.109127
_rcs3 | 1.035186 .0170553 2.10 0.036 1.002292 1.069159
_rcs4 | 1.034184 .0092842 3.74 0.000 1.016147 1.052542
_rcs5 | 1.023046 .0070432 3.31 0.001 1.009335 1.036944
_rcs6 | 1.011757 .0023494 5.03 0.000 1.007163 1.016372
_rcs_mot_egr_early1 | .8940461 .029204 -3.43 0.001 .8386011 .9531569
_rcs_mot_egr_early2 | 1.0064 .0256104 0.25 0.802 .9574357 1.057868
_rcs_mot_egr_early3 | .9968646 .0186336 -0.17 0.867 .9610043 1.034063
_rcs_mot_egr_early4 | .9767731 .0119638 -1.92 0.055 .9536037 1.000506
_rcs_mot_egr_late1 | .9194833 .0290814 -2.65 0.008 .8642155 .9782855
_rcs_mot_egr_late2 | 1.017925 .0254909 0.71 0.478 .9691697 1.069132
_rcs_mot_egr_late3 | .9910228 .0179238 -0.50 0.618 .9565082 1.026783
_rcs_mot_egr_late4 | .9845052 .0115111 -1.34 0.182 .9622003 1.007327
_cons | 8.2e+142 1.3e+144 20.44 0.000 1.6e+129 4.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21773.198
Iteration 1: log likelihood = -21757.232
Iteration 2: log likelihood = -21757.052
Iteration 3: log likelihood = -21757.052
Log likelihood = -21757.052 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997404 .108944 12.68 0.000 1.794895 2.222762
mot_egr_late | 1.655055 .0780107 10.69 0.000 1.509007 1.815238
tr_mod2 | 1.152192 .0429598 3.80 0.000 1.070996 1.239545
sex_dum2 | .5924574 .025558 -12.13 0.000 .5444239 .6447288
edad_ini_cons | .9733984 .0040332 -6.51 0.000 .9655254 .9813356
esc1 | 1.516898 .0833248 7.59 0.000 1.362068 1.689327
esc2 | 1.344006 .0693386 5.73 0.000 1.21475 1.487016
sus_prin2 | 1.195737 .0709137 3.01 0.003 1.064523 1.343126
sus_prin3 | 1.717194 .0823117 11.28 0.000 1.563212 1.886343
sus_prin4 | 1.1434 .079375 1.93 0.054 .9979474 1.310052
sus_prin5 | 1.355961 .1841005 2.24 0.025 1.039151 1.769359
fr_cons_sus_prin2 | .9775398 .0969695 -0.23 0.819 .8048174 1.18733
fr_cons_sus_prin3 | .9958867 .0799411 -0.05 0.959 .8509086 1.165566
fr_cons_sus_prin4 | 1.038311 .0863248 0.45 0.651 .8821833 1.22207
fr_cons_sus_prin5 | 1.089062 .0865895 1.07 0.283 .9319124 1.272713
cond_ocu2 | 1.087549 .0670782 1.36 0.174 .9637136 1.227296
cond_ocu3 | 1.145567 .2804273 0.56 0.579 .7090094 1.850926
cond_ocu4 | 1.240187 .0809838 3.30 0.001 1.091199 1.409517
cond_ocu5 | 1.332868 .1368416 2.80 0.005 1.089926 1.629962
cond_ocu6 | 1.211583 .0420009 5.54 0.000 1.131997 1.296765
policonsumo | 1.007226 .0431279 0.17 0.866 .9261464 1.095403
num_hij2 | 1.136081 .0394168 3.68 0.000 1.061393 1.216023
tenviv1 | 1.018197 .1150369 0.16 0.873 .8159472 1.270578
tenviv2 | 1.06857 .0803267 0.88 0.378 .9221817 1.238197
tenviv4 | 1.012021 .0420531 0.29 0.774 .9328658 1.097893
tenviv5 | .9928243 .0331953 -0.22 0.829 .9298486 1.060065
mzone2 | 1.416413 .052495 9.39 0.000 1.317173 1.52313
mzone3 | 1.544647 .0865246 7.76 0.000 1.38404 1.723892
n_off_vio | 1.461672 .0503344 11.02 0.000 1.366274 1.563732
n_off_acq | 2.796447 .0871162 33.01 0.000 2.630811 2.972512
n_off_sud | 1.376759 .0456413 9.64 0.000 1.290148 1.469185
n_off_oth | 1.702458 .0564749 16.04 0.000 1.595291 1.816824
psy_com2 | 1.049482 .0403452 1.26 0.209 .9733127 1.131613
dep2 | 1.032698 .0387492 0.86 0.391 .9594762 1.111507
rural2 | .9375497 .0520551 -1.16 0.245 .8408789 1.045334
rural3 | .8649515 .0540191 -2.32 0.020 .7652994 .9775795
porc_pobr | 1.698295 .3669077 2.45 0.014 1.112028 2.593646
susini2 | 1.098594 .0720668 1.43 0.152 .9660492 1.249325
susini3 | 1.271422 .0731906 4.17 0.000 1.135767 1.423279
susini4 | 1.155438 .0378925 4.41 0.000 1.083507 1.232145
susini5 | 1.378219 .1164337 3.80 0.000 1.167905 1.626405
ano_nac_corr | .8464745 .0067741 -20.83 0.000 .833301 .8598562
cohab2 | .863228 .0473357 -2.68 0.007 .7752634 .9611734
cohab3 | 1.075526 .0686735 1.14 0.254 .9490099 1.218908
cohab4 | .9447182 .0518784 -1.04 0.300 .8483191 1.052072
fis_com2 | 1.112293 .0326089 3.63 0.000 1.050182 1.178077
rc_x1 | .8446977 .0086673 -16.45 0.000 .8278798 .8618573
rc_x2 | .8807532 .0305064 -3.67 0.000 .8229461 .9426208
rc_x3 | 1.297712 .1196332 2.83 0.005 1.083199 1.554708
_rcs1 | 2.183589 .0635935 26.82 0.000 2.062438 2.311856
_rcs2 | 1.058197 .0242478 2.47 0.014 1.011724 1.106805
_rcs3 | 1.039365 .0177385 2.26 0.024 1.005174 1.07472
_rcs4 | 1.028443 .0107137 2.69 0.007 1.007657 1.049657
_rcs5 | 1.024723 .0074021 3.38 0.001 1.010317 1.039334
_rcs6 | 1.015785 .0041106 3.87 0.000 1.00776 1.023874
_rcs_mot_egr_early1 | .8934271 .0291893 -3.45 0.001 .8380103 .9525085
_rcs_mot_egr_early2 | 1.007665 .0254546 0.30 0.762 .9589898 1.058811
_rcs_mot_egr_early3 | .9968821 .0189805 -0.16 0.870 .9603665 1.034786
_rcs_mot_egr_early4 | .9836594 .0122165 -1.33 0.185 .9600046 1.007897
_rcs_mot_egr_early5 | .98638 .0080317 -1.68 0.092 .9707631 1.002248
_rcs_mot_egr_late1 | .9188929 .0290605 -2.67 0.007 .8636646 .9776528
_rcs_mot_egr_late2 | 1.019654 .0253873 0.78 0.434 .9710905 1.070646
_rcs_mot_egr_late3 | .9897403 .0182849 -0.56 0.577 .9545436 1.026235
_rcs_mot_egr_late4 | .9905663 .0117007 -0.80 0.422 .9678967 1.013767
_rcs_mot_egr_late5 | .9888569 .0075579 -1.47 0.143 .9741541 1.003782
_cons | 8.5e+142 1.4e+144 20.44 0.000 1.7e+129 4.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21774.744
Iteration 1: log likelihood = -21756.724
Iteration 2: log likelihood = -21756.455
Iteration 3: log likelihood = -21756.455
Log likelihood = -21756.455 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997094 .1089372 12.68 0.000 1.794599 2.222439
mot_egr_late | 1.655119 .0780268 10.69 0.000 1.509042 1.815337
tr_mod2 | 1.152165 .0429587 3.80 0.000 1.07097 1.239515
sex_dum2 | .5924394 .0255573 -12.14 0.000 .5444073 .6447093
edad_ini_cons | .9734006 .0040332 -6.51 0.000 .9655276 .9813377
esc1 | 1.516855 .0833227 7.58 0.000 1.362029 1.68928
esc2 | 1.343961 .0693364 5.73 0.000 1.214709 1.486966
sus_prin2 | 1.195693 .0709109 3.01 0.003 1.064483 1.343075
sus_prin3 | 1.717066 .0823051 11.28 0.000 1.563097 1.886202
sus_prin4 | 1.143289 .0793671 1.93 0.054 .9978509 1.309925
sus_prin5 | 1.35571 .1840649 2.24 0.025 1.03896 1.769027
fr_cons_sus_prin2 | .9775457 .0969701 -0.23 0.819 .8048223 1.187337
fr_cons_sus_prin3 | .995917 .0799435 -0.05 0.959 .8509345 1.165602
fr_cons_sus_prin4 | 1.038343 .0863276 0.45 0.651 .8822107 1.222108
fr_cons_sus_prin5 | 1.089111 .0865933 1.07 0.283 .9319541 1.272769
cond_ocu2 | 1.087583 .0670804 1.36 0.173 .9637436 1.227335
cond_ocu3 | 1.145173 .2803312 0.55 0.580 .7087652 1.850291
cond_ocu4 | 1.240257 .0809887 3.30 0.001 1.09126 1.409597
cond_ocu5 | 1.333009 .1368574 2.80 0.005 1.090039 1.630138
cond_ocu6 | 1.211578 .0420007 5.54 0.000 1.131992 1.296759
policonsumo | 1.007293 .0431309 0.17 0.865 .926208 1.095477
num_hij2 | 1.136074 .0394165 3.68 0.000 1.061387 1.216016
tenviv1 | 1.018119 .1150286 0.16 0.874 .8158838 1.270482
tenviv2 | 1.068496 .0803205 0.88 0.378 .9221185 1.23811
tenviv4 | 1.012007 .0420527 0.29 0.774 .9328527 1.097879
tenviv5 | .9927831 .033194 -0.22 0.828 .9298099 1.060021
mzone2 | 1.416393 .0524939 9.39 0.000 1.317155 1.523108
mzone3 | 1.544538 .0865186 7.76 0.000 1.383941 1.72377
n_off_vio | 1.461724 .0503368 11.02 0.000 1.366322 1.563788
n_off_acq | 2.796581 .0871219 33.01 0.000 2.630934 2.972658
n_off_sud | 1.376804 .0456435 9.65 0.000 1.290189 1.469234
n_off_oth | 1.70252 .0564777 16.04 0.000 1.595348 1.816892
psy_com2 | 1.049399 .0403426 1.25 0.210 .9732349 1.131525
dep2 | 1.032701 .0387493 0.86 0.391 .959479 1.11151
rural2 | .9375056 .0520532 -1.16 0.245 .8408384 1.045286
rural3 | .8648901 .0540154 -2.32 0.020 .7652449 .9775104
porc_pobr | 1.699421 .3671623 2.45 0.014 1.11275 2.5954
susini2 | 1.098358 .072051 1.43 0.153 .9658421 1.249055
susini3 | 1.271563 .0731986 4.17 0.000 1.135894 1.423436
susini4 | 1.155499 .0378945 4.41 0.000 1.083564 1.23221
susini5 | 1.378421 .1164498 3.80 0.000 1.168078 1.626642
ano_nac_corr | .8465233 .0067746 -20.82 0.000 .8333489 .8599059
cohab2 | .8632731 .0473386 -2.68 0.007 .7753032 .9612244
cohab3 | 1.075583 .0686773 1.14 0.254 .9490598 1.218973
cohab4 | .9447694 .0518814 -1.03 0.301 .8483648 1.052129
fis_com2 | 1.112366 .0326116 3.63 0.000 1.05025 1.178156
rc_x1 | .8447428 .0086679 -16.44 0.000 .8279238 .8619034
rc_x2 | .8807759 .0305071 -3.67 0.000 .8229674 .9426451
rc_x3 | 1.297619 .1196245 2.83 0.005 1.083121 1.554596
_rcs1 | 2.183786 .063685 26.78 0.000 2.062466 2.312243
_rcs2 | 1.05818 .024095 2.48 0.013 1.011993 1.106475
_rcs3 | 1.04076 .018017 2.31 0.021 1.00604 1.076679
_rcs4 | 1.024121 .0122415 1.99 0.046 1.000407 1.048397
_rcs5 | 1.029612 .0088727 3.39 0.001 1.012368 1.04715
_rcs6 | 1.013345 .0065743 2.04 0.041 1.000542 1.026313
_rcs_mot_egr_early1 | .8935616 .0292304 -3.44 0.001 .8380691 .9527286
_rcs_mot_egr_early2 | 1.007562 .0253678 0.30 0.765 .9590488 1.058529
_rcs_mot_egr_early3 | .9968596 .0192953 -0.16 0.871 .9597498 1.035404
_rcs_mot_egr_early4 | .9916444 .0133739 -0.62 0.534 .9657754 1.018206
_rcs_mot_egr_early5 | .9787079 .009604 -2.19 0.028 .9600643 .9977136
_rcs_mot_egr_early6 | .9970322 .0074551 -0.40 0.691 .9825271 1.011752
_rcs_mot_egr_late1 | .9187402 .0290963 -2.68 0.007 .8634464 .977575
_rcs_mot_egr_late2 | 1.020065 .0253494 0.80 0.424 .9715721 1.070979
_rcs_mot_egr_late3 | .9889673 .0187174 -0.59 0.558 .9529539 1.026342
_rcs_mot_egr_late4 | .9969325 .0129936 -0.24 0.814 .971788 1.022728
_rcs_mot_egr_late5 | .983393 .0092511 -1.78 0.075 .9654274 1.001693
_rcs_mot_egr_late6 | .9963664 .007096 -0.51 0.609 .9825551 1.010372
_cons | 7.6e+142 1.2e+144 20.43 0.000 1.5e+129 3.9e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21773.335
Iteration 1: log likelihood = -21757.553
Iteration 2: log likelihood = -21757.294
Iteration 3: log likelihood = -21757.294
Log likelihood = -21757.294 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.99597 .1088618 12.67 0.000 1.793613 2.221157
mot_egr_late | 1.654014 .0779617 10.68 0.000 1.508057 1.814096
tr_mod2 | 1.152154 .0429581 3.80 0.000 1.07096 1.239503
sex_dum2 | .5924598 .0255581 -12.13 0.000 .5444261 .6447314
edad_ini_cons | .9733979 .0040332 -6.51 0.000 .9655249 .9813351
esc1 | 1.516901 .0833253 7.59 0.000 1.362071 1.689331
esc2 | 1.344023 .0693396 5.73 0.000 1.214765 1.487034
sus_prin2 | 1.195675 .0709097 3.01 0.003 1.064468 1.343055
sus_prin3 | 1.717044 .0823035 11.28 0.000 1.563078 1.886176
sus_prin4 | 1.143312 .079369 1.93 0.054 .9978707 1.309952
sus_prin5 | 1.355888 .1840896 2.24 0.025 1.039096 1.769261
fr_cons_sus_prin2 | .9775307 .0969686 -0.23 0.819 .8048099 1.187319
fr_cons_sus_prin3 | .9958987 .079942 -0.05 0.959 .8509189 1.16558
fr_cons_sus_prin4 | 1.0383 .0863239 0.45 0.651 .8821741 1.222057
fr_cons_sus_prin5 | 1.089091 .0865918 1.07 0.283 .9319369 1.272746
cond_ocu2 | 1.087572 .0670795 1.36 0.173 .9637346 1.227322
cond_ocu3 | 1.145357 .2803762 0.55 0.579 .7088792 1.850588
cond_ocu4 | 1.240309 .0809915 3.30 0.001 1.091307 1.409655
cond_ocu5 | 1.332928 .1368483 2.80 0.005 1.089974 1.630037
cond_ocu6 | 1.211626 .0420024 5.54 0.000 1.132037 1.29681
policonsumo | 1.007287 .0431307 0.17 0.865 .9262024 1.09547
num_hij2 | 1.136104 .0394179 3.68 0.000 1.061414 1.216049
tenviv1 | 1.018203 .1150379 0.16 0.873 .815952 1.270587
tenviv2 | 1.068477 .0803198 0.88 0.378 .9221014 1.238089
tenviv4 | 1.012095 .0420565 0.29 0.772 .932933 1.097974
tenviv5 | .9928404 .0331959 -0.21 0.830 .9298637 1.060082
mzone2 | 1.416421 .052495 9.39 0.000 1.317181 1.523139
mzone3 | 1.544662 .0865254 7.76 0.000 1.384053 1.723909
n_off_vio | 1.461722 .0503366 11.02 0.000 1.36632 1.563786
n_off_acq | 2.796527 .0871202 33.01 0.000 2.630883 2.972601
n_off_sud | 1.376797 .0456432 9.65 0.000 1.290183 1.469227
n_off_oth | 1.702456 .0564755 16.04 0.000 1.595288 1.816824
psy_com2 | 1.049327 .0403407 1.25 0.210 .9731657 1.131448
dep2 | 1.032687 .0387487 0.86 0.391 .9594666 1.111496
rural2 | .9374317 .052049 -1.16 0.245 .8407724 1.045203
rural3 | .8648662 .054014 -2.32 0.020 .7652236 .9774837
porc_pobr | 1.700694 .3674298 2.46 0.014 1.113593 2.597321
susini2 | 1.098385 .0720532 1.43 0.153 .9658653 1.249088
susini3 | 1.271526 .0731966 4.17 0.000 1.135861 1.423395
susini4 | 1.155489 .0378944 4.41 0.000 1.083554 1.2322
susini5 | 1.378306 .1164408 3.80 0.000 1.16798 1.626508
ano_nac_corr | .8464876 .0067745 -20.82 0.000 .8333134 .8598701
cohab2 | .8632491 .0473371 -2.68 0.007 .7752819 .9611975
cohab3 | 1.075612 .0686793 1.14 0.254 .9490852 1.219006
cohab4 | .9447315 .0518794 -1.04 0.301 .8483307 1.052087
fis_com2 | 1.112438 .032614 3.63 0.000 1.050318 1.178233
rc_x1 | .8447081 .0086676 -16.45 0.000 .8278896 .8618682
rc_x2 | .8807775 .0305075 -3.67 0.000 .8229683 .9426474
rc_x3 | 1.297613 .1196251 2.83 0.005 1.083114 1.554592
_rcs1 | 2.181024 .0635149 26.78 0.000 2.060024 2.309133
_rcs2 | 1.059701 .0245709 2.50 0.012 1.012621 1.10897
_rcs3 | 1.037111 .0179713 2.10 0.035 1.002479 1.072939
_rcs4 | 1.030663 .0117098 2.66 0.008 1.007966 1.053871
_rcs5 | 1.022961 .0082386 2.82 0.005 1.006941 1.039237
_rcs6 | 1.012163 .0055021 2.22 0.026 1.001436 1.023005
_rcs_mot_egr_early1 | .8948917 .0292423 -3.40 0.001 .8393747 .9540806
_rcs_mot_egr_early2 | 1.006041 .0257516 0.24 0.814 .9568134 1.0578
_rcs_mot_egr_early3 | 1.002582 .0193203 0.13 0.894 .9654216 1.041174
_rcs_mot_egr_early4 | .9861639 .0126623 -1.09 0.278 .961656 1.011296
_rcs_mot_egr_early5 | .9856547 .0089505 -1.59 0.112 .9682674 1.003354
_rcs_mot_egr_early6 | .9930963 .007106 -0.97 0.333 .9792659 1.007122
_rcs_mot_egr_early7 | 1.000246 .0046725 0.05 0.958 .9911298 1.009446
_rcs_mot_egr_late1 | .9200125 .0290978 -2.64 0.008 .8647135 .9788479
_rcs_mot_egr_late2 | 1.018868 .0258263 0.74 0.461 .9694864 1.070765
_rcs_mot_egr_late3 | .9929478 .0189318 -0.37 0.710 .9565269 1.030755
_rcs_mot_egr_late4 | .9921675 .0122899 -0.63 0.526 .9683698 1.01655
_rcs_mot_egr_late5 | .9894218 .0085025 -1.24 0.216 .9728967 1.006228
_rcs_mot_egr_late6 | .9949339 .0066867 -0.76 0.450 .9819141 1.008126
_rcs_mot_egr_late7 | .9996005 .0042124 -0.09 0.924 .9913783 1.007891
_cons | 8.3e+142 1.3e+144 20.44 0.000 1.6e+129 4.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21771.037
Iteration 1: log likelihood = -21758.771
Iteration 2: log likelihood = -21758.686
Iteration 3: log likelihood = -21758.686
Log likelihood = -21758.686 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994216 .1086928 12.66 0.000 1.792166 2.219045
mot_egr_late | 1.650641 .0777562 10.64 0.000 1.505066 1.810298
tr_mod2 | 1.152139 .0429541 3.80 0.000 1.070953 1.239479
sex_dum2 | .5925387 .0255613 -12.13 0.000 .544499 .6448168
edad_ini_cons | .9733999 .0040332 -6.51 0.000 .965527 .9813369
esc1 | 1.517041 .0833337 7.59 0.000 1.362195 1.689488
esc2 | 1.344021 .0693397 5.73 0.000 1.214763 1.487033
sus_prin2 | 1.195453 .0708942 3.01 0.003 1.064274 1.342801
sus_prin3 | 1.716651 .0822796 11.27 0.000 1.562729 1.885733
sus_prin4 | 1.143296 .0793666 1.93 0.054 .9978586 1.309931
sus_prin5 | 1.355176 .183987 2.24 0.025 1.03856 1.768317
fr_cons_sus_prin2 | .9773867 .0969543 -0.23 0.818 .8046914 1.187144
fr_cons_sus_prin3 | .9956628 .0799232 -0.05 0.957 .8507171 1.165304
fr_cons_sus_prin4 | 1.038094 .0863069 0.45 0.653 .8819984 1.221814
fr_cons_sus_prin5 | 1.088928 .0865796 1.07 0.284 .9317966 1.272558
cond_ocu2 | 1.087592 .0670797 1.36 0.173 .9637538 1.227342
cond_ocu3 | 1.144708 .2802124 0.55 0.581 .7084836 1.849524
cond_ocu4 | 1.240402 .0809945 3.30 0.001 1.091394 1.409754
cond_ocu5 | 1.332931 .1368398 2.80 0.005 1.08999 1.630019
cond_ocu6 | 1.211857 .0420088 5.54 0.000 1.132256 1.297054
policonsumo | 1.007136 .0431233 0.17 0.868 .9260649 1.095303
num_hij2 | 1.13621 .0394228 3.68 0.000 1.061512 1.216165
tenviv1 | 1.018591 .1150757 0.16 0.870 .8162725 1.271056
tenviv2 | 1.06841 .0803147 0.88 0.379 .9220434 1.238011
tenviv4 | 1.012301 .042064 0.29 0.769 .9331247 1.098195
tenviv5 | .9929135 .0331978 -0.21 0.832 .9299332 1.060159
mzone2 | 1.416303 .05249 9.39 0.000 1.317072 1.52301
mzone3 | 1.544806 .0865317 7.76 0.000 1.384185 1.724065
n_off_vio | 1.461709 .0503345 11.02 0.000 1.36631 1.563768
n_off_acq | 2.796305 .0871135 33.01 0.000 2.630674 2.972364
n_off_sud | 1.37677 .045643 9.64 0.000 1.290155 1.469199
n_off_oth | 1.702261 .0564676 16.04 0.000 1.595108 1.816613
psy_com2 | 1.048502 .0402988 1.23 0.218 .9724199 1.130538
dep2 | 1.032652 .0387469 0.86 0.392 .9594344 1.111456
rural2 | .937162 .0520334 -1.17 0.242 .8405315 1.044902
rural3 | .8650328 .0540242 -2.32 0.020 .7653713 .9776715
porc_pobr | 1.707301 .3687346 2.48 0.013 1.118077 2.607045
susini2 | 1.098226 .0720415 1.43 0.153 .9657269 1.248903
susini3 | 1.27111 .0731728 4.17 0.000 1.135489 1.42293
susini4 | 1.155442 .0378932 4.41 0.000 1.08351 1.232151
susini5 | 1.378173 .1164274 3.80 0.000 1.167871 1.626346
ano_nac_corr | .8463991 .0067727 -20.84 0.000 .8332283 .8597781
cohab2 | .8633487 .0473401 -2.68 0.007 .7753757 .9613029
cohab3 | 1.075904 .0686944 1.15 0.252 .9493486 1.219329
cohab4 | .9447669 .0518799 -1.03 0.301 .848365 1.052123
fis_com2 | 1.112845 .0326242 3.65 0.000 1.050705 1.17866
rc_x1 | .8446229 .0086662 -16.46 0.000 .827807 .8617803
rc_x2 | .8807263 .0305069 -3.67 0.000 .8229183 .9425951
rc_x3 | 1.297888 .1196543 2.83 0.005 1.083337 1.55493
_rcs1 | 2.175386 .0585978 28.85 0.000 2.063516 2.293321
_rcs2 | 1.070975 .0075224 9.76 0.000 1.056332 1.08582
_rcs3 | 1.034473 .0057265 6.12 0.000 1.02331 1.045758
_rcs4 | 1.019821 .0039785 5.03 0.000 1.012053 1.027649
_rcs5 | 1.012767 .0028574 4.50 0.000 1.007182 1.018383
_rcs6 | 1.011836 .0022999 5.18 0.000 1.007339 1.016354
_rcs7 | 1.00744 .001876 3.98 0.000 1.00377 1.011124
_rcs_mot_egr_early1 | .8986482 .0272103 -3.53 0.000 .8468687 .9535937
_rcs_mot_egr_late1 | .9213079 .0268047 -2.82 0.005 .8702415 .9753709
_cons | 1.0e+143 1.6e+144 20.45 0.000 2.0e+129 5.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21771.776
Iteration 1: log likelihood = -21758.504
Iteration 2: log likelihood = -21758.403
Iteration 3: log likelihood = -21758.403
Log likelihood = -21758.403 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993176 .1086773 12.65 0.000 1.791159 2.217978
mot_egr_late | 1.651065 .0777912 10.64 0.000 1.505425 1.810795
tr_mod2 | 1.151936 .0429479 3.79 0.000 1.070762 1.239264
sex_dum2 | .5925422 .0255615 -12.13 0.000 .544502 .6448208
edad_ini_cons | .9734035 .0040332 -6.51 0.000 .9655306 .9813406
esc1 | 1.517041 .0833337 7.59 0.000 1.362195 1.689489
esc2 | 1.344006 .0693389 5.73 0.000 1.214749 1.487016
sus_prin2 | 1.19548 .0708964 3.01 0.003 1.064297 1.342832
sus_prin3 | 1.716719 .0822828 11.28 0.000 1.562791 1.885808
sus_prin4 | 1.143378 .0793727 1.93 0.054 .9979299 1.310026
sus_prin5 | 1.355428 .1840243 2.24 0.025 1.038747 1.768653
fr_cons_sus_prin2 | .9774172 .0969575 -0.23 0.818 .8047163 1.187182
fr_cons_sus_prin3 | .9957205 .0799279 -0.05 0.957 .8507663 1.165372
fr_cons_sus_prin4 | 1.038102 .0863073 0.45 0.653 .8820056 1.221823
fr_cons_sus_prin5 | 1.088968 .086582 1.07 0.284 .9318313 1.272602
cond_ocu2 | 1.087491 .0670737 1.36 0.174 .9636638 1.227229
cond_ocu3 | 1.144816 .2802398 0.55 0.581 .7085486 1.849701
cond_ocu4 | 1.240608 .0810069 3.30 0.001 1.091577 1.409986
cond_ocu5 | 1.333291 .1368782 2.80 0.005 1.090283 1.630463
cond_ocu6 | 1.211848 .0420083 5.54 0.000 1.132248 1.297044
policonsumo | 1.007153 .0431241 0.17 0.868 .9260805 1.095322
num_hij2 | 1.136227 .0394233 3.68 0.000 1.061527 1.216183
tenviv1 | 1.018645 .1150822 0.16 0.870 .8163149 1.271124
tenviv2 | 1.068284 .0803054 0.88 0.380 .921934 1.237866
tenviv4 | 1.012399 .0420683 0.30 0.767 .9332152 1.098302
tenviv5 | .993014 .0332013 -0.21 0.834 .930027 1.060267
mzone2 | 1.416421 .0524943 9.39 0.000 1.317182 1.523137
mzone3 | 1.545042 .0865447 7.77 0.000 1.384397 1.724328
n_off_vio | 1.461743 .0503361 11.02 0.000 1.366342 1.563806
n_off_acq | 2.796395 .0871156 33.01 0.000 2.630759 2.972458
n_off_sud | 1.376747 .0456418 9.64 0.000 1.290135 1.469173
n_off_oth | 1.70233 .0564698 16.04 0.000 1.595172 1.816686
psy_com2 | 1.048948 .0403212 1.24 0.214 .9728239 1.13103
dep2 | 1.032646 .0387469 0.86 0.392 .9594287 1.111451
rural2 | .9371051 .0520304 -1.17 0.242 .8404802 1.044838
rural3 | .8648783 .0540157 -2.32 0.020 .7652326 .9774994
porc_pobr | 1.705883 .3684478 2.47 0.013 1.117124 2.604938
susini2 | 1.098311 .0720482 1.43 0.153 .9658006 1.249003
susini3 | 1.271242 .0731806 4.17 0.000 1.135606 1.423078
susini4 | 1.155375 .0378912 4.40 0.000 1.083446 1.232079
susini5 | 1.378078 .1164201 3.80 0.000 1.167789 1.626235
ano_nac_corr | .8464091 .0067739 -20.84 0.000 .8332362 .8597903
cohab2 | .8631615 .0473304 -2.68 0.007 .7752065 .9610958
cohab3 | 1.075635 .0686783 1.14 0.253 .9491099 1.219027
cohab4 | .9445965 .0518706 -1.04 0.299 .8482118 1.051934
fis_com2 | 1.112781 .0326235 3.65 0.000 1.050643 1.178595
rc_x1 | .8446241 .0086671 -16.46 0.000 .8278066 .8617833
rc_x2 | .8807568 .0305079 -3.67 0.000 .822947 .9426276
rc_x3 | 1.297796 .1196459 2.83 0.005 1.08326 1.55482
_rcs1 | 2.169673 .0624901 26.89 0.000 2.050588 2.295674
_rcs2 | 1.065511 .0233964 2.89 0.004 1.020628 1.112369
_rcs3 | 1.03341 .006747 5.03 0.000 1.02027 1.046719
_rcs4 | 1.019614 .0040429 4.90 0.000 1.011721 1.027569
_rcs5 | 1.012734 .0028585 4.48 0.000 1.007147 1.018352
_rcs6 | 1.011833 .0023001 5.17 0.000 1.007335 1.016351
_rcs7 | 1.007434 .0018764 3.98 0.000 1.003763 1.011119
_rcs_mot_egr_early1 | .8995875 .0290274 -3.28 0.001 .8444566 .9583178
_rcs_mot_egr_early2 | .9999489 .0245841 -0.00 0.998 .9529074 1.049313
_rcs_mot_egr_late1 | .9255229 .0289351 -2.48 0.013 .8705137 .9840082
_rcs_mot_egr_late2 | 1.01011 .0242107 0.42 0.675 .9637549 1.058694
_cons | 1.0e+143 1.6e+144 20.45 0.000 2.0e+129 5.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21771.216
Iteration 1: log likelihood = -21758.102
Iteration 2: log likelihood = -21757.977
Iteration 3: log likelihood = -21757.977
Log likelihood = -21757.977 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994983 .1087932 12.66 0.000 1.792752 2.220026
mot_egr_late | 1.652895 .0778978 10.66 0.000 1.507057 1.812845
tr_mod2 | 1.152032 .0429519 3.80 0.000 1.070851 1.239369
sex_dum2 | .592524 .0255606 -12.13 0.000 .5444855 .6448008
edad_ini_cons | .9733962 .0040332 -6.51 0.000 .9655233 .9813334
esc1 | 1.517016 .0833318 7.59 0.000 1.362174 1.68946
esc2 | 1.343982 .0693375 5.73 0.000 1.214728 1.486989
sus_prin2 | 1.195686 .0709101 3.01 0.003 1.064478 1.343066
sus_prin3 | 1.716964 .0822979 11.28 0.000 1.563008 1.886085
sus_prin4 | 1.14346 .0793793 1.93 0.053 .998 1.310122
sus_prin5 | 1.355911 .1840923 2.24 0.025 1.039114 1.76929
fr_cons_sus_prin2 | .9774316 .0969588 -0.23 0.818 .8047283 1.187199
fr_cons_sus_prin3 | .9957081 .0799269 -0.05 0.957 .8507556 1.165358
fr_cons_sus_prin4 | 1.038157 .086312 0.45 0.652 .8820522 1.221888
fr_cons_sus_prin5 | 1.08898 .0865834 1.07 0.284 .9318418 1.272618
cond_ocu2 | 1.087432 .0670706 1.36 0.174 .9636109 1.227164
cond_ocu3 | 1.14526 .2803512 0.55 0.580 .7088207 1.850427
cond_ocu4 | 1.24032 .0809895 3.30 0.001 1.091321 1.409661
cond_ocu5 | 1.333224 .136873 2.80 0.005 1.090225 1.630385
cond_ocu6 | 1.211824 .0420081 5.54 0.000 1.132224 1.29702
policonsumo | 1.007203 .0431267 0.17 0.867 .9261256 1.095378
num_hij2 | 1.136174 .0394212 3.68 0.000 1.061478 1.216125
tenviv1 | 1.018483 .1150657 0.16 0.871 .816182 1.270926
tenviv2 | 1.068439 .0803178 0.88 0.379 .9220671 1.238047
tenviv4 | 1.012304 .0420647 0.29 0.769 .9331265 1.098199
tenviv5 | .9929252 .0331986 -0.21 0.832 .9299434 1.060173
mzone2 | 1.416455 .0524962 9.39 0.000 1.317213 1.523175
mzone3 | 1.544799 .0865318 7.76 0.000 1.384179 1.724059
n_off_vio | 1.461724 .0503343 11.02 0.000 1.366326 1.563783
n_off_acq | 2.796298 .0871094 33.01 0.000 2.630675 2.972349
n_off_sud | 1.376633 .0456375 9.64 0.000 1.290029 1.469051
n_off_oth | 1.702302 .0564676 16.04 0.000 1.595149 1.816654
psy_com2 | 1.048973 .0403259 1.24 0.214 .9728394 1.131064
dep2 | 1.032646 .0387471 0.86 0.392 .9594282 1.111451
rural2 | .9372233 .052037 -1.17 0.243 .8405861 1.04497
rural3 | .8649739 .0540213 -2.32 0.020 .7653178 .9776067
porc_pobr | 1.704107 .3681102 2.47 0.014 1.115902 2.602364
susini2 | 1.098514 .0720621 1.43 0.152 .9659777 1.249235
susini3 | 1.271244 .0731809 4.17 0.000 1.135608 1.423081
susini4 | 1.155332 .0378897 4.40 0.000 1.083406 1.232033
susini5 | 1.378162 .1164282 3.80 0.000 1.167858 1.626337
ano_nac_corr | .8463893 .0067738 -20.84 0.000 .8332164 .8597704
cohab2 | .8632036 .0473331 -2.68 0.007 .7752437 .9611435
cohab3 | 1.075653 .0686803 1.14 0.253 .9491246 1.21905
cohab4 | .9446286 .0518725 -1.04 0.300 .8482404 1.05197
fis_com2 | 1.112612 .0326185 3.64 0.000 1.050483 1.178416
rc_x1 | .8446008 .0086669 -16.46 0.000 .8277836 .8617596
rc_x2 | .8807822 .0305086 -3.66 0.000 .8229709 .9426545
rc_x3 | 1.297655 .1196324 2.83 0.005 1.083143 1.554649
_rcs1 | 2.178116 .0634464 26.72 0.000 2.057247 2.306087
_rcs2 | 1.059003 .0238631 2.54 0.011 1.01325 1.106822
_rcs3 | 1.043983 .01363 3.30 0.001 1.017608 1.071042
_rcs4 | 1.026343 .0086003 3.10 0.002 1.009624 1.043338
_rcs5 | 1.014911 .0037795 3.97 0.000 1.00753 1.022345
_rcs6 | 1.01219 .0023356 5.25 0.000 1.007622 1.016778
_rcs7 | 1.007428 .0018763 3.97 0.000 1.003758 1.011113
_rcs_mot_egr_early1 | .8960231 .0292594 -3.36 0.001 .8404723 .9552456
_rcs_mot_egr_early2 | 1.004613 .0249215 0.19 0.853 .9569361 1.054665
_rcs_mot_egr_early3 | .9871714 .016944 -0.75 0.452 .9545141 1.020946
_rcs_mot_egr_late1 | .9213882 .0291429 -2.59 0.010 .8660036 .9803149
_rcs_mot_egr_late2 | 1.016057 .0247089 0.66 0.512 .9687647 1.065658
_rcs_mot_egr_late3 | .985557 .0163484 -0.88 0.380 .9540301 1.018126
_cons | 1.0e+143 1.7e+144 20.45 0.000 2.1e+129 5.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21771.294
Iteration 1: log likelihood = -21756.351
Iteration 2: log likelihood = -21756.158
Iteration 3: log likelihood = -21756.158
Log likelihood = -21756.158 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996349 .1088877 12.67 0.000 1.793945 2.22159
mot_egr_late | 1.654038 .0779664 10.68 0.000 1.508073 1.81413
tr_mod2 | 1.152188 .0429592 3.80 0.000 1.070992 1.239539
sex_dum2 | .5925127 .0255602 -12.13 0.000 .544475 .6447887
edad_ini_cons | .9733931 .0040333 -6.51 0.000 .96552 .9813304
esc1 | 1.516922 .0833263 7.59 0.000 1.36209 1.689355
esc2 | 1.344008 .0693387 5.73 0.000 1.214752 1.487018
sus_prin2 | 1.195886 .0709232 3.02 0.003 1.064654 1.343294
sus_prin3 | 1.717377 .082321 11.28 0.000 1.563378 1.886545
sus_prin4 | 1.143603 .0793895 1.93 0.053 .9981241 1.310286
sus_prin5 | 1.356107 .1841214 2.24 0.025 1.039261 1.769552
fr_cons_sus_prin2 | .9775492 .0969704 -0.23 0.819 .8048252 1.187342
fr_cons_sus_prin3 | .9958468 .0799379 -0.05 0.959 .8508746 1.165519
fr_cons_sus_prin4 | 1.038295 .0863235 0.45 0.651 .8821695 1.222051
fr_cons_sus_prin5 | 1.088989 .086584 1.07 0.284 .9318491 1.272627
cond_ocu2 | 1.087459 .0670727 1.36 0.174 .9636338 1.227195
cond_ocu3 | 1.145939 .2805178 0.56 0.578 .70924 1.851525
cond_ocu4 | 1.240041 .0809719 3.29 0.001 1.091075 1.409346
cond_ocu5 | 1.33303 .1368566 2.80 0.005 1.090061 1.630156
cond_ocu6 | 1.211701 .0420049 5.54 0.000 1.132108 1.296891
policonsumo | 1.007179 .0431257 0.17 0.867 .9261038 1.095352
num_hij2 | 1.136059 .0394163 3.68 0.000 1.061373 1.216001
tenviv1 | 1.018288 .1150477 0.16 0.873 .81602 1.270693
tenviv2 | 1.06876 .080342 0.88 0.376 .9223433 1.238419
tenviv4 | 1.012177 .0420595 0.29 0.771 .9330093 1.098062
tenviv5 | .9929081 .033198 -0.21 0.831 .9299274 1.060154
mzone2 | 1.416448 .052497 9.39 0.000 1.317204 1.523169
mzone3 | 1.544853 .0865366 7.76 0.000 1.384223 1.724122
n_off_vio | 1.461591 .0503296 11.02 0.000 1.366202 1.56364
n_off_acq | 2.796088 .0871005 33.01 0.000 2.630481 2.972121
n_off_sud | 1.376569 .0456339 9.64 0.000 1.289972 1.468979
n_off_oth | 1.702351 .0564685 16.04 0.000 1.595195 1.816704
psy_com2 | 1.049439 .0403437 1.26 0.209 .973272 1.131566
dep2 | 1.032652 .0387476 0.86 0.392 .9594336 1.111458
rural2 | .9375485 .0520545 -1.16 0.245 .8408787 1.045332
rural3 | .8650102 .0540228 -2.32 0.020 .7653513 .977646
porc_pobr | 1.698436 .3669187 2.45 0.014 1.112144 2.593803
susini2 | 1.098978 .0720927 1.44 0.150 .9663851 1.249763
susini3 | 1.271312 .0731848 4.17 0.000 1.135668 1.423156
susini4 | 1.155276 .0378873 4.40 0.000 1.083355 1.231973
susini5 | 1.378139 .1164276 3.80 0.000 1.167836 1.626312
ano_nac_corr | .8463731 .0067735 -20.84 0.000 .8332008 .8597537
cohab2 | .8632557 .0473372 -2.68 0.007 .7752883 .9612042
cohab3 | 1.075562 .068676 1.14 0.254 .9490412 1.218949
cohab4 | .9447023 .0518775 -1.04 0.300 .8483049 1.052054
fis_com2 | 1.112252 .0326069 3.63 0.000 1.050145 1.178032
rc_x1 | .8446031 .0086665 -16.46 0.000 .8277868 .861761
rc_x2 | .8807168 .0305053 -3.67 0.000 .8229119 .9425822
rc_x3 | 1.297842 .1196459 2.83 0.005 1.083306 1.554865
_rcs1 | 2.180932 .0635016 26.78 0.000 2.059956 2.309013
_rcs2 | 1.05938 .0247302 2.47 0.013 1.012002 1.108976
_rcs3 | 1.033503 .0165884 2.05 0.040 1.001496 1.066532
_rcs4 | 1.032217 .0088449 3.70 0.000 1.015026 1.049699
_rcs5 | 1.024955 .0078718 3.21 0.001 1.009642 1.0405
_rcs6 | 1.016482 .0036501 4.55 0.000 1.009353 1.023662
_rcs7 | 1.007704 .0018819 4.11 0.000 1.004022 1.011399
_rcs_mot_egr_early1 | .8946681 .0292217 -3.41 0.001 .8391894 .9538145
_rcs_mot_egr_early2 | 1.006065 .0256422 0.24 0.812 .9570414 1.057599
_rcs_mot_egr_early3 | .9978209 .0185792 -0.12 0.907 .9620628 1.034908
_rcs_mot_egr_early4 | .976667 .0119932 -1.92 0.055 .9534413 1.000458
_rcs_mot_egr_late1 | .9200782 .0290987 -2.63 0.008 .8647774 .9789154
_rcs_mot_egr_late2 | 1.017408 .0255193 0.69 0.491 .9686002 1.068674
_rcs_mot_egr_late3 | .9920656 .017915 -0.44 0.659 .9575669 1.027807
_rcs_mot_egr_late4 | .9843022 .0115733 -1.35 0.178 .9618784 1.007249
_cons | 1.1e+143 1.7e+144 20.45 0.000 2.1e+129 5.5e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21770.851
Iteration 1: log likelihood = -21755.699
Iteration 2: log likelihood = -21755.515
Iteration 3: log likelihood = -21755.515
Log likelihood = -21755.515 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.998323 .1090133 12.69 0.000 1.795687 2.223826
mot_egr_late | 1.65572 .0780594 10.70 0.000 1.509582 1.816005
tr_mod2 | 1.15218 .0429597 3.80 0.000 1.070983 1.239532
sex_dum2 | .5925158 .0255604 -12.13 0.000 .5444778 .644792
edad_ini_cons | .9733941 .0040333 -6.51 0.000 .965521 .9813313
esc1 | 1.51694 .0833268 7.59 0.000 1.362107 1.689374
esc2 | 1.343974 .0693368 5.73 0.000 1.214721 1.48698
sus_prin2 | 1.195949 .0709274 3.02 0.003 1.064709 1.343366
sus_prin3 | 1.71755 .0823307 11.28 0.000 1.563533 1.886739
sus_prin4 | 1.143702 .0793962 1.93 0.053 .998211 1.3104
sus_prin5 | 1.356119 .1841231 2.24 0.025 1.03927 1.769568
fr_cons_sus_prin2 | .977542 .0969697 -0.23 0.819 .8048193 1.187333
fr_cons_sus_prin3 | .995827 .0799363 -0.05 0.958 .8508576 1.165496
fr_cons_sus_prin4 | 1.038318 .0863255 0.45 0.651 .8821891 1.222078
fr_cons_sus_prin5 | 1.088951 .0865811 1.07 0.284 .931816 1.272583
cond_ocu2 | 1.087433 .0670712 1.36 0.174 .9636111 1.227166
cond_ocu3 | 1.146057 .2805471 0.56 0.578 .7093129 1.851717
cond_ocu4 | 1.239861 .0809606 3.29 0.001 1.090915 1.409142
cond_ocu5 | 1.333214 .1368768 2.80 0.005 1.090209 1.630384
cond_ocu6 | 1.2117 .042005 5.54 0.000 1.132106 1.296889
policonsumo | 1.007088 .0431216 0.16 0.869 .92602 1.095252
num_hij2 | 1.136063 .0394162 3.68 0.000 1.061377 1.216005
tenviv1 | 1.018258 .1150437 0.16 0.873 .8159967 1.270654
tenviv2 | 1.068914 .0803531 0.89 0.375 .9224769 1.238596
tenviv4 | 1.012082 .0420554 0.29 0.773 .932922 1.097958
tenviv5 | .9928727 .0331969 -0.21 0.831 .929894 1.060117
mzone2 | 1.416407 .0524957 9.39 0.000 1.317166 1.523126
mzone3 | 1.544828 .0865366 7.76 0.000 1.384198 1.724098
n_off_vio | 1.461518 .0503265 11.02 0.000 1.366135 1.563561
n_off_acq | 2.796065 .0870976 33.01 0.000 2.630463 2.972092
n_off_sud | 1.376553 .0456326 9.64 0.000 1.289958 1.468961
n_off_oth | 1.702374 .0564683 16.04 0.000 1.595219 1.816727
psy_com2 | 1.049503 .0403477 1.26 0.209 .9733287 1.131639
dep2 | 1.032647 .0387476 0.86 0.392 .9594286 1.111453
rural2 | .9376928 .0520625 -1.16 0.247 .8410081 1.045493
rural3 | .8650981 .0540282 -2.32 0.020 .7654292 .9777452
porc_pobr | 1.695667 .3663316 2.44 0.015 1.110317 2.589608
susini2 | 1.099172 .0721058 1.44 0.149 .9665557 1.249985
susini3 | 1.271132 .0731749 4.17 0.000 1.135506 1.422956
susini4 | 1.155192 .0378844 4.40 0.000 1.083275 1.231882
susini5 | 1.378007 .116416 3.80 0.000 1.167725 1.626155
ano_nac_corr | .8463644 .0067736 -20.84 0.000 .833192 .8597451
cohab2 | .8632583 .0473372 -2.68 0.007 .775291 .9612067
cohab3 | 1.075529 .0686734 1.14 0.254 .9490133 1.218911
cohab4 | .944709 .0518778 -1.04 0.300 .8483111 1.052061
fis_com2 | 1.112121 .0326029 3.62 0.000 1.050022 1.177893
rc_x1 | .8445962 .0086666 -16.46 0.000 .8277797 .8617543
rc_x2 | .8806837 .0305038 -3.67 0.000 .8228815 .9425462
rc_x3 | 1.297992 .1196586 2.83 0.005 1.083433 1.555042
_rcs1 | 2.184558 .0637258 26.79 0.000 2.063161 2.313098
_rcs2 | 1.05637 .0240008 2.41 0.016 1.010362 1.104474
_rcs3 | 1.041222 .0174788 2.41 0.016 1.007521 1.076049
_rcs4 | 1.024409 .0102257 2.42 0.016 1.004562 1.044648
_rcs5 | 1.02358 .007238 3.30 0.001 1.009492 1.037865
_rcs6 | 1.023255 .0064622 3.64 0.000 1.010667 1.035999
_rcs7 | 1.010073 .0023432 4.32 0.000 1.005491 1.014676
_rcs_mot_egr_early1 | .8929487 .02922 -3.46 0.001 .8374765 .9520953
_rcs_mot_egr_early2 | 1.008489 .0252649 0.34 0.736 .9601667 1.059243
_rcs_mot_egr_early3 | .9951374 .0187619 -0.26 0.796 .959036 1.032598
_rcs_mot_egr_early4 | .9870117 .012298 -1.05 0.294 .9632 1.011412
_rcs_mot_egr_early5 | .9829696 .0088369 -1.91 0.056 .9658012 1.000443
_rcs_mot_egr_late1 | .9183836 .0290897 -2.69 0.007 .8631026 .9772053
_rcs_mot_egr_late2 | 1.020414 .0251965 0.82 0.413 .9722059 1.071013
_rcs_mot_egr_late3 | .9879878 .0180584 -0.66 0.508 .9532205 1.024023
_rcs_mot_egr_late4 | .9939436 .0118189 -0.51 0.609 .9710468 1.01738
_rcs_mot_egr_late5 | .9854007 .0084543 -1.71 0.086 .9689692 1.002111
_cons | 1.1e+143 1.8e+144 20.45 0.000 2.2e+129 5.6e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21770.791
Iteration 1: log likelihood = -21754.601
Iteration 2: log likelihood = -21754.398
Iteration 3: log likelihood = -21754.398
Log likelihood = -21754.398 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.998835 .109052 12.69 0.000 1.796128 2.224419
mot_egr_late | 1.656332 .0780983 10.70 0.000 1.510122 1.816698
tr_mod2 | 1.152153 .0429587 3.80 0.000 1.070959 1.239504
sex_dum2 | .5925128 .0255604 -12.13 0.000 .5444749 .6447891
edad_ini_cons | .9733955 .0040333 -6.51 0.000 .9655225 .9813327
esc1 | 1.516915 .0833256 7.59 0.000 1.362084 1.689346
esc2 | 1.343928 .0693345 5.73 0.000 1.21468 1.486929
sus_prin2 | 1.195944 .0709271 3.02 0.003 1.064704 1.34336
sus_prin3 | 1.717508 .0823284 11.28 0.000 1.563495 1.886691
sus_prin4 | 1.143615 .0793901 1.93 0.053 .9981351 1.310299
sus_prin5 | 1.355936 .1840973 2.24 0.025 1.039131 1.769326
fr_cons_sus_prin2 | .9775551 .096971 -0.23 0.819 .8048301 1.187349
fr_cons_sus_prin3 | .9958484 .079938 -0.05 0.959 .8508759 1.165521
fr_cons_sus_prin4 | 1.038364 .0863294 0.45 0.651 .8822282 1.222133
fr_cons_sus_prin5 | 1.088991 .0865843 1.07 0.284 .9318502 1.27263
cond_ocu2 | 1.087436 .0670716 1.36 0.174 .9636128 1.227169
cond_ocu3 | 1.14593 .2805162 0.56 0.578 .7092335 1.851512
cond_ocu4 | 1.239851 .0809602 3.29 0.001 1.090907 1.409132
cond_ocu5 | 1.333384 .1368956 2.80 0.005 1.090346 1.630596
cond_ocu6 | 1.211684 .0420044 5.54 0.000 1.132091 1.296872
policonsumo | 1.007135 .0431237 0.17 0.868 .9260637 1.095304
num_hij2 | 1.136066 .0394162 3.68 0.000 1.06138 1.216008
tenviv1 | 1.018208 .1150385 0.16 0.873 .8159561 1.270593
tenviv2 | 1.068913 .0803526 0.89 0.375 .9224767 1.238594
tenviv4 | 1.012055 .0420543 0.29 0.773 .9328973 1.09793
tenviv5 | .9928409 .0331959 -0.21 0.830 .9298642 1.060083
mzone2 | 1.416403 .0524953 9.39 0.000 1.317162 1.523121
mzone3 | 1.544713 .08653 7.76 0.000 1.384096 1.723969
n_off_vio | 1.461552 .0503279 11.02 0.000 1.366166 1.563597
n_off_acq | 2.796091 .087099 33.01 0.000 2.630487 2.972121
n_off_sud | 1.376559 .0456331 9.64 0.000 1.289963 1.468967
n_off_oth | 1.702407 .0564696 16.04 0.000 1.59525 1.816763
psy_com2 | 1.049439 .0403457 1.26 0.209 .9732688 1.131571
dep2 | 1.032646 .0387476 0.86 0.392 .9594277 1.111453
rural2 | .9376797 .052062 -1.16 0.246 .840996 1.045479
rural3 | .8650686 .0540265 -2.32 0.020 .7654028 .9777121
porc_pobr | 1.695905 .3663937 2.44 0.014 1.110459 2.590003
susini2 | 1.099069 .0720992 1.44 0.150 .9664646 1.249867
susini3 | 1.271254 .0731818 4.17 0.000 1.135616 1.423093
susini4 | 1.155211 .0378851 4.40 0.000 1.083294 1.231903
susini5 | 1.378103 .1164235 3.80 0.000 1.167808 1.626267
ano_nac_corr | .8463935 .0067739 -20.84 0.000 .8332205 .8597747
cohab2 | .8632811 .0473386 -2.68 0.007 .7753112 .9612325
cohab3 | 1.0755 .0686717 1.14 0.254 .9489871 1.218878
cohab4 | .9447224 .0518784 -1.04 0.300 .8483234 1.052076
fis_com2 | 1.112156 .0326042 3.63 0.000 1.050054 1.177931
rc_x1 | .8446225 .0086669 -16.46 0.000 .8278054 .8617812
rc_x2 | .8806917 .030504 -3.67 0.000 .8228892 .9425545
rc_x3 | 1.297963 .1196555 2.83 0.005 1.083409 1.555007
_rcs1 | 2.18594 .0637866 26.80 0.000 2.064429 2.314604
_rcs2 | 1.056465 .0239129 2.43 0.015 1.010621 1.104389
_rcs3 | 1.041998 .0176248 2.43 0.015 1.00802 1.077121
_rcs4 | 1.020084 .0112032 1.81 0.070 .9983613 1.04228
_rcs5 | 1.028441 .0079312 3.64 0.000 1.013013 1.044104
_rcs6 | 1.023306 .0061626 3.83 0.000 1.011298 1.035456
_rcs7 | 1.010088 .0037118 2.73 0.006 1.002839 1.017389
_rcs_mot_egr_early1 | .8923961 .0292124 -3.48 0.001 .8369389 .9515279
_rcs_mot_egr_early2 | 1.008291 .0252239 0.33 0.741 .9600451 1.058961
_rcs_mot_egr_early3 | .9959606 .0188117 -0.21 0.830 .9597645 1.033522
_rcs_mot_egr_early4 | .9936864 .0126394 -0.50 0.619 .9692199 1.018771
_rcs_mot_egr_early5 | .9771502 .0089467 -2.52 0.012 .9597714 .9948438
_rcs_mot_egr_early6 | .9938222 .0063556 -0.97 0.333 .9814431 1.006357
_rcs_mot_egr_late1 | .9177532 .029076 -2.71 0.007 .8624985 .9765478
_rcs_mot_egr_late2 | 1.020513 .025188 0.82 0.411 .9723203 1.071094
_rcs_mot_egr_late3 | .9886645 .0181192 -0.62 0.534 .9537817 1.024823
_rcs_mot_egr_late4 | .998819 .0121673 -0.10 0.923 .975254 1.022953
_rcs_mot_egr_late5 | .9818304 .0085816 -2.10 0.036 .965154 .9987949
_rcs_mot_egr_late6 | .9931051 .0059697 -1.15 0.250 .9814734 1.004875
_cons | 1.0e+143 1.7e+144 20.45 0.000 2.0e+129 5.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21771.354
Iteration 1: log likelihood = -21755.53
Iteration 2: log likelihood = -21755.311
Iteration 3: log likelihood = -21755.311
Log likelihood = -21755.311 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997678 .1089735 12.69 0.000 1.795116 2.223098
mot_egr_late | 1.655451 .0780458 10.69 0.000 1.509338 1.815708
tr_mod2 | 1.152152 .0429586 3.80 0.000 1.070958 1.239502
sex_dum2 | .5925107 .0255602 -12.13 0.000 .544473 .6447867
edad_ini_cons | .9733943 .0040332 -6.51 0.000 .9655213 .9813315
esc1 | 1.516966 .0833284 7.59 0.000 1.36213 1.689403
esc2 | 1.34398 .0693372 5.73 0.000 1.214727 1.486987
sus_prin2 | 1.195889 .0709232 3.02 0.003 1.064656 1.343297
sus_prin3 | 1.717437 .0823238 11.28 0.000 1.563433 1.886612
sus_prin4 | 1.143607 .0793892 1.93 0.053 .998128 1.310289
sus_prin5 | 1.35597 .1841023 2.24 0.025 1.039157 1.769372
fr_cons_sus_prin2 | .9775367 .0969691 -0.23 0.819 .804815 1.187326
fr_cons_sus_prin3 | .9958249 .0799361 -0.05 0.958 .8508559 1.165494
fr_cons_sus_prin4 | 1.038319 .0863255 0.45 0.651 .8821901 1.222079
fr_cons_sus_prin5 | 1.088985 .0865836 1.07 0.284 .9318459 1.272623
cond_ocu2 | 1.08741 .0670699 1.36 0.174 .9635906 1.227141
cond_ocu3 | 1.145826 .2804908 0.56 0.578 .7091695 1.851345
cond_ocu4 | 1.239927 .0809651 3.29 0.001 1.090973 1.409218
cond_ocu5 | 1.333243 .1368807 2.80 0.005 1.090232 1.630422
cond_ocu6 | 1.211655 .0420035 5.54 0.000 1.132064 1.296842
policonsumo | 1.007127 .0431234 0.17 0.868 .9260561 1.095295
num_hij2 | 1.136079 .0394167 3.68 0.000 1.061392 1.216022
tenviv1 | 1.01815 .1150316 0.16 0.874 .8159099 1.27052
tenviv2 | 1.068789 .0803434 0.88 0.376 .9223696 1.238451
tenviv4 | 1.012056 .0420545 0.29 0.773 .9328975 1.09793
tenviv5 | .9928473 .0331961 -0.21 0.830 .9298703 1.06009
mzone2 | 1.41643 .0524961 9.39 0.000 1.317188 1.52315
mzone3 | 1.544665 .0865271 7.76 0.000 1.384053 1.723915
n_off_vio | 1.461563 .0503284 11.02 0.000 1.366176 1.563609
n_off_acq | 2.796171 .087102 33.01 0.000 2.630561 2.972207
n_off_sud | 1.376599 .0456345 9.64 0.000 1.290001 1.469011
n_off_oth | 1.702413 .0564701 16.04 0.000 1.595255 1.81677
psy_com2 | 1.049422 .0403454 1.25 0.210 .9732528 1.131553
dep2 | 1.032641 .0387475 0.86 0.392 .9594226 1.111447
rural2 | .9377383 .0520655 -1.16 0.247 .8410482 1.045544
rural3 | .8650767 .0540271 -2.32 0.020 .7654099 .9777214
porc_pobr | 1.695811 .3663785 2.44 0.014 1.110392 2.589875
susini2 | 1.098973 .0720926 1.44 0.150 .9663808 1.249758
susini3 | 1.271228 .0731803 4.17 0.000 1.135593 1.423063
susini4 | 1.155228 .0378857 4.40 0.000 1.083309 1.23192
susini5 | 1.378076 .1164216 3.80 0.000 1.167784 1.626236
ano_nac_corr | .8463741 .0067737 -20.84 0.000 .8332015 .859755
cohab2 | .8632348 .0473359 -2.68 0.007 .7752699 .9611805
cohab3 | 1.075507 .0686721 1.14 0.254 .9489931 1.218886
cohab4 | .9446862 .0518762 -1.04 0.300 .8482912 1.052035
fis_com2 | 1.112172 .0326048 3.63 0.000 1.050069 1.177948
rc_x1 | .8445979 .0086666 -16.46 0.000 .8277812 .8617561
rc_x2 | .8807167 .030505 -3.67 0.000 .8229121 .9425816
rc_x3 | 1.297885 .1196487 2.83 0.005 1.083343 1.554913
_rcs1 | 2.18384 .0637044 26.78 0.000 2.062483 2.312336
_rcs2 | 1.057133 .0240271 2.44 0.015 1.011074 1.10529
_rcs3 | 1.041436 .0182699 2.31 0.021 1.006236 1.077867
_rcs4 | 1.022204 .0125991 1.78 0.075 .9978064 1.047199
_rcs5 | 1.026898 .0086148 3.16 0.002 1.010152 1.043923
_rcs6 | 1.019013 .0070723 2.71 0.007 1.005245 1.032969
_rcs7 | 1.012989 .0057206 2.29 0.022 1.001838 1.024263
_rcs_mot_egr_early1 | .8936175 .0292408 -3.44 0.001 .8381056 .9528061
_rcs_mot_egr_early2 | 1.007779 .0253156 0.31 0.758 .9593634 1.058639
_rcs_mot_egr_early3 | .99751 .0195055 -0.13 0.899 .9600033 1.036482
_rcs_mot_egr_early4 | .9938255 .0137915 -0.45 0.655 .967159 1.021227
_rcs_mot_egr_early5 | .9821212 .0094841 -1.87 0.062 .9637075 1.000887
_rcs_mot_egr_early6 | .9909485 .0078872 -1.14 0.253 .9756099 1.006528
_rcs_mot_egr_early7 | .9941931 .0064512 -0.90 0.369 .981629 1.006918
_rcs_mot_egr_late1 | .9186697 .0291009 -2.68 0.007 .8633674 .9775143
_rcs_mot_egr_late2 | 1.020671 .0253589 0.82 0.410 .9721591 1.071603
_rcs_mot_egr_late3 | .9878427 .018986 -0.64 0.525 .9513229 1.025764
_rcs_mot_egr_late4 | .9999002 .0134426 -0.01 0.994 .9738973 1.026597
_rcs_mot_egr_late5 | .9859117 .0090961 -1.54 0.124 .9682439 1.003902
_rcs_mot_egr_late6 | .9927735 .0075299 -0.96 0.339 .9781243 1.007642
_rcs_mot_egr_late7 | .9935366 .0061384 -1.05 0.294 .9815782 1.005641
_cons | 1.1e+143 1.7e+144 20.45 0.000 2.1e+129 5.5e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21769.852
Iteration 1: log likelihood = -21757.661
Iteration 2: log likelihood = -21757.57
Iteration 3: log likelihood = -21757.57
Log likelihood = -21757.57 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994719 .1087238 12.67 0.000 1.792612 2.219613
mot_egr_late | 1.650885 .0777693 10.64 0.000 1.505285 1.810569
tr_mod2 | 1.152141 .0429542 3.80 0.000 1.070954 1.239482
sex_dum2 | .5925674 .0255625 -12.13 0.000 .5445254 .644848
edad_ini_cons | .9733969 .0040332 -6.51 0.000 .9655239 .981334
esc1 | 1.517094 .0833365 7.59 0.000 1.362243 1.689547
esc2 | 1.344028 .0693399 5.73 0.000 1.214769 1.48704
sus_prin2 | 1.195555 .0709009 3.01 0.003 1.064364 1.342917
sus_prin3 | 1.716839 .0822894 11.28 0.000 1.562899 1.885942
sus_prin4 | 1.14343 .0793761 1.93 0.054 .997975 1.310084
sus_prin5 | 1.35532 .1840071 2.24 0.025 1.038669 1.768506
fr_cons_sus_prin2 | .9773897 .0969546 -0.23 0.818 .8046939 1.187148
fr_cons_sus_prin3 | .9956267 .0799204 -0.05 0.956 .8506861 1.165262
fr_cons_sus_prin4 | 1.038101 .0863075 0.45 0.653 .8820048 1.221823
fr_cons_sus_prin5 | 1.088872 .0865754 1.07 0.284 .9317476 1.272492
cond_ocu2 | 1.087555 .0670776 1.36 0.174 .9637207 1.227301
cond_ocu3 | 1.145132 .2803162 0.55 0.580 .708746 1.850209
cond_ocu4 | 1.240256 .0809837 3.30 0.001 1.091268 1.409585
cond_ocu5 | 1.332996 .1368464 2.80 0.005 1.090044 1.630099
cond_ocu6 | 1.211923 .0420111 5.54 0.000 1.132318 1.297125
policonsumo | 1.007051 .0431195 0.16 0.870 .9259875 1.095211
num_hij2 | 1.136213 .039423 3.68 0.000 1.061514 1.216169
tenviv1 | 1.018672 .1150846 0.16 0.870 .8163379 1.271157
tenviv2 | 1.068596 .0803291 0.88 0.377 .9222032 1.238228
tenviv4 | 1.012331 .0420651 0.29 0.768 .933153 1.098228
tenviv5 | .9929436 .0331987 -0.21 0.832 .9299614 1.060191
mzone2 | 1.41631 .052491 9.39 0.000 1.317078 1.523019
mzone3 | 1.544966 .0865412 7.77 0.000 1.384328 1.724245
n_off_vio | 1.461632 .0503305 11.02 0.000 1.366241 1.563683
n_off_acq | 2.796094 .0871033 33.01 0.000 2.630481 2.972132
n_off_sud | 1.376637 .0456378 9.64 0.000 1.290033 1.469055
n_off_oth | 1.7022 .0564636 16.04 0.000 1.595054 1.816543
psy_com2 | 1.048511 .0403003 1.23 0.218 .9724257 1.130549
dep2 | 1.032626 .0387461 0.86 0.392 .9594107 1.111429
rural2 | .9371837 .0520342 -1.17 0.243 .8405517 1.044925
rural3 | .8651018 .0540285 -2.32 0.020 .7654324 .9777495
porc_pobr | 1.705783 .368404 2.47 0.013 1.117087 2.604719
susini2 | 1.098581 .0720657 1.43 0.152 .9660376 1.249309
susini3 | 1.270953 .0731643 4.17 0.000 1.135348 1.422755
susini4 | 1.155308 .0378889 4.40 0.000 1.083384 1.232008
susini5 | 1.377966 .11641 3.80 0.000 1.167695 1.626102
ano_nac_corr | .8463477 .0067725 -20.85 0.000 .8331774 .8597261
cohab2 | .863356 .0473404 -2.68 0.007 .7753825 .9613108
cohab3 | 1.07588 .0686929 1.15 0.252 .9493278 1.219302
cohab4 | .9447474 .0518788 -1.04 0.301 .8483475 1.052101
fis_com2 | 1.112788 .0326222 3.65 0.000 1.050652 1.178599
rc_x1 | .8445783 .0086659 -16.46 0.000 .8277632 .861735
rc_x2 | .8806783 .0305052 -3.67 0.000 .8228736 .9425436
rc_x3 | 1.298076 .1196715 2.83 0.005 1.083494 1.555155
_rcs1 | 2.175739 .0586161 28.85 0.000 2.063834 2.293712
_rcs2 | 1.070472 .0075068 9.71 0.000 1.055859 1.085286
_rcs3 | 1.034489 .0057401 6.11 0.000 1.0233 1.045801
_rcs4 | 1.019486 .0040456 4.86 0.000 1.011587 1.027446
_rcs5 | 1.013811 .0028664 4.85 0.000 1.008208 1.019444
_rcs6 | 1.010353 .0023033 4.52 0.000 1.005848 1.014877
_rcs7 | 1.010766 .0020257 5.34 0.000 1.006804 1.014744
_rcs8 | 1.00537 .0016928 3.18 0.001 1.002057 1.008693
_rcs_mot_egr_early1 | .8983342 .0272051 -3.54 0.000 .8465649 .9532694
_rcs_mot_egr_late1 | .9211669 .026802 -2.82 0.005 .8701056 .9752246
_cons | 1.2e+143 1.9e+144 20.46 0.000 2.3e+129 5.9e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21770.703
Iteration 1: log likelihood = -21757.397
Iteration 2: log likelihood = -21757.289
Iteration 3: log likelihood = -21757.289
Log likelihood = -21757.289 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993674 .1087069 12.65 0.000 1.791602 2.218537
mot_egr_late | 1.651298 .077803 10.65 0.000 1.505636 1.811052
tr_mod2 | 1.151941 .0429482 3.79 0.000 1.070766 1.23927
sex_dum2 | .5925704 .0255627 -12.13 0.000 .544528 .6448515
edad_ini_cons | .9734004 .0040332 -6.51 0.000 .9655275 .9813376
esc1 | 1.517093 .0833364 7.59 0.000 1.362243 1.689547
esc2 | 1.344012 .0693392 5.73 0.000 1.214754 1.487022
sus_prin2 | 1.195586 .0709034 3.01 0.003 1.06439 1.342953
sus_prin3 | 1.716913 .0822929 11.28 0.000 1.562966 1.886023
sus_prin4 | 1.143514 .0793824 1.93 0.053 .9980479 1.310182
sus_prin5 | 1.355583 .184046 2.24 0.025 1.038865 1.768857
fr_cons_sus_prin2 | .9774203 .0969577 -0.23 0.818 .8047189 1.187185
fr_cons_sus_prin3 | .9956849 .0799251 -0.05 0.957 .8507358 1.16533
fr_cons_sus_prin4 | 1.03811 .0863081 0.45 0.653 .8820128 1.221833
fr_cons_sus_prin5 | 1.088912 .0865779 1.07 0.284 .9317832 1.272538
cond_ocu2 | 1.087452 .0670715 1.36 0.174 .9636296 1.227186
cond_ocu3 | 1.145252 .2803464 0.55 0.580 .7088185 1.850405
cond_ocu4 | 1.24046 .080996 3.30 0.001 1.091449 1.409815
cond_ocu5 | 1.333356 .1368846 2.80 0.005 1.090335 1.630541
cond_ocu6 | 1.211914 .0420106 5.54 0.000 1.13231 1.297115
policonsumo | 1.00707 .0431204 0.16 0.869 .9260044 1.095232
num_hij2 | 1.136229 .0394235 3.68 0.000 1.061529 1.216186
tenviv1 | 1.018723 .1150909 0.16 0.870 .816378 1.271222
tenviv2 | 1.06847 .0803199 0.88 0.378 .9220937 1.238082
tenviv4 | 1.012429 .0420694 0.30 0.766 .9332423 1.098334
tenviv5 | .9930433 .0332022 -0.21 0.835 .9300545 1.060298
mzone2 | 1.41643 .0524953 9.39 0.000 1.317189 1.523148
mzone3 | 1.5452 .086554 7.77 0.000 1.384538 1.724505
n_off_vio | 1.461667 .0503321 11.02 0.000 1.366273 1.563721
n_off_acq | 2.796184 .0871053 33.01 0.000 2.630568 2.972227
n_off_sud | 1.376613 .0456365 9.64 0.000 1.290011 1.469028
n_off_oth | 1.702269 .0564658 16.04 0.000 1.595119 1.816617
psy_com2 | 1.048959 .0403227 1.24 0.214 .9728318 1.131044
dep2 | 1.032621 .038746 0.86 0.392 .9594051 1.111424
rural2 | .9371277 .0520312 -1.17 0.242 .8405013 1.044863
rural3 | .8649477 .05402 -2.32 0.020 .7652941 .9775778
porc_pobr | 1.70434 .3681118 2.47 0.014 1.116116 2.602573
susini2 | 1.098669 .0720726 1.43 0.151 .9661138 1.249412
susini3 | 1.271085 .0731721 4.17 0.000 1.135465 1.422903
susini4 | 1.15524 .0378869 4.40 0.000 1.083319 1.231935
susini5 | 1.377869 .1164025 3.79 0.000 1.167611 1.625989
ano_nac_corr | .8463562 .0067736 -20.84 0.000 .8331838 .8597368
cohab2 | .8631684 .0473307 -2.68 0.007 .7752129 .9611032
cohab3 | 1.075611 .0686768 1.14 0.254 .949089 1.219
cohab4 | .9445768 .0518695 -1.04 0.299 .8481942 1.051912
fis_com2 | 1.112722 .0326213 3.64 0.000 1.050588 1.178531
rc_x1 | .844578 .0086667 -16.46 0.000 .8277612 .8617364
rc_x2 | .8807094 .0305062 -3.67 0.000 .8229028 .9425768
rc_x3 | 1.297981 .1196629 2.83 0.005 1.083414 1.555041
_rcs1 | 2.170319 .0625363 26.89 0.000 2.051147 2.296415
_rcs2 | 1.065316 .0233647 2.88 0.004 1.020492 1.112108
_rcs3 | 1.033424 .0068592 4.95 0.000 1.020067 1.046955
_rcs4 | 1.019234 .0041496 4.68 0.000 1.011133 1.027399
_rcs5 | 1.013758 .0028714 4.82 0.000 1.008145 1.019401
_rcs6 | 1.010343 .0023034 4.51 0.000 1.005839 1.014868
_rcs7 | 1.010763 .0020262 5.34 0.000 1.0068 1.014742
_rcs8 | 1.005365 .0016933 3.18 0.001 1.002052 1.008689
_rcs_mot_egr_early1 | .8991322 .0290259 -3.29 0.001 .8440049 .9578603
_rcs_mot_egr_early2 | .9996335 .0245826 -0.01 0.988 .9525951 1.048995
_rcs_mot_egr_late1 | .9252529 .0289362 -2.48 0.013 .8702422 .983741
_rcs_mot_egr_late2 | 1.009816 .0242143 0.41 0.684 .9634543 1.058408
_cons | 1.1e+143 1.8e+144 20.46 0.000 2.2e+129 5.8e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21770.164
Iteration 1: log likelihood = -21756.948
Iteration 2: log likelihood = -21756.812
Iteration 3: log likelihood = -21756.812
Log likelihood = -21756.812 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.995664 .1088343 12.67 0.000 1.793358 2.220793
mot_egr_late | 1.653263 .0779166 10.67 0.000 1.50739 1.813252
tr_mod2 | 1.15204 .0429523 3.80 0.000 1.070857 1.239377
sex_dum2 | .5925515 .0255618 -12.13 0.000 .5445108 .6448307
edad_ini_cons | .9733929 .0040333 -6.51 0.000 .9655198 .9813302
esc1 | 1.517067 .0833344 7.59 0.000 1.36222 1.689516
esc2 | 1.343989 .0693378 5.73 0.000 1.214735 1.486997
sus_prin2 | 1.195806 .070918 3.02 0.003 1.064584 1.343203
sus_prin3 | 1.717181 .0823092 11.28 0.000 1.563203 1.886324
sus_prin4 | 1.143604 .0793895 1.93 0.053 .9981248 1.310287
sus_prin5 | 1.356107 .1841195 2.24 0.025 1.039264 1.769547
fr_cons_sus_prin2 | .97744 .0969596 -0.23 0.818 .8047353 1.187209
fr_cons_sus_prin3 | .9956793 .0799247 -0.05 0.957 .850731 1.165324
fr_cons_sus_prin4 | 1.038172 .0863133 0.45 0.652 .8820652 1.221906
fr_cons_sus_prin5 | 1.08893 .0865796 1.07 0.284 .9317982 1.272559
cond_ocu2 | 1.087388 .0670681 1.36 0.174 .9635713 1.227114
cond_ocu3 | 1.145759 .2804733 0.56 0.578 .7091295 1.851233
cond_ocu4 | 1.240162 .0809779 3.30 0.001 1.091185 1.40948
cond_ocu5 | 1.333295 .1368802 2.80 0.005 1.090283 1.630472
cond_ocu6 | 1.211887 .0420103 5.54 0.000 1.132283 1.297087
policonsumo | 1.007124 .0431233 0.17 0.868 .9260531 1.095292
num_hij2 | 1.136176 .0394213 3.68 0.000 1.061481 1.216128
tenviv1 | 1.018554 .1150737 0.16 0.871 .8162388 1.271015
tenviv2 | 1.068635 .080333 0.88 0.377 .9222347 1.238275
tenviv4 | 1.01233 .0420657 0.29 0.768 .9331513 1.098228
tenviv5 | .9929542 .0331995 -0.21 0.833 .9299707 1.060203
mzone2 | 1.41647 .0524975 9.39 0.000 1.317225 1.523192
mzone3 | 1.544958 .0865411 7.77 0.000 1.38432 1.724237
n_off_vio | 1.461646 .0503302 11.02 0.000 1.366256 1.563696
n_off_acq | 2.796083 .0870987 33.01 0.000 2.630479 2.972112
n_off_sud | 1.376493 .045632 9.64 0.000 1.2899 1.4689
n_off_oth | 1.702243 .0564635 16.04 0.000 1.595097 1.816586
psy_com2 | 1.049005 .0403281 1.24 0.213 .9728675 1.131101
dep2 | 1.032621 .0387463 0.86 0.392 .9594046 1.111424
rural2 | .9372527 .0520382 -1.17 0.243 .8406133 1.045002
rural3 | .8650428 .0540256 -2.32 0.020 .7653789 .9776845
porc_pobr | 1.702313 .3677207 2.46 0.014 1.114729 2.599618
susini2 | 1.098889 .0720877 1.44 0.151 .9663059 1.249664
susini3 | 1.271092 .0731726 4.17 0.000 1.135471 1.422911
susini4 | 1.155191 .0378852 4.40 0.000 1.083274 1.231883
susini5 | 1.377941 .1164097 3.79 0.000 1.16767 1.626076
ano_nac_corr | .8463339 .0067735 -20.85 0.000 .8331616 .8597144
cohab2 | .8632054 .0473331 -2.68 0.007 .7752455 .9611452
cohab3 | 1.075618 .0686781 1.14 0.254 .9490935 1.21901
cohab4 | .9446039 .0518711 -1.04 0.299 .8482184 1.051942
fis_com2 | 1.112539 .0326158 3.64 0.000 1.050415 1.178337
rc_x1 | .8445519 .0086665 -16.46 0.000 .8277356 .8617098
rc_x2 | .8807365 .0305069 -3.67 0.000 .8229285 .9426054
rc_x3 | 1.297831 .1196484 2.83 0.005 1.083291 1.55486
_rcs1 | 2.179227 .0635091 26.73 0.000 2.05824 2.307327
_rcs2 | 1.058105 .0237671 2.51 0.012 1.012533 1.105728
_rcs3 | 1.044091 .0131433 3.43 0.001 1.018646 1.070172
_rcs4 | 1.026746 .0089621 3.02 0.002 1.00933 1.044463
_rcs5 | 1.016956 .0044515 3.84 0.000 1.008269 1.025719
_rcs6 | 1.011228 .0024876 4.54 0.000 1.006364 1.016115
_rcs7 | 1.010906 .0020325 5.40 0.000 1.006931 1.014898
_rcs8 | 1.005346 .0016937 3.17 0.002 1.002032 1.008671
_rcs_mot_egr_early1 | .8953048 .0292525 -3.38 0.001 .8397682 .9545141
_rcs_mot_egr_early2 | 1.004848 .0248569 0.20 0.845 .9572914 1.054767
_rcs_mot_egr_early3 | .9860753 .0169069 -0.82 0.413 .953489 1.019775
_rcs_mot_egr_late1 | .9209337 .0291376 -2.60 0.009 .8655597 .9798503
_rcs_mot_egr_late2 | 1.016246 .0246455 0.66 0.506 .969072 1.065717
_rcs_mot_egr_late3 | .9847418 .0163068 -0.93 0.353 .9532943 1.017227
_cons | 1.2e+143 1.9e+144 20.46 0.000 2.3e+129 6.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21770.155
Iteration 1: log likelihood = -21755.453
Iteration 2: log likelihood = -21755.261
Iteration 3: log likelihood = -21755.261
Log likelihood = -21755.261 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996442 .1088877 12.68 0.000 1.794037 2.221682
mot_egr_late | 1.653879 .0779524 10.67 0.000 1.50794 1.813942
tr_mod2 | 1.152187 .0429592 3.80 0.000 1.070991 1.239538
sex_dum2 | .5925419 .0255615 -12.13 0.000 .5445018 .6448205
edad_ini_cons | .9733907 .0040333 -6.51 0.000 .9655176 .9813281
esc1 | 1.516978 .0833293 7.59 0.000 1.36214 1.689416
esc2 | 1.344019 .0693392 5.73 0.000 1.214762 1.48703
sus_prin2 | 1.195975 .070929 3.02 0.003 1.064733 1.343396
sus_prin3 | 1.717541 .0823294 11.28 0.000 1.563526 1.886727
sus_prin4 | 1.143724 .0793981 1.93 0.053 .9982294 1.310425
sus_prin5 | 1.356258 .1841422 2.24 0.025 1.039377 1.76975
fr_cons_sus_prin2 | .9775446 .09697 -0.23 0.819 .8048214 1.187336
fr_cons_sus_prin3 | .9958099 .0799349 -0.05 0.958 .8508429 1.165476
fr_cons_sus_prin4 | 1.038292 .0863232 0.45 0.651 .8821671 1.222048
fr_cons_sus_prin5 | 1.088936 .08658 1.07 0.284 .9318034 1.272566
cond_ocu2 | 1.08742 .0670704 1.36 0.174 .9635997 1.227152
cond_ocu3 | 1.146355 .2806196 0.56 0.577 .709498 1.852198
cond_ocu4 | 1.239933 .0809636 3.29 0.001 1.090982 1.409221
cond_ocu5 | 1.333102 .1368637 2.80 0.005 1.09012 1.630244
cond_ocu6 | 1.21177 .0420072 5.54 0.000 1.132172 1.296964
policonsumo | 1.007105 .0431224 0.17 0.869 .9260361 1.095271
num_hij2 | 1.136072 .0394169 3.68 0.000 1.061385 1.216015
tenviv1 | 1.018385 .1150582 0.16 0.872 .816098 1.270813
tenviv2 | 1.068912 .0803539 0.89 0.375 .9224742 1.238597
tenviv4 | 1.012216 .042061 0.29 0.770 .9330457 1.098104
tenviv5 | .992945 .0331992 -0.21 0.832 .929962 1.060194
mzone2 | 1.416459 .052498 9.39 0.000 1.317213 1.523183
mzone3 | 1.545022 .0865465 7.77 0.000 1.384374 1.724312
n_off_vio | 1.46153 .0503262 11.02 0.000 1.366147 1.563572
n_off_acq | 2.795907 .0870916 33.01 0.000 2.630317 2.971922
n_off_sud | 1.376454 .0456294 9.64 0.000 1.289866 1.468855
n_off_oth | 1.702293 .0564647 16.04 0.000 1.595145 1.816638
psy_com2 | 1.049434 .0403445 1.26 0.209 .9732662 1.131564
dep2 | 1.032631 .0387469 0.86 0.392 .9594134 1.111435
rural2 | .9375493 .0520542 -1.16 0.245 .8408801 1.045332
rural3 | .8650675 .0540264 -2.32 0.020 .765402 .9777109
porc_pobr | 1.697267 .3666617 2.45 0.014 1.111385 2.592005
susini2 | 1.099291 .0721141 1.44 0.149 .9666592 1.250121
susini3 | 1.271162 .0731767 4.17 0.000 1.135534 1.42299
susini4 | 1.155152 .0378833 4.40 0.000 1.083238 1.23184
susini5 | 1.377914 .1164087 3.79 0.000 1.167646 1.626047
ano_nac_corr | .8463195 .0067733 -20.85 0.000 .8331477 .8596995
cohab2 | .8632532 .0473369 -2.68 0.007 .7752864 .9612011
cohab3 | 1.075537 .0686743 1.14 0.254 .9490191 1.218921
cohab4 | .9446749 .051876 -1.04 0.300 .8482803 1.052023
fis_com2 | 1.112219 .0326057 3.63 0.000 1.050115 1.177997
rc_x1 | .8445551 .0086661 -16.46 0.000 .8277395 .8617123
rc_x2 | .880675 .0305038 -3.67 0.000 .8228729 .9425374
rc_x3 | 1.29801 .1196615 2.83 0.005 1.083446 1.555067
_rcs1 | 2.180627 .0634791 26.78 0.000 2.059693 2.308661
_rcs2 | 1.05919 .024709 2.46 0.014 1.011851 1.108743
_rcs3 | 1.033475 .0162167 2.10 0.036 1.002175 1.065753
_rcs4 | 1.029507 .0087138 3.44 0.001 1.012569 1.046728
_rcs5 | 1.025319 .007766 3.30 0.001 1.01021 1.040653
_rcs6 | 1.017232 .0050574 3.44 0.001 1.007368 1.027193
_rcs7 | 1.012624 .0023559 5.39 0.000 1.008017 1.017252
_rcs8 | 1.005343 .0016939 3.16 0.002 1.002028 1.008668
_rcs_mot_egr_early1 | .8946142 .0292153 -3.41 0.001 .8391473 .9537474
_rcs_mot_egr_early2 | 1.005738 .0256002 0.22 0.822 .9567939 1.057187
_rcs_mot_egr_early3 | .9971664 .0185383 -0.15 0.879 .961486 1.034171
_rcs_mot_egr_early4 | .9781488 .0119486 -1.81 0.071 .9550082 1.00185
_rcs_mot_egr_late1 | .9202958 .0290951 -2.63 0.009 .8650014 .979125
_rcs_mot_egr_late2 | 1.016962 .0254781 0.67 0.502 .968232 1.068145
_rcs_mot_egr_late3 | .9918561 .0179016 -0.45 0.651 .957383 1.027571
_rcs_mot_egr_late4 | .9855871 .0115348 -1.24 0.215 .9632367 1.008456
_cons | 1.2e+143 2.0e+144 20.46 0.000 2.4e+129 6.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21770.259
Iteration 1: log likelihood = -21755.355
Iteration 2: log likelihood = -21755.161
Iteration 3: log likelihood = -21755.161
Log likelihood = -21755.161 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997427 .1089501 12.68 0.000 1.794907 2.222798
mot_egr_late | 1.65476 .0780025 10.68 0.000 1.508728 1.814927
tr_mod2 | 1.152193 .0429598 3.80 0.000 1.070996 1.239546
sex_dum2 | .5925362 .0255613 -12.13 0.000 .5444966 .6448144
edad_ini_cons | .973391 .0040333 -6.51 0.000 .9655179 .9813283
esc1 | 1.516987 .0833295 7.59 0.000 1.362148 1.689425
esc2 | 1.344006 .0693385 5.73 0.000 1.21475 1.487015
sus_prin2 | 1.196003 .0709309 3.02 0.003 1.064757 1.343428
sus_prin3 | 1.71763 .0823344 11.28 0.000 1.563606 1.886826
sus_prin4 | 1.143774 .0794014 1.94 0.053 .9982725 1.310482
sus_prin5 | 1.356299 .184148 2.24 0.025 1.039407 1.769804
fr_cons_sus_prin2 | .9775434 .0969698 -0.23 0.819 .8048205 1.187334
fr_cons_sus_prin3 | .9958002 .0799342 -0.05 0.958 .8508346 1.165465
fr_cons_sus_prin4 | 1.038312 .086325 0.45 0.651 .8821844 1.222072
fr_cons_sus_prin5 | 1.088925 .0865792 1.07 0.284 .9317937 1.272553
cond_ocu2 | 1.087407 .0670696 1.36 0.174 .9635875 1.227136
cond_ocu3 | 1.146421 .280636 0.56 0.577 .7095384 1.852305
cond_ocu4 | 1.239837 .0809578 3.29 0.001 1.090896 1.409112
cond_ocu5 | 1.333153 .1368701 2.80 0.005 1.09016 1.630309
cond_ocu6 | 1.211754 .0420068 5.54 0.000 1.132157 1.296947
policonsumo | 1.00706 .0431205 0.16 0.869 .9259942 1.095222
num_hij2 | 1.136067 .0394166 3.68 0.000 1.061381 1.21601
tenviv1 | 1.018358 .1150549 0.16 0.872 .8160769 1.270779
tenviv2 | 1.068973 .0803583 0.89 0.375 .9225272 1.238667
tenviv4 | 1.012151 .0420583 0.29 0.771 .9329854 1.098033
tenviv5 | .9929168 .0331983 -0.21 0.832 .9299354 1.060164
mzone2 | 1.416435 .0524972 9.39 0.000 1.317191 1.523157
mzone3 | 1.544975 .0865446 7.77 0.000 1.384331 1.724261
n_off_vio | 1.461498 .0503249 11.02 0.000 1.366118 1.563538
n_off_acq | 2.795913 .0870907 33.01 0.000 2.630324 2.971926
n_off_sud | 1.376449 .0456289 9.64 0.000 1.289862 1.468849
n_off_oth | 1.702312 .056465 16.04 0.000 1.595164 1.816658
psy_com2 | 1.04948 .0403474 1.26 0.209 .9733063 1.131615
dep2 | 1.032631 .038747 0.86 0.392 .9594134 1.111436
rural2 | .9376342 .052059 -1.16 0.246 .8409561 1.045427
rural3 | .8651159 .0540293 -2.32 0.020 .7654449 .9777654
porc_pobr | 1.695667 .3663271 2.44 0.015 1.110323 2.589595
susini2 | 1.099381 .0721203 1.44 0.149 .966738 1.250224
susini3 | 1.271084 .0731724 4.17 0.000 1.135463 1.422903
susini4 | 1.155111 .0378819 4.40 0.000 1.0832 1.231796
susini5 | 1.377843 .1164026 3.79 0.000 1.167586 1.625963
ano_nac_corr | .8463165 .0067733 -20.85 0.000 .8331445 .8596966
cohab2 | .8632487 .0473365 -2.68 0.007 .7752825 .9611957
cohab3 | 1.075515 .0686726 1.14 0.254 .9490002 1.218895
cohab4 | .9446783 .0518761 -1.04 0.300 .8482835 1.052027
fis_com2 | 1.112137 .0326033 3.63 0.000 1.050037 1.17791
rc_x1 | .8445525 .0086662 -16.46 0.000 .8277368 .8617098
rc_x2 | .8806608 .0305032 -3.67 0.000 .8228598 .9425219
rc_x3 | 1.298076 .1196669 2.83 0.005 1.083502 1.555144
_rcs1 | 2.182554 .0635989 26.78 0.000 2.061396 2.310834
_rcs2 | 1.056979 .0242743 2.41 0.016 1.010457 1.105643
_rcs3 | 1.038262 .0173349 2.25 0.025 1.004836 1.072799
_rcs4 | 1.026836 .009989 2.72 0.006 1.007444 1.046602
_rcs5 | 1.022767 .0075578 3.05 0.002 1.008061 1.037688
_rcs6 | 1.019518 .0067191 2.93 0.003 1.006434 1.032773
_rcs7 | 1.015708 .0044293 3.57 0.000 1.007063 1.024426
_rcs8 | 1.00592 .0017392 3.41 0.001 1.002517 1.009335
_rcs_mot_egr_early1 | .8937611 .0292167 -3.44 0.001 .8382934 .9528989
_rcs_mot_egr_early2 | 1.007545 .0254208 0.30 0.766 .9589327 1.058621
_rcs_mot_egr_early3 | .9964814 .0188833 -0.19 0.852 .9601497 1.034188
_rcs_mot_egr_early4 | .9839077 .0125787 -1.27 0.204 .9595602 1.008873
_rcs_mot_egr_early5 | .9875589 .0091946 -1.34 0.179 .9697013 1.005745
_rcs_mot_egr_late1 | .9193546 .0290925 -2.66 0.008 .8640666 .9781802
_rcs_mot_egr_late2 | 1.019271 .0253483 0.77 0.443 .9707805 1.070183
_rcs_mot_egr_late3 | .9897349 .0182676 -0.56 0.576 .9545709 1.026194
_rcs_mot_egr_late4 | .990603 .0121499 -0.77 0.441 .9670736 1.014705
_rcs_mot_egr_late5 | .9900026 .0087953 -1.13 0.258 .9729133 1.007392
_cons | 1.2e+143 2.0e+144 20.46 0.000 2.4e+129 6.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21769.562
Iteration 1: log likelihood = -21754.214
Iteration 2: log likelihood = -21754.005
Iteration 3: log likelihood = -21754.005
Log likelihood = -21754.005 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997937 .1089972 12.69 0.000 1.795331 2.223407
mot_egr_late | 1.655539 .0780564 10.69 0.000 1.509408 1.815819
tr_mod2 | 1.152159 .0429588 3.80 0.000 1.070964 1.239509
sex_dum2 | .5925477 .0255618 -12.13 0.000 .544507 .6448269
edad_ini_cons | .973392 .0040333 -6.51 0.000 .9655189 .9813293
esc1 | 1.516981 .0833292 7.59 0.000 1.362144 1.689419
esc2 | 1.343955 .0693358 5.73 0.000 1.214704 1.486959
sus_prin2 | 1.196044 .0709336 3.02 0.003 1.064792 1.343474
sus_prin3 | 1.717688 .0823376 11.29 0.000 1.563658 1.886891
sus_prin4 | 1.143771 .0794011 1.94 0.053 .9982707 1.310479
sus_prin5 | 1.356137 .1841252 2.24 0.025 1.039284 1.76959
fr_cons_sus_prin2 | .9775493 .0969704 -0.23 0.819 .8048253 1.187342
fr_cons_sus_prin3 | .9958004 .0799342 -0.05 0.958 .8508348 1.165465
fr_cons_sus_prin4 | 1.03836 .086329 0.45 0.651 .8822249 1.222128
fr_cons_sus_prin5 | 1.08892 .0865789 1.07 0.284 .9317896 1.272548
cond_ocu2 | 1.087384 .0670685 1.36 0.174 .9635671 1.227112
cond_ocu3 | 1.146317 .2806108 0.56 0.577 .709474 1.852138
cond_ocu4 | 1.239732 .0809508 3.29 0.001 1.090805 1.408993
cond_ocu5 | 1.333447 .1369015 2.80 0.005 1.090398 1.630671
cond_ocu6 | 1.211765 .0420071 5.54 0.000 1.132167 1.296959
policonsumo | 1.007054 .0431201 0.16 0.870 .925989 1.095215
num_hij2 | 1.136058 .0394161 3.68 0.000 1.061372 1.216
tenviv1 | 1.018293 .1150477 0.16 0.873 .816024 1.270698
tenviv2 | 1.069058 .0803641 0.89 0.374 .9226011 1.238764
tenviv4 | 1.012111 .0420565 0.29 0.772 .9329489 1.09799
tenviv5 | .9928843 .0331973 -0.21 0.831 .929905 1.060129
mzone2 | 1.41641 .0524962 9.39 0.000 1.317168 1.52313
mzone3 | 1.544895 .0865407 7.76 0.000 1.384258 1.724174
n_off_vio | 1.461478 .0503238 11.02 0.000 1.3661 1.563515
n_off_acq | 2.795897 .0870889 33.01 0.000 2.630311 2.971906
n_off_sud | 1.376429 .0456279 9.64 0.000 1.289843 1.468827
n_off_oth | 1.702343 .0564653 16.04 0.000 1.595194 1.81669
psy_com2 | 1.049447 .040347 1.26 0.209 .9732745 1.131582
dep2 | 1.032617 .0387466 0.86 0.392 .9593999 1.111421
rural2 | .937712 .0520635 -1.16 0.247 .8410256 1.045514
rural3 | .8651292 .0540303 -2.32 0.020 .7654565 .9777806
porc_pobr | 1.694676 .3661234 2.44 0.015 1.109661 2.588112
susini2 | 1.099417 .0721228 1.44 0.149 .9667692 1.250265
susini3 | 1.271095 .0731732 4.17 0.000 1.135473 1.422915
susini4 | 1.155071 .0378806 4.40 0.000 1.083163 1.231754
susini5 | 1.377891 .1164059 3.79 0.000 1.167627 1.626018
ano_nac_corr | .8463283 .0067735 -20.85 0.000 .8331561 .8597087
cohab2 | .8632853 .0473386 -2.68 0.007 .7753153 .9612366
cohab3 | 1.075503 .0686717 1.14 0.254 .9489898 1.218881
cohab4 | .9447012 .0518772 -1.04 0.300 .8483045 1.052052
fis_com2 | 1.112111 .0326024 3.62 0.000 1.050012 1.177882
rc_x1 | .8445636 .0086663 -16.46 0.000 .8277476 .8617213
rc_x2 | .8806507 .0305026 -3.67 0.000 .8228509 .9425106
rc_x3 | 1.298123 .1196704 2.83 0.005 1.083542 1.555198
_rcs1 | 2.183767 .0636749 26.79 0.000 2.062465 2.312202
_rcs2 | 1.05631 .0239803 2.41 0.016 1.010339 1.104371
_rcs3 | 1.040927 .0175598 2.38 0.017 1.007073 1.075918
_rcs4 | 1.020577 .0109976 1.89 0.059 .999248 1.042361
_rcs5 | 1.02445 .0076022 3.26 0.001 1.009658 1.039459
_rcs6 | 1.023259 .0065271 3.60 0.000 1.010546 1.036132
_rcs7 | 1.016599 .0055418 3.02 0.003 1.005795 1.027519
_rcs8 | 1.006784 .0023485 2.90 0.004 1.002191 1.011397
_rcs_mot_egr_early1 | .8933694 .0292226 -3.45 0.001 .8378917 .9525204
_rcs_mot_egr_early2 | 1.007995 .0252572 0.32 0.751 .9596877 1.058734
_rcs_mot_egr_early3 | .9964358 .0188188 -0.19 0.850 .9602259 1.034011
_rcs_mot_egr_early4 | .9924396 .0128253 -0.59 0.557 .9676182 1.017898
_rcs_mot_egr_early5 | .9791809 .0092385 -2.23 0.026 .9612401 .9974565
_rcs_mot_egr_early6 | .9943055 .0068921 -0.82 0.410 .9808886 1.007906
_rcs_mot_egr_late1 | .9187229 .029091 -2.68 0.007 .8634388 .9775467
_rcs_mot_egr_late2 | 1.020075 .0252162 0.80 0.421 .9718305 1.070715
_rcs_mot_egr_late3 | .9893422 .0181889 -0.58 0.560 .9543273 1.025642
_rcs_mot_egr_late4 | .997464 .0123825 -0.20 0.838 .9734876 1.022031
_rcs_mot_egr_late5 | .9838967 .0088688 -1.80 0.072 .9666667 1.001434
_rcs_mot_egr_late6 | .993561 .0064854 -0.99 0.322 .980931 1.006354
_cons | 1.2e+143 1.9e+144 20.46 0.000 2.4e+129 6.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.288
Iteration 1: log likelihood = -21753.439
Iteration 2: log likelihood = -21753.262
Iteration 3: log likelihood = -21753.262
Log likelihood = -21753.262 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.99917 .1090608 12.70 0.000 1.796446 2.224772
mot_egr_late | 1.656295 .078087 10.70 0.000 1.510106 1.816637
tr_mod2 | 1.152102 .0429572 3.80 0.000 1.07091 1.239449
sex_dum2 | .592538 .0255615 -12.13 0.000 .544498 .6448165
edad_ini_cons | .9733902 .0040333 -6.51 0.000 .9655171 .9813276
esc1 | 1.51706 .0833334 7.59 0.000 1.362214 1.689507
esc2 | 1.343982 .0693373 5.73 0.000 1.214729 1.486989
sus_prin2 | 1.196042 .0709331 3.02 0.003 1.064792 1.343471
sus_prin3 | 1.717728 .0823388 11.29 0.000 1.563696 1.886933
sus_prin4 | 1.143857 .0794066 1.94 0.053 .9983468 1.310576
sus_prin5 | 1.356165 .1841297 2.24 0.025 1.039305 1.769629
fr_cons_sus_prin2 | .9775278 .0969682 -0.23 0.819 .8048077 1.187315
fr_cons_sus_prin3 | .9957833 .0799327 -0.05 0.958 .8508204 1.165445
fr_cons_sus_prin4 | 1.038336 .0863268 0.45 0.651 .8822048 1.222099
fr_cons_sus_prin5 | 1.088901 .0865771 1.07 0.284 .9317742 1.272525
cond_ocu2 | 1.087372 .0670679 1.36 0.174 .9635556 1.227098
cond_ocu3 | 1.146209 .2805848 0.56 0.577 .7094064 1.851964
cond_ocu4 | 1.239787 .080954 3.29 0.001 1.090853 1.409054
cond_ocu5 | 1.333412 .1368979 2.80 0.005 1.09037 1.630628
cond_ocu6 | 1.211767 .0420075 5.54 0.000 1.132168 1.296961
policonsumo | 1.00698 .0431169 0.16 0.871 .9259211 1.095135
num_hij2 | 1.136087 .039417 3.68 0.000 1.0614 1.216031
tenviv1 | 1.018277 .1150457 0.16 0.873 .8160124 1.270678
tenviv2 | 1.06904 .0803627 0.89 0.374 .9225855 1.238743
tenviv4 | 1.012087 .0420554 0.29 0.772 .9329272 1.097964
tenviv5 | .992884 .0331973 -0.21 0.831 .9299046 1.060129
mzone2 | 1.416414 .0524965 9.39 0.000 1.317171 1.523135
mzone3 | 1.544975 .0865453 7.77 0.000 1.384329 1.724262
n_off_vio | 1.461399 .0503209 11.02 0.000 1.366027 1.56343
n_off_acq | 2.795887 .0870871 33.01 0.000 2.630305 2.971892
n_off_sud | 1.376398 .0456265 9.64 0.000 1.289815 1.468793
n_off_oth | 1.702362 .056465 16.04 0.000 1.595213 1.816708
psy_com2 | 1.049443 .0403477 1.26 0.209 .9732687 1.131578
dep2 | 1.032608 .0387464 0.86 0.392 .9593915 1.111412
rural2 | .9378244 .0520696 -1.16 0.248 .8411267 1.045639
rural3 | .8652451 .0540374 -2.32 0.020 .7655593 .9779114
porc_pobr | 1.69218 .3655869 2.43 0.015 1.108023 2.584308
susini2 | 1.099524 .0721298 1.45 0.148 .9668634 1.250387
susini3 | 1.270889 .0731618 4.16 0.000 1.135288 1.422686
susini4 | 1.155002 .0378785 4.39 0.000 1.083098 1.231681
susini5 | 1.377789 .1163976 3.79 0.000 1.16754 1.625899
ano_nac_corr | .8462858 .0067729 -20.85 0.000 .8331146 .8596651
cohab2 | .8632224 .0473351 -2.68 0.007 .775259 .9611666
cohab3 | 1.075468 .0686698 1.14 0.255 .9489592 1.218843
cohab4 | .9446505 .0518742 -1.04 0.300 .8482591 1.051995
fis_com2 | 1.112055 .0326006 3.62 0.000 1.04996 1.177823
rc_x1 | .8445153 .0086658 -16.47 0.000 .8277003 .8616719
rc_x2 | .8806626 .0305031 -3.67 0.000 .8228618 .9425235
rc_x3 | 1.298109 .1196693 2.83 0.005 1.08353 1.555182
_rcs1 | 2.185327 .0636982 26.82 0.000 2.06398 2.313808
_rcs2 | 1.056432 .0239472 2.42 0.015 1.010523 1.104426
_rcs3 | 1.041532 .0179732 2.36 0.018 1.006894 1.077361
_rcs4 | 1.021171 .012192 1.75 0.079 .9975525 1.045349
_rcs5 | 1.02564 .0078174 3.32 0.001 1.010432 1.041077
_rcs6 | 1.017843 .0064516 2.79 0.005 1.005276 1.030567
_rcs7 | 1.017792 .005468 3.28 0.001 1.007131 1.028566
_rcs8 | 1.010487 .0035294 2.99 0.003 1.003593 1.017428
_rcs_mot_egr_early1 | .8925943 .0291903 -3.47 0.001 .8371773 .9516796
_rcs_mot_egr_early2 | 1.008137 .0252453 0.32 0.746 .9598517 1.058851
_rcs_mot_egr_early3 | .9970088 .019128 -0.16 0.876 .9602147 1.035213
_rcs_mot_egr_early4 | .9927705 .0136002 -0.53 0.596 .9664692 1.019788
_rcs_mot_egr_early5 | .9833985 .0090596 -1.82 0.069 .9658014 1.001316
_rcs_mot_egr_early6 | .9917465 .0073191 -1.12 0.261 .9775044 1.006196
_rcs_mot_egr_early7 | .9924124 .0054993 -1.37 0.169 .9816923 1.00325
_rcs_mot_egr_late1 | .9181323 .0290547 -2.70 0.007 .8629162 .9768816
_rcs_mot_egr_late2 | 1.020529 .0252681 0.82 0.412 .972187 1.071275
_rcs_mot_egr_late3 | .9883483 .0185508 -0.62 0.532 .95265 1.025384
_rcs_mot_egr_late4 | .9988845 .0131879 -0.08 0.933 .9733682 1.02507
_rcs_mot_egr_late5 | .9871094 .0086495 -1.48 0.139 .9703014 1.004208
_rcs_mot_egr_late6 | .9935186 .0069294 -0.93 0.351 .9800295 1.007193
_rcs_mot_egr_late7 | .9917696 .0051289 -1.60 0.110 .981768 1.001873
_cons | 1.3e+143 2.2e+144 20.47 0.000 2.6e+129 6.8e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21766.583
Iteration 1: log likelihood = -21756.587
Iteration 2: log likelihood = -21756.515
Iteration 3: log likelihood = -21756.515
Log likelihood = -21756.515 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994446 .1087047 12.67 0.000 1.792374 2.2193
mot_egr_late | 1.650574 .0777506 10.64 0.000 1.505009 1.810219
tr_mod2 | 1.152095 .0429532 3.80 0.000 1.070911 1.239434
sex_dum2 | .592568 .0255625 -12.13 0.000 .5445259 .6448487
edad_ini_cons | .9733942 .0040333 -6.51 0.000 .965521 .9813315
esc1 | 1.517147 .0833394 7.59 0.000 1.362291 1.689606
esc2 | 1.344022 .0693396 5.73 0.000 1.214764 1.487034
sus_prin2 | 1.195669 .0709084 3.01 0.003 1.064464 1.343046
sus_prin3 | 1.717036 .0823002 11.28 0.000 1.563076 1.886161
sus_prin4 | 1.143622 .0793895 1.93 0.053 .9981426 1.310305
sus_prin5 | 1.355465 .1840273 2.24 0.025 1.038779 1.768696
fr_cons_sus_prin2 | .9773765 .0969533 -0.23 0.818 .8046831 1.187132
fr_cons_sus_prin3 | .9956079 .0799189 -0.05 0.956 .8506702 1.16524
fr_cons_sus_prin4 | 1.038121 .0863091 0.45 0.653 .8820218 1.221846
fr_cons_sus_prin5 | 1.088809 .0865705 1.07 0.285 .9316936 1.272419
cond_ocu2 | 1.087571 .0670789 1.36 0.173 .9637344 1.22732
cond_ocu3 | 1.145137 .2803176 0.55 0.580 .7087486 1.850217
cond_ocu4 | 1.240165 .0809768 3.30 0.001 1.09119 1.40948
cond_ocu5 | 1.333162 .1368638 2.80 0.005 1.090179 1.630303
cond_ocu6 | 1.212028 .042015 5.55 0.000 1.132416 1.297238
policonsumo | 1.006957 .0431154 0.16 0.871 .9259008 1.095109
num_hij2 | 1.136201 .0394225 3.68 0.000 1.061503 1.216155
tenviv1 | 1.018751 .1150937 0.16 0.869 .8164005 1.271255
tenviv2 | 1.068759 .0803416 0.88 0.376 .9223433 1.238417
tenviv4 | 1.012342 .0420653 0.30 0.768 .9331638 1.098239
tenviv5 | .9929404 .0331987 -0.21 0.832 .9299584 1.060188
mzone2 | 1.416268 .0524901 9.39 0.000 1.317037 1.522975
mzone3 | 1.545214 .0865564 7.77 0.000 1.384547 1.724524
n_off_vio | 1.461502 .0503248 11.02 0.000 1.366122 1.563541
n_off_acq | 2.795966 .0870948 33.01 0.000 2.630369 2.971987
n_off_sud | 1.376501 .0456324 9.64 0.000 1.289906 1.468908
n_off_oth | 1.702182 .0564605 16.04 0.000 1.595042 1.816519
psy_com2 | 1.048521 .0403021 1.23 0.218 .9724324 1.130563
dep2 | 1.032609 .0387454 0.86 0.392 .9593949 1.111411
rural2 | .937259 .0520381 -1.17 0.243 .8406197 1.045008
rural3 | .8652277 .0540361 -2.32 0.020 .7655444 .9778912
porc_pobr | 1.703026 .3678052 2.47 0.014 1.115285 2.600498
susini2 | 1.098935 .0720897 1.44 0.150 .9663475 1.249713
susini3 | 1.270675 .0731492 4.16 0.000 1.135098 1.422446
susini4 | 1.155152 .037884 4.40 0.000 1.083237 1.231842
susini5 | 1.377866 .1164017 3.79 0.000 1.16761 1.625984
ano_nac_corr | .8462894 .0067719 -20.86 0.000 .8331203 .8596668
cohab2 | .8633806 .047342 -2.68 0.007 .7754042 .9613388
cohab3 | 1.075941 .068697 1.15 0.252 .9493808 1.219372
cohab4 | .9447671 .0518804 -1.03 0.301 .8483644 1.052124
fis_com2 | 1.112694 .0326191 3.64 0.000 1.050564 1.178499
rc_x1 | .8445251 .0086653 -16.47 0.000 .8277111 .8616807
rc_x2 | .8806428 .0305039 -3.67 0.000 .8228405 .9425055
rc_x3 | 1.298207 .1196836 2.83 0.005 1.083604 1.555312
_rcs1 | 2.174885 .0585736 28.85 0.000 2.06306 2.292771
_rcs2 | 1.070073 .0074928 9.67 0.000 1.055488 1.08486
_rcs3 | 1.034745 .0057409 6.16 0.000 1.023554 1.046058
_rcs4 | 1.01905 .0040881 4.70 0.000 1.011069 1.027094
_rcs5 | 1.014734 .002878 5.16 0.000 1.009109 1.020391
_rcs6 | 1.009631 .0023394 4.14 0.000 1.005056 1.014226
_rcs7 | 1.010978 .0020009 5.52 0.000 1.007064 1.014907
_rcs8 | 1.007809 .0018127 4.32 0.000 1.004262 1.011368
_rcs9 | 1.004829 .0015562 3.11 0.002 1.001783 1.007883
_rcs_mot_egr_early1 | .8987001 .0272048 -3.53 0.000 .8469306 .9536341
_rcs_mot_egr_late1 | .9216378 .0268068 -2.81 0.005 .8705669 .9757047
_cons | 1.3e+143 2.1e+144 20.47 0.000 2.6e+129 6.7e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.501
Iteration 1: log likelihood = -21756.324
Iteration 2: log likelihood = -21756.234
Iteration 3: log likelihood = -21756.234
Log likelihood = -21756.234 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.99342 .1086892 12.65 0.000 1.791381 2.218246
mot_egr_late | 1.650987 .0777848 10.64 0.000 1.505359 1.810704
tr_mod2 | 1.151895 .0429471 3.79 0.000 1.070722 1.239221
sex_dum2 | .5925714 .0255628 -12.13 0.000 .5445289 .6448526
edad_ini_cons | .9733977 .0040333 -6.51 0.000 .9655246 .981335
esc1 | 1.517147 .0833393 7.59 0.000 1.362291 1.689606
esc2 | 1.344006 .0693389 5.73 0.000 1.21475 1.487017
sus_prin2 | 1.195697 .0709107 3.01 0.003 1.064488 1.343079
sus_prin3 | 1.717106 .0823035 11.28 0.000 1.563139 1.886238
sus_prin4 | 1.143705 .0793957 1.93 0.053 .998214 1.310401
sus_prin5 | 1.355718 .1840649 2.24 0.025 1.038969 1.769035
fr_cons_sus_prin2 | .9774072 .0969564 -0.23 0.818 .8047081 1.187169
fr_cons_sus_prin3 | .9956655 .0799235 -0.05 0.957 .8507193 1.165308
fr_cons_sus_prin4 | 1.038129 .0863096 0.45 0.653 .8820292 1.221856
fr_cons_sus_prin5 | 1.088848 .0865729 1.07 0.284 .9317284 1.272463
cond_ocu2 | 1.08747 .067073 1.36 0.174 .9636446 1.227207
cond_ocu3 | 1.145248 .2803459 0.55 0.580 .7088161 1.8504
cond_ocu4 | 1.240368 .080989 3.30 0.001 1.09137 1.409709
cond_ocu5 | 1.33352 .1369019 2.80 0.005 1.09047 1.630744
cond_ocu6 | 1.21202 .0420145 5.55 0.000 1.132408 1.297229
policonsumo | 1.006974 .0431163 0.16 0.871 .9259167 1.095128
num_hij2 | 1.136217 .039423 3.68 0.000 1.061518 1.216173
tenviv1 | 1.018803 .1151 0.16 0.869 .8164413 1.271321
tenviv2 | 1.068634 .0803324 0.88 0.377 .9222347 1.238273
tenviv4 | 1.01244 .0420697 0.30 0.766 .9332535 1.098346
tenviv5 | .9930402 .0332022 -0.21 0.835 .9300516 1.060295
mzone2 | 1.416386 .0524944 9.39 0.000 1.317146 1.523102
mzone3 | 1.545447 .0865691 7.77 0.000 1.384757 1.724784
n_off_vio | 1.461536 .0503263 11.02 0.000 1.366153 1.563579
n_off_acq | 2.796053 .0870968 33.01 0.000 2.630454 2.972079
n_off_sud | 1.376477 .0456311 9.64 0.000 1.289885 1.468882
n_off_oth | 1.70225 .0564627 16.04 0.000 1.595106 1.816591
psy_com2 | 1.048965 .0403244 1.24 0.214 .9728343 1.131052
dep2 | 1.032603 .0387453 0.86 0.393 .9593891 1.111405
rural2 | .9372028 .0520351 -1.17 0.243 .8405691 1.044946
rural3 | .8650745 .0540276 -2.32 0.020 .7654068 .9777205
porc_pobr | 1.701616 .3675199 2.46 0.014 1.114337 2.598403
susini2 | 1.099021 .0720964 1.44 0.150 .9664218 1.249814
susini3 | 1.270806 .073157 4.16 0.000 1.135214 1.422593
susini4 | 1.155085 .037882 4.40 0.000 1.083173 1.23177
susini5 | 1.377771 .1163944 3.79 0.000 1.167528 1.625873
ano_nac_corr | .8462982 .006773 -20.85 0.000 .8331269 .8596778
cohab2 | .8631945 .0473324 -2.68 0.007 .775236 .9611329
cohab3 | 1.075674 .068681 1.14 0.253 .9491435 1.219071
cohab4 | .9445976 .0518711 -1.04 0.299 .848212 1.051936
fis_com2 | 1.11263 .0326182 3.64 0.000 1.050501 1.178433
rc_x1 | .8445251 .0086662 -16.47 0.000 .8277095 .8616824
rc_x2 | .8806734 .0305049 -3.67 0.000 .8228692 .9425381
rc_x3 | 1.298114 .1196752 2.83 0.005 1.083526 1.555201
_rcs1 | 2.169294 .0624765 26.89 0.000 2.050234 2.295268
_rcs2 | 1.064767 .0232916 2.87 0.004 1.020081 1.11141
_rcs3 | 1.033604 .0069512 4.91 0.000 1.020069 1.047318
_rcs4 | 1.018752 .0042313 4.47 0.000 1.010492 1.027079
_rcs5 | 1.014651 .0028892 5.11 0.000 1.009004 1.02033
_rcs6 | 1.009615 .0023396 4.13 0.000 1.005039 1.01421
_rcs7 | 1.010976 .0020011 5.51 0.000 1.007061 1.014906
_rcs8 | 1.007804 .0018133 4.32 0.000 1.004256 1.011364
_rcs9 | 1.004827 .0015567 3.11 0.002 1.00178 1.007883
_rcs_mot_egr_early1 | .8995804 .0290253 -3.28 0.001 .8444534 .9583061
_rcs_mot_egr_early2 | .9998455 .0245655 -0.01 0.995 .952839 1.049171
_rcs_mot_egr_late1 | .9258 .0289406 -2.47 0.014 .8707802 .9842962
_rcs_mot_egr_late2 | 1.009969 .0241993 0.41 0.679 .963636 1.058531
_cons | 1.3e+143 2.1e+144 20.46 0.000 2.6e+129 6.6e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.028
Iteration 1: log likelihood = -21755.839
Iteration 2: log likelihood = -21755.727
Iteration 3: log likelihood = -21755.727
Log likelihood = -21755.727 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.995533 .1088242 12.67 0.000 1.793245 2.22064
mot_egr_late | 1.653041 .0779033 10.67 0.000 1.507193 1.813003
tr_mod2 | 1.151996 .0429512 3.80 0.000 1.070815 1.239331
sex_dum2 | .592552 .0255619 -12.13 0.000 .5445113 .6448313
edad_ini_cons | .9733899 .0040334 -6.51 0.000 .9655167 .9813274
esc1 | 1.517121 .0833373 7.59 0.000 1.362268 1.689576
esc2 | 1.343986 .0693376 5.73 0.000 1.214732 1.486994
sus_prin2 | 1.195931 .0709261 3.02 0.003 1.064694 1.343346
sus_prin3 | 1.717397 .082321 11.28 0.000 1.563398 1.886565
sus_prin4 | 1.143806 .0794036 1.94 0.053 .9983005 1.310518
sus_prin5 | 1.356283 .1841439 2.24 0.025 1.039398 1.769778
fr_cons_sus_prin2 | .9774309 .0969587 -0.23 0.818 .8047278 1.187198
fr_cons_sus_prin3 | .9956661 .0799236 -0.05 0.957 .8507198 1.165309
fr_cons_sus_prin4 | 1.038197 .0863153 0.45 0.652 .8820867 1.221936
fr_cons_sus_prin5 | 1.088869 .0865748 1.07 0.284 .9317461 1.272489
cond_ocu2 | 1.0874 .0670692 1.36 0.174 .9635816 1.227129
cond_ocu3 | 1.145808 .2804856 0.56 0.578 .7091597 1.851314
cond_ocu4 | 1.240066 .0809706 3.30 0.001 1.091102 1.409368
cond_ocu5 | 1.33347 .1368986 2.80 0.005 1.090426 1.630687
cond_ocu6 | 1.211991 .0420142 5.55 0.000 1.13238 1.297199
policonsumo | 1.00703 .0431192 0.16 0.870 .9259671 1.09519
num_hij2 | 1.136165 .0394208 3.68 0.000 1.06147 1.216116
tenviv1 | 1.018627 .1150823 0.16 0.870 .8162971 1.271107
tenviv2 | 1.068806 .0803461 0.89 0.376 .9223823 1.238474
tenviv4 | 1.012341 .0420658 0.30 0.768 .9331612 1.098239
tenviv5 | .9929518 .0331994 -0.21 0.832 .9299684 1.060201
mzone2 | 1.41643 .0524968 9.39 0.000 1.317186 1.523151
mzone3 | 1.545213 .0865567 7.77 0.000 1.384546 1.724524
n_off_vio | 1.461512 .0503243 11.02 0.000 1.366132 1.56355
n_off_acq | 2.795949 .0870898 33.01 0.000 2.630362 2.97196
n_off_sud | 1.376352 .0456264 9.64 0.000 1.28977 1.468748
n_off_oth | 1.702226 .0564604 16.04 0.000 1.595086 1.816562
psy_com2 | 1.049032 .0403305 1.25 0.213 .9728904 1.131133
dep2 | 1.032603 .0387456 0.86 0.393 .9593879 1.111405
rural2 | .9373355 .0520426 -1.17 0.244 .840688 1.045094
rural3 | .8651689 .0540331 -2.32 0.020 .7654911 .9778262
porc_pobr | 1.699323 .3670722 2.45 0.014 1.112775 2.595044
susini2 | 1.09926 .0721127 1.44 0.149 .9666303 1.250087
susini3 | 1.270811 .0731574 4.16 0.000 1.135219 1.422599
susini4 | 1.155028 .0378801 4.39 0.000 1.08312 1.23171
susini5 | 1.377829 .1164004 3.79 0.000 1.167576 1.625945
ano_nac_corr | .8462715 .0067729 -20.86 0.000 .8331004 .8596509
cohab2 | .863226 .0473345 -2.68 0.007 .7752636 .9611688
cohab3 | 1.075672 .0686818 1.14 0.253 .9491404 1.219071
cohab4 | .9446197 .0518724 -1.04 0.300 .8482317 1.051961
fis_com2 | 1.112433 .0326123 3.63 0.000 1.050316 1.178224
rc_x1 | .8444944 .0086659 -16.47 0.000 .8276793 .8616511
rc_x2 | .8807019 .0305057 -3.67 0.000 .8228962 .9425682
rc_x3 | 1.297958 .1196599 2.83 0.005 1.083397 1.555012
_rcs1 | 2.178749 .0634964 26.72 0.000 2.057786 2.306823
_rcs2 | 1.057313 .0236667 2.49 0.013 1.01193 1.104732
_rcs3 | 1.044204 .0127379 3.55 0.000 1.019534 1.069471
_rcs4 | 1.026732 .0091417 2.96 0.003 1.00897 1.044807
_rcs5 | 1.018645 .0049984 3.76 0.000 1.008896 1.028489
_rcs6 | 1.011085 .002781 4.01 0.000 1.005649 1.01655
_rcs7 | 1.011363 .0020396 5.60 0.000 1.007373 1.015369
_rcs8 | 1.007836 .0018138 4.34 0.000 1.004287 1.011397
_rcs9 | 1.00484 .0015573 3.12 0.002 1.001792 1.007896
_rcs_mot_egr_early1 | .8954568 .0292556 -3.38 0.001 .8399141 .9546725
_rcs_mot_egr_early2 | 1.005187 .0247957 0.21 0.834 .9577446 1.05498
_rcs_mot_egr_early3 | .9853329 .0168654 -0.86 0.388 .9528256 1.018949
_rcs_mot_egr_late1 | .9212508 .0291489 -2.59 0.010 .8658553 .9801903
_rcs_mot_egr_late2 | 1.016439 .0245803 0.67 0.500 .9693862 1.065775
_rcs_mot_egr_late3 | .9842898 .0162701 -0.96 0.338 .9529119 1.016701
_cons | 1.4e+143 2.2e+144 20.47 0.000 2.7e+129 7.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21766.999
Iteration 1: log likelihood = -21754.224
Iteration 2: log likelihood = -21754.067
Iteration 3: log likelihood = -21754.067
Log likelihood = -21754.067 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.996517 .1088915 12.68 0.000 1.794105 2.221765
mot_egr_late | 1.653828 .0779497 10.67 0.000 1.507894 1.813886
tr_mod2 | 1.152137 .042958 3.80 0.000 1.070943 1.239486
sex_dum2 | .5925432 .0255616 -12.13 0.000 .544503 .6448219
edad_ini_cons | .9733878 .0040334 -6.51 0.000 .9655145 .9813253
esc1 | 1.517029 .083332 7.59 0.000 1.362186 1.689473
esc2 | 1.344012 .0693387 5.73 0.000 1.214755 1.487022
sus_prin2 | 1.196095 .0709368 3.02 0.003 1.064838 1.343531
sus_prin3 | 1.717753 .0823409 11.29 0.000 1.563717 1.886962
sus_prin4 | 1.14392 .0794118 1.94 0.053 .9984 1.31065
sus_prin5 | 1.356386 .18416 2.25 0.025 1.039473 1.769918
fr_cons_sus_prin2 | .9775389 .0969694 -0.23 0.819 .8048168 1.187329
fr_cons_sus_prin3 | .9957961 .0799338 -0.05 0.958 .8508312 1.16546
fr_cons_sus_prin4 | 1.03832 .0863254 0.45 0.651 .8821907 1.22208
fr_cons_sus_prin5 | 1.088873 .0865751 1.07 0.284 .9317499 1.272493
cond_ocu2 | 1.087434 .0670716 1.36 0.174 .9636114 1.227168
cond_ocu3 | 1.146367 .2806228 0.56 0.577 .7095051 1.852218
cond_ocu4 | 1.239823 .0809554 3.29 0.001 1.090887 1.409093
cond_ocu5 | 1.333292 .1368836 2.80 0.005 1.090274 1.630476
cond_ocu6 | 1.21187 .0420109 5.54 0.000 1.132265 1.297072
policonsumo | 1.007006 .0431181 0.16 0.870 .9259453 1.095164
num_hij2 | 1.136061 .0394164 3.68 0.000 1.061374 1.216003
tenviv1 | 1.018447 .1150655 0.16 0.871 .8161474 1.270891
tenviv2 | 1.069096 .0803679 0.89 0.374 .9226328 1.23881
tenviv4 | 1.012224 .0420611 0.29 0.770 .9330539 1.098113
tenviv5 | .9929402 .0331991 -0.21 0.832 .9299574 1.060189
mzone2 | 1.416417 .0524972 9.39 0.000 1.317173 1.523139
mzone3 | 1.545266 .0865614 7.77 0.000 1.384591 1.724587
n_off_vio | 1.461391 .0503202 11.02 0.000 1.366019 1.563421
n_off_acq | 2.795762 .0870825 33.01 0.000 2.630189 2.971758
n_off_sud | 1.376315 .0456238 9.64 0.000 1.289737 1.468705
n_off_oth | 1.702273 .0564615 16.04 0.000 1.595131 1.816612
psy_com2 | 1.049463 .040347 1.26 0.209 .97329 1.131597
dep2 | 1.032611 .0387461 0.86 0.392 .9593951 1.111414
rural2 | .9376368 .0520588 -1.16 0.246 .8409591 1.045429
rural3 | .8651924 .0540339 -2.32 0.020 .765513 .9778513
porc_pobr | 1.694292 .3660157 2.44 0.015 1.109441 2.587452
susini2 | 1.099658 .0721389 1.45 0.148 .9669808 1.25054
susini3 | 1.270885 .0731617 4.16 0.000 1.135285 1.422682
susini4 | 1.154989 .0378782 4.39 0.000 1.083085 1.231667
susini5 | 1.377823 .1164012 3.79 0.000 1.167568 1.62594
ano_nac_corr | .8462639 .0067728 -20.86 0.000 .8330932 .8596429
cohab2 | .8632768 .0473385 -2.68 0.007 .7753071 .9612279
cohab3 | 1.075585 .0686776 1.14 0.254 .9490609 1.218976
cohab4 | .9446901 .0518773 -1.04 0.300 .8482932 1.052041
fis_com2 | 1.11211 .032602 3.62 0.000 1.050013 1.177881
rc_x1 | .8445048 .0086655 -16.47 0.000 .8276903 .8616609
rc_x2 | .8806374 .0305024 -3.67 0.000 .8228379 .942497
rc_x3 | 1.29815 .1196741 2.83 0.005 1.083563 1.555233
_rcs1 | 2.18017 .063448 26.78 0.000 2.059295 2.308141
_rcs2 | 1.058841 .024761 2.44 0.014 1.011406 1.108501
_rcs3 | 1.03218 .0157974 2.07 0.039 1.001678 1.063612
_rcs4 | 1.027769 .0087769 3.21 0.001 1.01071 1.045116
_rcs5 | 1.026472 .0074109 3.62 0.000 1.012049 1.0411
_rcs6 | 1.01857 .0058943 3.18 0.001 1.007082 1.030188
_rcs7 | 1.014985 .0031659 4.77 0.000 1.008799 1.021209
_rcs8 | 1.008592 .0018693 4.62 0.000 1.004935 1.012263
_rcs9 | 1.004765 .0015571 3.07 0.002 1.001718 1.007821
_rcs_mot_egr_early1 | .8947473 .0292105 -3.41 0.001 .8392889 .9538703
_rcs_mot_egr_early2 | 1.005969 .0256341 0.23 0.815 .9569606 1.057486
_rcs_mot_egr_early3 | .9977517 .0184892 -0.12 0.903 .9621637 1.034656
_rcs_mot_egr_early4 | .976951 .0119327 -1.91 0.056 .953841 1.000621
_rcs_mot_egr_late1 | .9205891 .0290987 -2.62 0.009 .8652874 .9794253
_rcs_mot_egr_late2 | 1.016946 .0255061 0.67 0.503 .9681639 1.068186
_rcs_mot_egr_late3 | .992782 .017854 -0.40 0.687 .9583984 1.028399
_rcs_mot_egr_late4 | .9843923 .011514 -1.34 0.179 .9620819 1.00722
_cons | 1.4e+143 2.3e+144 20.47 0.000 2.8e+129 7.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.099
Iteration 1: log likelihood = -21754.236
Iteration 2: log likelihood = -21754.085
Iteration 3: log likelihood = -21754.085
Log likelihood = -21754.085 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997304 .1089405 12.68 0.000 1.794802 2.222654
mot_egr_late | 1.654485 .0779859 10.68 0.000 1.508483 1.814617
tr_mod2 | 1.152141 .0429585 3.80 0.000 1.070947 1.239491
sex_dum2 | .5925385 .0255614 -12.13 0.000 .5444986 .6448169
edad_ini_cons | .9733892 .0040334 -6.51 0.000 .9655159 .9813267
esc1 | 1.517035 .0833321 7.59 0.000 1.362192 1.68948
esc2 | 1.343997 .0693379 5.73 0.000 1.214742 1.487005
sus_prin2 | 1.196104 .0709375 3.02 0.003 1.064846 1.343542
sus_prin3 | 1.717814 .0823444 11.29 0.000 1.563771 1.88703
sus_prin4 | 1.143951 .0794138 1.94 0.053 .9984275 1.310685
sus_prin5 | 1.35639 .1841606 2.25 0.025 1.039476 1.769923
fr_cons_sus_prin2 | .9775296 .0969684 -0.23 0.819 .8048092 1.187318
fr_cons_sus_prin3 | .9957843 .0799329 -0.05 0.958 .850821 1.165447
fr_cons_sus_prin4 | 1.038327 .0863262 0.45 0.651 .8821974 1.222089
fr_cons_sus_prin5 | 1.088864 .0865744 1.07 0.284 .9317415 1.272482
cond_ocu2 | 1.087424 .067071 1.36 0.174 .9636021 1.227156
cond_ocu3 | 1.146404 .2806321 0.56 0.577 .7095277 1.852278
cond_ocu4 | 1.239748 .0809511 3.29 0.001 1.09082 1.409009
cond_ocu5 | 1.333348 .1368903 2.80 0.005 1.090318 1.630547
cond_ocu6 | 1.211853 .0420104 5.54 0.000 1.132249 1.297054
policonsumo | 1.006967 .0431164 0.16 0.871 .9259091 1.095121
num_hij2 | 1.136064 .0394164 3.68 0.000 1.061377 1.216006
tenviv1 | 1.01843 .1150631 0.16 0.872 .816134 1.270868
tenviv2 | 1.069134 .0803705 0.89 0.374 .9226658 1.238853
tenviv4 | 1.012161 .0420585 0.29 0.771 .9329956 1.098044
tenviv5 | .9929145 .0331983 -0.21 0.832 .9299332 1.060161
mzone2 | 1.41639 .0524962 9.39 0.000 1.317148 1.52311
mzone3 | 1.545217 .0865595 7.77 0.000 1.384545 1.724534
n_off_vio | 1.461371 .0503194 11.02 0.000 1.366001 1.563399
n_off_acq | 2.795795 .0870829 33.01 0.000 2.630221 2.971792
n_off_sud | 1.37633 .0456241 9.64 0.000 1.289752 1.468721
n_off_oth | 1.702301 .0564623 16.04 0.000 1.595157 1.816641
psy_com2 | 1.049491 .0403491 1.26 0.209 .9733144 1.13163
dep2 | 1.032616 .0387464 0.86 0.392 .9594 1.11142
rural2 | .9377101 .052063 -1.16 0.247 .8410246 1.045511
rural3 | .865236 .0540366 -2.32 0.020 .7655517 .9779006
porc_pobr | 1.692992 .365744 2.44 0.015 1.108578 2.585493
susini2 | 1.099705 .0721422 1.45 0.147 .9670218 1.250594
susini3 | 1.270806 .0731573 4.16 0.000 1.135213 1.422594
susini4 | 1.154962 .0378773 4.39 0.000 1.08306 1.231638
susini5 | 1.377745 .1163943 3.79 0.000 1.167503 1.625848
ano_nac_corr | .846264 .0067729 -20.86 0.000 .833093 .8596433
cohab2 | .8632726 .0473381 -2.68 0.007 .7753035 .961223
cohab3 | 1.075568 .0686763 1.14 0.254 .9490473 1.218956
cohab4 | .9446968 .0518776 -1.04 0.300 .8482993 1.052048
fis_com2 | 1.112049 .0326004 3.62 0.000 1.049955 1.177816
rc_x1 | .8445045 .0086657 -16.47 0.000 .8276897 .8616609
rc_x2 | .8806261 .0305019 -3.67 0.000 .8228276 .9424847
rc_x3 | 1.298209 .119679 2.83 0.005 1.083613 1.555303
_rcs1 | 2.181622 .0635516 26.78 0.000 2.060552 2.309805
_rcs2 | 1.056645 .0242451 2.40 0.016 1.010178 1.105249
_rcs3 | 1.038056 .0170844 2.27 0.023 1.005106 1.072087
_rcs4 | 1.024967 .0095101 2.66 0.008 1.006496 1.043777
_rcs5 | 1.022214 .0078198 2.87 0.004 1.007002 1.037656
_rcs6 | 1.018831 .0061464 3.09 0.002 1.006855 1.030949
_rcs7 | 1.01836 .0056325 3.29 0.001 1.00738 1.029459
_rcs8 | 1.010631 .0027483 3.89 0.000 1.005259 1.016032
_rcs9 | 1.004951 .0015577 3.19 0.001 1.001902 1.008008
_rcs_mot_egr_early1 | .8940716 .0292181 -3.43 0.001 .8386006 .9532118
_rcs_mot_egr_early2 | 1.007565 .0253876 0.30 0.765 .9590146 1.058573
_rcs_mot_egr_early3 | .9965328 .0188519 -0.18 0.854 .9602604 1.034175
_rcs_mot_egr_early4 | .9847203 .012524 -1.21 0.226 .9604771 1.009575
_rcs_mot_egr_early5 | .9868826 .0090897 -1.43 0.152 .969227 1.00486
_rcs_mot_egr_late1 | .9199092 .0291008 -2.64 0.008 .8646048 .978751
_rcs_mot_egr_late2 | 1.019069 .0253137 0.76 0.447 .9706433 1.06991
_rcs_mot_egr_late3 | .9901626 .0182594 -0.54 0.592 .9550139 1.026605
_rcs_mot_egr_late4 | .9914839 .0121062 -0.70 0.484 .9680378 1.015498
_rcs_mot_egr_late5 | .9892696 .0087169 -1.22 0.221 .9723314 1.006503
_cons | 1.4e+143 2.3e+144 20.47 0.000 2.8e+129 7.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21766.716
Iteration 1: log likelihood = -21752.944
Iteration 2: log likelihood = -21752.764
Iteration 3: log likelihood = -21752.764
Log likelihood = -21752.764 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997182 .1089396 12.68 0.000 1.794682 2.222531
mot_egr_late | 1.654742 .0780053 10.68 0.000 1.508705 1.814916
tr_mod2 | 1.152138 .0429582 3.80 0.000 1.070945 1.239488
sex_dum2 | .5925471 .0255617 -12.13 0.000 .5445066 .6448262
edad_ini_cons | .9733883 .0040334 -6.51 0.000 .965515 .9813258
esc1 | 1.517015 .0833312 7.59 0.000 1.362173 1.689457
esc2 | 1.343963 .0693363 5.73 0.000 1.214711 1.486967
sus_prin2 | 1.196138 .0709399 3.02 0.003 1.064876 1.343581
sus_prin3 | 1.717794 .0823436 11.29 0.000 1.563753 1.887009
sus_prin4 | 1.143907 .0794109 1.94 0.053 .998389 1.310635
sus_prin5 | 1.356251 .1841402 2.24 0.025 1.039373 1.769738
fr_cons_sus_prin2 | .9775487 .0969704 -0.23 0.819 .8048248 1.187341
fr_cons_sus_prin3 | .9957947 .0799337 -0.05 0.958 .8508299 1.165459
fr_cons_sus_prin4 | 1.038358 .0863288 0.45 0.651 .8822226 1.222125
fr_cons_sus_prin5 | 1.088878 .0865757 1.07 0.284 .9317529 1.272499
cond_ocu2 | 1.087419 .0670708 1.36 0.174 .9635981 1.227152
cond_ocu3 | 1.146351 .280619 0.56 0.577 .7094945 1.852192
cond_ocu4 | 1.239708 .080948 3.29 0.001 1.090785 1.408962
cond_ocu5 | 1.33352 .1369092 2.80 0.005 1.090457 1.630761
cond_ocu6 | 1.211885 .0420114 5.54 0.000 1.132279 1.297088
policonsumo | 1.007007 .0431182 0.16 0.870 .9259457 1.095164
num_hij2 | 1.13606 .0394163 3.68 0.000 1.061374 1.216002
tenviv1 | 1.018373 .1150573 0.16 0.872 .8160882 1.2708
tenviv2 | 1.06919 .0803746 0.89 0.373 .9227142 1.238918
tenviv4 | 1.012191 .0420598 0.29 0.771 .9330233 1.098077
tenviv5 | .9929023 .0331979 -0.21 0.831 .9299218 1.060148
mzone2 | 1.416401 .0524966 9.39 0.000 1.317158 1.523122
mzone3 | 1.54519 .0865581 7.77 0.000 1.384521 1.724505
n_off_vio | 1.461392 .05032 11.02 0.000 1.366021 1.563421
n_off_acq | 2.79578 .087082 33.01 0.000 2.630207 2.971775
n_off_sud | 1.3763 .0456233 9.64 0.000 1.289723 1.468689
n_off_oth | 1.702303 .0564621 16.04 0.000 1.595159 1.816643
psy_com2 | 1.049428 .0403473 1.25 0.210 .9732546 1.131563
dep2 | 1.032593 .0387455 0.85 0.393 .9593786 1.111395
rural2 | .9376536 .0520601 -1.16 0.246 .8409735 1.045448
rural3 | .8651691 .0540325 -2.32 0.020 .7654922 .9778252
porc_pobr | 1.694022 .3659758 2.44 0.015 1.109241 2.587094
susini2 | 1.099652 .0721388 1.45 0.148 .9669746 1.250533
susini3 | 1.270909 .0731632 4.16 0.000 1.135306 1.422709
susini4 | 1.154955 .037877 4.39 0.000 1.083053 1.23163
susini5 | 1.377853 .1164025 3.79 0.000 1.167595 1.625973
ano_nac_corr | .8462774 .006773 -20.85 0.000 .8331061 .8596569
cohab2 | .8633273 .0473413 -2.68 0.007 .7753523 .9612844
cohab3 | 1.075623 .0686799 1.14 0.254 .9490954 1.219019
cohab4 | .9447268 .0518793 -1.04 0.300 .8483261 1.052082
fis_com2 | 1.112102 .0326022 3.62 0.000 1.050004 1.177872
rc_x1 | .8445193 .0086658 -16.47 0.000 .8277042 .861676
rc_x2 | .8806211 .0305017 -3.67 0.000 .822823 .9424792
rc_x3 | 1.298214 .1196794 2.83 0.005 1.083617 1.555309
_rcs1 | 2.182712 .0636525 26.77 0.000 2.061454 2.311103
_rcs2 | 1.05643 .0239218 2.42 0.015 1.010569 1.104372
_rcs3 | 1.041671 .0174794 2.43 0.015 1.007969 1.076499
_rcs4 | 1.019059 .010575 1.82 0.069 .9985415 1.039997
_rcs5 | 1.023838 .0077635 3.11 0.002 1.008734 1.039168
_rcs6 | 1.02429 .0070124 3.51 0.000 1.010638 1.038127
_rcs7 | 1.01683 .0055211 3.07 0.002 1.006066 1.027709
_rcs8 | 1.008229 .0044237 1.87 0.062 .9995959 1.016937
_rcs9 | 1.004994 .001661 3.01 0.003 1.001744 1.008255
_rcs_mot_egr_early1 | .893821 .0292323 -3.43 0.001 .8383244 .9529914
_rcs_mot_egr_early2 | 1.007491 .0251833 0.30 0.765 .9593227 1.058079
_rcs_mot_egr_early3 | .9958099 .0187688 -0.22 0.824 .959695 1.033284
_rcs_mot_egr_early4 | .9923276 .0131358 -0.58 0.561 .966913 1.01841
_rcs_mot_egr_early5 | .9776765 .0093869 -2.35 0.019 .9594505 .9962488
_rcs_mot_egr_early6 | .999451 .007263 -0.08 0.940 .9853167 1.013788
_rcs_mot_egr_late1 | .9192909 .0291136 -2.66 0.008 .8639641 .9781607
_rcs_mot_egr_late2 | 1.019355 .0251408 0.78 0.437 .9712522 1.069841
_rcs_mot_egr_late3 | .9888902 .0181904 -0.61 0.544 .9538727 1.025193
_rcs_mot_egr_late4 | .9974563 .0127224 -0.20 0.842 .97283 1.022706
_rcs_mot_egr_late5 | .9824061 .0090319 -1.93 0.054 .9648624 1.000269
_rcs_mot_egr_late6 | .9986935 .0068971 -0.19 0.850 .9852666 1.012303
_cons | 1.4e+143 2.2e+144 20.47 0.000 2.7e+129 6.9e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21766.59
Iteration 1: log likelihood = -21753.604
Iteration 2: log likelihood = -21753.449
Iteration 3: log likelihood = -21753.449
Log likelihood = -21753.449 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997382 .1089497 12.68 0.000 1.794863 2.222752
mot_egr_late | 1.654897 .0780104 10.69 0.000 1.50885 1.815081
tr_mod2 | 1.152105 .0429574 3.80 0.000 1.070912 1.239452
sex_dum2 | .5925419 .0255616 -12.13 0.000 .5445017 .6448206
edad_ini_cons | .9733881 .0040334 -6.51 0.000 .9655149 .9813256
esc1 | 1.517072 .083334 7.59 0.000 1.362225 1.68952
esc2 | 1.343992 .0693377 5.73 0.000 1.214737 1.487
sus_prin2 | 1.19612 .0709384 3.02 0.003 1.064859 1.34356
sus_prin3 | 1.717842 .0823455 11.29 0.000 1.563798 1.887061
sus_prin4 | 1.143956 .0794139 1.94 0.053 .9984319 1.31069
sus_prin5 | 1.35635 .1841546 2.24 0.025 1.039447 1.76987
fr_cons_sus_prin2 | .9775321 .0969687 -0.23 0.819 .8048112 1.187321
fr_cons_sus_prin3 | .9957729 .079932 -0.05 0.958 .8508114 1.165433
fr_cons_sus_prin4 | 1.038357 .0863286 0.45 0.651 .8822226 1.222124
fr_cons_sus_prin5 | 1.088863 .0865743 1.07 0.284 .9317409 1.272481
cond_ocu2 | 1.087396 .0670695 1.36 0.174 .963577 1.227125
cond_ocu3 | 1.146329 .2806139 0.56 0.577 .7094809 1.852157
cond_ocu4 | 1.23969 .080947 3.29 0.001 1.090769 1.408942
cond_ocu5 | 1.333508 .136908 2.80 0.005 1.090448 1.630747
cond_ocu6 | 1.211857 .0420107 5.54 0.000 1.132252 1.297058
policonsumo | 1.006955 .043116 0.16 0.871 .9258984 1.095108
num_hij2 | 1.13607 .0394165 3.68 0.000 1.061384 1.216013
tenviv1 | 1.018357 .115055 0.16 0.872 .8160754 1.270777
tenviv2 | 1.069157 .080372 0.89 0.374 .9226864 1.23888
tenviv4 | 1.012129 .0420571 0.29 0.772 .9329657 1.098009
tenviv5 | .9928898 .0331974 -0.21 0.831 .9299101 1.060135
mzone2 | 1.416388 .052496 9.39 0.000 1.317146 1.523107
mzone3 | 1.54515 .086556 7.77 0.000 1.384485 1.72446
n_off_vio | 1.461353 .0503186 11.02 0.000 1.365985 1.56338
n_off_acq | 2.795804 .087082 33.01 0.000 2.630232 2.971799
n_off_sud | 1.37631 .0456232 9.64 0.000 1.289733 1.468699
n_off_oth | 1.702316 .0564623 16.04 0.000 1.595173 1.816657
psy_com2 | 1.049452 .0403487 1.26 0.209 .9732763 1.13159
dep2 | 1.032599 .0387459 0.85 0.393 .9593837 1.111402
rural2 | .9377909 .0520678 -1.16 0.247 .8410965 1.045602
rural3 | .8652489 .0540375 -2.32 0.020 .7655627 .9779154
porc_pobr | 1.691856 .3655163 2.43 0.015 1.107812 2.583812
susini2 | 1.09972 .0721434 1.45 0.147 .9670348 1.250612
susini3 | 1.270796 .073157 4.16 0.000 1.135205 1.422584
susini4 | 1.154911 .0378757 4.39 0.000 1.083011 1.231584
susini5 | 1.377725 .1163922 3.79 0.000 1.167486 1.625823
ano_nac_corr | .8462552 .0067728 -20.86 0.000 .8330843 .8596343
cohab2 | .8632682 .0473378 -2.68 0.007 .7752996 .961218
cohab3 | 1.075557 .0686755 1.14 0.254 .9490375 1.218944
cohab4 | .9446813 .0518765 -1.04 0.300 .8482858 1.052031
fis_com2 | 1.112032 .0325999 3.62 0.000 1.049939 1.177798
rc_x1 | .844493 .0086656 -16.47 0.000 .8276784 .8616493
rc_x2 | .8806261 .0305018 -3.67 0.000 .8228276 .9424845
rc_x3 | 1.298223 .1196799 2.83 0.005 1.083625 1.555318
_rcs1 | 2.182216 .0635831 26.78 0.000 2.061088 2.310464
_rcs2 | 1.056121 .023938 2.41 0.016 1.010231 1.104097
_rcs3 | 1.041016 .0178549 2.34 0.019 1.006602 1.076606
_rcs4 | 1.020481 .0116627 1.77 0.076 .9978766 1.043597
_rcs5 | 1.023768 .0077657 3.10 0.002 1.00866 1.039102
_rcs6 | 1.020067 .0065998 3.07 0.002 1.007213 1.033085
_rcs7 | 1.017645 .0059154 3.01 0.003 1.006116 1.029305
_rcs8 | 1.012142 .0049531 2.47 0.014 1.00248 1.021896
_rcs9 | 1.006107 .0024119 2.54 0.011 1.001391 1.010845
_rcs_mot_egr_early1 | .8941368 .0292257 -3.42 0.001 .8386517 .9532928
_rcs_mot_egr_early2 | 1.008079 .0252291 0.32 0.748 .9598241 1.05876
_rcs_mot_egr_early3 | .997317 .0190564 -0.14 0.888 .9606577 1.035375
_rcs_mot_egr_early4 | .9928293 .0134507 -0.53 0.595 .9668133 1.019545
_rcs_mot_egr_early5 | .9831999 .0093242 -1.79 0.074 .9650936 1.001646
_rcs_mot_egr_early6 | .9904309 .0076354 -1.25 0.212 .9755783 1.00551
_rcs_mot_egr_early7 | .9961249 .0059226 -0.65 0.514 .9845842 1.007801
_rcs_mot_egr_late1 | .9195098 .029096 -2.65 0.008 .8642151 .9783424
_rcs_mot_egr_late2 | 1.020237 .0252427 0.81 0.418 .9719426 1.070931
_rcs_mot_egr_late3 | .9887386 .0185044 -0.61 0.545 .9531277 1.02568
_rcs_mot_egr_late4 | .9988333 .0129996 -0.09 0.929 .9736767 1.02464
_rcs_mot_egr_late5 | .9870227 .0088953 -1.45 0.147 .9697414 1.004612
_rcs_mot_egr_late6 | .992225 .0072872 -1.06 0.288 .9780447 1.006611
_rcs_mot_egr_late7 | .9954853 .0055635 -0.81 0.418 .9846405 1.00645
_cons | 1.4e+143 2.3e+144 20.47 0.000 2.8e+129 7.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21766.598
Iteration 1: log likelihood = -21756.569
Iteration 2: log likelihood = -21756.497
Iteration 3: log likelihood = -21756.497
Log likelihood = -21756.497 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.995005 .1087417 12.67 0.000 1.792865 2.219936
mot_egr_late | 1.65123 .0777869 10.65 0.000 1.505597 1.81095
tr_mod2 | 1.152087 .0429529 3.80 0.000 1.070903 1.239425
sex_dum2 | .5925593 .0255622 -12.13 0.000 .5445178 .6448394
edad_ini_cons | .9733933 .0040333 -6.51 0.000 .9655202 .9813307
esc1 | 1.517164 .0833404 7.59 0.000 1.362306 1.689625
esc2 | 1.344028 .0693399 5.73 0.000 1.21477 1.48704
sus_prin2 | 1.195677 .0709089 3.01 0.003 1.064471 1.343055
sus_prin3 | 1.717069 .0823016 11.28 0.000 1.563106 1.886198
sus_prin4 | 1.143657 .0793919 1.93 0.053 .9981729 1.310345
sus_prin5 | 1.35555 .1840391 2.24 0.025 1.038845 1.768808
fr_cons_sus_prin2 | .9773981 .0969554 -0.23 0.818 .8047008 1.187158
fr_cons_sus_prin3 | .9955986 .0799182 -0.05 0.956 .8506621 1.16523
fr_cons_sus_prin4 | 1.038162 .0863124 0.45 0.652 .8820562 1.221894
fr_cons_sus_prin5 | 1.088818 .0865712 1.07 0.285 .9317014 1.272429
cond_ocu2 | 1.087576 .0670793 1.36 0.173 .9637386 1.227326
cond_ocu3 | 1.145235 .2803416 0.55 0.580 .7088089 1.850375
cond_ocu4 | 1.240124 .0809738 3.30 0.001 1.091154 1.409432
cond_ocu5 | 1.333094 .1368569 2.80 0.005 1.090123 1.630219
cond_ocu6 | 1.212035 .0420153 5.55 0.000 1.132422 1.297246
policonsumo | 1.006936 .0431146 0.16 0.872 .9258821 1.095087
num_hij2 | 1.136184 .0394218 3.68 0.000 1.061487 1.216137
tenviv1 | 1.018808 .1151002 0.16 0.869 .8164464 1.271327
tenviv2 | 1.068776 .0803431 0.88 0.376 .9223577 1.238438
tenviv4 | 1.012343 .0420655 0.30 0.768 .9331646 1.098241
tenviv5 | .992938 .0331985 -0.21 0.832 .9299562 1.060185
mzone2 | 1.41625 .0524895 9.39 0.000 1.31702 1.522956
mzone3 | 1.545266 .0865592 7.77 0.000 1.384595 1.724583
n_off_vio | 1.461493 .0503242 11.02 0.000 1.366114 1.563531
n_off_acq | 2.79589 .0870917 33.01 0.000 2.6303 2.971905
n_off_sud | 1.376425 .0456299 9.64 0.000 1.289836 1.468827
n_off_oth | 1.702169 .0564598 16.04 0.000 1.59503 1.816504
psy_com2 | 1.048561 .0403038 1.23 0.217 .9724694 1.130607
dep2 | 1.032612 .0387455 0.86 0.392 .9593976 1.111414
rural2 | .9372561 .0520379 -1.17 0.243 .8406172 1.045005
rural3 | .8652488 .0540374 -2.32 0.020 .7655631 .9779149
porc_pobr | 1.702188 .3676275 2.46 0.014 1.114732 2.599229
susini2 | 1.099053 .0720981 1.44 0.150 .9664506 1.249849
susini3 | 1.270668 .0731488 4.16 0.000 1.135092 1.422438
susini4 | 1.155086 .037882 4.40 0.000 1.083175 1.231772
susini5 | 1.377773 .1163938 3.79 0.000 1.167531 1.625874
ano_nac_corr | .8462833 .006772 -20.86 0.000 .833114 .8596607
cohab2 | .8633655 .0473411 -2.68 0.007 .7753906 .9613218
cohab3 | 1.075912 .0686951 1.15 0.252 .9493558 1.219339
cohab4 | .9447512 .0518796 -1.03 0.301 .8483499 1.052107
fis_com2 | 1.112667 .0326182 3.64 0.000 1.050538 1.178469
rc_x1 | .8445221 .0086653 -16.47 0.000 .8277081 .8616777
rc_x2 | .8806221 .0305032 -3.67 0.000 .8228212 .9424834
rc_x3 | 1.298285 .1196907 2.83 0.005 1.083669 1.555405
_rcs1 | 2.175967 .0586235 28.86 0.000 2.064048 2.293955
_rcs2 | 1.069705 .0074857 9.63 0.000 1.055133 1.084477
_rcs3 | 1.034675 .0057468 6.14 0.000 1.023472 1.046
_rcs4 | 1.018769 .0041085 4.61 0.000 1.010748 1.026853
_rcs5 | 1.015335 .0029147 5.30 0.000 1.009639 1.021064
_rcs6 | 1.009808 .0023408 4.21 0.000 1.00523 1.014406
_rcs7 | 1.009701 .0020171 4.83 0.000 1.005756 1.013663
_rcs8 | 1.009854 .0018216 5.44 0.000 1.00629 1.013431
_rcs9 | 1.006303 .001682 3.76 0.000 1.003012 1.009605
_rcs10 | 1.003713 .0014437 2.58 0.010 1.000887 1.006546
_rcs_mot_egr_early1 | .8983045 .0272026 -3.54 0.000 .8465397 .9532346
_rcs_mot_egr_late1 | .9209944 .026797 -2.83 0.005 .8699427 .9750419
_cons | 1.3e+143 2.2e+144 20.47 0.000 2.7e+129 6.8e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.511
Iteration 1: log likelihood = -21756.311
Iteration 2: log likelihood = -21756.221
Iteration 3: log likelihood = -21756.221
Log likelihood = -21756.221 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.993991 .1087264 12.66 0.000 1.791884 2.218895
mot_egr_late | 1.65164 .0778207 10.65 0.000 1.505945 1.81143
tr_mod2 | 1.151889 .0429469 3.79 0.000 1.070716 1.239215
sex_dum2 | .5925627 .0255625 -12.13 0.000 .5445207 .6448433
edad_ini_cons | .9733969 .0040333 -6.51 0.000 .9655238 .9813342
esc1 | 1.517164 .0833404 7.59 0.000 1.362306 1.689625
esc2 | 1.344012 .0693392 5.73 0.000 1.214755 1.487023
sus_prin2 | 1.195705 .0709112 3.01 0.003 1.064495 1.343088
sus_prin3 | 1.717139 .082305 11.28 0.000 1.56317 1.886274
sus_prin4 | 1.143739 .0793981 1.93 0.053 .9982439 1.31044
sus_prin5 | 1.355802 .1840765 2.24 0.025 1.039033 1.769145
fr_cons_sus_prin2 | .9774286 .0969586 -0.23 0.818 .8047258 1.187195
fr_cons_sus_prin3 | .9956557 .0799228 -0.05 0.957 .8507108 1.165296
fr_cons_sus_prin4 | 1.03817 .0863129 0.45 0.652 .8820636 1.221903
fr_cons_sus_prin5 | 1.088857 .0865736 1.07 0.284 .931736 1.272474
cond_ocu2 | 1.087476 .0670734 1.36 0.174 .9636493 1.227213
cond_ocu3 | 1.145346 .28037 0.55 0.579 .7088767 1.850559
cond_ocu4 | 1.240325 .0809858 3.30 0.001 1.091332 1.409658
cond_ocu5 | 1.333448 .1368946 2.80 0.005 1.090411 1.630656
cond_ocu6 | 1.212027 .0420149 5.55 0.000 1.132414 1.297236
policonsumo | 1.006954 .0431154 0.16 0.871 .9258978 1.095106
num_hij2 | 1.1362 .0394224 3.68 0.000 1.061502 1.216154
tenviv1 | 1.018859 .1151063 0.17 0.869 .8164862 1.271391
tenviv2 | 1.068652 .0803341 0.88 0.377 .9222502 1.238295
tenviv4 | 1.01244 .0420698 0.30 0.766 .9332534 1.098346
tenviv5 | .9930367 .033202 -0.21 0.834 .9300484 1.060291
mzone2 | 1.416367 .0524937 9.39 0.000 1.317129 1.523082
mzone3 | 1.545498 .0865718 7.77 0.000 1.384803 1.72484
n_off_vio | 1.461527 .0503257 11.02 0.000 1.366145 1.563568
n_off_acq | 2.795977 .0870936 33.01 0.000 2.630383 2.971996
n_off_sud | 1.376401 .0456287 9.64 0.000 1.289814 1.468801
n_off_oth | 1.702236 .0564619 16.04 0.000 1.595093 1.816576
psy_com2 | 1.049001 .0403259 1.24 0.213 .9728674 1.131092
dep2 | 1.032606 .0387455 0.86 0.392 .9593919 1.111408
rural2 | .9372007 .052035 -1.17 0.243 .8405672 1.044943
rural3 | .865097 .054029 -2.32 0.020 .7654267 .9777458
porc_pobr | 1.700791 .367345 2.46 0.014 1.113792 2.597153
susini2 | 1.099139 .0721048 1.44 0.150 .9665247 1.24995
susini3 | 1.270797 .0731565 4.16 0.000 1.135207 1.422584
susini4 | 1.155019 .0378801 4.39 0.000 1.083112 1.231701
susini5 | 1.377678 .1163866 3.79 0.000 1.167449 1.625764
ano_nac_corr | .846292 .0067731 -20.85 0.000 .8331206 .8596717
cohab2 | .8631813 .0473316 -2.68 0.007 .7752241 .9611181
cohab3 | 1.075648 .0686793 1.14 0.253 .9491209 1.219042
cohab4 | .9445835 .0518704 -1.04 0.299 .8481991 1.05192
fis_com2 | 1.112603 .0326174 3.64 0.000 1.050476 1.178404
rc_x1 | .8445222 .0086662 -16.47 0.000 .8277064 .8616795
rc_x2 | .8806524 .0305042 -3.67 0.000 .8228496 .9425156
rc_x3 | 1.298193 .1196824 2.83 0.005 1.083592 1.555295
_rcs1 | 2.170458 .0625458 26.89 0.000 2.051268 2.296574
_rcs2 | 1.06449 .0232706 2.86 0.004 1.019843 1.11109
_rcs3 | 1.03351 .0070329 4.84 0.000 1.019818 1.047387
_rcs4 | 1.018444 .0042941 4.33 0.000 1.010062 1.026895
_rcs5 | 1.01522 .0029345 5.23 0.000 1.009485 1.020988
_rcs6 | 1.009786 .0023421 4.20 0.000 1.005206 1.014387
_rcs7 | 1.009694 .0020173 4.83 0.000 1.005747 1.013655
_rcs8 | 1.009853 .001822 5.43 0.000 1.006288 1.01343
_rcs9 | 1.006299 .0016826 3.76 0.000 1.003007 1.009603
_rcs10 | 1.003712 .001444 2.58 0.010 1.000886 1.006546
_rcs_mot_egr_early1 | .899159 .0290291 -3.29 0.001 .8440256 .9578937
_rcs_mot_egr_early2 | .9998157 .0245834 -0.01 0.994 .9527758 1.049178
_rcs_mot_egr_late1 | .9250996 .0289336 -2.49 0.013 .8700939 .9835827
_rcs_mot_egr_late2 | 1.009844 .0242127 0.41 0.683 .963486 1.058433
_cons | 1.3e+143 2.1e+144 20.46 0.000 2.6e+129 6.7e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.041
Iteration 1: log likelihood = -21755.801
Iteration 2: log likelihood = -21755.695
Iteration 3: log likelihood = -21755.695
Log likelihood = -21755.695 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.99613 .1088619 12.67 0.000 1.793772 2.221316
mot_egr_late | 1.653725 .0779398 10.67 0.000 1.507809 1.813762
tr_mod2 | 1.151991 .0429511 3.79 0.000 1.07081 1.239326
sex_dum2 | .5925426 .0255615 -12.13 0.000 .5445025 .6448213
edad_ini_cons | .9733889 .0040334 -6.51 0.000 .9655156 .9813263
esc1 | 1.517138 .0833383 7.59 0.000 1.362283 1.689595
esc2 | 1.343991 .0693378 5.73 0.000 1.214736 1.486999
sus_prin2 | 1.195943 .070927 3.02 0.003 1.064704 1.343359
sus_prin3 | 1.717435 .0823228 11.28 0.000 1.563433 1.886607
sus_prin4 | 1.143843 .0794062 1.94 0.053 .9983331 1.310561
sus_prin5 | 1.356376 .1841567 2.25 0.025 1.039469 1.7699
fr_cons_sus_prin2 | .9774529 .0969609 -0.23 0.818 .8047459 1.187225
fr_cons_sus_prin3 | .9956553 .0799228 -0.05 0.957 .8507104 1.165296
fr_cons_sus_prin4 | 1.038239 .0863188 0.45 0.652 .8821227 1.221986
fr_cons_sus_prin5 | 1.088878 .0865756 1.07 0.284 .9317538 1.272499
cond_ocu2 | 1.087405 .0670697 1.36 0.174 .9635858 1.227135
cond_ocu3 | 1.145913 .2805113 0.56 0.578 .7092241 1.851483
cond_ocu4 | 1.240016 .080967 3.29 0.001 1.091058 1.40931
cond_ocu5 | 1.333395 .1368911 2.80 0.005 1.090364 1.630595
cond_ocu6 | 1.211998 .0420146 5.55 0.000 1.132386 1.297207
policonsumo | 1.007009 .0431184 0.16 0.870 .9259479 1.095167
num_hij2 | 1.136146 .0394201 3.68 0.000 1.061452 1.216096
tenviv1 | 1.018682 .1150886 0.16 0.870 .8163413 1.271176
tenviv2 | 1.068828 .080348 0.89 0.376 .922401 1.2385
tenviv4 | 1.012339 .0420659 0.30 0.768 .9331596 1.098237
tenviv5 | .9929465 .0331992 -0.21 0.832 .9299635 1.060195
mzone2 | 1.416411 .0524961 9.39 0.000 1.317169 1.523131
mzone3 | 1.54526 .0865591 7.77 0.000 1.384588 1.724576
n_off_vio | 1.461501 .0503237 11.02 0.000 1.366123 1.563538
n_off_acq | 2.795869 .0870865 33.01 0.000 2.630288 2.971873
n_off_sud | 1.376272 .0456238 9.63 0.000 1.289694 1.468662
n_off_oth | 1.702211 .0564595 16.04 0.000 1.595072 1.816545
psy_com2 | 1.049068 .0403321 1.25 0.213 .972923 1.131172
dep2 | 1.032605 .0387457 0.86 0.392 .9593904 1.111408
rural2 | .9373348 .0520425 -1.17 0.244 .8406874 1.045093
rural3 | .8651945 .0540346 -2.32 0.020 .7655138 .977855
porc_pobr | 1.69846 .3668893 2.45 0.014 1.112205 2.593736
susini2 | 1.099386 .0721217 1.44 0.149 .96674 1.250231
susini3 | 1.270801 .0731568 4.16 0.000 1.13521 1.422588
susini4 | 1.15496 .037878 4.39 0.000 1.083056 1.231638
susini5 | 1.377737 .1163926 3.79 0.000 1.167498 1.625836
ano_nac_corr | .846265 .006773 -20.86 0.000 .8330938 .8596444
cohab2 | .863214 .0473338 -2.68 0.007 .7752529 .9611554
cohab3 | 1.075647 .0686801 1.14 0.253 .9491187 1.219043
cohab4 | .9446066 .0518718 -1.04 0.299 .8482198 1.051946
fis_com2 | 1.112402 .0326113 3.63 0.000 1.050287 1.178191
rc_x1 | .8444911 .0086659 -16.47 0.000 .827676 .8616479
rc_x2 | .8806806 .0305049 -3.67 0.000 .8228764 .9425454
rc_x3 | 1.298037 .1196672 2.83 0.005 1.083463 1.555106
_rcs1 | 2.179973 .0635525 26.73 0.000 2.058904 2.308161
_rcs2 | 1.056742 .0236335 2.47 0.014 1.011422 1.104093
_rcs3 | 1.043798 .0123705 3.62 0.000 1.019832 1.068327
_rcs4 | 1.026637 .0092259 2.93 0.003 1.008713 1.044879
_rcs5 | 1.019824 .0054655 3.66 0.000 1.009168 1.030592
_rcs6 | 1.011844 .003123 3.81 0.000 1.005742 1.017984
_rcs7 | 1.010422 .0021452 4.88 0.000 1.006227 1.014636
_rcs8 | 1.010044 .0018317 5.51 0.000 1.006461 1.013641
_rcs9 | 1.006294 .0016831 3.75 0.000 1.003001 1.009598
_rcs10 | 1.003741 .0014448 2.59 0.009 1.000914 1.006577
_rcs_mot_egr_early1 | .895023 .0292511 -3.39 0.001 .8394896 .9542301
_rcs_mot_egr_early2 | 1.00533 .0247854 0.22 0.829 .9579064 1.055101
_rcs_mot_egr_early3 | .9851059 .0168425 -0.88 0.380 .9526422 1.018676
_rcs_mot_egr_late1 | .9205178 .0291337 -2.62 0.009 .8651518 .9794271
_rcs_mot_egr_late2 | 1.016514 .0245669 0.68 0.498 .9694864 1.065823
_rcs_mot_egr_late3 | .9839882 .0162513 -0.98 0.328 .9526462 1.016361
_cons | 1.4e+143 2.3e+144 20.47 0.000 2.8e+129 7.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.051
Iteration 1: log likelihood = -21754.243
Iteration 2: log likelihood = -21754.08
Iteration 3: log likelihood = -21754.08
Log likelihood = -21754.08 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997004 .108921 12.68 0.000 1.794537 2.222314
mot_egr_late | 1.654403 .077979 10.68 0.000 1.508414 1.814521
tr_mod2 | 1.152134 .042958 3.80 0.000 1.070941 1.239483
sex_dum2 | .5925341 .0255613 -12.13 0.000 .5444944 .6448121
edad_ini_cons | .9733868 .0040334 -6.51 0.000 .9655134 .9813243
esc1 | 1.517047 .083333 7.59 0.000 1.362202 1.689493
esc2 | 1.344018 .0693391 5.73 0.000 1.214761 1.487028
sus_prin2 | 1.196108 .0709377 3.02 0.003 1.064849 1.343547
sus_prin3 | 1.71779 .0823427 11.29 0.000 1.563751 1.887004
sus_prin4 | 1.143959 .0794145 1.94 0.053 .9984339 1.310694
sus_prin5 | 1.35649 .1841744 2.25 0.025 1.039553 1.770055
fr_cons_sus_prin2 | .9775585 .0969713 -0.23 0.819 .8048329 1.187353
fr_cons_sus_prin3 | .9957842 .0799329 -0.05 0.958 .8508209 1.165446
fr_cons_sus_prin4 | 1.038359 .0863287 0.45 0.651 .8822242 1.222126
fr_cons_sus_prin5 | 1.088882 .0865758 1.07 0.284 .9317572 1.272503
cond_ocu2 | 1.087438 .067072 1.36 0.174 .9636148 1.227173
cond_ocu3 | 1.146477 .2806497 0.56 0.577 .7095726 1.852395
cond_ocu4 | 1.239779 .0809522 3.29 0.001 1.090849 1.409042
cond_ocu5 | 1.333215 .1368759 2.80 0.005 1.090212 1.630384
cond_ocu6 | 1.21188 .0420114 5.54 0.000 1.132274 1.297082
policonsumo | 1.006987 .0431174 0.16 0.871 .9259276 1.095143
num_hij2 | 1.136043 .0394157 3.68 0.000 1.061358 1.215984
tenviv1 | 1.018505 .1150721 0.16 0.871 .8161937 1.270963
tenviv2 | 1.069114 .0803695 0.89 0.374 .9226471 1.238831
tenviv4 | 1.012224 .0420612 0.29 0.770 .9330536 1.098113
tenviv5 | .9929364 .0331989 -0.21 0.832 .9299539 1.060184
mzone2 | 1.416401 .0524966 9.39 0.000 1.317157 1.523121
mzone3 | 1.545315 .086564 7.77 0.000 1.384634 1.724641
n_off_vio | 1.461382 .0503196 11.02 0.000 1.366012 1.563411
n_off_acq | 2.795687 .0870793 33.01 0.000 2.63012 2.971677
n_off_sud | 1.376235 .0456212 9.63 0.000 1.289662 1.46862
n_off_oth | 1.702259 .0564607 16.04 0.000 1.595119 1.816597
psy_com2 | 1.049494 .0403483 1.26 0.209 .9733186 1.131631
dep2 | 1.032613 .0387462 0.86 0.392 .9593975 1.111417
rural2 | .9376334 .0520585 -1.16 0.246 .8409561 1.045425
rural3 | .8652179 .0540354 -2.32 0.020 .7655357 .9778801
porc_pobr | 1.693483 .3658439 2.44 0.015 1.108907 2.586225
susini2 | 1.099782 .0721477 1.45 0.147 .9670884 1.250682
susini3 | 1.270873 .073161 4.16 0.000 1.135274 1.422668
susini4 | 1.154923 .0378762 4.39 0.000 1.083022 1.231596
susini5 | 1.377724 .1163928 3.79 0.000 1.167485 1.625824
ano_nac_corr | .8462549 .0067728 -20.86 0.000 .8330841 .8596339
cohab2 | .8632646 .0473378 -2.68 0.007 .7752963 .9612143
cohab3 | 1.075564 .0686762 1.14 0.254 .9490433 1.218953
cohab4 | .9446782 .0518767 -1.04 0.300 .8482824 1.052028
fis_com2 | 1.112083 .0326012 3.62 0.000 1.049987 1.177851
rc_x1 | .8444988 .0086655 -16.47 0.000 .8276843 .8616548
rc_x2 | .8806174 .0305017 -3.67 0.000 .8228192 .9424756
rc_x3 | 1.298224 .119681 2.83 0.005 1.083624 1.555322
_rcs1 | 2.181217 .0635022 26.79 0.000 2.060239 2.309298
_rcs2 | 1.058216 .0246991 2.42 0.015 1.010897 1.107749
_rcs3 | 1.032322 .0154706 2.12 0.034 1.002442 1.063094
_rcs4 | 1.026407 .0089123 3.00 0.003 1.009087 1.044024
_rcs5 | 1.026214 .0070161 3.78 0.000 1.012555 1.040058
_rcs6 | 1.019311 .0062217 3.13 0.002 1.007189 1.031579
_rcs7 | 1.015466 .0041168 3.79 0.000 1.00743 1.023567
_rcs8 | 1.012006 .0022504 5.37 0.000 1.007605 1.016427
_rcs9 | 1.006598 .0016911 3.91 0.000 1.003289 1.009917
_rcs10 | 1.003681 .0014442 2.55 0.011 1.000854 1.006515
_rcs_mot_egr_early1 | .8943924 .0292099 -3.42 0.001 .8389359 .9535148
_rcs_mot_egr_early2 | 1.006231 .0255944 0.24 0.807 .9572972 1.057667
_rcs_mot_egr_early3 | .9970685 .0184634 -0.16 0.874 .9615298 1.033921
_rcs_mot_egr_early4 | .9773376 .0119101 -1.88 0.060 .9542709 1.000962
_rcs_mot_egr_late1 | .9199471 .0290871 -2.64 0.008 .8646679 .9787603
_rcs_mot_egr_late2 | 1.017139 .0254622 0.68 0.497 .9684389 1.068289
_rcs_mot_egr_late3 | .9920479 .0178315 -0.44 0.657 .9577072 1.02762
_rcs_mot_egr_late4 | .9847347 .0114957 -1.32 0.188 .9624594 1.007526
_cons | 1.4e+143 2.3e+144 20.47 0.000 2.8e+129 7.3e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.13
Iteration 1: log likelihood = -21754.179
Iteration 2: log likelihood = -21754.017
Iteration 3: log likelihood = -21754.017
Log likelihood = -21754.017 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.998001 .1089861 12.69 0.000 1.795414 2.223447
mot_egr_late | 1.655264 .0780299 10.69 0.000 1.50918 1.815487
tr_mod2 | 1.152135 .0429584 3.80 0.000 1.070941 1.239485
sex_dum2 | .5925288 .025561 -12.13 0.000 .5444896 .6448065
edad_ini_cons | .9733878 .0040334 -6.51 0.000 .9655145 .9813253
esc1 | 1.517049 .0833329 7.59 0.000 1.362204 1.689495
esc2 | 1.344 .0693381 5.73 0.000 1.214745 1.487009
sus_prin2 | 1.196119 .0709385 3.02 0.003 1.064858 1.343559
sus_prin3 | 1.717855 .0823464 11.29 0.000 1.563809 1.887076
sus_prin4 | 1.143988 .0794164 1.94 0.053 .9984595 1.310727
sus_prin5 | 1.35648 .1841731 2.25 0.025 1.039545 1.770041
fr_cons_sus_prin2 | .977554 .0969709 -0.23 0.819 .8048292 1.187347
fr_cons_sus_prin3 | .9957762 .0799323 -0.05 0.958 .8508141 1.165437
fr_cons_sus_prin4 | 1.038372 .0863298 0.45 0.651 .882235 1.222141
fr_cons_sus_prin5 | 1.088874 .0865752 1.07 0.284 .9317498 1.272494
cond_ocu2 | 1.087428 .0670714 1.36 0.174 .9636059 1.227162
cond_ocu3 | 1.146503 .2806563 0.56 0.577 .7095884 1.852438
cond_ocu4 | 1.2397 .0809476 3.29 0.001 1.090778 1.408954
cond_ocu5 | 1.33327 .1368827 2.80 0.005 1.090254 1.630453
cond_ocu6 | 1.211856 .0420107 5.54 0.000 1.132252 1.297057
policonsumo | 1.006947 .0431156 0.16 0.872 .9258906 1.095099
num_hij2 | 1.136044 .0394156 3.68 0.000 1.061359 1.215984
tenviv1 | 1.018475 .1150683 0.16 0.871 .8161699 1.270925
tenviv2 | 1.06916 .0803727 0.89 0.374 .9226875 1.238884
tenviv4 | 1.012159 .0420585 0.29 0.771 .9329933 1.098042
tenviv5 | .9929087 .0331981 -0.21 0.831 .9299279 1.060155
mzone2 | 1.416376 .0524957 9.39 0.000 1.317134 1.523095
mzone3 | 1.545259 .0865617 7.77 0.000 1.384583 1.724581
n_off_vio | 1.46136 .0503188 11.02 0.000 1.365991 1.563387
n_off_acq | 2.795711 .0870795 33.01 0.000 2.630143 2.971701
n_off_sud | 1.376251 .0456215 9.63 0.000 1.289678 1.468637
n_off_oth | 1.702283 .0564615 16.04 0.000 1.595141 1.816621
psy_com2 | 1.049535 .0403509 1.26 0.209 .973355 1.131677
dep2 | 1.032618 .0387465 0.86 0.392 .9594015 1.111422
rural2 | .9377129 .0520631 -1.16 0.247 .8410272 1.045514
rural3 | .8652589 .054038 -2.32 0.020 .765572 .9779263
porc_pobr | 1.692107 .3655563 2.43 0.015 1.107994 2.584152
susini2 | 1.099831 .0721511 1.45 0.147 .9671314 1.250738
susini3 | 1.270805 .0731572 4.16 0.000 1.135212 1.422592
susini4 | 1.154899 .0378753 4.39 0.000 1.083 1.231571
susini5 | 1.377669 .1163879 3.79 0.000 1.167438 1.625758
ano_nac_corr | .8462598 .0067729 -20.86 0.000 .8330886 .8596392
cohab2 | .8632583 .0473373 -2.68 0.007 .7752907 .961207
cohab3 | 1.075538 .0686743 1.14 0.254 .9490207 1.218922
cohab4 | .944681 .0518768 -1.04 0.300 .848285 1.052031
fis_com2 | 1.112013 .0325992 3.62 0.000 1.049921 1.177778
rc_x1 | .8445035 .0086657 -16.47 0.000 .8276887 .8616599
rc_x2 | .8806061 .0305012 -3.67 0.000 .8228089 .9424632
rc_x3 | 1.298281 .1196856 2.83 0.005 1.083673 1.555389
_rcs1 | 2.182958 .0636144 26.79 0.000 2.061769 2.311269
_rcs2 | 1.056177 .0242929 2.38 0.017 1.009621 1.10488
_rcs3 | 1.036939 .0168052 2.24 0.025 1.004519 1.070406
_rcs4 | 1.024651 .0093331 2.67 0.008 1.006521 1.043108
_rcs5 | 1.022615 .0079791 2.87 0.004 1.007095 1.038374
_rcs6 | 1.018456 .0058283 3.20 0.001 1.007097 1.029944
_rcs7 | 1.017975 .0059145 3.07 0.002 1.006449 1.029634
_rcs8 | 1.015019 .0042452 3.56 0.000 1.006733 1.023374
_rcs9 | 1.007763 .0019977 3.90 0.000 1.003855 1.011686
_rcs10 | 1.003714 .0014437 2.58 0.010 1.000888 1.006548
_rcs_mot_egr_early1 | .8935876 .0292142 -3.44 0.001 .8381246 .9527208
_rcs_mot_egr_early2 | 1.007766 .025425 0.31 0.759 .9591456 1.05885
_rcs_mot_egr_early3 | .9969338 .0188515 -0.16 0.871 .9606618 1.034575
_rcs_mot_egr_early4 | .9836998 .012559 -1.29 0.198 .95939 1.008626
_rcs_mot_egr_early5 | .9869295 .0091979 -1.41 0.158 .9690656 1.005123
_rcs_mot_egr_late1 | .91912 .0290865 -2.67 0.008 .8638435 .9779336
_rcs_mot_egr_late2 | 1.019166 .0253498 0.76 0.445 .9706734 1.070082
_rcs_mot_egr_late3 | .9905663 .0182591 -0.51 0.607 .9554179 1.027008
_rcs_mot_egr_late4 | .9903514 .0121374 -0.79 0.429 .9668459 1.014428
_rcs_mot_egr_late5 | .9893849 .0088244 -1.20 0.231 .9722397 1.006832
_cons | 1.4e+143 2.3e+144 20.47 0.000 2.8e+129 7.2e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21767.049
Iteration 1: log likelihood = -21753.384
Iteration 2: log likelihood = -21753.19
Iteration 3: log likelihood = -21753.189
Log likelihood = -21753.189 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.997579 .1089671 12.68 0.000 1.795029 2.222986
mot_egr_late | 1.655151 .0780287 10.69 0.000 1.50907 1.815373
tr_mod2 | 1.152129 .0429581 3.80 0.000 1.070936 1.239478
sex_dum2 | .5925392 .0255615 -12.13 0.000 .5444991 .6448178
edad_ini_cons | .9733884 .0040334 -6.51 0.000 .9655151 .9813259
esc1 | 1.517036 .0833324 7.59 0.000 1.362193 1.689481
esc2 | 1.343965 .0693364 5.73 0.000 1.214713 1.48697
sus_prin2 | 1.196147 .0709404 3.02 0.003 1.064884 1.343592
sus_prin3 | 1.717859 .0823466 11.29 0.000 1.563812 1.88708
sus_prin4 | 1.143957 .0794143 1.94 0.053 .9984324 1.310692
sus_prin5 | 1.35636 .1841557 2.24 0.025 1.039455 1.769881
fr_cons_sus_prin2 | .977562 .0969717 -0.23 0.819 .8048358 1.187357
fr_cons_sus_prin3 | .9957781 .0799325 -0.05 0.958 .8508157 1.165439
fr_cons_sus_prin4 | 1.038396 .086332 0.45 0.650 .8822553 1.22217
fr_cons_sus_prin5 | 1.08888 .0865759 1.07 0.284 .9317549 1.272501
cond_ocu2 | 1.087414 .0670707 1.36 0.174 .9635927 1.227146
cond_ocu3 | 1.146457 .2806452 0.56 0.577 .7095602 1.852364
cond_ocu4 | 1.239642 .0809435 3.29 0.001 1.090727 1.408887
cond_ocu5 | 1.333459 .1369031 2.80 0.005 1.090408 1.630687
cond_ocu6 | 1.211878 .0420114 5.54 0.000 1.132272 1.297081
policonsumo | 1.006975 .0431168 0.16 0.871 .9259167 1.09513
num_hij2 | 1.136037 .0394154 3.68 0.000 1.061352 1.215977
tenviv1 | 1.018425 .1150631 0.16 0.872 .8161298 1.270864
tenviv2 | 1.069196 .0803752 0.89 0.373 .9227196 1.238926
tenviv4 | 1.012163 .0420587 0.29 0.771 .9329966 1.098046
tenviv5 | .9928913 .0331975 -0.21 0.831 .9299116 1.060136
mzone2 | 1.416375 .0524957 9.39 0.000 1.317134 1.523094
mzone3 | 1.545205 .0865589 7.77 0.000 1.384534 1.724521
n_off_vio | 1.461377 .0503191 11.02 0.000 1.366008 1.563405
n_off_acq | 2.795717 .087079 33.01 0.000 2.63015 2.971706
n_off_sud | 1.37623 .0456208 9.63 0.000 1.289658 1.468614
n_off_oth | 1.702298 .0564615 16.04 0.000 1.595156 1.816637
psy_com2 | 1.049478 .0403494 1.26 0.209 .9733011 1.131618
dep2 | 1.032605 .038746 0.86 0.393 .9593899 1.111409
rural2 | .937708 .052063 -1.16 0.247 .8410223 1.045509
rural3 | .865226 .054036 -2.32 0.020 .7655427 .9778894
porc_pobr | 1.692449 .365638 2.44 0.015 1.108208 2.584698
susini2 | 1.099801 .0721492 1.45 0.147 .9671049 1.250704
susini3 | 1.270871 .0731611 4.16 0.000 1.135271 1.422666
susini4 | 1.154884 .0378748 4.39 0.000 1.082987 1.231556
susini5 | 1.377726 .116392 3.79 0.000 1.167487 1.625823
ano_nac_corr | .846267 .006773 -20.86 0.000 .8330957 .8596466
cohab2 | .8633073 .0473401 -2.68 0.007 .7753345 .9612619
cohab3 | 1.075573 .0686765 1.14 0.254 .9490514 1.218962
cohab4 | .9447162 .0518787 -1.04 0.300 .8483166 1.05207
fis_com2 | 1.112043 .0326002 3.62 0.000 1.049949 1.177809
rc_x1 | .8445108 .0086658 -16.47 0.000 .8276958 .8616674
rc_x2 | .8806019 .030501 -3.67 0.000 .8228051 .9424585
rc_x3 | 1.298294 .1196866 2.83 0.005 1.083685 1.555404
_rcs1 | 2.183011 .0636611 26.77 0.000 2.061737 2.31142
_rcs2 | 1.055847 .0239538 2.40 0.017 1.009927 1.103855
_rcs3 | 1.040733 .0174746 2.38 0.017 1.00704 1.075552
_rcs4 | 1.019882 .0102194 1.96 0.049 1.000048 1.04011
_rcs5 | 1.021174 .007971 2.68 0.007 1.00567 1.036917
_rcs6 | 1.022146 .0067926 3.30 0.001 1.00892 1.035547
_rcs7 | 1.019371 .0055066 3.55 0.000 1.008635 1.030221
_rcs8 | 1.013806 .0052326 2.66 0.008 1.003602 1.024114
_rcs9 | 1.007493 .0031224 2.41 0.016 1.001392 1.013631
_rcs10 | 1.003871 .0014516 2.67 0.008 1.00103 1.00672
_rcs_mot_egr_early1 | .8936947 .0292374 -3.44 0.001 .838189 .952876
_rcs_mot_egr_early2 | 1.007704 .0252155 0.31 0.759 .9594748 1.058357
_rcs_mot_egr_early3 | .9960873 .0189349 -0.21 0.837 .9596583 1.033899
_rcs_mot_egr_early4 | .9918339 .013269 -0.61 0.540 .9661651 1.018185
_rcs_mot_egr_early5 | .9792606 .0094507 -2.17 0.030 .9609118 .9979599
_rcs_mot_egr_early6 | .9969668 .0072379 -0.42 0.676 .9828812 1.011254
_rcs_mot_egr_late1 | .9190636 .0291038 -2.67 0.008 .8637553 .9779134
_rcs_mot_egr_late2 | 1.019548 .0251701 0.78 0.433 .97139 1.070093
_rcs_mot_egr_late3 | .989258 .0183569 -0.58 0.561 .9539256 1.025899
_rcs_mot_egr_late4 | .9968346 .0128774 -0.25 0.806 .9719122 1.022396
_rcs_mot_egr_late5 | .9839967 .0091115 -1.74 0.081 .9662996 1.002018
_rcs_mot_egr_late6 | .9963313 .0068978 -0.53 0.595 .9829031 1.009943
_cons | 1.4e+143 2.3e+144 20.47 0.000 2.7e+129 7.1e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Iteration 0: log likelihood = -21766.794
Iteration 1: log likelihood = -21753.247
Iteration 2: log likelihood = -21753.087
Iteration 3: log likelihood = -21753.087
Log likelihood = -21753.087 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.998602 .1090246 12.69 0.000 1.795944 2.224127
mot_egr_late | 1.655938 .0780664 10.70 0.000 1.509787 1.816237
tr_mod2 | 1.152104 .0429575 3.80 0.000 1.070912 1.239452
sex_dum2 | .5925318 .0255612 -12.13 0.000 .5444922 .6448099
edad_ini_cons | .9733877 .0040334 -6.51 0.000 .9655144 .9813252
esc1 | 1.517096 .0833355 7.59 0.000 1.362247 1.689548
esc2 | 1.343997 .069338 5.73 0.000 1.214742 1.487006
sus_prin2 | 1.196122 .0709385 3.02 0.003 1.064862 1.343562
sus_prin3 | 1.717861 .0823459 11.29 0.000 1.563816 1.887081
sus_prin4 | 1.14398 .0794155 1.94 0.053 .9984535 1.310718
sus_prin5 | 1.356393 .1841605 2.25 0.025 1.039479 1.769926
fr_cons_sus_prin2 | .9775534 .0969708 -0.23 0.819 .8048287 1.187347
fr_cons_sus_prin3 | .9957581 .0799308 -0.05 0.958 .8507986 1.165416
fr_cons_sus_prin4 | 1.03838 .0863304 0.45 0.651 .8822418 1.222151
fr_cons_sus_prin5 | 1.088873 .086575 1.07 0.284 .9317495 1.272493
cond_ocu2 | 1.087397 .0670697 1.36 0.174 .9635773 1.227127
cond_ocu3 | 1.146407 .2806332 0.56 0.577 .7095291 1.852284
cond_ocu4 | 1.239645 .0809438 3.29 0.001 1.09073 1.408891
cond_ocu5 | 1.333378 .1368948 2.80 0.005 1.090341 1.630588
cond_ocu6 | 1.211851 .0420106 5.54 0.000 1.132246 1.297052
policonsumo | 1.006935 .0431151 0.16 0.872 .9258798 1.095086
num_hij2 | 1.136058 .039416 3.68 0.000 1.061373 1.216
tenviv1 | 1.018383 .1150582 0.16 0.872 .8160964 1.270811
tenviv2 | 1.069165 .0803729 0.89 0.374 .9226927 1.23889
tenviv4 | 1.012127 .0420572 0.29 0.772 .9329638 1.098007
tenviv5 | .9928809 .0331971 -0.21 0.831 .9299019 1.060125
mzone2 | 1.416379 .0524958 9.39 0.000 1.317137 1.523098
mzone3 | 1.545192 .086558 7.77 0.000 1.384523 1.724506
n_off_vio | 1.461338 .0503178 11.02 0.000 1.365971 1.563363
n_off_acq | 2.795723 .0870787 33.01 0.000 2.630157 2.971711
n_off_sud | 1.376225 .0456205 9.63 0.000 1.289653 1.468608
n_off_oth | 1.70231 .0564617 16.04 0.000 1.595168 1.816649
psy_com2 | 1.04949 .0403503 1.26 0.209 .9733115 1.131632
dep2 | 1.032607 .0387462 0.86 0.392 .9593907 1.11141
rural2 | .9378072 .0520686 -1.16 0.247 .8411113 1.045619
rural3 | .8652947 .0540403 -2.32 0.021 .7656035 .977967
porc_pobr | 1.690759 .3652812 2.43 0.015 1.107091 2.582142
susini2 | 1.099826 .0721508 1.45 0.147 .9671268 1.250733
susini3 | 1.270763 .0731551 4.16 0.000 1.135175 1.422547
susini4 | 1.154844 .0378737 4.39 0.000 1.082949 1.231513
susini5 | 1.377635 .1163845 3.79 0.000 1.16741 1.625716
ano_nac_corr | .8462415 .0067727 -20.86 0.000 .8330708 .8596204
cohab2 | .8632571 .0473373 -2.68 0.007 .7752895 .9612058
cohab3 | 1.075535 .0686742 1.14 0.254 .949018 1.218919
cohab4 | .9446706 .051876 -1.04 0.300 .848276 1.052019
fis_com2 | 1.112001 .0325989 3.62 0.000 1.04991 1.177765
rc_x1 | .8444806 .0086655 -16.47 0.000 .8276662 .8616366
rc_x2 | .8806123 .0305014 -3.67 0.000 .8228147 .9424697
rc_x3 | 1.298277 .1196849 2.83 0.005 1.083671 1.555384
_rcs1 | 2.185078 .0637766 26.78 0.000 2.063587 2.313723
_rcs2 | 1.055923 .023811 2.41 0.016 1.010271 1.103638
_rcs3 | 1.042578 .0177688 2.45 0.014 1.008327 1.077993
_rcs4 | 1.018564 .0112665 1.66 0.096 .9967194 1.040887
_rcs5 | 1.023181 .0077483 3.03 0.002 1.008106 1.03848
_rcs6 | 1.021206 .0066831 3.21 0.001 1.008191 1.034389
_rcs7 | 1.015694 .0060333 2.62 0.009 1.003938 1.027588
_rcs8 | 1.014828 .0049269 3.03 0.002 1.005217 1.02453
_rcs9 | 1.010352 .0044857 2.32 0.020 1.001598 1.019182
_rcs10 | 1.004603 .0017111 2.70 0.007 1.001255 1.007963
_rcs_mot_egr_early1 | .8928551 .0292306 -3.46 0.001 .8373635 .952024
_rcs_mot_egr_early2 | 1.007849 .0251209 0.31 0.754 .9597961 1.058307
_rcs_mot_egr_early3 | .9957277 .0189667 -0.22 0.822 .9592389 1.033604
_rcs_mot_egr_early4 | .9940312 .0136482 -0.44 0.663 .9676379 1.021144
_rcs_mot_egr_early5 | .9815713 .0093436 -1.95 0.051 .963428 1.000056
_rcs_mot_egr_early6 | .9926189 .007666 -0.96 0.337 .977707 1.007758
_rcs_mot_egr_early7 | .9948613 .0062023 -0.83 0.409 .982779 1.007092
_rcs_mot_egr_late1 | .9181215 .0290938 -2.70 0.007 .8628333 .9769524
_rcs_mot_egr_late2 | 1.019955 .0251359 0.80 0.423 .9718606 1.07043
_rcs_mot_egr_late3 | .9872037 .0184272 -0.69 0.490 .9517397 1.023989
_rcs_mot_egr_late4 | 1.000015 .0132482 0.00 0.999 .9743827 1.026321
_rcs_mot_egr_late5 | .9853975 .0089503 -1.62 0.105 .9680104 1.003097
_rcs_mot_egr_late6 | .9944397 .0072987 -0.76 0.447 .980237 1.008848
_rcs_mot_egr_late7 | .9942846 .0058899 -0.97 0.333 .9828074 1.005896
_cons | 1.5e+143 2.4e+144 20.47 0.000 2.9e+129 7.6e+156
---------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
.
We obtained a summary of distributions by AICs and BICs.
. *file:///G:/Mi%20unidad/Alvacast/SISTRAT%202019%20(github)/_supp_mstates/stata/1806.01615.pdf
. *rcs - restricted cubic splines on log hazard scale
. *rp - Royston-Parmar model (restricted cubic spline on log cumulative hazard scale)
. qui count if _d == 1
. // we count the amount of cases with the event in the strata
. //we call the estimates stored, and the results...
. estimates stat m_nostag_rp*, n(`r(N)')
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
m_nostag_r~1 | 5,144 . -21856.23 53 43818.46 44165.38
m_nostag_r~2 | 5,144 . -21796.4 55 43702.8 44062.8
m_nostag_r~3 | 5,144 . -21782.69 57 43679.38 44052.48
m_nostag_r~4 | 5,144 . -21779.04 59 43676.08 44062.27
m_nostag_r~5 | 5,144 . -21775.39 61 43672.78 44072.06
m_nostag_r~6 | 5,144 . -21771.64 63 43669.29 44081.66
m_nostag_r~7 | 5,144 . -21771.29 65 43672.57 44098.04
m_nostag_r~1 | 5,144 . -21791.03 54 43690.06 44043.53
m_nostag_r~2 | 5,144 . -21790.58 56 43693.16 44059.72
m_nostag_r~3 | 5,144 . -21776.98 58 43669.96 44049.61
m_nostag_r~4 | 5,144 . -21772.97 60 43665.93 44058.67
m_nostag_r~5 | 5,144 . -21769.17 62 43662.35 44068.18
m_nostag_r~6 | 5,144 . -21765.53 64 43659.05 44077.97
m_nostag_r~7 | 5,144 . -21765.13 66 43662.26 44094.27
m_nostag_r~1 | 5,144 . -21773.48 55 43656.95 44016.96
m_nostag_r~2 | 5,144 . -21773.21 57 43660.43 44033.53
m_nostag_r~3 | 5,144 . -21772.71 59 43663.42 44049.61
m_nostag_r~4 | 5,144 . -21770.43 61 43662.86 44062.14
m_nostag_r~5 | 5,144 . -21765.32 63 43656.64 44069.01
m_nostag_r~6 | 5,144 . -21761.52 65 43653.04 44078.51
m_nostag_r~7 | 5,144 . -21760.97 67 43655.95 44094.5
m_nostag_r~1 | 5,144 . -21768.78 56 43649.56 44016.12
m_nostag_r~2 | 5,144 . -21768.48 58 43652.97 44032.61
m_nostag_r~3 | 5,144 . -21767.44 60 43654.88 44047.62
m_nostag_r~4 | 5,144 . -21766.34 62 43656.69 44062.51
m_nostag_r~5 | 5,144 . -21764.89 64 43657.78 44076.7
m_nostag_r~6 | 5,144 . -21759.2 66 43650.41 44082.42
m_nostag_r~7 | 5,144 . -21759.04 68 43654.08 44099.18
m_nostag_r~1 | 5,144 . -21763.63 57 43641.26 44014.36
m_nostag_r~2 | 5,144 . -21763.33 59 43644.67 44030.86
m_nostag_r~3 | 5,144 . -21762.91 61 43647.83 44047.11
m_nostag_r~4 | 5,144 . -21759.3 63 43644.6 44056.97
m_nostag_r~5 | 5,144 . -21761.05 65 43652.1 44077.56
m_nostag_r~6 | 5,144 . -21758.09 67 43650.18 44088.74
m_nostag_r~7 | 5,144 . -21757.88 69 43653.75 44105.4
m_nostag_r~1 | 5,144 . -21759.87 58 43635.74 44015.39
m_nostag_r~2 | 5,144 . -21759.58 60 43639.17 44031.9
m_nostag_r~3 | 5,144 . -21759.09 62 43642.18 44048.01
m_nostag_r~4 | 5,144 . -21757.32 64 43642.63 44061.55
m_nostag_r~5 | 5,144 . -21757.05 66 43646.1 44078.11
m_nostag_r~6 | 5,144 . -21756.46 68 43648.91 44094.01
m_nostag_r~7 | 5,144 . -21757.29 70 43654.59 44112.78
m_nostag_r~1 | 5,144 . -21758.69 59 43635.37 44021.56
m_nostag_r~2 | 5,144 . -21758.4 61 43638.81 44038.09
m_nostag_r~3 | 5,144 . -21757.98 63 43641.95 44054.33
m_nostag_r~4 | 5,144 . -21756.16 65 43642.32 44067.78
m_nostag_r~5 | 5,144 . -21755.52 67 43645.03 44083.59
m_nostag_r~6 | 5,144 . -21754.4 69 43646.8 44098.44
m_nostag_r~7 | 5,144 . -21755.31 71 43652.62 44117.36
m_nostag_r~1 | 5,144 . -21757.57 60 43635.14 44027.88
m_nostag_r~2 | 5,144 . -21757.29 62 43638.58 44044.4
m_nostag_r~3 | 5,144 . -21756.81 64 43641.62 44060.54
m_nostag_r~4 | 5,144 . -21755.26 66 43642.52 44074.53
m_nostag_r~5 | 5,144 . -21755.16 68 43646.32 44091.42
m_nostag_r~6 | 5,144 . -21754 70 43648.01 44106.2
m_nostag_r~7 | 5,144 . -21753.26 72 43650.52 44121.81
m_nostag_r~1 | 5,144 . -21756.51 61 43635.03 44034.31
m_nostag_r~2 | 5,144 . -21756.23 63 43638.47 44050.84
m_nostag_r~3 | 5,144 . -21755.73 65 43641.45 44066.92
m_nostag_r~4 | 5,144 . -21754.07 67 43642.13 44080.69
m_nostag_r~5 | 5,144 . -21754.09 69 43646.17 44097.82
m_nostag_r~6 | 5,144 . -21752.76 71 43647.53 44112.26
m_nostag_r~7 | 5,144 . -21753.45 73 43652.9 44130.73
m_nostag_r~1 | 5,144 . -21756.5 62 43636.99 44042.82
m_nostag_r~2 | 5,144 . -21756.22 64 43640.44 44059.36
m_nostag_r~3 | 5,144 . -21755.69 66 43643.39 44075.4
m_nostag_r~4 | 5,144 . -21754.08 68 43644.16 44089.26
m_nostag_r~5 | 5,144 . -21754.02 70 43648.03 44106.22
m_nostag_r~6 | 5,144 . -21753.19 72 43650.38 44121.66
m_nostag_r~7 | 5,144 . -21753.09 74 43654.17 44138.55
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_1=r(S)
.
. ** to order AICs
. *https://www.statalist.org/forums/forum/general-stata-discussion/general/1665263-sorting-matrix-including-rownames
. mata :
------------------------------------------------- mata (type end to exit) ----------------------------------------------------------------------------
:
: void st_sort_matrix(
> //argumento de la matriz
> string scalar matname,
> //argumento de las columnas
> real rowvector columns
> )
> {
> string matrix rownames
> real colvector sort_order
> // defino una base
> //Y = st_matrix(matname)
> //[.,(1, 2, 3, 4, 6, 5)]
> //ordeno las columnas
> rownames = st_matrixrowstripe(matname) //[.,(1, 2, 3, 4, 6, 5)]
> sort_order = order(st_matrix(matname), (columns))
> st_replacematrix(matname, st_matrix(matname)[sort_order,.])
> st_matrixrowstripe(matname, rownames[sort_order,.])
> }
:
: end
------------------------------------------------------------------------------------------------------------------------------------------------------
. //mata: mata drop st_sort_matrix()
.
. mata : st_sort_matrix("stats_1", 5) // 5 AIC, 6 BIC
.
. global st_rownames : rownames stats_1
.
. //matrix colname stats_1 = mod N ll0 ll df AIC BIC
.
. *di "$st_rownames"
. esttab matrix(stats_1) using "testreg_aic_bic_mrl_23_1_pris_m1.csv", replace
(output written to testreg_aic_bic_mrl_23_1_pris_m1.csv)
. esttab matrix(stats_1) using "testreg_aic_bic_mrl_23_1_pris_m1.html", replace
(output written to testreg_aic_bic_mrl_23_1_pris_m1.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 | 5144 | . | -21756.51 | 61 | 43635.03 | 44034.31 |
| m_nostag_rp8_tvc_1 | 5144 | . | -21757.57 | 60 | 43635.14 | 44027.88 |
| m_nostag_rp7_tvc_1 | 5144 | . | -21758.69 | 59 | 43635.37 | 44021.56 |
| m_nostag_rp6_tvc_1 | 5144 | . | -21759.87 | 58 | 43635.74 | 44015.39 |
| m_nostag_rp10_tvc_1 | 5144 | . | -21756.5 | 62 | 43636.99 | 44042.82 |
| m_nostag_rp9_tvc_2 | 5144 | . | -21756.23 | 63 | 43638.47 | 44050.84 |
| m_nostag_rp8_tvc_2 | 5144 | . | -21757.29 | 62 | 43638.58 | 44044.4 |
| m_nostag_rp7_tvc_2 | 5144 | . | -21758.4 | 61 | 43638.81 | 44038.09 |
| m_nostag_rp6_tvc_2 | 5144 | . | -21759.58 | 60 | 43639.17 | 44031.9 |
| m_nostag_rp10_tvc_2 | 5144 | . | -21756.22 | 64 | 43640.44 | 44059.36 |
| m_nostag_rp5_tvc_1 | 5144 | . | -21763.63 | 57 | 43641.26 | 44014.36 |
| m_nostag_rp9_tvc_3 | 5144 | . | -21755.73 | 65 | 43641.45 | 44066.92 |
| m_nostag_rp8_tvc_3 | 5144 | . | -21756.81 | 64 | 43641.62 | 44060.54 |
| m_nostag_rp7_tvc_3 | 5144 | . | -21757.98 | 63 | 43641.95 | 44054.33 |
| m_nostag_rp9_tvc_4 | 5144 | . | -21754.07 | 67 | 43642.13 | 44080.69 |
| m_nostag_rp6_tvc_3 | 5144 | . | -21759.09 | 62 | 43642.18 | 44048.01 |
| m_nostag_rp7_tvc_4 | 5144 | . | -21756.16 | 65 | 43642.32 | 44067.78 |
| m_nostag_rp8_tvc_4 | 5144 | . | -21755.26 | 66 | 43642.52 | 44074.53 |
| m_nostag_rp6_tvc_4 | 5144 | . | -21757.32 | 64 | 43642.63 | 44061.55 |
| m_nostag_rp10_tvc_3 | 5144 | . | -21755.69 | 66 | 43643.39 | 44075.4 |
| m_nostag_rp10_tvc_4 | 5144 | . | -21754.08 | 68 | 43644.16 | 44089.26 |
| m_nostag_rp5_tvc_4 | 5144 | . | -21759.3 | 63 | 43644.6 | 44056.97 |
| m_nostag_rp5_tvc_2 | 5144 | . | -21763.33 | 59 | 43644.67 | 44030.86 |
| m_nostag_rp7_tvc_5 | 5144 | . | -21755.52 | 67 | 43645.03 | 44083.59 |
| m_nostag_rp6_tvc_5 | 5144 | . | -21757.05 | 66 | 43646.1 | 44078.11 |
| m_nostag_rp9_tvc_5 | 5144 | . | -21754.09 | 69 | 43646.17 | 44097.82 |
| m_nostag_rp8_tvc_5 | 5144 | . | -21755.16 | 68 | 43646.32 | 44091.42 |
| m_nostag_rp7_tvc_6 | 5144 | . | -21754.4 | 69 | 43646.8 | 44098.44 |
| m_nostag_rp9_tvc_6 | 5144 | . | -21752.76 | 71 | 43647.53 | 44112.26 |
| m_nostag_rp5_tvc_3 | 5144 | . | -21762.91 | 61 | 43647.83 | 44047.11 |
| m_nostag_rp8_tvc_6 | 5144 | . | -21754 | 70 | 43648.01 | 44106.2 |
| m_nostag_rp10_tvc_5 | 5144 | . | -21754.02 | 70 | 43648.03 | 44106.22 |
| m_nostag_rp6_tvc_6 | 5144 | . | -21756.46 | 68 | 43648.91 | 44094.01 |
| m_nostag_rp4_tvc_1 | 5144 | . | -21768.78 | 56 | 43649.56 | 44016.12 |
| m_nostag_rp5_tvc_6 | 5144 | . | -21758.09 | 67 | 43650.18 | 44088.74 |
| m_nostag_rp10_tvc_6 | 5144 | . | -21753.19 | 72 | 43650.38 | 44121.66 |
| m_nostag_rp4_tvc_6 | 5144 | . | -21759.2 | 66 | 43650.41 | 44082.42 |
| m_nostag_rp8_tvc_7 | 5144 | . | -21753.26 | 72 | 43650.52 | 44121.81 |
| m_nostag_rp5_tvc_5 | 5144 | . | -21761.05 | 65 | 43652.1 | 44077.56 |
| m_nostag_rp7_tvc_7 | 5144 | . | -21755.31 | 71 | 43652.62 | 44117.36 |
| m_nostag_rp9_tvc_7 | 5144 | . | -21753.45 | 73 | 43652.9 | 44130.73 |
| m_nostag_rp4_tvc_2 | 5144 | . | -21768.48 | 58 | 43652.97 | 44032.61 |
| m_nostag_rp3_tvc_6 | 5144 | . | -21761.52 | 65 | 43653.04 | 44078.51 |
| m_nostag_rp5_tvc_7 | 5144 | . | -21757.88 | 69 | 43653.75 | 44105.4 |
| m_nostag_rp4_tvc_7 | 5144 | . | -21759.04 | 68 | 43654.08 | 44099.18 |
| m_nostag_rp10_tvc_7 | 5144 | . | -21753.09 | 74 | 43654.17 | 44138.55 |
| m_nostag_rp6_tvc_7 | 5144 | . | -21757.29 | 70 | 43654.59 | 44112.78 |
| m_nostag_rp4_tvc_3 | 5144 | . | -21767.44 | 60 | 43654.88 | 44047.62 |
| m_nostag_rp3_tvc_7 | 5144 | . | -21760.97 | 67 | 43655.95 | 44094.5 |
| m_nostag_rp3_tvc_5 | 5144 | . | -21765.32 | 63 | 43656.64 | 44069.01 |
| m_nostag_rp4_tvc_4 | 5144 | . | -21766.34 | 62 | 43656.69 | 44062.51 |
| m_nostag_rp3_tvc_1 | 5144 | . | -21773.48 | 55 | 43656.95 | 44016.96 |
| m_nostag_rp4_tvc_5 | 5144 | . | -21764.89 | 64 | 43657.78 | 44076.7 |
| m_nostag_rp2_tvc_6 | 5144 | . | -21765.53 | 64 | 43659.05 | 44077.97 |
| m_nostag_rp3_tvc_2 | 5144 | . | -21773.21 | 57 | 43660.43 | 44033.53 |
| m_nostag_rp2_tvc_7 | 5144 | . | -21765.13 | 66 | 43662.26 | 44094.27 |
| m_nostag_rp2_tvc_5 | 5144 | . | -21769.17 | 62 | 43662.35 | 44068.18 |
| m_nostag_rp3_tvc_4 | 5144 | . | -21770.43 | 61 | 43662.86 | 44062.14 |
| m_nostag_rp3_tvc_3 | 5144 | . | -21772.71 | 59 | 43663.42 | 44049.61 |
| m_nostag_rp2_tvc_4 | 5144 | . | -21772.97 | 60 | 43665.93 | 44058.67 |
| m_nostag_rp1_tvc_6 | 5144 | . | -21771.64 | 63 | 43669.29 | 44081.66 |
| m_nostag_rp2_tvc_3 | 5144 | . | -21776.98 | 58 | 43669.96 | 44049.61 |
| m_nostag_rp1_tvc_7 | 5144 | . | -21771.29 | 65 | 43672.57 | 44098.04 |
| m_nostag_rp1_tvc_5 | 5144 | . | -21775.39 | 61 | 43672.78 | 44072.06 |
| m_nostag_rp1_tvc_4 | 5144 | . | -21779.04 | 59 | 43676.08 | 44062.27 |
| m_nostag_rp1_tvc_3 | 5144 | . | -21782.69 | 57 | 43679.38 | 44052.48 |
| m_nostag_rp2_tvc_1 | 5144 | . | -21791.03 | 54 | 43690.06 | 44043.53 |
| m_nostag_rp2_tvc_2 | 5144 | . | -21790.58 | 56 | 43693.16 | 44059.72 |
| m_nostag_rp1_tvc_2 | 5144 | . | -21796.4 | 55 | 43702.8 | 44062.8 |
| m_nostag_rp1_tvc_1 | 5144 | . | -21856.23 | 53 | 43818.46 | 44165.38 |
In the case of the more flexible parametric models (non-standard), we selected the models that showed the best trade-off between lower complexity and better fit. This is why we also considered the BIC. If a model with fewer parameters had greater or equal AIC (or differences lower than 4) but also had better BIC (<=3), we favoured the model with fewer parameters.
.
. *The per(1000) option multiplies the hazard rate by 1000 as it is easier to interpret the rate per 1000 years than per person per year.
.
. range tt 0 7 56
(70,807 missing values generated)
.
. estimates replay m_nostag_rp6_tvc_1, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp6_tvc_1
------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -21759.872 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994852 .1087314 12.67 0.000 1.792731 2.219761
mot_egr_late | 1.651233 .0777884 10.65 0.000 1.505598 1.810956
tr_mod2 | 1.152116 .0429533 3.80 0.000 1.070932 1.239455
sex_dum2 | .5924607 .0255581 -12.13 0.000 .544427 .6447322
edad_ini_cons | .9734059 .0040331 -6.51 0.000 .9655331 .9813429
esc1 | 1.516986 .0833309 7.59 0.000 1.362145 1.689428
esc2 | 1.344025 .06934 5.73 0.000 1.214766 1.487037
sus_prin2 | 1.195219 .0708791 3.01 0.003 1.064068 1.342535
sus_prin3 | 1.716276 .0822599 11.27 0.000 1.56239 1.885318
sus_prin4 | 1.142999 .0793454 1.93 0.054 .9975999 1.309589
sus_prin5 | 1.354906 .1839488 2.24 0.025 1.038354 1.76796
fr_cons_sus_prin2 | .977386 .0969542 -0.23 0.818 .8046908 1.187143
fr_cons_sus_prin3 | .9957392 .0799293 -0.05 0.958 .8507824 1.165394
fr_cons_sus_prin4 | 1.038108 .086308 0.45 0.653 .882011 1.221831
fr_cons_sus_prin5 | 1.08905 .0865887 1.07 0.283 .9319019 1.272699
cond_ocu2 | 1.087743 .0670889 1.36 0.173 .9638878 1.227513
cond_ocu3 | 1.143944 .2800258 0.55 0.583 .70801 1.84829
cond_ocu4 | 1.240744 .0810192 3.30 0.001 1.091691 1.410148
cond_ocu5 | 1.332646 .1368113 2.80 0.005 1.089756 1.629673
cond_ocu6 | 1.211739 .0420048 5.54 0.000 1.132146 1.296928
policonsumo | 1.007253 .0431286 0.17 0.866 .9261719 1.095431
num_hij2 | 1.136224 .0394231 3.68 0.000 1.061525 1.21618
tenviv1 | 1.018513 .1150669 0.16 0.871 .8162099 1.270959
tenviv2 | 1.068074 .0802883 0.88 0.381 .9217552 1.23762
tenviv4 | 1.012196 .0420598 0.29 0.770 .933028 1.098082
tenviv5 | .9928354 .0331953 -0.22 0.830 .9298599 1.060076
mzone2 | 1.416263 .0524875 9.39 0.000 1.317037 1.522965
mzone3 | 1.544621 .0865199 7.76 0.000 1.384022 1.723855
n_off_vio | 1.461835 .0503418 11.03 0.000 1.366423 1.563909
n_off_acq | 2.796745 .0871343 33.01 0.000 2.631075 2.972847
n_off_sud | 1.376993 .0456524 9.65 0.000 1.290361 1.469441
n_off_oth | 1.702386 .0564758 16.04 0.000 1.595218 1.816754
psy_com2 | 1.048481 .0402961 1.23 0.218 .9724036 1.130511
dep2 | 1.032711 .0387488 0.86 0.391 .9594905 1.11152
rural2 | .9370524 .0520279 -1.17 0.242 .8404322 1.044781
rural3 | .8649187 .054017 -2.32 0.020 .7652705 .9775424
porc_pobr | 1.709119 .3691358 2.48 0.013 1.119256 2.609846
susini2 | 1.097617 .0720002 1.42 0.156 .9651941 1.248208
susini3 | 1.271345 .0731854 4.17 0.000 1.135701 1.423191
susini4 | 1.15569 .0379013 4.41 0.000 1.083742 1.232415
susini5 | 1.378443 .1164494 3.80 0.000 1.1681 1.626662
ano_nac_corr | .8465336 .0067735 -20.82 0.000 .8333613 .859914
cohab2 | .8633277 .0473393 -2.68 0.007 .7753563 .9612803
cohab3 | 1.07592 .0686957 1.15 0.252 .9493631 1.219349
cohab4 | .9448006 .0518821 -1.03 0.301 .8483947 1.052162
fis_com2 | 1.112992 .0326294 3.65 0.000 1.050843 1.178818
rc_x1 | .8447476 .0086673 -16.44 0.000 .8279298 .8619071
rc_x2 | .8807967 .0305094 -3.66 0.000 .822984 .9426706
rc_x3 | 1.297611 .1196287 2.83 0.005 1.083106 1.554598
_rcs1 | 2.177066 .0586595 28.87 0.000 2.065078 2.295126
_rcs2 | 1.071884 .0075279 9.88 0.000 1.057231 1.086741
_rcs3 | 1.033961 .0056547 6.11 0.000 1.022937 1.045104
_rcs4 | 1.019485 .0038677 5.09 0.000 1.011932 1.027094
_rcs5 | 1.012627 .0028211 4.50 0.000 1.007113 1.018171
_rcs6 | 1.01034 .0021956 4.73 0.000 1.006046 1.014653
_rcs_mot_egr_early1 | .8978523 .0271955 -3.56 0.000 .8461013 .9527685
_rcs_mot_egr_late1 | .9205885 .0267904 -2.84 0.004 .8695497 .974623
_cons | 7.4e+142 1.2e+144 20.43 0.000 1.5e+129 3.7e+156
---------------------------------------------------------------------------------------
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 replay m_nostag_rp6_tvc_1, eform //estimates restore m_nostag_rp5_tvc_1
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_nostag_rp6_tvc_1
------------------------------------------------------------------------------------------------------------------------------------------------------
Log likelihood = -21759.872 Number of obs = 70,863
---------------------------------------------------------------------------------------
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
----------------------+----------------------------------------------------------------
xb |
mot_egr_early | 1.994852 .1087314 12.67 0.000 1.792731 2.219761
mot_egr_late | 1.651233 .0777884 10.65 0.000 1.505598 1.810956
tr_mod2 | 1.152116 .0429533 3.80 0.000 1.070932 1.239455
sex_dum2 | .5924607 .0255581 -12.13 0.000 .544427 .6447322
edad_ini_cons | .9734059 .0040331 -6.51 0.000 .9655331 .9813429
esc1 | 1.516986 .0833309 7.59 0.000 1.362145 1.689428
esc2 | 1.344025 .06934 5.73 0.000 1.214766 1.487037
sus_prin2 | 1.195219 .0708791 3.01 0.003 1.064068 1.342535
sus_prin3 | 1.716276 .0822599 11.27 0.000 1.56239 1.885318
sus_prin4 | 1.142999 .0793454 1.93 0.054 .9975999 1.309589
sus_prin5 | 1.354906 .1839488 2.24 0.025 1.038354 1.76796
fr_cons_sus_prin2 | .977386 .0969542 -0.23 0.818 .8046908 1.187143
fr_cons_sus_prin3 | .9957392 .0799293 -0.05 0.958 .8507824 1.165394
fr_cons_sus_prin4 | 1.038108 .086308 0.45 0.653 .882011 1.221831
fr_cons_sus_prin5 | 1.08905 .0865887 1.07 0.283 .9319019 1.272699
cond_ocu2 | 1.087743 .0670889 1.36 0.173 .9638878 1.227513
cond_ocu3 | 1.143944 .2800258 0.55 0.583 .70801 1.84829
cond_ocu4 | 1.240744 .0810192 3.30 0.001 1.091691 1.410148
cond_ocu5 | 1.332646 .1368113 2.80 0.005 1.089756 1.629673
cond_ocu6 | 1.211739 .0420048 5.54 0.000 1.132146 1.296928
policonsumo | 1.007253 .0431286 0.17 0.866 .9261719 1.095431
num_hij2 | 1.136224 .0394231 3.68 0.000 1.061525 1.21618
tenviv1 | 1.018513 .1150669 0.16 0.871 .8162099 1.270959
tenviv2 | 1.068074 .0802883 0.88 0.381 .9217552 1.23762
tenviv4 | 1.012196 .0420598 0.29 0.770 .933028 1.098082
tenviv5 | .9928354 .0331953 -0.22 0.830 .9298599 1.060076
mzone2 | 1.416263 .0524875 9.39 0.000 1.317037 1.522965
mzone3 | 1.544621 .0865199 7.76 0.000 1.384022 1.723855
n_off_vio | 1.461835 .0503418 11.03 0.000 1.366423 1.563909
n_off_acq | 2.796745 .0871343 33.01 0.000 2.631075 2.972847
n_off_sud | 1.376993 .0456524 9.65 0.000 1.290361 1.469441
n_off_oth | 1.702386 .0564758 16.04 0.000 1.595218 1.816754
psy_com2 | 1.048481 .0402961 1.23 0.218 .9724036 1.130511
dep2 | 1.032711 .0387488 0.86 0.391 .9594905 1.11152
rural2 | .9370524 .0520279 -1.17 0.242 .8404322 1.044781
rural3 | .8649187 .054017 -2.32 0.020 .7652705 .9775424
porc_pobr | 1.709119 .3691358 2.48 0.013 1.119256 2.609846
susini2 | 1.097617 .0720002 1.42 0.156 .9651941 1.248208
susini3 | 1.271345 .0731854 4.17 0.000 1.135701 1.423191
susini4 | 1.15569 .0379013 4.41 0.000 1.083742 1.232415
susini5 | 1.378443 .1164494 3.80 0.000 1.1681 1.626662
ano_nac_corr | .8465336 .0067735 -20.82 0.000 .8333613 .859914
cohab2 | .8633277 .0473393 -2.68 0.007 .7753563 .9612803
cohab3 | 1.07592 .0686957 1.15 0.252 .9493631 1.219349
cohab4 | .9448006 .0518821 -1.03 0.301 .8483947 1.052162
fis_com2 | 1.112992 .0326294 3.65 0.000 1.050843 1.178818
rc_x1 | .8447476 .0086673 -16.44 0.000 .8279298 .8619071
rc_x2 | .8807967 .0305094 -3.66 0.000 .822984 .9426706
rc_x3 | 1.297611 .1196287 2.83 0.005 1.083106 1.554598
_rcs1 | 2.177066 .0586595 28.87 0.000 2.065078 2.295126
_rcs2 | 1.071884 .0075279 9.88 0.000 1.057231 1.086741
_rcs3 | 1.033961 .0056547 6.11 0.000 1.022937 1.045104
_rcs4 | 1.019485 .0038677 5.09 0.000 1.011932 1.027094
_rcs5 | 1.012627 .0028211 4.50 0.000 1.007113 1.018171
_rcs6 | 1.01034 .0021956 4.73 0.000 1.006046 1.014653
_rcs_mot_egr_early1 | .8978523 .0271955 -3.56 0.000 .8461013 .9527685
_rcs_mot_egr_late1 | .9205885 .0267904 -2.84 0.004 .8695497 .974623
_cons | 7.4e+142 1.2e+144 20.43 0.000 1.5e+129 3.7e+156
---------------------------------------------------------------------------------------
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)
.
. // 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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6tvc2_pris_m1.gph saved)
.
. *https://www.pauldickman.com/software/stata/sex-differences/
.
. estimates restore m_nostag_rp6_tvc_1 //estimates restore m_nostag_rp5_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)
(70,816 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)
(70,839 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)
(70,839 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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_stddif_s_pris_m1.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)
> */
. estimates restore m_nostag_rp6_tvc_1 //estimates restore m_nostag_rp5_tvc_1
(results m_nostag_rp6_tvc_1 are active now)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 1 mot_egr_late 0) timevar(tt) ci contrast(difference) ///
> atvar(s_tr_comp s_early_drop) contrastvar(sdiff_tr_comp_early_drop)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) ci contrast(difference) ///
> atvar(s_tr_comp0 s_late_drop) contrastvar(sdiff_tr_comp_late_drop)
.
. stpm2_standsurv, at1(mot_egr_early 1 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) ci contrast(difference) ///
> atvar(s_early_drop0 s_late_drop0) contrastvar(sdiff_early_late_drop)
.
. cap noi drop s_tr_comp0 s_early_drop0 s_late_drop0
. twoway (rarea s_tr_comp_lci s_tr_comp_uci tt, color(gs2%35)) ///
> (rarea s_early_drop_lci s_early_drop_uci tt, color(gs6%35)) ///
> (rarea s_late_drop_lci s_late_drop_uci tt, color(gs10%35)) ///
> (line km _t if motivodeegreso_mod_imp_rec==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_s_pris_m1.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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_stdif_s2_pris_m1.gph saved)
.
. estimates restore m_nostag_rp6_tvc_1
(results m_nostag_rp6_tvc_1 are active now)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 1 mot_egr_late 0) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_h0 rmst_h1) contrastvar(rmstdiff_tr_comp_early_drop)
.
. stpm2_standsurv, at1(mot_egr_early 0 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_h00 rmst_h2) contrastvar(rmstdiff_tr_comp_late_drop)
.
. stpm2_standsurv, at1(mot_egr_early 1 mot_egr_late 0) at2(mot_egr_early 0 mot_egr_late 1) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_h11 rmst_h22) contrastvar(rmstdiff_early_late_drop)
.
. cap noi drop rmst_h00 rmst_h11 rmst_h22
. twoway (rarea rmstdiff_tr_comp_early_drop_lci rmstdiff_tr_comp_early_drop_uci tt, color(gs2%35)) ///
> (line rmstdiff_tr_comp_early_drop tt, lcolor(gs2)) ///
> (rarea rmstdiff_tr_comp_late_drop_lci rmstdiff_tr_comp_late_drop_uci tt, color(gs6%35)) ///
> (line rmstdiff_tr_comp_late_drop tt, lcolor(gs6)) ///
> (rarea rmstdiff_early_late_drop_lci rmstdiff_early_late_drop_uci tt, color(gs10%35)) ///
> (line rmstdiff_early_late_drop tt, lcolor(gs10)) ///
> (line zero tt, lcolor(black%20) lwidth(thick)) ///
> , ylabel(, format(%3.1f)) ///
> ytitle("Difference in RMST (years)") ///
> xtitle("Years from baseline treatment outcome") ///
> legend(order( 1 "Early vs. Tr. completion" 3 "Late vs. Tr. completion" 5 "Late vs. Early dropout") ring(0) pos(7) cols(1) region(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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp6_stdif_rmst_pris_m1.gph saved)
Saved at= 03:43:42 8 Apr 2023
=============================================================================
=============================================================================
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)
(55066 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. }
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16755.389
Iteration 1: log pseudolikelihood = -16719.59
Iteration 2: log pseudolikelihood = -16719.326
Iteration 3: log pseudolikelihood = -16719.326
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16719.326 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485363 .0861119 6.82 0.000 1.325823 1.664102
_rcs1 | 2.171931 .0774908 21.74 0.000 2.02524 2.329246
_rcs_tr_outcome1 | .9269235 .0346176 -2.03 0.042 .8614979 .9973178
_cons | .0384615 .0021079 -59.45 0.000 .0345443 .0428229
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16710.919
Iteration 1: log pseudolikelihood = -16698.058
Iteration 2: log pseudolikelihood = -16698.006
Iteration 3: log pseudolikelihood = -16698.006
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16698.006 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.495703 .0867606 6.94 0.000 1.334965 1.675794
_rcs1 | 2.171931 .0774908 21.74 0.000 2.02524 2.329246
_rcs_tr_outcome1 | .9358186 .035495 -1.75 0.080 .8687726 1.008039
_rcs_tr_outcome2 | 1.064915 .0102863 6.51 0.000 1.044944 1.085268
_cons | .0384615 .0021079 -59.45 0.000 .0345443 .0428229
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16705.86
Iteration 1: log pseudolikelihood = -16696.046
Iteration 2: log pseudolikelihood = -16695.996
Iteration 3: log pseudolikelihood = -16695.996
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16695.996 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.495054 .086731 6.93 0.000 1.334372 1.675084
_rcs1 | 2.171931 .0774908 21.74 0.000 2.02524 2.329246
_rcs_tr_outcome1 | .9373405 .0355504 -1.71 0.088 .8701898 1.009673
_rcs_tr_outcome2 | 1.062045 .0094521 6.76 0.000 1.04368 1.080733
_rcs_tr_outcome3 | 1.01827 .007108 2.59 0.009 1.004433 1.032297
_cons | .0384615 .0021079 -59.45 0.000 .0345443 .0428229
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16703.701
Iteration 1: log pseudolikelihood = -16694.222
Iteration 2: log pseudolikelihood = -16694.161
Iteration 3: log pseudolikelihood = -16694.161
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16694.161 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.494982 .0867272 6.93 0.000 1.334308 1.675005
_rcs1 | 2.171931 .0774908 21.74 0.000 2.02524 2.329246
_rcs_tr_outcome1 | .9370154 .0355468 -1.71 0.086 .869872 1.009341
_rcs_tr_outcome2 | 1.062112 .009905 6.46 0.000 1.042875 1.081704
_rcs_tr_outcome3 | 1.017211 .0072532 2.39 0.017 1.003094 1.031526
_rcs_tr_outcome4 | 1.012817 .0052385 2.46 0.014 1.002602 1.023137
_cons | .0384615 .0021079 -59.45 0.000 .0345443 .0428229
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16699.3
Iteration 1: log pseudolikelihood = -16692.6
Iteration 2: log pseudolikelihood = -16692.583
Iteration 3: log pseudolikelihood = -16692.583
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16692.583 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.494817 .0867191 6.93 0.000 1.334158 1.674823
_rcs1 | 2.171931 .0774908 21.74 0.000 2.02524 2.329246
_rcs_tr_outcome1 | .9370803 .0355549 -1.71 0.087 .8699219 1.009423
_rcs_tr_outcome2 | 1.061681 .0098426 6.46 0.000 1.042564 1.081149
_rcs_tr_outcome3 | 1.01745 .0074385 2.37 0.018 1.002974 1.032134
_rcs_tr_outcome4 | 1.014089 .0053999 2.63 0.009 1.00356 1.024728
_rcs_tr_outcome5 | 1.009624 .0039299 2.46 0.014 1.00195 1.017356
_cons | .0384615 .0021079 -59.45 0.000 .0345443 .0428229
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16699.371
Iteration 1: log pseudolikelihood = -16691.032
Iteration 2: log pseudolikelihood = -16691.005
Iteration 3: log pseudolikelihood = -16691.005
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16691.005 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.494818 .086719 6.93 0.000 1.334158 1.674824
_rcs1 | 2.171931 .0774908 21.74 0.000 2.02524 2.329246
_rcs_tr_outcome1 | .9369359 .0355504 -1.72 0.086 .8697862 1.00927
_rcs_tr_outcome2 | 1.061635 .0101132 6.28 0.000 1.041997 1.081643
_rcs_tr_outcome3 | 1.015608 .0076486 2.06 0.040 1.000727 1.03071
_rcs_tr_outcome4 | 1.015878 .0054909 2.91 0.004 1.005173 1.026697
_rcs_tr_outcome5 | 1.009412 .004088 2.31 0.021 1.001431 1.017456
_rcs_tr_outcome6 | 1.008913 .0032157 2.78 0.005 1.00263 1.015235
_cons | .0384615 .0021079 -59.45 0.000 .0345443 .0428229
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16698.753
Iteration 1: log pseudolikelihood = -16690.416
Iteration 2: log pseudolikelihood = -16690.389
Iteration 3: log pseudolikelihood = -16690.389
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16690.389 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.494807 .0867185 6.93 0.000 1.334149 1.674812
_rcs1 | 2.171931 .0774908 21.74 0.000 2.02524 2.329246
_rcs_tr_outcome1 | .9369314 .035552 -1.72 0.086 .8697788 1.009269
_rcs_tr_outcome2 | 1.061562 .0102562 6.18 0.000 1.04165 1.081856
_rcs_tr_outcome3 | 1.014908 .0078259 1.92 0.055 .9996852 1.030363
_rcs_tr_outcome4 | 1.016624 .0056083 2.99 0.003 1.005691 1.027675
_rcs_tr_outcome5 | 1.00866 .0041223 2.11 0.035 1.000613 1.016772
_rcs_tr_outcome6 | 1.010502 .0033518 3.15 0.002 1.003954 1.017092
_rcs_tr_outcome7 | 1.006042 .0027683 2.19 0.029 1.000631 1.011483
_cons | .0384615 .0021079 -59.45 0.000 .0345443 .0428229
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16706.906
Iteration 1: log pseudolikelihood = -16695.376
Iteration 2: log pseudolikelihood = -16695.336
Iteration 3: log pseudolikelihood = -16695.336
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16695.336 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490397 .086993 6.84 0.000 1.329285 1.671036
_rcs1 | 2.208218 .0884756 19.77 0.000 2.041443 2.388618
_rcs2 | 1.060586 .0105656 5.90 0.000 1.040079 1.081498
_rcs_tr_outcome1 | .919381 .0381406 -2.03 0.043 .8475851 .9972585
_cons | .0385918 .0021282 -59.02 0.000 .0346381 .0429967
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16706.535
Iteration 1: log pseudolikelihood = -16695.011
Iteration 2: log pseudolikelihood = -16694.959
Iteration 3: log pseudolikelihood = -16694.959
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16694.959 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.49098 .086779 6.86 0.000 1.330239 1.671144
_rcs1 | 2.196673 .0896558 19.28 0.000 2.027796 2.379615
_rcs2 | 1.045415 .0296421 1.57 0.117 .9889026 1.105157
_rcs_tr_outcome1 | .925278 .0395987 -1.81 0.070 .8508318 1.006238
_rcs_tr_outcome2 | 1.018653 .0305081 0.62 0.537 .9605792 1.080237
_cons | .0385834 .0021226 -59.17 0.000 .0346396 .0429762
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16701.744
Iteration 1: log pseudolikelihood = -16693.133
Iteration 2: log pseudolikelihood = -16693.081
Iteration 3: log pseudolikelihood = -16693.081
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16693.081 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490357 .0867416 6.86 0.000 1.329685 1.670444
_rcs1 | 2.195884 .0892579 19.35 0.000 2.027729 2.377984
_rcs2 | 1.044303 .0293838 1.54 0.123 .9882713 1.103512
_rcs_tr_outcome1 | .9271637 .0395172 -1.77 0.076 .8528582 1.007943
_rcs_tr_outcome2 | 1.017067 .0299441 0.57 0.565 .9600389 1.077483
_rcs_tr_outcome3 | 1.015322 .0073418 2.10 0.035 1.001034 1.029815
_cons | .0385822 .0021223 -59.17 0.000 .0346389 .0429744
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16699.317
Iteration 1: log pseudolikelihood = -16691.175
Iteration 2: log pseudolikelihood = -16691.114
Iteration 3: log pseudolikelihood = -16691.114
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16691.114 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490262 .0867456 6.85 0.000 1.329584 1.670358
_rcs1 | 2.196673 .0896558 19.28 0.000 2.027796 2.379615
_rcs2 | 1.045415 .0296421 1.57 0.117 .9889026 1.105157
_rcs_tr_outcome1 | .9264613 .0396548 -1.78 0.074 .8519101 1.007536
_rcs_tr_outcome2 | 1.016223 .0301763 0.54 0.588 .9587672 1.077123
_rcs_tr_outcome3 | 1.012452 .0078277 1.60 0.109 .9972259 1.027911
_rcs_tr_outcome4 | 1.012817 .0052385 2.46 0.014 1.002602 1.023137
_cons | .0385834 .0021226 -59.17 0.000 .0346396 .0429762
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16694.895
Iteration 1: log pseudolikelihood = -16689.524
Iteration 2: log pseudolikelihood = -16689.507
Iteration 3: log pseudolikelihood = -16689.507
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16689.507 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490093 .0867393 6.85 0.000 1.329427 1.670176
_rcs1 | 2.19684 .089738 19.27 0.000 2.027814 2.379956
_rcs2 | 1.045649 .0296888 1.57 0.116 .9890496 1.105488
_rcs_tr_outcome1 | .926447 .0396915 -1.78 0.075 .8518298 1.0076
_rcs_tr_outcome2 | 1.015764 .0300753 0.53 0.597 .9584948 1.076454
_rcs_tr_outcome3 | 1.011248 .0083723 1.35 0.177 .9949707 1.027791
_rcs_tr_outcome4 | 1.013512 .0054083 2.52 0.012 1.002968 1.024168
_rcs_tr_outcome5 | 1.009695 .0039309 2.48 0.013 1.00202 1.017429
_cons | .0385836 .0021227 -59.16 0.000 .0346397 .0429766
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16694.988
Iteration 1: log pseudolikelihood = -16687.985
Iteration 2: log pseudolikelihood = -16687.958
Iteration 3: log pseudolikelihood = -16687.958
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16687.958 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490098 .0867374 6.85 0.000 1.329435 1.670177
_rcs1 | 2.196673 .0896558 19.28 0.000 2.027796 2.379615
_rcs2 | 1.045415 .0296421 1.57 0.117 .9889026 1.105157
_rcs_tr_outcome1 | .9263827 .0396572 -1.79 0.074 .8518274 1.007463
_rcs_tr_outcome2 | 1.016066 .0300557 0.54 0.590 .9588331 1.076715
_rcs_tr_outcome3 | 1.008703 .0087735 1.00 0.319 .9916529 1.026046
_rcs_tr_outcome4 | 1.014706 .005535 2.68 0.007 1.003916 1.025613
_rcs_tr_outcome5 | 1.009412 .004088 2.31 0.021 1.001431 1.017456
_rcs_tr_outcome6 | 1.008913 .0032157 2.78 0.005 1.00263 1.015235
_cons | .0385834 .0021226 -59.17 0.000 .0346396 .0429762
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16694.366
Iteration 1: log pseudolikelihood = -16687.365
Iteration 2: log pseudolikelihood = -16687.338
Iteration 3: log pseudolikelihood = -16687.338
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16687.338 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490087 .0867371 6.85 0.000 1.329424 1.670166
_rcs1 | 2.196698 .0896681 19.28 0.000 2.027798 2.379666
_rcs2 | 1.04545 .0296491 1.57 0.117 .9889246 1.105207
_rcs_tr_outcome1 | .9263665 .0396629 -1.79 0.074 .851801 1.007459
_rcs_tr_outcome2 | 1.016114 .0300157 0.54 0.588 .9589553 1.07668
_rcs_tr_outcome3 | 1.007181 .0091869 0.78 0.433 .9893344 1.025349
_rcs_tr_outcome4 | 1.014987 .0056952 2.65 0.008 1.003886 1.026211
_rcs_tr_outcome5 | 1.0085 .0041227 2.07 0.038 1.000452 1.016613
_rcs_tr_outcome6 | 1.010521 .003352 3.16 0.002 1.003973 1.017112
_rcs_tr_outcome7 | 1.006035 .0027682 2.19 0.029 1.000624 1.011476
_cons | .0385834 .0021226 -59.17 0.000 .0346396 .0429763
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16700.908
Iteration 1: log pseudolikelihood = -16691.835
Iteration 2: log pseudolikelihood = -16691.799
Iteration 3: log pseudolikelihood = -16691.799
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16691.799 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.489655 .0868415 6.84 0.000 1.328813 1.669966
_rcs1 | 2.21412 .0888949 19.80 0.000 2.046568 2.395389
_rcs2 | 1.057547 .0095345 6.21 0.000 1.039023 1.0764
_rcs3 | 1.020245 .0072364 2.83 0.005 1.00616 1.034527
_rcs_tr_outcome1 | .9188452 .0381735 -2.04 0.042 .8469917 .9967944
_cons | .03859 .0021267 -59.06 0.000 .0346389 .0429917
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16700.51
Iteration 1: log pseudolikelihood = -16691.535
Iteration 2: log pseudolikelihood = -16691.488
Iteration 3: log pseudolikelihood = -16691.488
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16691.488 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490004 .0866668 6.86 0.000 1.329465 1.669929
_rcs1 | 2.203766 .0891738 19.53 0.000 2.03574 2.385661
_rcs2 | 1.044172 .0270475 1.67 0.095 .9924835 1.098553
_rcs3 | 1.019448 .0074736 2.63 0.009 1.004904 1.034201
_rcs_tr_outcome1 | .9241327 .0390122 -1.87 0.062 .8507479 1.003848
_rcs_tr_outcome2 | 1.016419 .027909 0.59 0.553 .9631647 1.072619
_cons | .0385855 .0021223 -59.18 0.000 .0346422 .0429777
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16700.875
Iteration 1: log pseudolikelihood = -16691.281
Iteration 2: log pseudolikelihood = -16691.217
Iteration 3: log pseudolikelihood = -16691.217
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16691.217 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490836 .0869427 6.85 0.000 1.329809 1.671361
_rcs1 | 2.207467 .0914272 19.12 0.000 2.035354 2.394135
_rcs2 | 1.041654 .0251856 1.69 0.091 .9934422 1.092205
_rcs3 | 1.027603 .0214081 1.31 0.191 .9864887 1.07043
_rcs_tr_outcome1 | .9222509 .039998 -1.87 0.062 .8470957 1.004074
_rcs_tr_outcome2 | 1.019576 .0262646 0.75 0.452 .9693761 1.072375
_rcs_tr_outcome3 | .990918 .0217734 -0.42 0.678 .9491489 1.034525
_cons | .0385704 .0021264 -59.05 0.000 .03462 .0429715
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16698.999
Iteration 1: log pseudolikelihood = -16689.797
Iteration 2: log pseudolikelihood = -16689.723
Iteration 3: log pseudolikelihood = -16689.723
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16689.723 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490509 .086868 6.85 0.000 1.329615 1.670872
_rcs1 | 2.205635 .0910309 19.17 0.000 2.034243 2.391467
_rcs2 | 1.042 .0257059 1.67 0.095 .9928165 1.093621
_rcs3 | 1.024653 .0208663 1.20 0.232 .9845607 1.066377
_rcs_tr_outcome1 | .9227271 .0398895 -1.86 0.063 .8477656 1.004317
_rcs_tr_outcome2 | 1.020223 .0268595 0.76 0.447 .9689145 1.074249
_rcs_tr_outcome3 | .9915858 .0212407 -0.39 0.693 .9508167 1.034103
_rcs_tr_outcome4 | 1.007867 .0067442 1.17 0.242 .9947351 1.021173
_cons | .0385763 .0021253 -59.08 0.000 .0346278 .0429751
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16694.329
Iteration 1: log pseudolikelihood = -16687.794
Iteration 2: log pseudolikelihood = -16687.764
Iteration 3: log pseudolikelihood = -16687.764
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16687.764 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.49065 .0869404 6.84 0.000 1.329629 1.671172
_rcs1 | 2.207626 .0914079 19.13 0.000 2.035546 2.394252
_rcs2 | 1.041417 .0250681 1.69 0.092 .993425 1.091727
_rcs3 | 1.028043 .0213647 1.33 0.183 .9870102 1.070782
_rcs_tr_outcome1 | .9219117 .0399929 -1.87 0.061 .8467669 1.003725
_rcs_tr_outcome2 | 1.021347 .0262063 0.82 0.410 .9712538 1.074024
_rcs_tr_outcome3 | .9888795 .0205999 -0.54 0.591 .9493176 1.03009
_rcs_tr_outcome4 | 1.003848 .0094378 0.41 0.683 .9855195 1.022517
_rcs_tr_outcome5 | 1.009084 .0039466 2.31 0.021 1.001378 1.016848
_cons | .0385692 .0021265 -59.04 0.000 .0346187 .0429705
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16694.386
Iteration 1: log pseudolikelihood = -16686.266
Iteration 2: log pseudolikelihood = -16686.226
Iteration 3: log pseudolikelihood = -16686.226
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16686.226 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.4906 .0869307 6.84 0.000 1.329596 1.671101
_rcs1 | 2.207467 .0914272 19.12 0.000 2.035354 2.394135
_rcs2 | 1.041654 .0251856 1.69 0.091 .9934422 1.092205
_rcs3 | 1.027603 .0214081 1.31 0.191 .9864887 1.07043
_rcs_tr_outcome1 | .9218528 .0399939 -1.88 0.061 .8467063 1.003669
_rcs_tr_outcome2 | 1.021547 .0263639 0.83 0.409 .9711595 1.074548
_rcs_tr_outcome3 | .9880459 .0197962 -0.60 0.548 .949998 1.027618
_rcs_tr_outcome4 | 1.003067 .0111483 0.28 0.783 .981453 1.025157
_rcs_tr_outcome5 | 1.006728 .0045638 1.48 0.139 .9978225 1.015713
_rcs_tr_outcome6 | 1.008913 .0032157 2.78 0.005 1.00263 1.015235
_cons | .0385704 .0021264 -59.05 0.000 .03462 .0429715
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16693.747
Iteration 1: log pseudolikelihood = -16685.618
Iteration 2: log pseudolikelihood = -16685.577
Iteration 3: log pseudolikelihood = -16685.577
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16685.577 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.490615 .0869362 6.84 0.000 1.329601 1.671127
_rcs1 | 2.207626 .0914558 19.12 0.000 2.03546 2.394354
_rcs2 | 1.04165 .0251515 1.69 0.091 .9935019 1.092131
_rcs3 | 1.027826 .0214095 1.32 0.188 .9867088 1.070656
_rcs_tr_outcome1 | .9217762 .0400022 -1.88 0.061 .846615 1.00361
_rcs_tr_outcome2 | 1.022047 .0263402 0.85 0.397 .9717031 1.074999
_rcs_tr_outcome3 | .9875108 .0191317 -0.65 0.517 .9507163 1.025729
_rcs_tr_outcome4 | 1.002061 .0121622 0.17 0.865 .9785046 1.026184
_rcs_tr_outcome5 | 1.004008 .0054194 0.74 0.459 .9934425 1.014686
_rcs_tr_outcome6 | 1.009787 .0033911 2.90 0.004 1.003162 1.016455
_rcs_tr_outcome7 | 1.006105 .0027693 2.21 0.027 1.000692 1.011547
_cons | .0385699 .0021265 -59.04 0.000 .0346194 .0429712
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16697.906
Iteration 1: log pseudolikelihood = -16685.984
Iteration 2: log pseudolikelihood = -16685.904
Iteration 3: log pseudolikelihood = -16685.904
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16685.904 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.486404 .0869163 6.78 0.000 1.325451 1.666902
_rcs1 | 2.209794 .0887971 19.73 0.000 2.042431 2.39087
_rcs2 | 1.057875 .0102869 5.79 0.000 1.037904 1.07823
_rcs3 | 1.017145 .0071375 2.42 0.015 1.003251 1.031231
_rcs4 | 1.018891 .0056904 3.35 0.001 1.007798 1.030105
_rcs_tr_outcome1 | .9208024 .0383399 -1.98 0.048 .848642 .9990985
_cons | .0386568 .0021347 -58.91 0.000 .0346913 .0430755
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16697.438
Iteration 1: log pseudolikelihood = -16685.628
Iteration 2: log pseudolikelihood = -16685.536
Iteration 3: log pseudolikelihood = -16685.536
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16685.536 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.486824 .0867297 6.80 0.000 1.326194 1.666909
_rcs1 | 2.198688 .0892559 19.41 0.000 2.030528 2.380774
_rcs2 | 1.043329 .0285859 1.55 0.122 .9887799 1.100888
_rcs3 | 1.015727 .007735 2.05 0.040 1.00068 1.031001
_rcs4 | 1.018847 .005671 3.35 0.001 1.007792 1.030023
_rcs_tr_outcome1 | .9264911 .0393462 -1.80 0.072 .8524962 1.006909
_rcs_tr_outcome2 | 1.017967 .0296389 0.61 0.541 .9615022 1.077748
_cons | .0386512 .0021299 -59.03 0.000 .0346942 .0430596
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16698.01
Iteration 1: log pseudolikelihood = -16685.242
Iteration 2: log pseudolikelihood = -16685.127
Iteration 3: log pseudolikelihood = -16685.127
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16685.127 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488175 .0870598 6.80 0.000 1.32696 1.668977
_rcs1 | 2.205225 .0911888 19.12 0.000 2.033549 2.391394
_rcs2 | 1.040629 .0264792 1.57 0.118 .9900039 1.093844
_rcs3 | 1.027031 .0203348 1.35 0.178 .9879392 1.06767
_rcs4 | 1.021049 .0073491 2.89 0.004 1.006746 1.035555
_rcs_tr_outcome1 | .9231661 .040001 -1.85 0.065 .8480024 1.004992
_rcs_tr_outcome2 | 1.020812 .0278273 0.76 0.450 .9677029 1.076836
_rcs_tr_outcome3 | .9868875 .0209655 -0.62 0.534 .9466396 1.028847
_cons | .0386258 .0021345 -58.88 0.000 .0346609 .0430442
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16697.99
Iteration 1: log pseudolikelihood = -16683.098
Iteration 2: log pseudolikelihood = -16682.833
Iteration 3: log pseudolikelihood = -16682.833
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16682.833 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488651 .0868457 6.82 0.000 1.327807 1.668978
_rcs1 | 2.208409 .0916179 19.10 0.000 2.035948 2.395479
_rcs2 | 1.043109 .0306766 1.44 0.151 .9846841 1.105001
_rcs3 | 1.016916 .0197665 0.86 0.388 .9789033 1.056405
_rcs4 | 1.039122 .0172759 2.31 0.021 1.005807 1.07354
_rcs_tr_outcome1 | .921538 .040035 -1.88 0.060 .8463187 1.003443
_rcs_tr_outcome2 | 1.018217 .0314108 0.59 0.558 .9584777 1.081681
_rcs_tr_outcome3 | 1.000289 .0207124 0.01 0.989 .9605066 1.04172
_rcs_tr_outcome4 | .9746857 .01697 -1.47 0.141 .9419863 1.00852
_cons | .0386251 .0021302 -59.00 0.000 .0346677 .0430343
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16693.446
Iteration 1: log pseudolikelihood = -16683.99
Iteration 2: log pseudolikelihood = -16683.888
Iteration 3: log pseudolikelihood = -16683.887
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16683.887 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487559 .0869335 6.80 0.000 1.326568 1.668087
_rcs1 | 2.204742 .0909806 19.16 0.000 2.033444 2.39047
_rcs2 | 1.041721 .0284378 1.50 0.134 .9874484 1.098976
_rcs3 | 1.021608 .0199519 1.09 0.274 .9832418 1.061471
_rcs4 | 1.029309 .0156498 1.90 0.057 .9990882 1.060443
_rcs_tr_outcome1 | .9237657 .0399324 -1.83 0.067 .8487235 1.005443
_rcs_tr_outcome2 | 1.019509 .0293315 0.67 0.502 .9636114 1.07865
_rcs_tr_outcome3 | .9996387 .0203503 -0.02 0.986 .9605381 1.040331
_rcs_tr_outcome4 | .9839484 .0158591 -1.00 0.315 .953351 1.015528
_rcs_tr_outcome5 | .998916 .0069469 -0.16 0.876 .9853927 1.012625
_cons | .0386386 .0021338 -58.91 0.000 .0346748 .0430555
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16693.574
Iteration 1: log pseudolikelihood = -16680.515
Iteration 2: log pseudolikelihood = -16680.317
Iteration 3: log pseudolikelihood = -16680.317
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.317 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488194 .0868348 6.81 0.000 1.327372 1.6685
_rcs1 | 2.207353 .0913878 19.12 0.000 2.03531 2.393937
_rcs2 | 1.042707 .0301643 1.45 0.148 .9852313 1.103536
_rcs3 | 1.017921 .0197581 0.92 0.360 .9799237 1.057393
_rcs4 | 1.037095 .0170623 2.21 0.027 1.004187 1.071081
_rcs_tr_outcome1 | .9220592 .039978 -1.87 0.061 .8469407 1.00384
_rcs_tr_outcome2 | 1.018513 .0310449 0.60 0.547 .9594479 1.081214
_rcs_tr_outcome3 | 1.004308 .0198248 0.22 0.828 .9661937 1.043925
_rcs_tr_outcome4 | .9839598 .0150828 -1.05 0.291 .9548377 1.01397
_rcs_tr_outcome5 | .9864926 .0110075 -1.22 0.223 .9651526 1.008305
_rcs_tr_outcome6 | 1.005747 .0035058 1.64 0.100 .9988994 1.012642
_cons | .0386298 .0021308 -58.99 0.000 .0346714 .0430401
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16693.001
Iteration 1: log pseudolikelihood = -16679.706
Iteration 2: log pseudolikelihood = -16679.51
Iteration 3: log pseudolikelihood = -16679.51
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16679.51 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488253 .0868309 6.81 0.000 1.327438 1.668551
_rcs1 | 2.207671 .0914768 19.11 0.000 2.035467 2.394444
_rcs2 | 1.042932 .0303798 1.44 0.149 .9850568 1.104208
_rcs3 | 1.017469 .0197804 0.89 0.373 .979429 1.056986
_rcs4 | 1.037768 .0171701 2.24 0.025 1.004655 1.071973
_rcs_tr_outcome1 | .9218814 .0400045 -1.87 0.061 .8467159 1.00372
_rcs_tr_outcome2 | 1.018368 .0313008 0.59 0.554 .9588312 1.081602
_rcs_tr_outcome3 | 1.005209 .0193785 0.27 0.788 .9679362 1.043917
_rcs_tr_outcome4 | .9885296 .0140849 -0.81 0.418 .9613056 1.016525
_rcs_tr_outcome5 | .9816722 .0124322 -1.46 0.144 .9576055 1.006344
_rcs_tr_outcome6 | 1.000342 .0056108 0.06 0.951 .9894051 1.0114
_rcs_tr_outcome7 | 1.00528 .002784 1.90 0.057 .9998384 1.010752
_cons | .0386287 .0021306 -58.99 0.000 .0346707 .0430386
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.543
Iteration 1: log pseudolikelihood = -16682.157
Iteration 2: log pseudolikelihood = -16682.135
Iteration 3: log pseudolikelihood = -16682.135
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16682.135 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.484681 .0869404 6.75 0.000 1.323696 1.665245
_rcs1 | 2.207979 .0886593 19.73 0.000 2.040872 2.388769
_rcs2 | 1.056976 .0099806 5.87 0.000 1.037594 1.07672
_rcs3 | 1.01759 .0072571 2.45 0.014 1.003465 1.031913
_rcs4 | 1.017505 .005605 3.15 0.002 1.006579 1.02855
_rcs5 | 1.01451 .0041432 3.53 0.000 1.006422 1.022663
_rcs_tr_outcome1 | .9220181 .0384098 -1.95 0.051 .8497276 1.000459
_cons | .0386872 .0021384 -58.84 0.000 .0347151 .0431138
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.173
Iteration 1: log pseudolikelihood = -16681.793
Iteration 2: log pseudolikelihood = -16681.769
Iteration 3: log pseudolikelihood = -16681.769
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16681.769 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485068 .0867662 6.77 0.000 1.324385 1.665246
_rcs1 | 2.196898 .0888145 19.47 0.000 2.029542 2.378053
_rcs2 | 1.042571 .0278521 1.56 0.119 .9893867 1.098615
_rcs3 | 1.015751 .0083195 1.91 0.056 .9995748 1.032188
_rcs4 | 1.017283 .0055928 3.12 0.002 1.00638 1.028304
_rcs5 | 1.014508 .0041267 3.54 0.000 1.006452 1.022628
_rcs_tr_outcome1 | .9277112 .0392306 -1.77 0.076 .8539208 1.007878
_rcs_tr_outcome2 | 1.01788 .0291661 0.62 0.536 .9622906 1.07668
_cons | .0386823 .0021339 -58.96 0.000 .0347182 .0430991
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.709
Iteration 1: log pseudolikelihood = -16681.699
Iteration 2: log pseudolikelihood = -16681.664
Iteration 3: log pseudolikelihood = -16681.664
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16681.664 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485794 .0870385 6.76 0.000 1.324631 1.666566
_rcs1 | 2.200843 .0905152 19.18 0.000 2.030398 2.385595
_rcs2 | 1.041284 .0267626 1.57 0.115 .9901293 1.095081
_rcs3 | 1.021936 .0189866 1.17 0.243 .9853926 1.059835
_rcs4 | 1.019667 .0094373 2.10 0.035 1.001337 1.038332
_rcs5 | 1.014534 .004166 3.51 0.000 1.006402 1.022732
_rcs_tr_outcome1 | .9256945 .0398759 -1.79 0.073 .8507475 1.007244
_rcs_tr_outcome2 | 1.018929 .0280557 0.68 0.496 .9653985 1.075428
_rcs_tr_outcome3 | .9928501 .0207782 -0.34 0.732 .9529495 1.034421
_cons | .0386687 .0021382 -58.82 0.000 .034697 .043095
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.209
Iteration 1: log pseudolikelihood = -16678.473
Iteration 2: log pseudolikelihood = -16678.36
Iteration 3: log pseudolikelihood = -16678.36
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16678.36 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487582 .0869785 6.79 0.000 1.326513 1.668209
_rcs1 | 2.207141 .0911097 19.18 0.000 2.035602 2.393135
_rcs2 | 1.042402 .0305428 1.42 0.156 .9842261 1.104018
_rcs3 | 1.011391 .0189103 0.61 0.545 .974998 1.049142
_rcs4 | 1.037038 .015366 2.45 0.014 1.007354 1.067596
_rcs5 | 1.022561 .0068768 3.32 0.001 1.009172 1.036129
_rcs_tr_outcome1 | .9222968 .0399029 -1.87 0.062 .8473127 1.003917
_rcs_tr_outcome2 | 1.018475 .0312786 0.60 0.551 .9589784 1.081662
_rcs_tr_outcome3 | 1.002858 .0203855 0.14 0.888 .9636886 1.04362
_rcs_tr_outcome4 | .9735169 .0148319 -1.76 0.078 .9448766 1.003025
_cons | .0386444 .0021346 -58.90 0.000 .0346791 .0430631
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.367
Iteration 1: log pseudolikelihood = -16678.385
Iteration 2: log pseudolikelihood = -16678.247
Iteration 3: log pseudolikelihood = -16678.247
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16678.247 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487863 .0868734 6.81 0.000 1.326975 1.668257
_rcs1 | 2.20757 .0906214 19.29 0.000 2.036913 2.392526
_rcs2 | 1.039565 .0273173 1.48 0.140 .9873799 1.094509
_rcs3 | 1.017012 .0195115 0.88 0.379 .97948 1.055982
_rcs4 | 1.028039 .0159939 1.78 0.075 .9971651 1.05987
_rcs5 | 1.031078 .0119392 2.64 0.008 1.007941 1.054746
_rcs_tr_outcome1 | .9219519 .0396741 -1.89 0.059 .8473811 1.003085
_rcs_tr_outcome2 | 1.021274 .0284544 0.76 0.450 .9669998 1.078595
_rcs_tr_outcome3 | 1.000431 .020541 0.02 0.983 .9609704 1.041511
_rcs_tr_outcome4 | .9864298 .0162198 -0.83 0.406 .9551464 1.018738
_rcs_tr_outcome5 | .979192 .0119614 -1.72 0.085 .9560264 1.002919
_cons | .0386413 .0021331 -58.94 0.000 .0346787 .0430567
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16690.089
Iteration 1: log pseudolikelihood = -16677.581
Iteration 2: log pseudolikelihood = -16677.43
Iteration 3: log pseudolikelihood = -16677.43
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16677.43 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487176 .0868694 6.79 0.000 1.3263 1.667566
_rcs1 | 2.205743 .0903821 19.31 0.000 2.035524 2.390196
_rcs2 | 1.040123 .0277124 1.48 0.140 .9872017 1.095882
_rcs3 | 1.016672 .0194027 0.87 0.386 .9793462 1.055421
_rcs4 | 1.028893 .0159435 1.84 0.066 .9981137 1.060621
_rcs5 | 1.027857 .0107083 2.64 0.008 1.007082 1.049061
_rcs_tr_outcome1 | .922835 .03962 -1.87 0.061 .8483587 1.00385
_rcs_tr_outcome2 | 1.020985 .0288385 0.74 0.462 .9659986 1.079101
_rcs_tr_outcome3 | .9999301 .0205398 -0.00 0.997 .9604725 1.041009
_rcs_tr_outcome4 | .9932255 .0153791 -0.44 0.661 .9635357 1.02383
_rcs_tr_outcome5 | .9789891 .0116637 -1.78 0.075 .9563935 1.002119
_rcs_tr_outcome6 | .995085 .0062654 -0.78 0.434 .9828804 1.007441
_cons | .0386535 .0021342 -58.92 0.000 .0346888 .0430712
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.751
Iteration 1: log pseudolikelihood = -16677.107
Iteration 2: log pseudolikelihood = -16676.971
Iteration 3: log pseudolikelihood = -16676.971
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.971 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487247 .0868503 6.80 0.000 1.326403 1.667594
_rcs1 | 2.205988 .0904565 19.29 0.000 2.035634 2.390599
_rcs2 | 1.040042 .0275615 1.48 0.138 .9874009 1.095488
_rcs3 | 1.016831 .0194348 0.87 0.383 .9794437 1.055645
_rcs4 | 1.028367 .0159383 1.80 0.071 .9975984 1.060085
_rcs5 | 1.028582 .0115717 2.50 0.012 1.00615 1.051514
_rcs_tr_outcome1 | .9226753 .0396496 -1.87 0.061 .8481462 1.003753
_rcs_tr_outcome2 | 1.021158 .0286567 0.75 0.456 .9665087 1.078898
_rcs_tr_outcome3 | .9997565 .0205247 -0.01 0.991 .9603275 1.040804
_rcs_tr_outcome4 | .997478 .0144867 -0.17 0.862 .9694848 1.026279
_rcs_tr_outcome5 | .9811328 .0114363 -1.63 0.102 .9589722 1.003806
_rcs_tr_outcome6 | .9879281 .0094315 -1.27 0.203 .9696145 1.006588
_rcs_tr_outcome7 | 1.000281 .003577 0.08 0.937 .9932948 1.007316
_cons | .0386519 .0021338 -58.93 0.000 .0346882 .0430687
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.697
Iteration 1: log pseudolikelihood = -16681.432
Iteration 2: log pseudolikelihood = -16681.401
Iteration 3: log pseudolikelihood = -16681.401
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16681.401 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.484982 .0868838 6.76 0.000 1.324094 1.66542
_rcs1 | 2.208811 .0888698 19.70 0.000 2.04132 2.390044
_rcs2 | 1.05676 .0100638 5.80 0.000 1.037218 1.07667
_rcs3 | 1.016274 .0073935 2.22 0.026 1.001886 1.030868
_rcs4 | 1.016758 .0056678 2.98 0.003 1.00571 1.027927
_rcs5 | 1.015413 .004346 3.57 0.000 1.00693 1.023966
_rcs6 | 1.009694 .0031323 3.11 0.002 1.003573 1.015852
_rcs_tr_outcome1 | .9214131 .0384779 -1.96 0.050 .8490016 1.000001
_cons | .0386822 .0021367 -58.88 0.000 .0347131 .0431052
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.34
Iteration 1: log pseudolikelihood = -16681.081
Iteration 2: log pseudolikelihood = -16681.048
Iteration 3: log pseudolikelihood = -16681.048
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16681.048 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485352 .0867096 6.78 0.000 1.324767 1.665404
_rcs1 | 2.197883 .0891574 19.41 0.000 2.029904 2.379762
_rcs2 | 1.042624 .0280827 1.55 0.121 .9890107 1.099144
_rcs3 | 1.014251 .0087486 1.64 0.101 .9972478 1.031543
_rcs4 | 1.01636 .0056581 2.91 0.004 1.005331 1.02751
_rcs5 | 1.01539 .0043335 3.58 0.000 1.006932 1.023919
_rcs6 | 1.009665 .0031226 3.11 0.002 1.003563 1.015804
_rcs_tr_outcome1 | .9270237 .0393496 -1.79 0.074 .8530209 1.007446
_rcs_tr_outcome2 | 1.017579 .029493 0.60 0.548 .9613846 1.077057
_cons | .0386776 .0021322 -59.00 0.000 .0347163 .0430908
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.825
Iteration 1: log pseudolikelihood = -16680.936
Iteration 2: log pseudolikelihood = -16680.892
Iteration 3: log pseudolikelihood = -16680.892
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.892 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.486228 .0870027 6.77 0.000 1.325124 1.666917
_rcs1 | 2.202143 .0907616 19.15 0.000 2.031249 2.387415
_rcs2 | 1.040681 .0269987 1.54 0.124 .989087 1.094966
_rcs3 | 1.020752 .0183026 1.15 0.252 .9855029 1.057262
_rcs4 | 1.019837 .0111334 1.80 0.072 .998248 1.041893
_rcs5 | 1.015974 .0048434 3.32 0.001 1.006525 1.025511
_rcs6 | 1.009688 .0031319 3.11 0.002 1.003568 1.015845
_rcs_tr_outcome1 | .9248354 .0399466 -1.81 0.070 .849764 1.006539
_rcs_tr_outcome2 | 1.019246 .0284242 0.68 0.494 .9650305 1.076507
_rcs_tr_outcome3 | .9917582 .0211685 -0.39 0.698 .9511245 1.034128
_cons | .0386612 .0021367 -58.86 0.000 .0346922 .0430843
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.111
Iteration 1: log pseudolikelihood = -16678.179
Iteration 2: log pseudolikelihood = -16678.044
Iteration 3: log pseudolikelihood = -16678.044
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16678.044 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.48761 .086853 6.80 0.000 1.32676 1.667961
_rcs1 | 2.20724 .0912996 19.14 0.000 2.035357 2.393637
_rcs2 | 1.042371 .0305866 1.41 0.157 .9841134 1.104077
_rcs3 | 1.008868 .0185128 0.48 0.630 .9732288 1.045813
_rcs4 | 1.031602 .0144531 2.22 0.026 1.00366 1.060322
_rcs5 | 1.028602 .0106241 2.73 0.006 1.007988 1.049637
_rcs6 | 1.01128 .0033631 3.37 0.001 1.00471 1.017893
_rcs_tr_outcome1 | .922217 .039982 -1.87 0.062 .8470907 1.004006
_rcs_tr_outcome2 | 1.018433 .0313469 0.59 0.553 .9588102 1.081762
_rcs_tr_outcome3 | 1.002894 .0205477 0.14 0.888 .9634196 1.043987
_rcs_tr_outcome4 | .9739002 .0160849 -1.60 0.109 .9428791 1.005942
_cons | .0386438 .0021322 -58.96 0.000 .0346828 .0430572
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.06
Iteration 1: log pseudolikelihood = -16678.042
Iteration 2: log pseudolikelihood = -16677.941
Iteration 3: log pseudolikelihood = -16677.941
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16677.941 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487839 .0868645 6.81 0.000 1.326967 1.668213
_rcs1 | 2.207288 .0905936 19.29 0.000 2.036682 2.392185
_rcs2 | 1.039827 .0279719 1.45 0.147 .9864231 1.096122
_rcs3 | 1.013654 .0192744 0.71 0.476 .9765722 1.052144
_rcs4 | 1.025095 .0155145 1.64 0.102 .9951332 1.055958
_rcs5 | 1.030391 .0111286 2.77 0.006 1.008809 1.052436
_rcs6 | 1.016518 .0056979 2.92 0.003 1.005412 1.027747
_rcs_tr_outcome1 | .9220467 .0396475 -1.89 0.059 .8475235 1.003123
_rcs_tr_outcome2 | 1.020806 .0291328 0.72 0.471 .9652739 1.079532
_rcs_tr_outcome3 | 1.002075 .0203402 0.10 0.919 .9629919 1.042745
_rcs_tr_outcome4 | .984264 .0161958 -0.96 0.335 .9530271 1.016525
_rcs_tr_outcome5 | .9826498 .0101765 -1.69 0.091 .9629053 1.002799
_cons | .0386415 .0021327 -58.95 0.000 .0346795 .043056
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.282
Iteration 1: log pseudolikelihood = -16676.468
Iteration 2: log pseudolikelihood = -16676.322
Iteration 3: log pseudolikelihood = -16676.322
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.322 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488144 .0868386 6.81 0.000 1.327316 1.668459
_rcs1 | 2.209267 .0914365 19.15 0.000 2.037131 2.395949
_rcs2 | 1.039731 .0263375 1.54 0.124 .9893705 1.092654
_rcs3 | 1.018563 .01988 0.94 0.346 .9803352 1.058282
_rcs4 | 1.018684 .0167531 1.13 0.260 .9863719 1.052054
_rcs5 | 1.036411 .012887 2.88 0.004 1.011458 1.061979
_rcs6 | 1.011424 .0082802 1.39 0.165 .9953246 1.027784
_rcs_tr_outcome1 | .9211018 .0399349 -1.90 0.058 .8460641 1.002794
_rcs_tr_outcome2 | 1.021068 .0276299 0.77 0.441 .968325 1.076683
_rcs_tr_outcome3 | .9970988 .0208604 -0.14 0.890 .95704 1.038834
_rcs_tr_outcome4 | .9972452 .0172623 -0.16 0.873 .9639791 1.031659
_rcs_tr_outcome5 | .9739495 .012736 -2.02 0.044 .9493046 .9992342
_rcs_tr_outcome6 | .9975174 .0087639 -0.28 0.777 .9804876 1.014843
_cons | .038634 .0021313 -58.98 0.000 .0346746 .0430455
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.878
Iteration 1: log pseudolikelihood = -16676.64
Iteration 2: log pseudolikelihood = -16676.477
Iteration 3: log pseudolikelihood = -16676.477
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.477 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487566 .0868207 6.80 0.000 1.326773 1.667846
_rcs1 | 2.207526 .0908686 19.24 0.000 2.036421 2.393006
_rcs2 | 1.039945 .0266153 1.53 0.126 .989067 1.093441
_rcs3 | 1.017735 .0195675 0.91 0.361 .9800967 1.056818
_rcs4 | 1.020201 .0164755 1.24 0.216 .9884154 1.053009
_rcs5 | 1.034361 .0123553 2.83 0.005 1.010426 1.058862
_rcs6 | 1.010746 .007221 1.50 0.135 .9966916 1.024998
_rcs_tr_outcome1 | .9220494 .0397386 -1.88 0.060 .847362 1.00332
_rcs_tr_outcome2 | 1.021308 .0277832 0.78 0.438 .9682803 1.07724
_rcs_tr_outcome3 | .9968625 .0209712 -0.15 0.881 .9565955 1.038824
_rcs_tr_outcome4 | 1.001266 .0165106 0.08 0.939 .9694227 1.034154
_rcs_tr_outcome5 | .9769411 .0122006 -1.87 0.062 .9533188 1.001149
_rcs_tr_outcome6 | .9903972 .0087127 -1.10 0.273 .973467 1.007622
_rcs_tr_outcome7 | 1.000837 .0053324 0.16 0.875 .9904397 1.011343
_cons | .0386445 .0021321 -58.97 0.000 .0346837 .0430576
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.386
Iteration 1: log pseudolikelihood = -16680.834
Iteration 2: log pseudolikelihood = -16680.8
Iteration 3: log pseudolikelihood = -16680.8
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.8 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.484986 .0868923 6.76 0.000 1.324083 1.665442
_rcs1 | 2.208837 .0888629 19.70 0.000 2.041359 2.390056
_rcs2 | 1.056529 .0100916 5.76 0.000 1.036934 1.076495
_rcs3 | 1.015758 .0075407 2.11 0.035 1.001086 1.030646
_rcs4 | 1.016438 .0058576 2.83 0.005 1.005022 1.027984
_rcs5 | 1.014022 .0043025 3.28 0.001 1.005624 1.02249
_rcs6 | 1.013285 .0034069 3.93 0.000 1.006629 1.019984
_rcs7 | 1.005696 .0028527 2.00 0.045 1.000121 1.011303
_rcs_tr_outcome1 | .9213658 .0384684 -1.96 0.050 .8489716 .9999333
_cons | .0386822 .0021368 -58.88 0.000 .0347128 .0431054
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.045
Iteration 1: log pseudolikelihood = -16680.481
Iteration 2: log pseudolikelihood = -16680.448
Iteration 3: log pseudolikelihood = -16680.448
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.448 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485357 .0867164 6.78 0.000 1.324759 1.665423
_rcs1 | 2.197916 .0892034 19.40 0.000 2.029854 2.379894
_rcs2 | 1.042442 .0280901 1.54 0.123 .9888147 1.098977
_rcs3 | 1.013494 .009233 1.47 0.141 .9955577 1.031752
_rcs4 | 1.015902 .0058461 2.74 0.006 1.004508 1.027425
_rcs5 | 1.013953 .0043036 3.26 0.001 1.005553 1.022423
_rcs6 | 1.01326 .0033938 3.93 0.000 1.00663 1.019933
_rcs7 | 1.005685 .0028423 2.01 0.045 1.00013 1.011271
_rcs_tr_outcome1 | .9269724 .0393674 -1.79 0.074 .8529376 1.007433
_rcs_tr_outcome2 | 1.017574 .0296555 0.60 0.550 .961079 1.07739
_cons | .0386775 .0021324 -59.00 0.000 .034716 .043091
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.496
Iteration 1: log pseudolikelihood = -16680.33
Iteration 2: log pseudolikelihood = -16680.286
Iteration 3: log pseudolikelihood = -16680.286
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.286 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.486242 .0870062 6.77 0.000 1.325133 1.666939
_rcs1 | 2.202186 .0907295 19.16 0.000 2.03135 2.38739
_rcs2 | 1.040313 .0270952 1.52 0.129 .9885398 1.094797
_rcs3 | 1.019695 .0176052 1.13 0.259 .9857667 1.054791
_rcs4 | 1.019835 .0121846 1.64 0.100 .9962306 1.043998
_rcs5 | 1.015095 .0055205 2.75 0.006 1.004333 1.025973
_rcs6 | 1.013411 .0034918 3.87 0.000 1.00659 1.020278
_rcs7 | 1.005697 .002844 2.01 0.045 1.000138 1.011287
_rcs_tr_outcome1 | .9247803 .039923 -1.81 0.070 .8497515 1.006434
_rcs_tr_outcome2 | 1.019312 .0286519 0.68 0.496 .964674 1.077044
_rcs_tr_outcome3 | .9916571 .0212167 -0.39 0.695 .950933 1.034125
_cons | .038661 .0021367 -58.86 0.000 .0346919 .0430842
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.731
Iteration 1: log pseudolikelihood = -16677.701
Iteration 2: log pseudolikelihood = -16677.561
Iteration 3: log pseudolikelihood = -16677.561
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16677.561 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487596 .0868409 6.80 0.000 1.326767 1.66792
_rcs1 | 2.207178 .0913124 19.14 0.000 2.035273 2.393603
_rcs2 | 1.04216 .030514 1.41 0.158 .9840373 1.103716
_rcs3 | 1.007844 .0180681 0.44 0.663 .9730459 1.043886
_rcs4 | 1.027674 .0137248 2.04 0.041 1.001123 1.054929
_rcs5 | 1.028564 .0119422 2.43 0.015 1.005422 1.052239
_rcs6 | 1.018756 .0055224 3.43 0.001 1.007989 1.029637
_rcs7 | 1.006035 .0028059 2.16 0.031 1.000551 1.01155
_rcs_tr_outcome1 | .9222208 .0399861 -1.87 0.062 .8470871 1.004019
_rcs_tr_outcome2 | 1.018594 .0313636 0.60 0.550 .9589408 1.081958
_rcs_tr_outcome3 | 1.002399 .0206237 0.12 0.907 .9627816 1.043647
_rcs_tr_outcome4 | .9744341 .0164094 -1.54 0.124 .9427972 1.007133
_cons | .038644 .0021321 -58.97 0.000 .0346832 .0430571
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16687.698
Iteration 1: log pseudolikelihood = -16676.899
Iteration 2: log pseudolikelihood = -16676.769
Iteration 3: log pseudolikelihood = -16676.769
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.769 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488547 .0868682 6.82 0.000 1.327664 1.668925
_rcs1 | 2.208772 .0907841 19.28 0.000 2.037817 2.394069
_rcs2 | 1.038843 .0275676 1.44 0.151 .9861922 1.094304
_rcs3 | 1.013592 .0192936 0.71 0.478 .9764745 1.052121
_rcs4 | 1.020613 .0142284 1.46 0.143 .9931032 1.048884
_rcs5 | 1.027537 .0110373 2.53 0.011 1.00613 1.049399
_rcs6 | 1.0262 .0089403 2.97 0.003 1.008826 1.043873
_rcs7 | 1.008977 .003242 2.78 0.005 1.002643 1.015351
_rcs_tr_outcome1 | .9211198 .0396871 -1.91 0.057 .8465283 1.002284
_rcs_tr_outcome2 | 1.021727 .029007 0.76 0.449 .9664271 1.080191
_rcs_tr_outcome3 | 1.000807 .0204373 0.04 0.968 .9615419 1.041676
_rcs_tr_outcome4 | .9862913 .0157599 -0.86 0.388 .9558811 1.017669
_rcs_tr_outcome5 | .9795059 .0112809 -1.80 0.072 .9576434 1.001868
_cons | .0386288 .0021316 -58.96 0.000 .034669 .043041
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16687.481
Iteration 1: log pseudolikelihood = -16676.332
Iteration 2: log pseudolikelihood = -16676.193
Iteration 3: log pseudolikelihood = -16676.193
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.193 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.48816 .0869598 6.80 0.000 1.32712 1.668742
_rcs1 | 2.208547 .0907843 19.28 0.000 2.037592 2.393845
_rcs2 | 1.038862 .0264985 1.49 0.135 .9882025 1.092118
_rcs3 | 1.016565 .01978 0.84 0.398 .9785273 1.056082
_rcs4 | 1.016652 .0159393 1.05 0.292 .9858869 1.048378
_rcs5 | 1.031203 .0122402 2.59 0.010 1.00749 1.055475
_rcs6 | 1.023923 .0083244 2.91 0.004 1.007737 1.04037
_rcs7 | 1.007554 .0051744 1.47 0.143 .9974633 1.017747
_rcs_tr_outcome1 | .921384 .0396472 -1.90 0.057 .8468635 1.002462
_rcs_tr_outcome2 | 1.02161 .0280017 0.78 0.435 .9681761 1.077994
_rcs_tr_outcome3 | .9991747 .0204243 -0.04 0.968 .959935 1.040018
_rcs_tr_outcome4 | .9950242 .0165905 -0.30 0.765 .9630329 1.028078
_rcs_tr_outcome5 | .9758425 .0123781 -1.93 0.054 .9518809 1.000407
_rcs_tr_outcome6 | .9939948 .0074473 -0.80 0.421 .9795049 1.008699
_cons | .0386349 .0021338 -58.91 0.000 .0346712 .0430518
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.332
Iteration 1: log pseudolikelihood = -16676.039
Iteration 2: log pseudolikelihood = -16675.813
Iteration 3: log pseudolikelihood = -16675.813
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16675.813 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488118 .0868335 6.81 0.000 1.327299 1.668422
_rcs1 | 2.209025 .0914188 19.15 0.000 2.036921 2.395669
_rcs2 | 1.039083 .0262808 1.52 0.130 .9888297 1.091891
_rcs3 | 1.018315 .0203809 0.91 0.364 .9791429 1.059055
_rcs4 | 1.015384 .0175095 0.89 0.376 .9816394 1.050288
_rcs5 | 1.032519 .0128044 2.58 0.010 1.007726 1.057923
_rcs6 | 1.022748 .00943 2.44 0.015 1.004432 1.041399
_rcs7 | 1.003929 .008362 0.47 0.638 .9876726 1.020452
_rcs_tr_outcome1 | .9211986 .0399375 -1.89 0.058 .8461558 1.002897
_rcs_tr_outcome2 | 1.021634 .0276572 0.79 0.429 .9688394 1.077305
_rcs_tr_outcome3 | .9966544 .0213771 -0.16 0.876 .9556245 1.039446
_rcs_tr_outcome4 | 1.001221 .0181259 0.07 0.946 .9663175 1.037385
_rcs_tr_outcome5 | .9768921 .0127552 -1.79 0.073 .9522095 1.002214
_rcs_tr_outcome6 | .9880258 .0096811 -1.23 0.219 .9692322 1.007184
_rcs_tr_outcome7 | 1.002105 .0087909 0.24 0.811 .9850229 1.019484
_cons | .0386344 .0021312 -58.98 0.000 .0346752 .0430458
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.703
Iteration 1: log pseudolikelihood = -16680.482
Iteration 2: log pseudolikelihood = -16680.429
Iteration 3: log pseudolikelihood = -16680.429
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.429 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.484864 .0868729 6.76 0.000 1.323995 1.665277
_rcs1 | 2.208466 .0889643 19.67 0.000 2.040805 2.389901
_rcs2 | 1.056426 .0101138 5.73 0.000 1.036788 1.076436
_rcs3 | 1.015124 .007673 1.99 0.047 1.000196 1.030275
_rcs4 | 1.016196 .005933 2.75 0.006 1.004634 1.027891
_rcs5 | 1.012441 .0042784 2.93 0.003 1.00409 1.020861
_rcs6 | 1.013904 .0035426 3.95 0.000 1.006985 1.020872
_rcs7 | 1.009676 .0029084 3.34 0.001 1.003991 1.015392
_rcs8 | 1.004239 .0027416 1.55 0.121 .99888 1.009627
_rcs_tr_outcome1 | .9215648 .0385429 -1.95 0.051 .8490354 1.00029
_cons | .0386845 .0021367 -58.88 0.000 .0347155 .0431074
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.384
Iteration 1: log pseudolikelihood = -16680.13
Iteration 2: log pseudolikelihood = -16680.076
Iteration 3: log pseudolikelihood = -16680.076
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.076 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.48523 .0866958 6.78 0.000 1.324669 1.665252
_rcs1 | 2.197537 .0893338 19.37 0.000 2.029239 2.379792
_rcs2 | 1.042364 .0280846 1.54 0.124 .9887474 1.098888
_rcs3 | 1.012732 .0095581 1.34 0.180 .9941712 1.03164
_rcs4 | 1.015539 .0059326 2.64 0.008 1.003978 1.027233
_rcs5 | 1.012303 .0042964 2.88 0.004 1.003918 1.020759
_rcs6 | 1.013881 .0035297 3.96 0.000 1.006987 1.020823
_rcs7 | 1.009644 .0028997 3.34 0.001 1.003977 1.015344
_rcs8 | 1.004242 .0027286 1.56 0.119 .9989081 1.009604
_rcs_tr_outcome1 | .9271796 .03946 -1.78 0.076 .8529772 1.007837
_rcs_tr_outcome2 | 1.01758 .0297605 0.60 0.551 .9608906 1.077614
_cons | .03868 .0021321 -59.00 0.000 .0347188 .043093
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.802
Iteration 1: log pseudolikelihood = -16679.996
Iteration 2: log pseudolikelihood = -16679.931
Iteration 3: log pseudolikelihood = -16679.931
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16679.931 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.4861 .0869918 6.77 0.000 1.325016 1.666766
_rcs1 | 2.201704 .0908699 19.12 0.000 2.030615 2.387207
_rcs2 | 1.04027 .0272052 1.51 0.131 .9882926 1.094982
_rcs3 | 1.018419 .0171216 1.09 0.278 .9854085 1.052536
_rcs4 | 1.019499 .012654 1.56 0.120 .994997 1.044605
_rcs5 | 1.01391 .0063825 2.19 0.028 1.001477 1.026496
_rcs6 | 1.014233 .0038426 3.73 0.000 1.00673 1.021793
_rcs7 | 1.009717 .002927 3.34 0.001 1.003996 1.01547
_rcs8 | 1.004241 .0027308 1.56 0.120 .9989034 1.009608
_rcs_tr_outcome1 | .9250369 .0400244 -1.80 0.072 .8498247 1.006906
_rcs_tr_outcome2 | 1.019218 .0288344 0.67 0.501 .9642422 1.077329
_rcs_tr_outcome3 | .9920318 .0213306 -0.37 0.710 .9510933 1.034732
_cons | .0386637 .0021367 -58.86 0.000 .0346947 .0430867
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.973
Iteration 1: log pseudolikelihood = -16677.559
Iteration 2: log pseudolikelihood = -16677.414
Iteration 3: log pseudolikelihood = -16677.414
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16677.414 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487246 .0868123 6.80 0.000 1.326469 1.66751
_rcs1 | 2.206077 .0913324 19.11 0.000 2.034139 2.392549
_rcs2 | 1.042092 .0304534 1.41 0.158 .9840819 1.103522
_rcs3 | 1.00687 .017771 0.39 0.698 .9726355 1.04231
_rcs4 | 1.024324 .0130518 1.89 0.059 .9990597 1.050227
_rcs5 | 1.026145 .011962 2.21 0.027 1.002965 1.04986
_rcs6 | 1.022469 .0077157 2.94 0.003 1.007457 1.037703
_rcs7 | 1.011768 .0033731 3.51 0.000 1.005178 1.018401
_rcs8 | 1.004326 .0026889 1.61 0.107 .99907 1.00961
_rcs_tr_outcome1 | .9228236 .040059 -1.85 0.064 .8475568 1.004775
_rcs_tr_outcome2 | 1.018652 .0313801 0.60 0.549 .958968 1.08205
_rcs_tr_outcome3 | 1.002336 .0207926 0.11 0.910 .9624005 1.043929
_rcs_tr_outcome4 | .9754021 .0163523 -1.49 0.137 .943873 1.007984
_cons | .0386504 .0021321 -58.97 0.000 .0346895 .0430635
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.217
Iteration 1: log pseudolikelihood = -16676.61
Iteration 2: log pseudolikelihood = -16676.47
Iteration 3: log pseudolikelihood = -16676.47
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.47 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488132 .0868039 6.81 0.000 1.327364 1.668371
_rcs1 | 2.207582 .0908177 19.25 0.000 2.03657 2.392955
_rcs2 | 1.038525 .027296 1.44 0.150 .9863808 1.093427
_rcs3 | 1.013723 .019371 0.71 0.476 .9764585 1.052409
_rcs4 | 1.017858 .0133746 1.35 0.178 .9919786 1.044412
_rcs5 | 1.021804 .0110225 2.00 0.046 1.000427 1.043638
_rcs6 | 1.028027 .0099912 2.84 0.004 1.00863 1.047797
_rcs7 | 1.017773 .0059255 3.03 0.002 1.006225 1.029453
_rcs8 | 1.005275 .002633 2.01 0.045 1.000127 1.010448
_rcs_tr_outcome1 | .921736 .0397744 -1.89 0.059 .8469853 1.003084
_rcs_tr_outcome2 | 1.021949 .0289252 0.77 0.443 .9668006 1.080243
_rcs_tr_outcome3 | .9996908 .020695 -0.01 0.988 .9599412 1.041086
_rcs_tr_outcome4 | .9883554 .0160564 -0.72 0.471 .9573812 1.020332
_rcs_tr_outcome5 | .9791179 .0117888 -1.75 0.080 .9562828 1.002498
_cons | .0386367 .0021313 -58.98 0.000 .0346773 .0430481
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.395
Iteration 1: log pseudolikelihood = -16675.386
Iteration 2: log pseudolikelihood = -16675.24
Iteration 3: log pseudolikelihood = -16675.24
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16675.24 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487909 .0868113 6.81 0.000 1.327129 1.668166
_rcs1 | 2.2079 .0910835 19.20 0.000 2.036406 2.393836
_rcs2 | 1.038467 .0261119 1.50 0.133 .9885291 1.090927
_rcs3 | 1.01743 .0200029 0.88 0.379 .9789709 1.0574
_rcs4 | 1.012686 .0150971 0.85 0.398 .9835248 1.042713
_rcs5 | 1.025997 .0116124 2.27 0.023 1.003488 1.049011
_rcs6 | 1.029229 .0096258 3.08 0.002 1.010535 1.04827
_rcs7 | 1.013123 .0071854 1.84 0.066 .9991374 1.027305
_rcs8 | 1.004982 .0033249 1.50 0.133 .9984868 1.01152
_rcs_tr_outcome1 | .9217938 .0398313 -1.88 0.059 .8469404 1.003263
_rcs_tr_outcome2 | 1.021938 .0278966 0.79 0.427 .9686985 1.078103
_rcs_tr_outcome3 | .9975602 .0204932 -0.12 0.905 .9581922 1.038546
_rcs_tr_outcome4 | .9976418 .0167237 -0.14 0.888 .9653965 1.030964
_rcs_tr_outcome5 | .9739666 .0125929 -2.04 0.041 .9495951 .9989636
_rcs_tr_outcome6 | .9962374 .0081664 -0.46 0.646 .9803594 1.012372
_cons | .0386387 .0021313 -58.98 0.000 .0346793 .0430502
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.745
Iteration 1: log pseudolikelihood = -16676.124
Iteration 2: log pseudolikelihood = -16675.915
Iteration 3: log pseudolikelihood = -16675.915
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16675.915 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487548 .0869351 6.80 0.000 1.326554 1.668079
_rcs1 | 2.207169 .0910302 19.20 0.000 2.035774 2.392994
_rcs2 | 1.038682 .0260453 1.51 0.130 .9888682 1.091005
_rcs3 | 1.01778 .0204931 0.88 0.381 .9783962 1.058748
_rcs4 | 1.012629 .0166426 0.76 0.445 .9805298 1.045779
_rcs5 | 1.026109 .0117785 2.25 0.025 1.003281 1.049456
_rcs6 | 1.027515 .0099572 2.80 0.005 1.008183 1.047217
_rcs7 | 1.013776 .0070594 1.96 0.049 1.000034 1.027707
_rcs8 | 1.00422 .0052804 0.80 0.423 .9939242 1.014623
_rcs_tr_outcome1 | .9222102 .0398335 -1.87 0.061 .8473515 1.003682
_rcs_tr_outcome2 | 1.021862 .0276651 0.80 0.424 .969053 1.077549
_rcs_tr_outcome3 | .9969352 .0211096 -0.14 0.885 .9564079 1.03918
_rcs_tr_outcome4 | 1.001783 .0175467 0.10 0.919 .9679763 1.036771
_rcs_tr_outcome5 | .9779457 .0125224 -1.74 0.082 .9537077 1.0028
_rcs_tr_outcome6 | .9878958 .0091878 -1.31 0.190 .9700513 1.006069
_rcs_tr_outcome7 | .9991638 .0068135 -0.12 0.902 .9858984 1.012608
_cons | .0386453 .0021345 -58.90 0.000 .0346803 .0430636
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.341
Iteration 1: log pseudolikelihood = -16680.337
Iteration 2: log pseudolikelihood = -16680.286
Iteration 3: log pseudolikelihood = -16680.286
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.286 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485076 .0868533 6.76 0.000 1.324241 1.665446
_rcs1 | 2.208906 .0890725 19.65 0.000 2.041048 2.390569
_rcs2 | 1.056214 .0100446 5.75 0.000 1.036709 1.076085
_rcs3 | 1.015185 .0077845 1.97 0.049 1.000042 1.030558
_rcs4 | 1.015401 .0060514 2.56 0.010 1.00361 1.027331
_rcs5 | 1.011995 .0043096 2.80 0.005 1.003583 1.020477
_rcs6 | 1.013086 .0035864 3.67 0.000 1.006081 1.020139
_rcs7 | 1.011437 .0029895 3.85 0.000 1.005595 1.017313
_rcs8 | 1.006862 .0026883 2.56 0.010 1.001607 1.012145
_rcs9 | 1.003868 .0023646 1.64 0.101 .9992442 1.008513
_rcs_tr_outcome1 | .921358 .0385675 -1.96 0.050 .8487849 1.000136
_cons | .0386803 .0021358 -58.90 0.000 .0347128 .0431013
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.071
Iteration 1: log pseudolikelihood = -16679.984
Iteration 2: log pseudolikelihood = -16679.933
Iteration 3: log pseudolikelihood = -16679.933
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16679.933 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485441 .0866767 6.78 0.000 1.324912 1.66542
_rcs1 | 2.197972 .0893973 19.36 0.000 2.029559 2.380361
_rcs2 | 1.042187 .0279184 1.54 0.123 .9888796 1.098368
_rcs3 | 1.012662 .0098738 1.29 0.197 .9934931 1.0322
_rcs4 | 1.014624 .0060666 2.43 0.015 1.002804 1.026585
_rcs5 | 1.011795 .0043413 2.73 0.006 1.003322 1.02034
_rcs6 | 1.013044 .0035777 3.67 0.000 1.006056 1.020081
_rcs7 | 1.01141 .0029785 3.85 0.000 1.005589 1.017265
_rcs8 | 1.006844 .0026788 2.56 0.010 1.001608 1.012108
_rcs9 | 1.003864 .0023545 1.64 0.100 .9992601 1.00849
_rcs_tr_outcome1 | .9269723 .0394582 -1.78 0.075 .8527739 1.007627
_rcs_tr_outcome2 | 1.017576 .0297094 0.60 0.551 .9609819 1.077504
_cons | .0386758 .0021313 -59.02 0.000 .0347162 .043087
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.541
Iteration 1: log pseudolikelihood = -16679.838
Iteration 2: log pseudolikelihood = -16679.775
Iteration 3: log pseudolikelihood = -16679.775
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16679.775 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.486343 .0869777 6.77 0.000 1.325282 1.666977
_rcs1 | 2.202316 .0909661 19.11 0.000 2.031052 2.388022
_rcs2 | 1.039965 .026985 1.51 0.131 .9883974 1.094222
_rcs3 | 1.018285 .01664 1.11 0.268 .9861878 1.051426
_rcs4 | 1.018901 .0130171 1.47 0.143 .9937043 1.044736
_rcs5 | 1.013857 .0070998 1.97 0.049 1.000036 1.027868
_rcs6 | 1.013703 .0042415 3.25 0.001 1.005424 1.022051
_rcs7 | 1.011582 .003084 3.78 0.000 1.005556 1.017645
_rcs8 | 1.006862 .0026842 2.57 0.010 1.001615 1.012137
_rcs9 | 1.003883 .0023563 1.65 0.099 .9992753 1.008512
_rcs_tr_outcome1 | .9247409 .0400304 -1.81 0.071 .849519 1.006623
_rcs_tr_outcome2 | 1.019252 .028717 0.68 0.499 .9644933 1.077119
_rcs_tr_outcome3 | .9916665 .0212898 -0.39 0.697 .950805 1.034284
_cons | .0386589 .0021359 -58.88 0.000 .0346913 .0430803
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.741
Iteration 1: log pseudolikelihood = -16677.225
Iteration 2: log pseudolikelihood = -16677.074
Iteration 3: log pseudolikelihood = -16677.074
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16677.074 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487664 .0868097 6.81 0.000 1.326889 1.667919
_rcs1 | 2.207078 .0914651 19.10 0.000 2.034897 2.393829
_rcs2 | 1.042043 .0304314 1.41 0.158 .9840738 1.103428
_rcs3 | 1.006065 .0172867 0.35 0.725 .9727475 1.040523
_rcs4 | 1.021518 .0128293 1.70 0.090 .9966797 1.046975
_rcs5 | 1.02506 .0115746 2.19 0.028 1.002623 1.047999
_rcs6 | 1.023903 .009035 2.68 0.007 1.006347 1.041766
_rcs7 | 1.016201 .0048404 3.37 0.001 1.006758 1.025732
_rcs8 | 1.007715 .0026971 2.87 0.004 1.002442 1.013015
_rcs9 | 1.003865 .0023397 1.65 0.098 .9992892 1.008461
_rcs_tr_outcome1 | .9223068 .0400661 -1.86 0.063 .847029 1.004275
_rcs_tr_outcome2 | 1.01865 .0313922 0.60 0.549 .9589438 1.082074
_rcs_tr_outcome3 | 1.002584 .0207079 0.12 0.901 .962808 1.044004
_rcs_tr_outcome4 | .9745182 .0163834 -1.54 0.125 .9429306 1.007164
_cons | .0386425 .0021312 -58.99 0.000 .0346832 .0430537
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.184
Iteration 1: log pseudolikelihood = -16676.706
Iteration 2: log pseudolikelihood = -16676.564
Iteration 3: log pseudolikelihood = -16676.564
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.564 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488419 .0868238 6.82 0.000 1.327615 1.6687
_rcs1 | 2.208319 .0909808 19.23 0.000 2.03701 2.394036
_rcs2 | 1.038516 .0272819 1.44 0.150 .9863973 1.093388
_rcs3 | 1.013261 .0192345 0.69 0.488 .9762551 1.05167
_rcs4 | 1.016874 .0128518 1.32 0.186 .9919944 1.042378
_rcs5 | 1.019066 .0110941 1.73 0.083 .9975519 1.041043
_rcs6 | 1.025486 .0095064 2.71 0.007 1.007022 1.044288
_rcs7 | 1.022222 .0078847 2.85 0.004 1.006884 1.037793
_rcs8 | 1.011083 .0036575 3.05 0.002 1.00394 1.018277
_rcs9 | 1.003998 .0023245 1.72 0.085 .9994526 1.008565
_rcs_tr_outcome1 | .9213807 .0398106 -1.90 0.058 .846566 1.002807
_rcs_tr_outcome2 | 1.021782 .028971 0.76 0.447 .966549 1.080172
_rcs_tr_outcome3 | .9997765 .0208092 -0.01 0.991 .959812 1.041405
_rcs_tr_outcome4 | .9876719 .0161196 -0.76 0.447 .9565781 1.019776
_rcs_tr_outcome5 | .9800804 .0116652 -1.69 0.091 .9574816 1.003213
_cons | .0386307 .002131 -58.98 0.000 .0346719 .0430415
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.338
Iteration 1: log pseudolikelihood = -16674.607
Iteration 2: log pseudolikelihood = -16674.431
Iteration 3: log pseudolikelihood = -16674.431
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16674.431 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488636 .086817 6.82 0.000 1.327842 1.668901
_rcs1 | 2.210317 .0918829 19.08 0.000 2.037371 2.397944
_rcs2 | 1.038559 .0258064 1.52 0.128 .9891917 1.090391
_rcs3 | 1.019175 .0204973 0.94 0.345 .9797822 1.060151
_rcs4 | 1.010749 .0142382 0.76 0.448 .9832245 1.039044
_rcs5 | 1.021238 .0114884 1.87 0.062 .9989673 1.044005
_rcs6 | 1.030844 .0104869 2.99 0.003 1.010494 1.051604
_rcs7 | 1.01789 .0075222 2.40 0.016 1.003253 1.032741
_rcs8 | 1.006731 .0057141 1.18 0.237 .9955932 1.017993
_rcs9 | 1.004114 .0024012 1.72 0.086 .9994187 1.008831
_rcs_tr_outcome1 | .9205607 .0400898 -1.90 0.057 .8452461 1.002586
_rcs_tr_outcome2 | 1.021605 .0278007 0.79 0.432 .9685442 1.077573
_rcs_tr_outcome3 | .9956393 .0208248 -0.21 0.834 .9556487 1.037303
_rcs_tr_outcome4 | .9983981 .0170354 -0.09 0.925 .9655614 1.032352
_rcs_tr_outcome5 | .9728486 .0127179 -2.11 0.035 .9482385 .9980974
_rcs_tr_outcome6 | .9995317 .0085665 -0.05 0.956 .982882 1.016464
_cons | .0386237 .0021299 -59.01 0.000 .0346669 .0430321
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.074
Iteration 1: log pseudolikelihood = -16675.33
Iteration 2: log pseudolikelihood = -16675.098
Iteration 3: log pseudolikelihood = -16675.098
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16675.098 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488226 .0867931 6.82 0.000 1.327477 1.668441
_rcs1 | 2.209244 .0916729 19.10 0.000 2.03668 2.396428
_rcs2 | 1.038524 .0258595 1.52 0.129 .9890575 1.090465
_rcs3 | 1.01881 .020783 0.91 0.361 .9788793 1.060369
_rcs4 | 1.010651 .0161432 0.66 0.507 .9795009 1.042791
_rcs5 | 1.022297 .0112569 2.00 0.045 1.00047 1.044599
_rcs6 | 1.028913 .0104466 2.81 0.005 1.008641 1.049593
_rcs7 | 1.019119 .0080275 2.40 0.016 1.003507 1.034975
_rcs8 | 1.006501 .0070261 0.93 0.353 .9928243 1.020367
_rcs9 | 1.00313 .0035627 0.88 0.379 .9961717 1.010137
_rcs_tr_outcome1 | .9211564 .0400372 -1.89 0.059 .8459344 1.003067
_rcs_tr_outcome2 | 1.021749 .0276708 0.79 0.427 .9689292 1.077448
_rcs_tr_outcome3 | .9960211 .0210787 -0.19 0.851 .9555527 1.038203
_rcs_tr_outcome4 | 1.002065 .0177293 0.12 0.907 .967912 1.037423
_rcs_tr_outcome5 | .9764375 .012612 -1.85 0.065 .9520286 1.001472
_rcs_tr_outcome6 | .9883866 .0095222 -1.21 0.225 .9698985 1.007227
_rcs_tr_outcome7 | 1.001808 .0078048 0.23 0.817 .9866268 1.017222
_cons | .0386316 .0021302 -59.01 0.000 .0346742 .0430407
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.877
Iteration 1: log pseudolikelihood = -16680.078
Iteration 2: log pseudolikelihood = -16680.027
Iteration 3: log pseudolikelihood = -16680.027
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16680.027 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.484826 .0869187 6.75 0.000 1.323878 1.665341
_rcs1 | 2.208212 .0888196 19.70 0.000 2.040814 2.389341
_rcs2 | 1.05594 .009944 5.78 0.000 1.036629 1.075611
_rcs3 | 1.015366 .0078247 1.98 0.048 1.000145 1.030819
_rcs4 | 1.014044 .0061348 2.31 0.021 1.002091 1.02614
_rcs5 | 1.01251 .004297 2.93 0.003 1.004123 1.020967
_rcs6 | 1.011534 .0035688 3.25 0.001 1.004563 1.018553
_rcs7 | 1.012225 .0031223 3.94 0.000 1.006123 1.018363
_rcs8 | 1.009251 .0026172 3.55 0.000 1.004134 1.014394
_rcs9 | 1.004979 .002644 1.89 0.059 .9998105 1.010175
_rcs10 | 1.003374 .002064 1.64 0.101 .9993373 1.007428
_rcs_tr_outcome1 | .9217378 .038476 -1.95 0.051 .8493287 1.00032
_cons | .0386851 .0021376 -58.86 0.000 .0347144 .0431101
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.646
Iteration 1: log pseudolikelihood = -16679.736
Iteration 2: log pseudolikelihood = -16679.683
Iteration 3: log pseudolikelihood = -16679.683
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16679.683 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.485183 .0867463 6.77 0.000 1.324534 1.665316
_rcs1 | 2.197428 .089181 19.40 0.000 2.029408 2.37936
_rcs2 | 1.042126 .0277331 1.55 0.121 .9891636 1.097925
_rcs3 | 1.012817 .0100346 1.29 0.199 .9933388 1.032676
_rcs4 | 1.013179 .0061683 2.15 0.032 1.001161 1.025341
_rcs5 | 1.012255 .0043423 2.84 0.005 1.00378 1.020801
_rcs6 | 1.011456 .003567 3.23 0.001 1.004489 1.018472
_rcs7 | 1.012205 .0031109 3.95 0.000 1.006126 1.01832
_rcs8 | 1.009221 .0026098 3.55 0.000 1.004119 1.014349
_rcs9 | 1.004975 .0026322 1.89 0.058 .9998293 1.010148
_rcs10 | 1.003359 .0020573 1.64 0.102 .9993345 1.007399
_rcs_tr_outcome1 | .9272795 .0393665 -1.78 0.075 .8532455 1.007737
_rcs_tr_outcome2 | 1.017336 .0295838 0.59 0.554 .9609738 1.077003
_cons | .0386807 .0021332 -58.97 0.000 .0347177 .0430961
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.154
Iteration 1: log pseudolikelihood = -16679.576
Iteration 2: log pseudolikelihood = -16679.513
Iteration 3: log pseudolikelihood = -16679.513
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16679.513 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.486103 .0870411 6.76 0.000 1.324934 1.666878
_rcs1 | 2.201804 .0907221 19.16 0.000 2.030983 2.386993
_rcs2 | 1.03976 .0267006 1.52 0.129 .9887226 1.093431
_rcs3 | 1.018293 .016215 1.14 0.255 .9870035 1.050575
_rcs4 | 1.017595 .013123 1.35 0.176 .9921972 1.043644
_rcs5 | 1.014668 .0076476 1.93 0.053 .9997887 1.029768
_rcs6 | 1.01248 .0047302 2.65 0.008 1.003251 1.021794
_rcs7 | 1.012506 .0033408 3.77 0.000 1.00598 1.019075
_rcs8 | 1.009309 .002646 3.53 0.000 1.004136 1.014509
_rcs9 | 1.004982 .0026353 1.90 0.058 .99983 1.01016
_rcs10 | 1.003378 .0020645 1.64 0.101 .9993398 1.007432
_rcs_tr_outcome1 | .9250287 .0399261 -1.81 0.071 .8499936 1.006688
_rcs_tr_outcome2 | 1.019127 .0284884 0.68 0.498 .9647931 1.076521
_rcs_tr_outcome3 | .9914526 .0212424 -0.40 0.689 .9506804 1.033973
_cons | .0386635 .0021377 -58.83 0.000 .0346928 .0430887
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16689.348
Iteration 1: log pseudolikelihood = -16677.048
Iteration 2: log pseudolikelihood = -16676.901
Iteration 3: log pseudolikelihood = -16676.901
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.901 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487369 .0868629 6.80 0.000 1.326503 1.667743
_rcs1 | 2.206366 .091189 19.15 0.000 2.034686 2.392532
_rcs2 | 1.041936 .0301401 1.42 0.156 .984506 1.102716
_rcs3 | 1.006181 .0169159 0.37 0.714 .9735671 1.039888
_rcs4 | 1.018327 .0126418 1.46 0.143 .9938485 1.043408
_rcs5 | 1.023924 .0108421 2.23 0.026 1.002893 1.045396
_rcs6 | 1.023041 .0096038 2.43 0.015 1.004389 1.042038
_rcs7 | 1.019216 .0063245 3.07 0.002 1.006896 1.031688
_rcs8 | 1.011557 .0031993 3.63 0.000 1.005306 1.017847
_rcs9 | 1.005381 .0025853 2.09 0.037 1.000326 1.010461
_rcs10 | 1.00327 .0020596 1.59 0.112 .9992418 1.007315
_rcs_tr_outcome1 | .922701 .0399537 -1.86 0.063 .847624 1.004428
_rcs_tr_outcome2 | 1.0185 .031134 0.60 0.549 .9592708 1.081387
_rcs_tr_outcome3 | 1.002425 .0206577 0.12 0.906 .9627432 1.043742
_rcs_tr_outcome4 | .9749059 .0161743 -1.53 0.126 .9437146 1.007128
_cons | .0386479 .0021329 -58.95 0.000 .0346858 .0430627
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.806
Iteration 1: log pseudolikelihood = -16676.448
Iteration 2: log pseudolikelihood = -16676.29
Iteration 3: log pseudolikelihood = -16676.29
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16676.29 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.488202 .0868602 6.81 0.000 1.327336 1.668565
_rcs1 | 2.207738 .0907262 19.27 0.000 2.036891 2.392916
_rcs2 | 1.038566 .0272631 1.44 0.149 .9864828 1.093399
_rcs3 | 1.012461 .0188857 0.66 0.507 .976114 1.050161
_rcs4 | 1.014956 .0125079 1.20 0.228 .9907344 1.039769
_rcs5 | 1.018097 .0108831 1.68 0.093 .996988 1.039652
_rcs6 | 1.022282 .0090765 2.48 0.013 1.004647 1.040228
_rcs7 | 1.02416 .0086234 2.84 0.005 1.007397 1.041202
_rcs8 | 1.016677 .0055844 3.01 0.003 1.005791 1.027682
_rcs9 | 1.007176 .0027553 2.61 0.009 1.00179 1.012591
_rcs10 | 1.003204 .0020536 1.56 0.118 .9991874 1.007237
_rcs_tr_outcome1 | .9217011 .0397008 -1.89 0.058 .8470831 1.002892
_rcs_tr_outcome2 | 1.02149 .0289287 0.75 0.453 .9663357 1.079792
_rcs_tr_outcome3 | 1.000722 .0207381 0.03 0.972 .9608904 1.042205
_rcs_tr_outcome4 | .9868766 .016013 -0.81 0.416 .9559855 1.018766
_rcs_tr_outcome5 | .9800628 .0116381 -1.70 0.090 .957516 1.003141
_cons | .038635 .0021321 -58.96 0.000 .0346743 .0430482
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.504
Iteration 1: log pseudolikelihood = -16675.36
Iteration 2: log pseudolikelihood = -16675.195
Iteration 3: log pseudolikelihood = -16675.195
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16675.195 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487847 .0868878 6.80 0.000 1.326934 1.668272
_rcs1 | 2.207953 .0911955 19.18 0.000 2.036256 2.394127
_rcs2 | 1.038623 .0259659 1.52 0.130 .9889578 1.090783
_rcs3 | 1.017563 .0203889 0.87 0.385 .9783762 1.05832
_rcs4 | 1.010503 .0135956 0.78 0.437 .9842048 1.037505
_rcs5 | 1.017543 .0112891 1.57 0.117 .9956556 1.039912
_rcs6 | 1.026912 .0103172 2.64 0.008 1.006888 1.047333
_rcs7 | 1.023442 .0079558 2.98 0.003 1.007967 1.039154
_rcs8 | 1.012096 .0066313 1.84 0.067 .9991816 1.025177
_rcs9 | 1.005615 .0041072 1.37 0.170 .9975972 1.013698
_rcs10 | 1.003429 .0020657 1.66 0.096 .9993885 1.007486
_rcs_tr_outcome1 | .9218114 .0398616 -1.88 0.060 .8469033 1.003345
_rcs_tr_outcome2 | 1.021253 .0279008 0.77 0.441 .9680071 1.077429
_rcs_tr_outcome3 | .9974634 .0209786 -0.12 0.904 .957182 1.03944
_rcs_tr_outcome4 | .9970067 .0170415 -0.18 0.861 .9641593 1.030973
_rcs_tr_outcome5 | .9746809 .012616 -1.98 0.048 .9502649 .9997242
_rcs_tr_outcome6 | .997735 .008504 -0.27 0.790 .9812059 1.014543
_cons | .0386394 .0021327 -58.94 0.000 .0346775 .0430539
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -16688.701
Iteration 1: log pseudolikelihood = -16675.163
Iteration 2: log pseudolikelihood = -16674.905
Iteration 3: log pseudolikelihood = -16674.905
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -16674.905 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.487801 .0868845 6.80 0.000 1.326894 1.66822
_rcs1 | 2.208186 .0913945 19.14 0.000 2.036129 2.394782
_rcs2 | 1.038641 .0258414 1.52 0.128 .9892079 1.090544
_rcs3 | 1.018445 .0210248 0.89 0.376 .9780595 1.060498
_rcs4 | 1.009706 .0153383 0.64 0.525 .9800861 1.04022
_rcs5 | 1.018659 .0109114 1.73 0.084 .9974961 1.040271
_rcs6 | 1.026399 .0103178 2.59 0.010 1.006375 1.046822
_rcs7 | 1.023878 .0092591 2.61 0.009 1.00589 1.042187
_rcs8 | 1.011958 .006368 1.89 0.059 .9995542 1.024517
_rcs9 | 1.003189 .0067173 0.48 0.634 .990109 1.016441
_rcs10 | 1.002927 .002404 1.22 0.223 .9982262 1.00765
_rcs_tr_outcome1 | .9217066 .0399384 -1.88 0.060 .8466606 1.003405
_rcs_tr_outcome2 | 1.02126 .0276515 0.78 0.437 .9684768 1.07692
_rcs_tr_outcome3 | .9966191 .021283 -0.16 0.874 .9557662 1.039218
_rcs_tr_outcome4 | 1.001331 .017807 0.07 0.940 .9670309 1.036847
_rcs_tr_outcome5 | .9770218 .0126336 -1.80 0.072 .9525716 1.0021
_rcs_tr_outcome6 | .9882084 .0094758 -1.24 0.216 .9698096 1.006956
_rcs_tr_outcome7 | 1.002968 .0086113 0.35 0.730 .9862314 1.019989
_cons | .03864 .0021329 -58.94 0.000 .0346779 .0430548
------------------------------------------------------------------------------------
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. }
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
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 = -16977.946
Iteration 1: log pseudolikelihood = -16935.159
Iteration 2: log pseudolikelihood = -16934.756
Iteration 3: log pseudolikelihood = -16934.756
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 55,066
Wald chi2(1) = 40.66
Log pseudolikelihood = -16934.756 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.424311 .0789998 6.38 0.000 1.277593 1.587877
_cons | .0111139 .0005807 -86.12 0.000 .0100321 .0123123
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
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 = -16977.946
Iteration 1: log pseudolikelihood = -16771.522
Iteration 2: log pseudolikelihood = -16768.772
Iteration 3: log pseudolikelihood = -16768.771
Fitting full model:
Iteration 0: log pseudolikelihood = -16768.771
Iteration 1: log pseudolikelihood = -16723.962
Iteration 2: log pseudolikelihood = -16723.523
Iteration 3: log pseudolikelihood = -16723.523
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 55,066
Wald chi2(1) = 43.16
Log pseudolikelihood = -16723.523 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.435973 .0790909 6.57 0.000 1.289031 1.599664
_cons | .0169244 .0009239 -74.72 0.000 .015207 .0188357
-------------+----------------------------------------------------------------
/ln_p | -.302611 .0166248 -18.20 0.000 -.335195 -.2700271
-------------+----------------------------------------------------------------
p | .7388865 .0122838 .7151986 .7633588
1/p | 1.353388 .0224998 1.31 1.398213
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
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 = -16979.337
Iteration 1: log pseudolikelihood = -16760.887
Iteration 2: log pseudolikelihood = -16752.187
Iteration 3: log pseudolikelihood = -16752.175
Iteration 4: log pseudolikelihood = -16752.175
Fitting full model:
Iteration 0: log pseudolikelihood = -16752.175
Iteration 1: log pseudolikelihood = -16707.927
Iteration 2: log pseudolikelihood = -16707.498
Iteration 3: log pseudolikelihood = -16707.498
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 55,066
Wald chi2(1) = 42.77
Log pseudolikelihood = -16707.498 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.43275 .078778 6.54 0.000 1.286377 1.595779
_cons | .0178681 .0010015 -71.81 0.000 .0160092 .0199429
-------------+----------------------------------------------------------------
/gamma | -.18565 .0112163 -16.55 0.000 -.2076337 -.1636664
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
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 = -30603.161 (not concave)
Iteration 1: log pseudolikelihood = -20770.326
Iteration 2: log pseudolikelihood = -17606.003
Iteration 3: log pseudolikelihood = -16784.211
Iteration 4: log pseudolikelihood = -16746.036
Iteration 5: log pseudolikelihood = -16745.582
Iteration 6: log pseudolikelihood = -16745.581
Fitting full model:
Iteration 0: log pseudolikelihood = -16745.581
Iteration 1: log pseudolikelihood = -16697.195
Iteration 2: log pseudolikelihood = -16696.027
Iteration 3: log pseudolikelihood = -16696.024
Iteration 4: log pseudolikelihood = -16696.024
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 55,066
Wald chi2(1) = 48.88
Log pseudolikelihood = -16696.024 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5683051 .0459344 -6.99 0.000 .4850444 .665858
_cons | 698.6579 83.00426 55.13 0.000 553.5251 881.8441
-------------+----------------------------------------------------------------
/lnsigma | 1.124791 .0176662 63.67 0.000 1.090166 1.159416
-------------+----------------------------------------------------------------
sigma | 3.079573 .0544044 2.974767 3.188071
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -32787.589
Iteration 2: log likelihood = -32706.552
Iteration 3: log likelihood = -32705.896
Iteration 4: log likelihood = -32705.896
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -35654.71
Iteration 1: log likelihood = -35654.71
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 = -17008.906
Iteration 1: log pseudolikelihood = -16780.875
Iteration 2: log pseudolikelihood = -16764.032
Iteration 3: log pseudolikelihood = -16764.03
Iteration 4: log pseudolikelihood = -16764.03
Fitting full model:
Iteration 0: log pseudolikelihood = -16764.03
Iteration 1: log pseudolikelihood = -16719.283
Iteration 2: log pseudolikelihood = -16718.023
Iteration 3: log pseudolikelihood = -16718.019
Iteration 4: log pseudolikelihood = -16718.019
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 55,066
Wald chi2(1) = 43.34
Log pseudolikelihood = -16718.019 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .6066286 .0460604 -6.58 0.000 .522748 .7039687
_cons | 219.9336 21.46738 55.25 0.000 181.6382 266.303
-------------+----------------------------------------------------------------
/lngamma | .2800416 .0167568 16.71 0.000 .2471988 .3128844
-------------+----------------------------------------------------------------
gamma | 1.323185 .0221724 1.280434 1.367363
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
. *}
.
. qui count if _d == 1
. // we count the amount of cases with the event in the strata
. //we call the estimates stored, and the results...
. estimates stat m_stipw_nostag_*, n(`r(N)')
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | N ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
m_stipw_no~1 | 3,433 . -16719.33 4 33446.65 33471.22
m_stipw_no~2 | 3,433 . -16698.01 5 33406.01 33436.72
m_stipw_no~3 | 3,433 . -16696 6 33403.99 33440.84
m_stipw_no~4 | 3,433 . -16694.16 7 33402.32 33445.31
m_stipw_no~5 | 3,433 . -16692.58 8 33401.17 33450.3
m_stipw_no~6 | 3,433 . -16691.01 9 33400.01 33455.28
m_stipw_no~7 | 3,433 . -16690.39 10 33400.78 33462.19
m_stipw_no~1 | 3,433 . -16695.34 5 33400.67 33431.38
m_stipw_no~2 | 3,433 . -16694.96 6 33401.92 33438.77
m_stipw_no~3 | 3,433 . -16693.08 7 33400.16 33443.15
m_stipw_no~4 | 3,433 . -16691.11 8 33398.23 33447.36
m_stipw_no~5 | 3,433 . -16689.51 9 33397.01 33452.29
m_stipw_no~6 | 3,433 . -16687.96 10 33395.92 33457.33
m_stipw_no~7 | 3,433 . -16687.34 11 33396.68 33464.23
m_stipw_no~1 | 3,433 . -16691.8 6 33395.6 33432.45
m_stipw_no~2 | 3,433 . -16691.49 7 33396.98 33439.96
m_stipw_no~3 | 3,433 . -16691.22 8 33398.43 33447.56
m_stipw_no~4 | 3,433 . -16689.72 9 33397.45 33452.72
m_stipw_no~5 | 3,433 . -16687.76 10 33395.53 33456.94
m_stipw_no~6 | 3,433 . -16686.23 11 33394.45 33462.01
m_stipw_no~7 | 3,433 . -16685.58 12 33395.15 33468.85
m_stipw_no~1 | 3,433 . -16685.9 7 33385.81 33428.8
m_stipw_no~2 | 3,433 . -16685.54 8 33387.07 33436.2
m_stipw_no~3 | 3,433 . -16685.13 9 33388.25 33443.52
m_stipw_no~4 | 3,433 . -16682.83 10 33385.67 33447.08
m_stipw_no~5 | 3,433 . -16683.89 11 33389.77 33457.33
m_stipw_no~6 | 3,433 . -16680.32 12 33384.63 33458.33
m_stipw_no~7 | 3,433 . -16679.51 13 33385.02 33464.86
m_stipw_no~1 | 3,433 . -16682.13 8 33380.27 33429.4
m_stipw_no~2 | 3,433 . -16681.77 9 33381.54 33436.81
m_stipw_no~3 | 3,433 . -16681.66 10 33383.33 33444.74
m_stipw_no~4 | 3,433 . -16678.36 11 33378.72 33446.27
m_stipw_no~5 | 3,433 . -16678.25 12 33380.49 33454.19
m_stipw_no~6 | 3,433 . -16677.43 13 33380.86 33460.7
m_stipw_no~7 | 3,433 . -16676.97 14 33381.94 33467.92
m_stipw_no~1 | 3,433 . -16681.4 9 33380.8 33436.07
m_stipw_no~2 | 3,433 . -16681.05 10 33382.1 33443.51
m_stipw_no~3 | 3,433 . -16680.89 11 33383.78 33451.34
m_stipw_no~4 | 3,433 . -16678.04 12 33380.09 33453.78
m_stipw_no~5 | 3,433 . -16677.94 13 33381.88 33461.72
m_stipw_no~6 | 3,433 . -16676.32 14 33380.64 33466.62
m_stipw_no~7 | 3,433 . -16676.48 15 33382.95 33475.07
m_stipw_no~1 | 3,433 . -16680.8 10 33381.6 33443.01
m_stipw_no~2 | 3,433 . -16680.45 11 33382.9 33450.45
m_stipw_no~3 | 3,433 . -16680.29 12 33384.57 33458.27
m_stipw_no~4 | 3,433 . -16677.56 13 33381.12 33460.96
m_stipw_no~5 | 3,433 . -16676.77 14 33381.54 33467.51
m_stipw_no~6 | 3,433 . -16676.19 15 33382.39 33474.5
m_stipw_no~7 | 3,433 . -16675.81 16 33383.63 33481.88
m_stipw_no~1 | 3,433 . -16680.43 11 33382.86 33450.41
m_stipw_no~2 | 3,433 . -16680.08 12 33384.15 33457.85
m_stipw_no~3 | 3,433 . -16679.93 13 33385.86 33465.7
m_stipw_no~4 | 3,433 . -16677.41 14 33382.83 33468.8
m_stipw_no~5 | 3,433 . -16676.47 15 33382.94 33475.06
m_stipw_no~6 | 3,433 . -16675.24 16 33382.48 33480.74
m_stipw_no~7 | 3,433 . -16675.92 17 33385.83 33490.23
m_stipw_no~1 | 3,433 . -16680.29 12 33384.57 33458.27
m_stipw_no~2 | 3,433 . -16679.93 13 33385.87 33465.7
m_stipw_no~3 | 3,433 . -16679.77 14 33387.55 33473.53
m_stipw_no~4 | 3,433 . -16677.07 15 33384.15 33476.27
m_stipw_no~5 | 3,433 . -16676.56 16 33385.13 33483.39
m_stipw_no~6 | 3,433 . -16674.43 17 33382.86 33487.26
m_stipw_no~7 | 3,433 . -16675.1 18 33386.2 33496.74
m_stipw_no~1 | 3,433 . -16680.03 13 33386.05 33465.89
m_stipw_no~2 | 3,433 . -16679.68 14 33387.37 33473.34
m_stipw_no~3 | 3,433 . -16679.51 15 33389.03 33481.14
m_stipw_no~4 | 3,433 . -16676.9 16 33385.8 33484.06
m_stipw_no~5 | 3,433 . -16676.29 17 33386.58 33490.98
m_stipw_no~6 | 3,433 . -16675.19 18 33386.39 33496.93
m_stipw_no~7 | 3,433 . -16674.9 19 33387.81 33504.49
m_stipw_no~p | 3,433 -16977.95 -16934.76 2 33873.51 33885.79
m_stipw_no~i | 3,433 -16768.77 -16723.52 3 33453.05 33471.47
m_stipw_no~m | 3,433 -16752.17 -16707.5 3 33421 33439.42
m_stipw_no~n | 3,433 -16745.58 -16696.02 3 33398.05 33416.47
m_stipw_no~g | 3,433 -16764.03 -16718.02 3 33442.04 33460.46
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_2=r(S)
. mata : st_sort_matrix("stats_2", 5) // 5 AIC, 6 BIC
. esttab matrix(stats_2) using "testreg_aic_bic_mrl_23_2_pris_m1.csv", replace
(output written to testreg_aic_bic_mrl_23_2_pris_m1.csv)
. esttab matrix(stats_2) using "testreg_aic_bic_mrl_23_2_pris_m1.html", replace
(output written to testreg_aic_bic_mrl_23_2_pris_m1.html)
| stats_2 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m_stipw_nostag_rp5_tvcdf4 | 3433 | . | -16678.36 | 11 | 33378.72 | 33446.27 |
| m_stipw_nostag_rp6_tvcdf4 | 3433 | . | -16678.04 | 12 | 33380.09 | 33453.78 |
| m_stipw_nostag_rp5_tvcdf1 | 3433 | . | -16682.13 | 8 | 33380.27 | 33429.4 |
| m_stipw_nostag_rp5_tvcdf5 | 3433 | . | -16678.25 | 12 | 33380.49 | 33454.19 |
| m_stipw_nostag_rp6_tvcdf6 | 3433 | . | -16676.32 | 14 | 33380.64 | 33466.62 |
| m_stipw_nostag_rp6_tvcdf1 | 3433 | . | -16681.4 | 9 | 33380.8 | 33436.07 |
| m_stipw_nostag_rp5_tvcdf6 | 3433 | . | -16677.43 | 13 | 33380.86 | 33460.7 |
| m_stipw_nostag_rp7_tvcdf4 | 3433 | . | -16677.56 | 13 | 33381.12 | 33460.96 |
| m_stipw_nostag_rp5_tvcdf2 | 3433 | . | -16681.77 | 9 | 33381.54 | 33436.81 |
| m_stipw_nostag_rp7_tvcdf5 | 3433 | . | -16676.77 | 14 | 33381.54 | 33467.51 |
| m_stipw_nostag_rp7_tvcdf1 | 3433 | . | -16680.8 | 10 | 33381.6 | 33443.01 |
| m_stipw_nostag_rp6_tvcdf5 | 3433 | . | -16677.94 | 13 | 33381.88 | 33461.72 |
| m_stipw_nostag_rp5_tvcdf7 | 3433 | . | -16676.97 | 14 | 33381.94 | 33467.92 |
| m_stipw_nostag_rp6_tvcdf2 | 3433 | . | -16681.05 | 10 | 33382.1 | 33443.51 |
| m_stipw_nostag_rp7_tvcdf6 | 3433 | . | -16676.19 | 15 | 33382.39 | 33474.5 |
| m_stipw_nostag_rp8_tvcdf6 | 3433 | . | -16675.24 | 16 | 33382.48 | 33480.74 |
| m_stipw_nostag_rp8_tvcdf4 | 3433 | . | -16677.41 | 14 | 33382.83 | 33468.8 |
| m_stipw_nostag_rp8_tvcdf1 | 3433 | . | -16680.43 | 11 | 33382.86 | 33450.41 |
| m_stipw_nostag_rp9_tvcdf6 | 3433 | . | -16674.43 | 17 | 33382.86 | 33487.26 |
| m_stipw_nostag_rp7_tvcdf2 | 3433 | . | -16680.45 | 11 | 33382.9 | 33450.45 |
| m_stipw_nostag_rp8_tvcdf5 | 3433 | . | -16676.47 | 15 | 33382.94 | 33475.06 |
| m_stipw_nostag_rp6_tvcdf7 | 3433 | . | -16676.48 | 15 | 33382.95 | 33475.07 |
| m_stipw_nostag_rp5_tvcdf3 | 3433 | . | -16681.66 | 10 | 33383.33 | 33444.74 |
| m_stipw_nostag_rp7_tvcdf7 | 3433 | . | -16675.81 | 16 | 33383.63 | 33481.88 |
| m_stipw_nostag_rp6_tvcdf3 | 3433 | . | -16680.89 | 11 | 33383.78 | 33451.34 |
| m_stipw_nostag_rp9_tvcdf4 | 3433 | . | -16677.07 | 15 | 33384.15 | 33476.27 |
| m_stipw_nostag_rp8_tvcdf2 | 3433 | . | -16680.08 | 12 | 33384.15 | 33457.85 |
| m_stipw_nostag_rp9_tvcdf1 | 3433 | . | -16680.29 | 12 | 33384.57 | 33458.27 |
| m_stipw_nostag_rp7_tvcdf3 | 3433 | . | -16680.29 | 12 | 33384.57 | 33458.27 |
| m_stipw_nostag_rp4_tvcdf6 | 3433 | . | -16680.32 | 12 | 33384.63 | 33458.33 |
| m_stipw_nostag_rp4_tvcdf7 | 3433 | . | -16679.51 | 13 | 33385.02 | 33464.86 |
| m_stipw_nostag_rp9_tvcdf5 | 3433 | . | -16676.56 | 16 | 33385.13 | 33483.39 |
| m_stipw_nostag_rp4_tvcdf4 | 3433 | . | -16682.83 | 10 | 33385.67 | 33447.08 |
| m_stipw_nostag_rp10_tvcdf4 | 3433 | . | -16676.9 | 16 | 33385.8 | 33484.06 |
| m_stipw_nostag_rp4_tvcdf1 | 3433 | . | -16685.9 | 7 | 33385.81 | 33428.8 |
| m_stipw_nostag_rp8_tvcdf7 | 3433 | . | -16675.92 | 17 | 33385.83 | 33490.23 |
| m_stipw_nostag_rp8_tvcdf3 | 3433 | . | -16679.93 | 13 | 33385.86 | 33465.7 |
| m_stipw_nostag_rp9_tvcdf2 | 3433 | . | -16679.93 | 13 | 33385.87 | 33465.7 |
| m_stipw_nostag_rp10_tvcdf1 | 3433 | . | -16680.03 | 13 | 33386.05 | 33465.89 |
| m_stipw_nostag_rp9_tvcdf7 | 3433 | . | -16675.1 | 18 | 33386.2 | 33496.74 |
| m_stipw_nostag_rp10_tvcdf6 | 3433 | . | -16675.19 | 18 | 33386.39 | 33496.93 |
| m_stipw_nostag_rp10_tvcdf5 | 3433 | . | -16676.29 | 17 | 33386.58 | 33490.98 |
| m_stipw_nostag_rp4_tvcdf2 | 3433 | . | -16685.54 | 8 | 33387.07 | 33436.2 |
| m_stipw_nostag_rp10_tvcdf2 | 3433 | . | -16679.68 | 14 | 33387.37 | 33473.34 |
| m_stipw_nostag_rp9_tvcdf3 | 3433 | . | -16679.77 | 14 | 33387.55 | 33473.53 |
| m_stipw_nostag_rp10_tvcdf7 | 3433 | . | -16674.9 | 19 | 33387.81 | 33504.49 |
| m_stipw_nostag_rp4_tvcdf3 | 3433 | . | -16685.13 | 9 | 33388.25 | 33443.52 |
| m_stipw_nostag_rp10_tvcdf3 | 3433 | . | -16679.51 | 15 | 33389.03 | 33481.14 |
| m_stipw_nostag_rp4_tvcdf5 | 3433 | . | -16683.89 | 11 | 33389.77 | 33457.33 |
| m_stipw_nostag_rp3_tvcdf6 | 3433 | . | -16686.23 | 11 | 33394.45 | 33462.01 |
| m_stipw_nostag_rp3_tvcdf7 | 3433 | . | -16685.58 | 12 | 33395.15 | 33468.85 |
| m_stipw_nostag_rp3_tvcdf5 | 3433 | . | -16687.76 | 10 | 33395.53 | 33456.94 |
| m_stipw_nostag_rp3_tvcdf1 | 3433 | . | -16691.8 | 6 | 33395.6 | 33432.45 |
| m_stipw_nostag_rp2_tvcdf6 | 3433 | . | -16687.96 | 10 | 33395.92 | 33457.33 |
| m_stipw_nostag_rp2_tvcdf7 | 3433 | . | -16687.34 | 11 | 33396.68 | 33464.23 |
| m_stipw_nostag_rp3_tvcdf2 | 3433 | . | -16691.49 | 7 | 33396.98 | 33439.96 |
| m_stipw_nostag_rp2_tvcdf5 | 3433 | . | -16689.51 | 9 | 33397.01 | 33452.29 |
| m_stipw_nostag_rp3_tvcdf4 | 3433 | . | -16689.72 | 9 | 33397.45 | 33452.72 |
| m_stipw_nostag_logn | 3433 | -16745.58 | -16696.02 | 3 | 33398.05 | 33416.47 |
| m_stipw_nostag_rp2_tvcdf4 | 3433 | . | -16691.11 | 8 | 33398.23 | 33447.36 |
| m_stipw_nostag_rp3_tvcdf3 | 3433 | . | -16691.22 | 8 | 33398.43 | 33447.56 |
| m_stipw_nostag_rp1_tvcdf6 | 3433 | . | -16691.01 | 9 | 33400.01 | 33455.28 |
| m_stipw_nostag_rp2_tvcdf3 | 3433 | . | -16693.08 | 7 | 33400.16 | 33443.15 |
| m_stipw_nostag_rp2_tvcdf1 | 3433 | . | -16695.34 | 5 | 33400.67 | 33431.38 |
| m_stipw_nostag_rp1_tvcdf7 | 3433 | . | -16690.39 | 10 | 33400.78 | 33462.19 |
| m_stipw_nostag_rp1_tvcdf5 | 3433 | . | -16692.58 | 8 | 33401.17 | 33450.3 |
| m_stipw_nostag_rp2_tvcdf2 | 3433 | . | -16694.96 | 6 | 33401.92 | 33438.77 |
| m_stipw_nostag_rp1_tvcdf4 | 3433 | . | -16694.16 | 7 | 33402.32 | 33445.31 |
| m_stipw_nostag_rp1_tvcdf3 | 3433 | . | -16696 | 6 | 33403.99 | 33440.84 |
| m_stipw_nostag_rp1_tvcdf2 | 3433 | . | -16698.01 | 5 | 33406.01 | 33436.72 |
| m_stipw_nostag_gom | 3433 | -16752.17 | -16707.5 | 3 | 33421 | 33439.42 |
| m_stipw_nostag_llog | 3433 | -16764.03 | -16718.02 | 3 | 33442.04 | 33460.46 |
| m_stipw_nostag_rp1_tvcdf1 | 3433 | . | -16719.33 | 4 | 33446.65 | 33471.22 |
| m_stipw_nostag_wei | 3433 | -16768.77 | -16723.52 | 3 | 33453.05 | 33471.47 |
| m_stipw_nostag_exp | 3433 | -16977.95 | -16934.76 | 2 | 33873.51 | 33885.79 |
. estimates replay m_stipw_nostag_rp5_tvcdf1, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m_stipw_nostag_rp5_tvcdf1
------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -16682.135 Number of obs = 55,066
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.484681 .0869404 6.75 0.000 1.323696 1.665245
_rcs1 | 2.207979 .0886593 19.73 0.000 2.040872 2.388769
_rcs2 | 1.056976 .0099806 5.87 0.000 1.037594 1.07672
_rcs3 | 1.01759 .0072571 2.45 0.014 1.003465 1.031913
_rcs4 | 1.017505 .005605 3.15 0.002 1.006579 1.02855
_rcs5 | 1.01451 .0041432 3.53 0.000 1.006422 1.022663
_rcs_tr_outcome1 | .9220181 .0384098 -1.95 0.051 .8497276 1.000459
_cons | .0386872 .0021384 -58.84 0.000 .0347151 .0431138
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m_stipw_nostag_rp5_tvcdf1
(results m_stipw_nostag_rp5_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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_a_pris_m1.gph saved)
.
.
. twoway (rarea rmst_comp_a_lci rmst_comp_a_uci tt, color(gs7%35)) ///
> (rarea rmst_late_a_lci rmst_late_a_uci tt, color(gs2%35)) ///
> (line rmst_comp_a tt, lcolor(gs7) lwidth(thick)) ///
> (line rmst_late_a tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Restricted Mean Survival Times (standardized)") ///
> legend(order(1 "Tr. completion" 2 "Late dropout") ring(0) pos(5) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(rmst_std_fin_a, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdiff_rmst_a_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdiff_rmst_a_pris_m1.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
(35,789 observations deleted)
.
. recode motivodeegreso_mod_imp_rec (1=0 "Tr. Completion") (2/3=1 "Early dropout"), gen(tr_outcome)
(35074 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. }
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11955.599
Iteration 1: log pseudolikelihood = -11930.555
Iteration 2: log pseudolikelihood = -11930.423
Iteration 3: log pseudolikelihood = -11930.423
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11930.423 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.460065 .2190035 2.52 0.012 1.088167 1.959064
_rcs1 | 2.052663 .1862902 7.92 0.000 1.718172 2.452273
_rcs_tr_outcome1 | .978702 .0903099 -0.23 0.816 .8167811 1.172722
_cons | .0455238 .0067016 -20.99 0.000 .0341139 .0607499
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11924.027
Iteration 1: log pseudolikelihood = -11917.512
Iteration 2: log pseudolikelihood = -11917.499
Iteration 3: log pseudolikelihood = -11917.499
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11917.499 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.476279 .221484 2.60 0.009 1.10018 1.980947
_rcs1 | 2.052663 .1862902 7.92 0.000 1.718172 2.452273
_rcs_tr_outcome1 | .9902442 .091814 -0.11 0.916 .8256959 1.187584
_rcs_tr_outcome2 | 1.069628 .0159935 4.50 0.000 1.038737 1.101439
_cons | .0455238 .0067016 -20.99 0.000 .0341139 .0607499
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11925.967
Iteration 1: log pseudolikelihood = -11917.028
Iteration 2: log pseudolikelihood = -11917
Iteration 3: log pseudolikelihood = -11917
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11917 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.476169 .2214732 2.60 0.009 1.10009 1.980815
_rcs1 | 2.052663 .1862902 7.92 0.000 1.718172 2.452273
_rcs_tr_outcome1 | .9918004 .0919613 -0.09 0.929 .8269886 1.189458
_rcs_tr_outcome2 | 1.066029 .0154073 4.42 0.000 1.036255 1.096659
_rcs_tr_outcome3 | 1.014936 .0114471 1.31 0.189 .9927462 1.037622
_cons | .0455238 .0067016 -20.99 0.000 .0341139 .0607499
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11932.041
Iteration 1: log pseudolikelihood = -11917.085
Iteration 2: log pseudolikelihood = -11916.852
Iteration 3: log pseudolikelihood = -11916.852
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11916.852 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.476206 .2214798 2.60 0.009 1.100116 1.980867
_rcs1 | 2.052663 .1862902 7.92 0.000 1.718172 2.452273
_rcs_tr_outcome1 | .9919887 .0919808 -0.09 0.931 .8271423 1.189688
_rcs_tr_outcome2 | 1.065299 .0156618 4.30 0.000 1.03504 1.096442
_rcs_tr_outcome3 | 1.017124 .0117897 1.46 0.143 .994277 1.040496
_rcs_tr_outcome4 | 1.005514 .0080952 0.68 0.495 .9897725 1.021506
_cons | .0455238 .0067016 -20.99 0.000 .0341139 .0607499
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11932.45
Iteration 1: log pseudolikelihood = -11916.161
Iteration 2: log pseudolikelihood = -11915.936
Iteration 3: log pseudolikelihood = -11915.935
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11915.935 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.47601 .2214523 2.60 0.009 1.099968 1.98061
_rcs1 | 2.052663 .1862902 7.92 0.000 1.718172 2.452273
_rcs_tr_outcome1 | .9926887 .0920474 -0.08 0.937 .8277233 1.190532
_rcs_tr_outcome2 | 1.063693 .0151334 4.34 0.000 1.034442 1.093772
_rcs_tr_outcome3 | 1.02039 .0115378 1.79 0.074 .9980254 1.043257
_rcs_tr_outcome4 | 1.005778 .0082393 0.70 0.482 .9897581 1.022057
_rcs_tr_outcome5 | 1.008179 .0064456 1.27 0.203 .9956247 1.020892
_cons | .0455238 .0067016 -20.99 0.000 .0341139 .0607499
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11940.095
Iteration 1: log pseudolikelihood = -11914.963
Iteration 2: log pseudolikelihood = -11914.075
Iteration 3: log pseudolikelihood = -11914.072
Iteration 4: log pseudolikelihood = -11914.072
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11914.072 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.475843 .2214261 2.59 0.009 1.099845 1.980382
_rcs1 | 2.052663 .1862902 7.92 0.000 1.718172 2.452273
_rcs_tr_outcome1 | .9930457 .0920818 -0.08 0.940 .8280188 1.190963
_rcs_tr_outcome2 | 1.062794 .0148581 4.36 0.000 1.034068 1.092318
_rcs_tr_outcome3 | 1.022466 .011398 1.99 0.046 1.000368 1.045051
_rcs_tr_outcome4 | 1.004738 .008319 0.57 0.568 .9885646 1.021176
_rcs_tr_outcome5 | 1.007216 .00644 1.12 0.261 .9946722 1.019917
_rcs_tr_outcome6 | 1.010561 .005437 1.95 0.051 .9999607 1.021274
_cons | .0455238 .0067016 -20.99 0.000 .0341139 .0607499
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11930.324
Iteration 1: log pseudolikelihood = -11912.553
Iteration 2: log pseudolikelihood = -11912.119
Iteration 3: log pseudolikelihood = -11912.118
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11912.118 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.475827 .2214224 2.59 0.009 1.099834 1.980357
_rcs1 | 2.052663 .1862902 7.92 0.000 1.718172 2.452273
_rcs_tr_outcome1 | .9930287 .0920719 -0.08 0.940 .8280181 1.190923
_rcs_tr_outcome2 | 1.061516 .0140055 4.52 0.000 1.034418 1.089324
_rcs_tr_outcome3 | 1.025374 .0108372 2.37 0.018 1.004352 1.046836
_rcs_tr_outcome4 | 1.000817 .0084996 0.10 0.923 .9842959 1.017615
_rcs_tr_outcome5 | 1.009847 .0063405 1.56 0.119 .9974965 1.022351
_rcs_tr_outcome6 | 1.006413 .0055147 1.17 0.243 .9956626 1.01728
_rcs_tr_outcome7 | 1.010545 .0046103 2.30 0.021 1.001549 1.019621
_cons | .0455238 .0067016 -20.99 0.000 .0341139 .0607499
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11913.385
Iteration 1: log pseudolikelihood = -11913.211
Iteration 2: log pseudolikelihood = -11913.211
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11913.211 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.464143 .2221816 2.51 0.012 1.087464 1.971297
_rcs1 | 2.068621 .2072984 7.25 0.000 1.699735 2.517565
_rcs2 | 1.057901 .0233171 2.55 0.011 1.013174 1.104603
_rcs_tr_outcome1 | .9793893 .1013669 -0.20 0.841 .7995689 1.199651
_cons | .0458551 .0068352 -20.68 0.000 .0342378 .0614142
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11913.604
Iteration 1: log pseudolikelihood = -11912.526
Iteration 2: log pseudolikelihood = -11912.525
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11912.525 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466937 .2229844 2.52 0.012 1.088988 1.976059
_rcs1 | 2.062398 .1983991 7.52 0.000 1.708002 2.490328
_rcs2 | 1.044894 .045372 1.01 0.312 .9596457 1.137715
_rcs_tr_outcome1 | .9855699 .0966386 -0.15 0.882 .8132502 1.194403
_rcs_tr_outcome2 | 1.023671 .0470043 0.51 0.610 .9355686 1.120071
_cons | .0458137 .0068369 -20.66 0.000 .0341955 .0613795
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11915.816
Iteration 1: log pseudolikelihood = -11912.053
Iteration 2: log pseudolikelihood = -11912.035
Iteration 3: log pseudolikelihood = -11912.035
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11912.035 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466823 .2229758 2.52 0.012 1.08889 1.975928
_rcs1 | 2.062374 .1983804 7.53 0.000 1.708009 2.490261
_rcs2 | 1.044836 .0453374 1.01 0.312 .9596504 1.137584
_rcs_tr_outcome1 | .9871286 .0967862 -0.13 0.895 .8145447 1.196279
_rcs_tr_outcome2 | 1.020378 .0465605 0.44 0.658 .9330826 1.11584
_rcs_tr_outcome3 | 1.01165 .0118639 0.99 0.323 .9886628 1.035172
_cons | .0458135 .0068369 -20.66 0.000 .0341952 .0613792
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11921.619
Iteration 1: log pseudolikelihood = -11912.099
Iteration 2: log pseudolikelihood = -11911.878
Iteration 3: log pseudolikelihood = -11911.878
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11911.878 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466864 .2229802 2.52 0.012 1.088924 1.975979
_rcs1 | 2.062399 .1983995 7.52 0.000 1.708002 2.49033
_rcs2 | 1.044894 .045372 1.01 0.312 .9596456 1.137715
_rcs_tr_outcome1 | .9873059 .0968134 -0.13 0.896 .8146752 1.196517
_rcs_tr_outcome2 | 1.019881 .04642 0.43 0.665 .9328396 1.115044
_rcs_tr_outcome3 | 1.011542 .0129499 0.90 0.370 .9864768 1.037245
_rcs_tr_outcome4 | 1.005514 .0080952 0.68 0.495 .9897725 1.021506
_cons | .0458137 .0068369 -20.66 0.000 .0341954 .0613794
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11922.007
Iteration 1: log pseudolikelihood = -11911.145
Iteration 2: log pseudolikelihood = -11910.931
Iteration 3: log pseudolikelihood = -11910.931
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11910.931 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466662 .2229465 2.52 0.012 1.088778 1.975699
_rcs1 | 2.062462 .1984593 7.52 0.000 1.707967 2.490534
_rcs2 | 1.045042 .0453911 1.01 0.310 .9597585 1.137903
_rcs_tr_outcome1 | .9879589 .0968997 -0.12 0.902 .8151779 1.197362
_rcs_tr_outcome2 | 1.018386 .0460388 0.40 0.687 .9320334 1.112738
_rcs_tr_outcome3 | 1.013526 .0132958 1.02 0.306 .9877993 1.039924
_rcs_tr_outcome4 | 1.005126 .0082577 0.62 0.534 .989071 1.021442
_rcs_tr_outcome5 | 1.008267 .0064476 1.29 0.198 .9957089 1.020983
_cons | .0458143 .0068369 -20.66 0.000 .034196 .0613799
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11929.673
Iteration 1: log pseudolikelihood = -11909.976
Iteration 2: log pseudolikelihood = -11909.101
Iteration 3: log pseudolikelihood = -11909.098
Iteration 4: log pseudolikelihood = -11909.098
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11909.098 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466504 .2229261 2.52 0.012 1.088656 1.975496
_rcs1 | 2.062399 .1983995 7.52 0.000 1.708002 2.49033
_rcs2 | 1.044894 .045372 1.01 0.312 .9596456 1.137715
_rcs_tr_outcome1 | .9883579 .0969192 -0.12 0.905 .8155389 1.197799
_rcs_tr_outcome2 | 1.017788 .0458052 0.39 0.695 .9318574 1.111643
_rcs_tr_outcome3 | 1.014942 .0135225 1.11 0.266 .9887815 1.041795
_rcs_tr_outcome4 | 1.003381 .0084152 0.40 0.687 .987022 1.020011
_rcs_tr_outcome5 | 1.007216 .00644 1.12 0.261 .9946722 1.019917
_rcs_tr_outcome6 | 1.010561 .005437 1.95 0.051 .9999607 1.021274
_cons | .0458137 .0068369 -20.66 0.000 .0341954 .0613794
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11919.886
Iteration 1: log pseudolikelihood = -11907.555
Iteration 2: log pseudolikelihood = -11907.133
Iteration 3: log pseudolikelihood = -11907.132
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11907.132 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466485 .22292 2.52 0.012 1.088646 1.975461
_rcs1 | 2.062425 .1984238 7.52 0.000 1.707988 2.490413
_rcs2 | 1.044954 .0453802 1.01 0.311 .9596911 1.137793
_rcs_tr_outcome1 | .988323 .0969166 -0.12 0.905 .8155088 1.197758
_rcs_tr_outcome2 | 1.016607 .0454203 0.37 0.712 .9313711 1.109643
_rcs_tr_outcome3 | 1.01735 .0133351 1.31 0.189 .9915463 1.043825
_rcs_tr_outcome4 | .999031 .0086656 -0.11 0.911 .9821903 1.016161
_rcs_tr_outcome5 | 1.009665 .0063415 1.53 0.126 .9973116 1.022171
_rcs_tr_outcome6 | 1.006438 .0055152 1.17 0.242 .9956867 1.017306
_rcs_tr_outcome7 | 1.010537 .0046103 2.30 0.022 1.001541 1.019614
_cons | .045814 .0068369 -20.66 0.000 .0341957 .0613796
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11912.119
Iteration 1: log pseudolikelihood = -11910.578
Iteration 2: log pseudolikelihood = -11910.576
Iteration 3: log pseudolikelihood = -11910.576
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11910.576 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.461589 .2243505 2.47 0.013 1.081852 1.974617
_rcs1 | 2.060831 .2121194 7.03 0.000 1.684337 2.521481
_rcs2 | 1.063564 .024411 2.68 0.007 1.01678 1.112502
_rcs3 | .9883161 .0240396 -0.48 0.629 .9423048 1.036574
_rcs_tr_outcome1 | .9823005 .1038261 -0.17 0.866 .7985002 1.208408
_cons | .0458974 .0068677 -20.59 0.000 .034231 .0615397
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11912.302
Iteration 1: log pseudolikelihood = -11909.871
Iteration 2: log pseudolikelihood = -11909.864
Iteration 3: log pseudolikelihood = -11909.864
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11909.864 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.464633 .2245945 2.49 0.013 1.084429 1.978137
_rcs1 | 2.054092 .2045567 7.23 0.000 1.689869 2.496817
_rcs2 | 1.04998 .0475291 1.08 0.281 .9608377 1.147393
_rcs3 | .9868944 .0253224 -0.51 0.607 .9384907 1.037795
_rcs_tr_outcome1 | .9888816 .1006598 -0.11 0.913 .8100264 1.207228
_rcs_tr_outcome2 | 1.024986 .0515179 0.49 0.623 .9288268 1.1311
_cons | .0458521 .0068604 -20.60 0.000 .0341981 .0614776
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11911.19
Iteration 1: log pseudolikelihood = -11901.903
Iteration 2: log pseudolikelihood = -11901.833
Iteration 3: log pseudolikelihood = -11901.833
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11901.833 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468596 .2209456 2.55 0.011 1.093558 1.972254
_rcs1 | 2.049086 .1990245 7.39 0.000 1.693887 2.478769
_rcs2 | 1.059036 .053667 1.13 0.258 .9589059 1.169623
_rcs3 | .9519068 .048078 -0.98 0.329 .8621895 1.05096
_rcs_tr_outcome1 | .9935318 .09833 -0.07 0.948 .8183485 1.206217
_rcs_tr_outcome2 | 1.006603 .0530419 0.12 0.901 .9078309 1.116121
_rcs_tr_outcome3 | 1.066214 .0551815 1.24 0.215 .9633645 1.180043
_cons | .0457586 .0067556 -20.89 0.000 .0342613 .061114
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11915.663
Iteration 1: log pseudolikelihood = -11901.724
Iteration 2: log pseudolikelihood = -11901.458
Iteration 3: log pseudolikelihood = -11901.457
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11901.457 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468682 .2210167 2.55 0.011 1.093537 1.972523
_rcs1 | 2.049074 .1989415 7.39 0.000 1.694009 2.478561
_rcs2 | 1.058998 .0537211 1.13 0.258 .9587722 1.169702
_rcs3 | .9515793 .0474795 -0.99 0.320 .8629267 1.04934
_rcs_tr_outcome1 | .993889 .0984187 -0.06 0.951 .8185565 1.206777
_rcs_tr_outcome2 | 1.00344 .0526474 0.07 0.948 .9053813 1.112119
_rcs_tr_outcome3 | 1.065198 .0540135 1.25 0.213 .9644243 1.176502
_rcs_tr_outcome4 | 1.017935 .0146974 1.23 0.218 .9895324 1.047153
_cons | .0457561 .006756 -20.89 0.000 .0342584 .0611128
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11917.145
Iteration 1: log pseudolikelihood = -11900.825
Iteration 2: log pseudolikelihood = -11900.559
Iteration 3: log pseudolikelihood = -11900.559
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11900.559 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468608 .2209207 2.55 0.011 1.093606 1.9722
_rcs1 | 2.049128 .198907 7.39 0.000 1.694119 2.478532
_rcs2 | 1.059121 .0537703 1.13 0.258 .9588072 1.169931
_rcs3 | .9513922 .0478965 -0.99 0.322 .8619996 1.050055
_rcs_tr_outcome1 | .994308 .0983271 -0.06 0.954 .8191163 1.206969
_rcs_tr_outcome2 | 1.000953 .0523092 0.02 0.985 .9035044 1.108911
_rcs_tr_outcome3 | 1.063278 .0511091 1.28 0.202 .9676798 1.16832
_rcs_tr_outcome4 | 1.026273 .0222284 1.20 0.231 .9836179 1.070778
_rcs_tr_outcome5 | 1.009204 .0065403 1.41 0.157 .9964667 1.022105
_cons | .0457553 .0067547 -20.89 0.000 .0342596 .0611084
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11925.318
Iteration 1: log pseudolikelihood = -11899.838
Iteration 2: log pseudolikelihood = -11898.908
Iteration 3: log pseudolikelihood = -11898.905
Iteration 4: log pseudolikelihood = -11898.905
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11898.905 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468271 .2208986 2.55 0.011 1.093313 1.971823
_rcs1 | 2.049086 .1990245 7.39 0.000 1.693887 2.478769
_rcs2 | 1.059036 .053667 1.13 0.258 .9589059 1.169623
_rcs3 | .9519068 .048078 -0.98 0.329 .8621895 1.05096
_rcs_tr_outcome1 | .9947793 .0984579 -0.05 0.958 .8193687 1.207742
_rcs_tr_outcome2 | .9996929 .051999 -0.01 0.995 .9027997 1.106985
_rcs_tr_outcome3 | 1.0608 .0484208 1.29 0.196 .970018 1.160077
_rcs_tr_outcome4 | 1.029337 .026904 1.11 0.269 .9779343 1.083442
_rcs_tr_outcome5 | 1.013042 .0088175 1.49 0.137 .9959069 1.030473
_rcs_tr_outcome6 | 1.010561 .005437 1.95 0.051 .9999607 1.021274
_cons | .0457586 .0067556 -20.89 0.000 .0342613 .061114
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11915.654
Iteration 1: log pseudolikelihood = -11897.602
Iteration 2: log pseudolikelihood = -11897.127
Iteration 3: log pseudolikelihood = -11897.126
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11897.126 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468132 .2209122 2.55 0.011 1.093159 1.971727
_rcs1 | 2.049008 .1990854 7.38 0.000 1.693711 2.478837
_rcs2 | 1.058855 .0535595 1.13 0.258 .9589159 1.169209
_rcs3 | .9523363 .0481514 -0.97 0.334 .8624868 1.051546
_rcs_tr_outcome1 | .9948763 .098518 -0.05 0.959 .8193671 1.20798
_rcs_tr_outcome2 | .9982917 .051556 -0.03 0.974 .9021896 1.104631
_rcs_tr_outcome3 | 1.060816 .0466726 1.34 0.180 .973173 1.156353
_rcs_tr_outcome4 | 1.02659 .028719 0.94 0.348 .9718172 1.08445
_rcs_tr_outcome5 | 1.019151 .0115611 1.67 0.094 .9967415 1.042064
_rcs_tr_outcome6 | 1.008008 .0057695 1.39 0.163 .9967633 1.01938
_rcs_tr_outcome7 | 1.010492 .0046098 2.29 0.022 1.001498 1.019568
_cons | .0457608 .0067566 -20.89 0.000 .0342621 .0611186
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11922.381
Iteration 1: log pseudolikelihood = -11904.397
Iteration 2: log pseudolikelihood = -11904.19
Iteration 3: log pseudolikelihood = -11904.19
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11904.19 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.459249 .2246906 2.45 0.014 1.079105 1.973307
_rcs1 | 2.062567 .212032 7.04 0.000 1.686182 2.522967
_rcs2 | 1.070516 .0276285 2.64 0.008 1.017711 1.126059
_rcs3 | .9809722 .0257192 -0.73 0.464 .9318367 1.032699
_rcs4 | 1.016138 .0173345 0.94 0.348 .9827242 1.050687
_rcs_tr_outcome1 | .985015 .106174 -0.14 0.889 .7974298 1.216727
_cons | .0459427 .0068824 -20.56 0.000 .0342534 .0616211
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11922.498
Iteration 1: log pseudolikelihood = -11903.473
Iteration 2: log pseudolikelihood = -11903.236
Iteration 3: log pseudolikelihood = -11903.236
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11903.236 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.463002 .2245793 2.48 0.013 1.082881 1.976557
_rcs1 | 2.055206 .2043015 7.25 0.000 1.691376 2.497299
_rcs2 | 1.054925 .0512851 1.10 0.271 .9590485 1.160387
_rcs3 | .9783982 .0284927 -0.75 0.453 .9241174 1.035867
_rcs4 | 1.016224 .0172061 0.95 0.342 .9830542 1.050513
_rcs_tr_outcome1 | .9923742 .1036713 -0.07 0.942 .8086343 1.217864
_rcs_tr_outcome2 | 1.029248 .057333 0.52 0.605 .9227943 1.147982
_cons | .0458873 .0068676 -20.59 0.000 .0342216 .0615297
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11923.835
Iteration 1: log pseudolikelihood = -11897.029
Iteration 2: log pseudolikelihood = -11896.201
Iteration 3: log pseudolikelihood = -11896.2
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11896.2 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.46429 .2220426 2.52 0.012 1.087808 1.97107
_rcs1 | 2.052339 .2000299 7.38 0.000 1.695459 2.484339
_rcs2 | 1.071167 .058806 1.25 0.210 .961894 1.192855
_rcs3 | .9476867 .0480146 -1.06 0.289 .8581014 1.046625
_rcs4 | 1.00786 .0205517 0.38 0.701 .9683735 1.048956
_rcs_tr_outcome1 | .9951794 .1008629 -0.05 0.962 .8158886 1.213869
_rcs_tr_outcome2 | 1.001943 .057495 0.03 0.973 .8953611 1.121213
_rcs_tr_outcome3 | 1.062055 .0551458 1.16 0.246 .9592894 1.17583
_cons | .0458391 .0067929 -20.80 0.000 .0342844 .0612881
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11921.42
Iteration 1: log pseudolikelihood = -11890.649
Iteration 2: log pseudolikelihood = -11889.094
Iteration 3: log pseudolikelihood = -11889.088
Iteration 4: log pseudolikelihood = -11889.088
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11889.088 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468529 .2212821 2.55 0.011 1.093002 1.973076
_rcs1 | 2.070291 .1988161 7.58 0.000 1.715094 2.499049
_rcs2 | 1.084359 .0674697 1.30 0.193 .9598659 1.224998
_rcs3 | .9351937 .0460569 -1.36 0.174 .8491437 1.029964
_rcs4 | 1.028544 .03668 0.79 0.430 .9591075 1.103008
_rcs_tr_outcome1 | .9835424 .0962838 -0.17 0.865 .8118295 1.191575
_rcs_tr_outcome2 | .9824227 .0628109 -0.28 0.781 .8667164 1.113576
_rcs_tr_outcome3 | 1.087608 .0550263 1.66 0.097 .984933 1.200986
_rcs_tr_outcome4 | .9776093 .0357413 -0.62 0.536 .9100085 1.050232
_cons | .0457618 .0067671 -20.86 0.000 .0342476 .0611471
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11918.492
Iteration 1: log pseudolikelihood = -11890.723
Iteration 2: log pseudolikelihood = -11889.579
Iteration 3: log pseudolikelihood = -11889.576
Iteration 4: log pseudolikelihood = -11889.576
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11889.576 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467023 .2210613 2.54 0.011 1.091873 1.97107
_rcs1 | 2.066151 .1980044 7.57 0.000 1.712337 2.493072
_rcs2 | 1.082821 .0662983 1.30 0.194 .9603727 1.220881
_rcs3 | .9361282 .0463783 -1.33 0.183 .8495022 1.031588
_rcs4 | 1.024311 .0349527 0.70 0.481 .9580457 1.09516
_rcs_tr_outcome1 | .9870136 .0965966 -0.13 0.894 .8147384 1.195716
_rcs_tr_outcome2 | .9797448 .0616297 -0.33 0.745 .8661021 1.108299
_rcs_tr_outcome3 | 1.091374 .0544523 1.75 0.080 .9897017 1.203491
_rcs_tr_outcome4 | .9935121 .0344645 -0.19 0.851 .9282081 1.063411
_rcs_tr_outcome5 | .99787 .0136667 -0.16 0.876 .97144 1.025019
_cons | .0457826 .0067671 -20.86 0.000 .0342677 .0611667
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11928.412
Iteration 1: log pseudolikelihood = -11887.505
Iteration 2: log pseudolikelihood = -11885.216
Iteration 3: log pseudolikelihood = -11885.209
Iteration 4: log pseudolikelihood = -11885.209
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11885.209 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468726 .2213053 2.55 0.011 1.093158 1.973324
_rcs1 | 2.071757 .1992297 7.57 0.000 1.715866 2.501464
_rcs2 | 1.085661 .0681567 1.31 0.190 .9599674 1.227811
_rcs3 | .9338644 .0456737 -1.40 0.162 .8485024 1.027814
_rcs4 | 1.029322 .0365847 0.81 0.416 .9600579 1.103583
_rcs_tr_outcome1 | .9834508 .0963729 -0.17 0.865 .8115951 1.191697
_rcs_tr_outcome2 | .9753362 .0629056 -0.39 0.699 .8595181 1.106761
_rcs_tr_outcome3 | 1.094149 .0519696 1.89 0.058 .996888 1.200899
_rcs_tr_outcome4 | 1.002801 .0324911 0.09 0.931 .9410991 1.068548
_rcs_tr_outcome5 | .9882626 .0232333 -0.50 0.616 .9437593 1.034864
_rcs_tr_outcome6 | 1.008926 .0058869 1.52 0.128 .9974537 1.020531
_cons | .0457517 .0067662 -20.86 0.000 .0342392 .0611351
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11919.328
Iteration 1: log pseudolikelihood = -11886.103
Iteration 2: log pseudolikelihood = -11884.36
Iteration 3: log pseudolikelihood = -11884.354
Iteration 4: log pseudolikelihood = -11884.354
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11884.354 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468058 .221187 2.55 0.011 1.092687 1.97238
_rcs1 | 2.070027 .1987599 7.58 0.000 1.714926 2.498658
_rcs2 | 1.084522 .0675011 1.30 0.192 .9599732 1.22523
_rcs3 | .9349665 .046022 -1.37 0.172 .8489795 1.029663
_rcs4 | 1.028104 .036579 0.78 0.436 .9588535 1.102357
_rcs_tr_outcome1 | .9847749 .0963893 -0.16 0.875 .8128713 1.193032
_rcs_tr_outcome2 | .9747064 .0622291 -0.40 0.688 .860062 1.104633
_rcs_tr_outcome3 | 1.093805 .0509521 1.92 0.054 .9983637 1.19837
_rcs_tr_outcome4 | 1.008138 .0306662 0.27 0.790 .9497899 1.07007
_rcs_tr_outcome5 | .9918948 .026068 -0.31 0.757 .942096 1.044326
_rcs_tr_outcome6 | .998856 .0109027 -0.10 0.916 .9777142 1.020455
_rcs_tr_outcome7 | 1.010082 .0046531 2.18 0.029 1.001003 1.019243
_cons | .0457628 .0067665 -20.86 0.000 .0342495 .0611465
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11914.228
Iteration 1: log pseudolikelihood = -11902.84
Iteration 2: log pseudolikelihood = -11902.708
Iteration 3: log pseudolikelihood = -11902.708
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11902.708 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.45838 .2242483 2.45 0.014 1.07891 1.971315
_rcs1 | 2.061657 .2106781 7.08 0.000 1.687459 2.518835
_rcs2 | 1.068726 .0263794 2.69 0.007 1.018254 1.121699
_rcs3 | .9832419 .0285715 -0.58 0.561 .9288076 1.040866
_rcs4 | 1.008102 .016547 0.49 0.623 .9761867 1.041061
_rcs5 | 1.013112 .0127276 1.04 0.300 .9884715 1.038368
_rcs_tr_outcome1 | .9856256 .1056019 -0.14 0.893 .798937 1.215938
_cons | .0459564 .0068797 -20.57 0.000 .0342705 .0616271
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11914.534
Iteration 1: log pseudolikelihood = -11901.888
Iteration 2: log pseudolikelihood = -11901.725
Iteration 3: log pseudolikelihood = -11901.725
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11901.725 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.462106 .2241826 2.48 0.013 1.082593 1.97466
_rcs1 | 2.054108 .202515 7.30 0.000 1.69318 2.491974
_rcs2 | 1.05305 .0486964 1.12 0.264 .9618042 1.152952
_rcs3 | .9802491 .0321917 -0.61 0.544 .9191422 1.045418
_rcs4 | 1.00785 .0165322 0.48 0.634 .9759632 1.040779
_rcs5 | 1.013247 .0125141 1.07 0.287 .9890147 1.038074
_rcs_tr_outcome1 | .99319 .1026817 -0.07 0.947 .8110176 1.216282
_rcs_tr_outcome2 | 1.029662 .0567543 0.53 0.596 .9242235 1.147129
_cons | .0459017 .0068653 -20.60 0.000 .0342388 .0615374
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11915.694
Iteration 1: log pseudolikelihood = -11894.18
Iteration 2: log pseudolikelihood = -11893.717
Iteration 3: log pseudolikelihood = -11893.717
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11893.717 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.463699 .2216465 2.52 0.012 1.087815 1.969465
_rcs1 | 2.051167 .1978561 7.45 0.000 1.697829 2.478039
_rcs2 | 1.069721 .0558069 1.29 0.196 .9657478 1.184888
_rcs3 | .9507903 .0469396 -1.02 0.307 .8631012 1.047388
_rcs4 | .9935942 .023351 -0.27 0.785 .9488652 1.040432
_rcs5 | 1.012765 .0127984 1.00 0.316 .9879883 1.038162
_rcs_tr_outcome1 | .9959572 .0995481 -0.04 0.968 .8187686 1.211491
_rcs_tr_outcome2 | 1.001911 .0559886 0.03 0.973 .8979715 1.117882
_rcs_tr_outcome3 | 1.064842 .0534882 1.25 0.211 .9650026 1.175012
_cons | .0458464 .006789 -20.82 0.000 .0342972 .0612846
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11914.905
Iteration 1: log pseudolikelihood = -11889.143
Iteration 2: log pseudolikelihood = -11887.962
Iteration 3: log pseudolikelihood = -11887.958
Iteration 4: log pseudolikelihood = -11887.958
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11887.958 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467589 .2208148 2.55 0.011 1.092778 1.970956
_rcs1 | 2.067371 .1942275 7.73 0.000 1.719685 2.485353
_rcs2 | 1.083025 .0641619 1.35 0.178 .9642966 1.216372
_rcs3 | .9353003 .0484077 -1.29 0.196 .8450764 1.035157
_rcs4 | 1.010795 .033635 0.32 0.747 .9469749 1.078915
_rcs5 | 1.018791 .0168927 1.12 0.262 .9862143 1.052444
_rcs_tr_outcome1 | .9860479 .0941877 -0.15 0.883 .8176942 1.189064
_rcs_tr_outcome2 | .9855196 .0601714 -0.24 0.811 .8743689 1.1108
_rcs_tr_outcome3 | 1.088564 .0566315 1.63 0.103 .98304 1.205416
_rcs_tr_outcome4 | .9802357 .0334936 -0.58 0.559 .9167393 1.04813
_cons | .0457776 .0067613 -20.88 0.000 .0342714 .061147
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11914.812
Iteration 1: log pseudolikelihood = -11889.007
Iteration 2: log pseudolikelihood = -11887.946
Iteration 3: log pseudolikelihood = -11887.943
Iteration 4: log pseudolikelihood = -11887.943
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11887.943 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468072 .2209869 2.55 0.011 1.092992 1.971867
_rcs1 | 2.067476 .1933872 7.77 0.000 1.721159 2.483476
_rcs2 | 1.082365 .0636679 1.35 0.178 .964503 1.21463
_rcs3 | .9351157 .0507554 -1.24 0.216 .8407455 1.040078
_rcs4 | 1.00946 .03528 0.27 0.788 .9426273 1.08103
_rcs5 | 1.020448 .0270043 0.76 0.444 .9688698 1.074772
_rcs_tr_outcome1 | .9855767 .0940738 -0.15 0.879 .8174154 1.188333
_rcs_tr_outcome2 | .982749 .0594745 -0.29 0.774 .8728291 1.106512
_rcs_tr_outcome3 | 1.091192 .0604928 1.57 0.115 .9788422 1.216436
_rcs_tr_outcome4 | .9963529 .0357709 -0.10 0.919 .9286532 1.068988
_rcs_tr_outcome5 | .9879766 .0268902 -0.44 0.657 .9366538 1.042112
_cons | .04577 .006761 -20.88 0.000 .0342644 .061139
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11927.309
Iteration 1: log pseudolikelihood = -11888.66
Iteration 2: log pseudolikelihood = -11886.413
Iteration 3: log pseudolikelihood = -11886.407
Iteration 4: log pseudolikelihood = -11886.407
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11886.407 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467773 .2208148 2.55 0.011 1.092955 1.971131
_rcs1 | 2.0694 .1940543 7.76 0.000 1.721966 2.486935
_rcs2 | 1.087093 .0656134 1.38 0.166 .9658086 1.223608
_rcs3 | .9323864 .0497658 -1.31 0.190 .8397758 1.03521
_rcs4 | 1.012446 .0353849 0.35 0.723 .9454153 1.08423
_rcs5 | 1.016198 .0250357 0.65 0.514 .9682943 1.066471
_rcs_tr_outcome1 | .9850854 .0940086 -0.16 0.875 .8170376 1.187697
_rcs_tr_outcome2 | .9761943 .0606164 -0.39 0.698 .8643333 1.102532
_rcs_tr_outcome3 | 1.095163 .0592995 1.68 0.093 .9848928 1.217779
_rcs_tr_outcome4 | 1.005436 .0337829 0.16 0.872 .941356 1.073878
_rcs_tr_outcome5 | .9892528 .026167 -0.41 0.683 .9392732 1.041892
_rcs_tr_outcome6 | 1.002699 .0141454 0.19 0.848 .9753545 1.030811
_cons | .0457699 .006759 -20.88 0.000 .0342673 .0611337
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11914.419
Iteration 1: log pseudolikelihood = -11885.604
Iteration 2: log pseudolikelihood = -11884.16
Iteration 3: log pseudolikelihood = -11884.156
Iteration 4: log pseudolikelihood = -11884.156
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11884.156 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467779 .2208269 2.55 0.011 1.092944 1.971168
_rcs1 | 2.068393 .1935994 7.76 0.000 1.721716 2.484876
_rcs2 | 1.084832 .064675 1.37 0.172 .9651975 1.219296
_rcs3 | .9335132 .0502121 -1.28 0.201 .8401093 1.037302
_rcs4 | 1.010869 .0353099 0.31 0.757 .9439789 1.082499
_rcs5 | 1.018473 .0264295 0.71 0.481 .9679673 1.071613
_rcs_tr_outcome1 | .9855371 .0940018 -0.15 0.879 .8174934 1.188124
_rcs_tr_outcome2 | .9763697 .0598105 -0.39 0.696 .8659072 1.100924
_rcs_tr_outcome3 | 1.094726 .0591853 1.67 0.094 .9846591 1.217096
_rcs_tr_outcome4 | 1.010792 .0315818 0.34 0.731 .9507498 1.074626
_rcs_tr_outcome5 | .9946209 .0248894 -0.22 0.829 .9470155 1.044619
_rcs_tr_outcome6 | .9920739 .0213212 -0.37 0.711 .951153 1.034755
_rcs_tr_outcome7 | 1.006239 .0075028 0.83 0.404 .991641 1.021052
_cons | .04577 .0067591 -20.88 0.000 .0342673 .0611339
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11916.679
Iteration 1: log pseudolikelihood = -11903.713
Iteration 2: log pseudolikelihood = -11903.537
Iteration 3: log pseudolikelihood = -11903.536
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11903.536 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.458756 .2242011 2.46 0.014 1.079341 1.971545
_rcs1 | 2.061576 .2100129 7.10 0.000 1.688447 2.517163
_rcs2 | 1.0674 .0253232 2.75 0.006 1.018904 1.118205
_rcs3 | .9867405 .0305006 -0.43 0.666 .9287352 1.048369
_rcs4 | 1.000411 .016692 0.02 0.980 .968225 1.033668
_rcs5 | 1.01379 .0130464 1.06 0.287 .9885392 1.039685
_rcs6 | 1.005381 .0073954 0.73 0.466 .9909901 1.019981
_rcs_tr_outcome1 | .9852008 .1049547 -0.14 0.889 .7995491 1.21396
_cons | .0459494 .0068781 -20.58 0.000 .0342662 .0616161
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11916.79
Iteration 1: log pseudolikelihood = -11902.732
Iteration 2: log pseudolikelihood = -11902.524
Iteration 3: log pseudolikelihood = -11902.524
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11902.524 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.462506 .2241595 2.48 0.013 1.083012 1.974977
_rcs1 | 2.053863 .2015844 7.33 0.000 1.694443 2.489522
_rcs2 | 1.051517 .0467134 1.13 0.258 .9638334 1.147178
_rcs3 | .983528 .0345481 -0.47 0.636 .9180933 1.053626
_rcs4 | .9997385 .0167272 -0.02 0.988 .9674856 1.033067
_rcs5 | 1.013933 .0128152 1.09 0.274 .9891239 1.039364
_rcs6 | 1.005516 .0072019 0.77 0.442 .9914996 1.019731
_rcs_tr_outcome1 | .9929114 .1018463 -0.07 0.945 .8120819 1.214007
_rcs_tr_outcome2 | 1.030108 .0555703 0.55 0.582 .9267525 1.14499
_cons | .0458943 .0068638 -20.60 0.000 .0342339 .0615264
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11916.99
Iteration 1: log pseudolikelihood = -11894.829
Iteration 2: log pseudolikelihood = -11894.333
Iteration 3: log pseudolikelihood = -11894.333
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11894.333 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.464536 .2215538 2.52 0.012 1.088757 1.970013
_rcs1 | 2.051023 .1968302 7.49 0.000 1.699352 2.47547
_rcs2 | 1.068173 .0534823 1.32 0.188 .9683283 1.178312
_rcs3 | .9557497 .0462748 -0.93 0.350 .8692231 1.05089
_rcs4 | .98235 .0257198 -0.68 0.496 .9332117 1.034076
_rcs5 | 1.009882 .0142853 0.70 0.487 .9822682 1.038273
_rcs6 | 1.005829 .0068591 0.85 0.394 .9924746 1.019363
_rcs_tr_outcome1 | .9954981 .0986292 -0.05 0.964 .8197992 1.208853
_rcs_tr_outcome2 | 1.002956 .0547242 0.05 0.957 .9012349 1.116159
_rcs_tr_outcome3 | 1.065456 .0531038 1.27 0.203 .966297 1.174791
_cons | .0458316 .0067847 -20.82 0.000 .0342892 .0612594
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11917.109
Iteration 1: log pseudolikelihood = -11890.037
Iteration 2: log pseudolikelihood = -11889.071
Iteration 3: log pseudolikelihood = -11889.069
Iteration 4: log pseudolikelihood = -11889.069
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11889.069 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.46812 .2207496 2.55 0.011 1.093385 1.971288
_rcs1 | 2.066305 .1931753 7.76 0.000 1.720351 2.481829
_rcs2 | 1.080361 .0607655 1.37 0.169 .9675927 1.206272
_rcs3 | .9404121 .0484483 -1.19 0.233 .850092 1.040329
_rcs4 | .9951228 .0310708 -0.16 0.876 .9360512 1.057922
_rcs5 | 1.021357 .023517 0.92 0.359 .9762895 1.068506
_rcs6 | 1.006582 .0073943 0.89 0.372 .9921931 1.021179
_rcs_tr_outcome1 | .9863636 .0935334 -0.14 0.885 .8190691 1.187828
_rcs_tr_outcome2 | .9884734 .0575596 -0.20 0.842 .8818583 1.107978
_rcs_tr_outcome3 | 1.086905 .0557536 1.62 0.104 .9829439 1.201862
_rcs_tr_outcome4 | .9803739 .0346735 -0.56 0.575 .914717 1.050744
_cons | .0457678 .0067587 -20.89 0.000 .0342658 .0611307
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11915.942
Iteration 1: log pseudolikelihood = -11890.56
Iteration 2: log pseudolikelihood = -11889.669
Iteration 3: log pseudolikelihood = -11889.667
Iteration 4: log pseudolikelihood = -11889.667
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11889.667 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468293 .2212053 2.55 0.011 1.092886 1.972651
_rcs1 | 2.065618 .1913268 7.83 0.000 1.722693 2.476807
_rcs2 | 1.080015 .0601808 1.38 0.167 .9682753 1.204649
_rcs3 | .9399327 .0540285 -1.08 0.281 .839786 1.052022
_rcs4 | .9933446 .0338265 -0.20 0.845 .9292099 1.061906
_rcs5 | 1.019764 .0258157 0.77 0.439 .9704006 1.071638
_rcs6 | 1.009301 .0126889 0.74 0.461 .9847354 1.03448
_rcs_tr_outcome1 | .9865029 .0932238 -0.14 0.886 .8197104 1.187234
_rcs_tr_outcome2 | .9859464 .056498 -0.25 0.805 .8812044 1.103138
_rcs_tr_outcome3 | 1.088502 .0622622 1.48 0.138 .9730617 1.217637
_rcs_tr_outcome4 | .9988116 .0363086 -0.03 0.974 .9301239 1.072572
_rcs_tr_outcome5 | .9894975 .02351 -0.44 0.657 .9444753 1.036666
_cons | .0457664 .0067624 -20.87 0.000 .0342589 .0611392
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11916.886
Iteration 1: log pseudolikelihood = -11887.37
Iteration 2: log pseudolikelihood = -11886.038
Iteration 3: log pseudolikelihood = -11886.034
Iteration 4: log pseudolikelihood = -11886.034
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11886.034 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.468372 .2211758 2.55 0.011 1.093006 1.972648
_rcs1 | 2.066814 .1927549 7.78 0.000 1.721539 2.481339
_rcs2 | 1.080822 .0600686 1.40 0.162 .9692752 1.205206
_rcs3 | .9425454 .0565148 -0.99 0.324 .8380394 1.060084
_rcs4 | .9956366 .0363761 -0.12 0.905 .9268335 1.069547
_rcs5 | 1.02205 .0284414 0.78 0.433 .9677985 1.079342
_rcs6 | .9980085 .01557 -0.13 0.898 .9679537 1.028997
_rcs_tr_outcome1 | .9862466 .0938719 -0.15 0.884 .8184032 1.188512
_rcs_tr_outcome2 | .9833199 .0563526 -0.29 0.769 .8788479 1.100211
_rcs_tr_outcome3 | 1.084792 .0661505 1.33 0.182 .9625879 1.222511
_rcs_tr_outcome4 | 1.009141 .0378123 0.24 0.808 .9376863 1.086041
_rcs_tr_outcome5 | .9854859 .0281325 -0.51 0.609 .9318615 1.042196
_rcs_tr_outcome6 | 1.012578 .016707 0.76 0.449 .9803563 1.045858
_cons | .0457554 .0067635 -20.87 0.000 .0342468 .0611316
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11912.856
Iteration 1: log pseudolikelihood = -11886.905
Iteration 2: log pseudolikelihood = -11885.718
Iteration 3: log pseudolikelihood = -11885.714
Iteration 4: log pseudolikelihood = -11885.714
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11885.714 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467353 .2205671 2.55 0.011 1.092912 1.970081
_rcs1 | 2.066261 .1910248 7.85 0.000 1.72382 2.476728
_rcs2 | 1.084213 .0616372 1.42 0.155 .9698935 1.212007
_rcs3 | .9389089 .0552217 -1.07 0.284 .8366816 1.053627
_rcs4 | .9972597 .0360351 -0.08 0.939 .9290752 1.070448
_rcs5 | 1.018818 .0277975 0.68 0.494 .9657673 1.074783
_rcs6 | 1.000636 .0149138 0.04 0.966 .9718287 1.030298
_rcs_tr_outcome1 | .9871246 .0926773 -0.14 0.890 .8212133 1.186555
_rcs_tr_outcome2 | .9782336 .0572227 -0.38 0.707 .8722697 1.09707
_rcs_tr_outcome3 | 1.090095 .0653728 1.44 0.150 .9692105 1.226058
_rcs_tr_outcome4 | 1.011777 .036138 0.33 0.743 .9433703 1.085144
_rcs_tr_outcome5 | .9926854 .0262245 -0.28 0.781 .9425944 1.045438
_rcs_tr_outcome6 | .9987524 .019062 -0.07 0.948 .9620818 1.036821
_rcs_tr_outcome7 | 1.010927 .0108914 1.01 0.313 .9898045 1.032501
_cons | .0457728 .0067554 -20.90 0.000 .0342754 .0611269
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11911.728
Iteration 1: log pseudolikelihood = -11902.069
Iteration 2: log pseudolikelihood = -11901.953
Iteration 3: log pseudolikelihood = -11901.953
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11901.953 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.457133 .2247825 2.44 0.015 1.076936 1.971554
_rcs1 | 2.058274 .2086302 7.12 0.000 1.687423 2.510628
_rcs2 | 1.066326 .0244754 2.80 0.005 1.019418 1.115393
_rcs3 | .99023 .0317001 -0.31 0.759 .9300079 1.054352
_rcs4 | .9955205 .0171198 -0.26 0.794 .9625255 1.029647
_rcs5 | 1.013933 .0127774 1.10 0.272 .9891964 1.039288
_rcs6 | 1.008072 .0103816 0.78 0.435 .9879283 1.028626
_rcs7 | .9999426 .0065172 -0.01 0.993 .9872504 1.012798
_rcs_tr_outcome1 | .9878206 .104393 -0.12 0.908 .8030138 1.215159
_cons | .0459738 .006891 -20.55 0.000 .0342707 .0616733
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11911.9
Iteration 1: log pseudolikelihood = -11901.124
Iteration 2: log pseudolikelihood = -11900.982
Iteration 3: log pseudolikelihood = -11900.982
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11900.982 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.460931 .2248527 2.46 0.014 1.08049 1.975325
_rcs1 | 2.050957 .2005794 7.34 0.000 1.69321 2.48429
_rcs2 | 1.050754 .0451709 1.15 0.249 .9658477 1.143124
_rcs3 | .9870052 .0358734 -0.36 0.719 .9191405 1.059881
_rcs4 | .9945879 .017209 -0.31 0.754 .9614244 1.028895
_rcs5 | 1.013977 .0126504 1.11 0.266 .9894838 1.039077
_rcs6 | 1.00819 .0101933 0.81 0.420 .9884082 1.028368
_rcs7 | 1.000219 .0062319 0.04 0.972 .9880792 1.012509
_rcs_tr_outcome1 | .9952008 .1014779 -0.05 0.962 .8149219 1.215361
_rcs_tr_outcome2 | 1.029512 .0541341 0.55 0.580 .9286957 1.141273
_cons | .045918 .0068787 -20.57 0.000 .034235 .061588
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11911.905
Iteration 1: log pseudolikelihood = -11893.272
Iteration 2: log pseudolikelihood = -11892.951
Iteration 3: log pseudolikelihood = -11892.951
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11892.951 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.463251 .2222112 2.51 0.012 1.086561 1.970532
_rcs1 | 2.049007 .1960029 7.50 0.000 1.698712 2.471538
_rcs2 | 1.067888 .051829 1.35 0.176 .9709866 1.174459
_rcs3 | .9607466 .0460237 -0.84 0.403 .874647 1.055322
_rcs4 | .9766792 .0265014 -0.87 0.384 .9260945 1.030027
_rcs5 | 1.007315 .015593 0.47 0.638 .9772118 1.038345
_rcs6 | 1.007405 .0104394 0.71 0.476 .987151 1.028075
_rcs7 | 1.000614 .0059825 0.10 0.918 .9889573 1.012409
_rcs_tr_outcome1 | .9971212 .0981903 -0.03 0.977 .8221043 1.209397
_rcs_tr_outcome2 | 1.002436 .0531976 0.05 0.963 .9034102 1.112317
_rcs_tr_outcome3 | 1.064426 .0523506 1.27 0.204 .9666103 1.172139
_cons | .0458519 .0067994 -20.79 0.000 .0342872 .0613172
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11912.093
Iteration 1: log pseudolikelihood = -11887.763
Iteration 2: log pseudolikelihood = -11886.945
Iteration 3: log pseudolikelihood = -11886.944
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11886.944 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467881 .2216706 2.54 0.011 1.091811 1.973487
_rcs1 | 2.06644 .1929847 7.77 0.000 1.720795 2.481513
_rcs2 | 1.080512 .0592444 1.41 0.158 .9704172 1.203098
_rcs3 | .9443784 .048227 -1.12 0.262 .8544316 1.043794
_rcs4 | .9871114 .0296855 -0.43 0.666 .9306105 1.047043
_rcs5 | 1.021052 .0258776 0.82 0.411 .9715715 1.073051
_rcs6 | 1.012383 .013903 0.90 0.370 .9854968 1.040002
_rcs7 | 1.000889 .0059686 0.15 0.882 .989259 1.012656
_rcs_tr_outcome1 | .9862507 .0931433 -0.15 0.883 .819593 1.186797
_rcs_tr_outcome2 | .9883745 .0560858 -0.21 0.837 .8843408 1.104647
_rcs_tr_outcome3 | 1.085747 .0547106 1.63 0.103 .9836409 1.198451
_rcs_tr_outcome4 | .9774867 .0358577 -0.62 0.535 .909674 1.050355
_cons | .0457713 .0067744 -20.84 0.000 .0342461 .0611751
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11911.326
Iteration 1: log pseudolikelihood = -11889.036
Iteration 2: log pseudolikelihood = -11888.343
Iteration 3: log pseudolikelihood = -11888.342
Iteration 4: log pseudolikelihood = -11888.342
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11888.342 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467296 .2218527 2.54 0.011 1.090981 1.973413
_rcs1 | 2.064021 .1902708 7.86 0.000 1.722846 2.472758
_rcs2 | 1.079694 .0586566 1.41 0.158 .9706382 1.201003
_rcs3 | .9444315 .0550986 -0.98 0.327 .8423856 1.058839
_rcs4 | .985483 .0322525 -0.45 0.655 .9242541 1.050768
_rcs5 | 1.017509 .0249931 0.71 0.480 .9696841 1.067693
_rcs6 | 1.013459 .0209145 0.65 0.517 .9732855 1.055291
_rcs7 | 1.002492 .0068813 0.36 0.717 .989095 1.01607
_rcs_tr_outcome1 | .9878007 .0925824 -0.13 0.896 .8220341 1.186995
_rcs_tr_outcome2 | .9864214 .0550379 -0.25 0.806 .8842381 1.100413
_rcs_tr_outcome3 | 1.086609 .062054 1.45 0.146 .9715454 1.215301
_rcs_tr_outcome4 | .9979683 .036993 -0.05 0.956 .9280346 1.073172
_rcs_tr_outcome5 | .9894295 .0257888 -0.41 0.683 .9401537 1.041288
_cons | .0457819 .0067758 -20.84 0.000 .0342542 .0611891
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11904.851
Iteration 1: log pseudolikelihood = -11882.45
Iteration 2: log pseudolikelihood = -11881.744
Iteration 3: log pseudolikelihood = -11881.743
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11881.743 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.467661 .2222282 2.53 0.011 1.090786 1.974748
_rcs1 | 2.064664 .1925434 7.77 0.000 1.719766 2.478732
_rcs2 | 1.079057 .0571477 1.44 0.151 .9726663 1.197084
_rcs3 | .9504121 .0607694 -0.80 0.426 .8384672 1.077303
_rcs4 | .9852464 .0361499 -0.41 0.685 .9168815 1.058709
_rcs5 | 1.023733 .028681 0.84 0.402 .9690345 1.081518
_rcs6 | 1.004946 .0195497 0.25 0.800 .9673502 1.044002
_rcs7 | .9918159 .0106708 -0.76 0.445 .9711205 1.012952
_rcs_tr_outcome1 | .9874249 .0942207 -0.13 0.894 .8189963 1.190491
_rcs_tr_outcome2 | .9849547 .053589 -0.28 0.781 .8853285 1.095792
_rcs_tr_outcome3 | 1.078362 .0684916 1.19 0.235 .9521405 1.221317
_rcs_tr_outcome4 | 1.010546 .0393936 0.27 0.788 .9362121 1.090782
_rcs_tr_outcome5 | .9838038 .0291172 -0.55 0.581 .9283588 1.04256
_rcs_tr_outcome6 | 1.018104 .0163792 1.12 0.265 .9865019 1.050718
_cons | .0457657 .0067818 -20.81 0.000 .0342298 .0611892
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11911.139
Iteration 1: log pseudolikelihood = -11877.752
Iteration 2: log pseudolikelihood = -11876.312
Iteration 3: log pseudolikelihood = -11876.308
Iteration 4: log pseudolikelihood = -11876.308
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11876.308 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.469321 .2210599 2.56 0.011 1.09409 1.973242
_rcs1 | 2.068553 .1916277 7.85 0.000 1.725094 2.480395
_rcs2 | 1.085269 .061105 1.45 0.146 .9718769 1.211891
_rcs3 | .9428528 .0613109 -0.90 0.366 .8300282 1.071014
_rcs4 | .9916527 .0370743 -0.22 0.823 .9215869 1.067045
_rcs5 | 1.020231 .0295919 0.69 0.490 .9638496 1.07991
_rcs6 | 1.008375 .0219523 0.38 0.702 .9662539 1.052331
_rcs7 | .9826086 .015198 -1.13 0.257 .953268 1.012852
_rcs_tr_outcome1 | .9854012 .0931811 -0.16 0.876 .8186949 1.186053
_rcs_tr_outcome2 | .9781135 .0565657 -0.38 0.702 .8732992 1.095508
_rcs_tr_outcome3 | 1.087523 .0716373 1.27 0.203 .9558027 1.237397
_rcs_tr_outcome4 | 1.009242 .0387026 0.24 0.810 .9361665 1.088021
_rcs_tr_outcome5 | .9898222 .0293738 -0.34 0.730 .9338928 1.049101
_rcs_tr_outcome6 | .998055 .0223992 -0.09 0.931 .9551049 1.042936
_rcs_tr_outcome7 | 1.028431 .0165821 1.74 0.082 .9964384 1.06145
_cons | .0457254 .0067508 -20.90 0.000 .0342363 .06107
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11898.386
Iteration 1: log pseudolikelihood = -11892.172
Iteration 2: log pseudolikelihood = -11892.12
Iteration 3: log pseudolikelihood = -11892.12
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11892.12 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.453084 .2255623 2.41 0.016 1.07191 1.969802
_rcs1 | 2.050672 .2051706 7.18 0.000 1.685517 2.494935
_rcs2 | 1.064973 .0234359 2.86 0.004 1.020016 1.111912
_rcs3 | .9949882 .0322779 -0.15 0.877 .933694 1.060306
_rcs4 | .9885636 .0180648 -0.63 0.529 .9537838 1.024612
_rcs5 | 1.012444 .0126418 0.99 0.322 .987967 1.037527
_rcs6 | 1.007601 .011586 0.66 0.510 .9851468 1.030567
_rcs7 | 1.008345 .0074422 1.13 0.260 .9938637 1.023038
_rcs8 | .9932732 .0069321 -0.97 0.333 .979779 1.006953
_rcs_tr_outcome1 | .9932125 .1028706 -0.07 0.948 .8107373 1.216758
_cons | .0460436 .006919 -20.48 0.000 .0342973 .0618129
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11898.454
Iteration 1: log pseudolikelihood = -11891.449
Iteration 2: log pseudolikelihood = -11891.379
Iteration 3: log pseudolikelihood = -11891.379
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11891.379 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.456505 .2258849 2.42 0.015 1.074735 1.973886
_rcs1 | 2.044556 .1984335 7.37 0.000 1.690386 2.472931
_rcs2 | 1.051325 .044218 1.19 0.234 .9681356 1.141663
_rcs3 | .9922332 .0363575 -0.21 0.831 .9234725 1.066114
_rcs4 | .9875261 .0183434 -0.68 0.499 .9522203 1.024141
_rcs5 | 1.012403 .0126463 0.99 0.324 .9879175 1.037495
_rcs6 | 1.007708 .0114127 0.68 0.498 .9855863 1.030327
_rcs7 | 1.008508 .00721 1.19 0.236 .9944753 1.022739
_rcs8 | .9935624 .0067168 -0.96 0.339 .9804845 1.006815
_rcs_tr_outcome1 | .9995231 .1003926 -0.00 0.996 .8209141 1.216993
_rcs_tr_outcome2 | 1.025691 .0530295 0.49 0.624 .9268473 1.135075
_cons | .0459931 .0069122 -20.49 0.000 .0342585 .0617472
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11898.6
Iteration 1: log pseudolikelihood = -11884.355
Iteration 2: log pseudolikelihood = -11884.106
Iteration 3: log pseudolikelihood = -11884.106
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11884.106 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.459075 .2233881 2.47 0.014 1.080828 1.969694
_rcs1 | 2.043273 .1942997 7.51 0.000 1.695835 2.461893
_rcs2 | 1.068174 .0504615 1.40 0.163 .9737117 1.1718
_rcs3 | .9681254 .0456153 -0.69 0.492 .882725 1.061788
_rcs4 | .9702608 .0274029 -1.07 0.285 .9180115 1.025484
_rcs5 | 1.00358 .0171068 0.21 0.834 .9706046 1.037675
_rcs6 | 1.005638 .0121833 0.46 0.643 .9820403 1.029802
_rcs7 | 1.008567 .0069599 1.24 0.216 .9950179 1.022301
_rcs8 | .9941131 .0065722 -0.89 0.372 .9813148 1.007078
_rcs_tr_outcome1 | 1.001193 .0974566 0.01 0.990 .8272966 1.211641
_rcs_tr_outcome2 | 1.00067 .0514937 0.01 0.990 .9046666 1.106861
_rcs_tr_outcome3 | 1.061027 .0517023 1.22 0.224 .9643805 1.167358
_cons | .0459265 .0068363 -20.70 0.000 .0343051 .0614848
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11898.709
Iteration 1: log pseudolikelihood = -11879.826
Iteration 2: log pseudolikelihood = -11879.3
Iteration 3: log pseudolikelihood = -11879.299
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11879.299 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.463119 .2230136 2.50 0.013 1.085267 1.972526
_rcs1 | 2.058772 .1902359 7.81 0.000 1.71773 2.467524
_rcs2 | 1.080508 .0573301 1.46 0.144 .9737883 1.198923
_rcs3 | .9524821 .0487802 -0.95 0.342 .8615165 1.053053
_rcs4 | .9761226 .0279457 -0.84 0.399 .9228584 1.032461
_rcs5 | 1.015067 .0247421 0.61 0.540 .9677135 1.064738
_rcs6 | 1.012956 .0178143 0.73 0.464 .9786358 1.04848
_rcs7 | 1.009678 .0076871 1.27 0.206 .9947236 1.024858
_rcs8 | .9943864 .0064165 -0.87 0.383 .9818895 1.007042
_rcs_tr_outcome1 | .9914046 .0919666 -0.09 0.926 .8265899 1.189082
_rcs_tr_outcome2 | .9869208 .0541208 -0.24 0.810 .8863476 1.098906
_rcs_tr_outcome3 | 1.080342 .0548621 1.52 0.128 .977992 1.193403
_rcs_tr_outcome4 | .9819461 .0329656 -0.54 0.587 .9194145 1.048731
_cons | .0458554 .006816 -20.74 0.000 .0342663 .061364
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11898.3
Iteration 1: log pseudolikelihood = -11879.937
Iteration 2: log pseudolikelihood = -11879.476
Iteration 3: log pseudolikelihood = -11879.475
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11879.475 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.463835 .2230766 2.50 0.012 1.085865 1.973369
_rcs1 | 2.059005 .1881002 7.91 0.000 1.721456 2.462741
_rcs2 | 1.079675 .0565933 1.46 0.144 .9742619 1.196494
_rcs3 | .9517686 .0546907 -0.86 0.390 .8503926 1.06523
_rcs4 | .9754053 .0295106 -0.82 0.410 .9192471 1.034994
_rcs5 | 1.013035 .0241968 0.54 0.588 .9667029 1.061587
_rcs6 | 1.013912 .0233551 0.60 0.549 .9691552 1.060736
_rcs7 | 1.012495 .0131579 0.96 0.339 .9870316 1.038615
_rcs8 | .9951302 .0061321 -0.79 0.428 .9831838 1.007222
_rcs_tr_outcome1 | .9908385 .0911438 -0.10 0.920 .8273776 1.186594
_rcs_tr_outcome2 | .9855681 .0529179 -0.27 0.787 .8871218 1.094939
_rcs_tr_outcome3 | 1.081982 .0600235 1.42 0.156 .9705079 1.20626
_rcs_tr_outcome4 | .9983297 .0348185 -0.05 0.962 .9323668 1.068959
_rcs_tr_outcome5 | .9877361 .026817 -0.45 0.649 .9365497 1.04172
_cons | .0458456 .0068123 -20.74 0.000 .0342623 .061345
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11897.082
Iteration 1: log pseudolikelihood = -11878.464
Iteration 2: log pseudolikelihood = -11877.854
Iteration 3: log pseudolikelihood = -11877.853
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11877.853 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.46144 .222958 2.49 0.013 1.083731 1.970792
_rcs1 | 2.055188 .1864397 7.94 0.000 1.720416 2.455103
_rcs2 | 1.08143 .0565429 1.50 0.134 .9760974 1.19813
_rcs3 | .9534696 .0607772 -0.75 0.455 .8414891 1.080352
_rcs4 | .9756506 .0343449 -0.70 0.484 .9106054 1.045342
_rcs5 | 1.015093 .0246912 0.62 0.538 .9678349 1.064659
_rcs6 | 1.01098 .0227059 0.49 0.627 .9674422 1.056477
_rcs7 | 1.003413 .0142921 0.24 0.811 .9757886 1.03182
_rcs8 | .9918665 .0075469 -1.07 0.283 .9771845 1.006769
_rcs_tr_outcome1 | .994651 .0908681 -0.06 0.953 .8315871 1.18969
_rcs_tr_outcome2 | .9818061 .0525579 -0.34 0.732 .8840145 1.090416
_rcs_tr_outcome3 | 1.079281 .0660446 1.25 0.212 .9572978 1.216809
_rcs_tr_outcome4 | 1.010678 .0388679 0.28 0.782 .9372981 1.089802
_rcs_tr_outcome5 | .9898551 .0279704 -0.36 0.718 .9365246 1.046222
_rcs_tr_outcome6 | 1.01025 .016244 0.63 0.526 .9789085 1.042594
_cons | .0458757 .0068161 -20.74 0.000 .0342857 .0613836
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11901.063
Iteration 1: log pseudolikelihood = -11871.985
Iteration 2: log pseudolikelihood = -11870.812
Iteration 3: log pseudolikelihood = -11870.809
Iteration 4: log pseudolikelihood = -11870.809
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11870.809 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.460691 .2214945 2.50 0.012 1.085138 1.966219
_rcs1 | 2.053068 .1830502 8.07 0.000 1.723895 2.445096
_rcs2 | 1.08582 .0597213 1.50 0.134 .9748564 1.209413
_rcs3 | .9476994 .0650697 -0.78 0.434 .8283741 1.084213
_rcs4 | .9792856 .0365874 -0.56 0.575 .9101382 1.053686
_rcs5 | 1.011993 .025804 0.47 0.640 .9626607 1.063853
_rcs6 | 1.012112 .0240902 0.51 0.613 .9659802 1.060446
_rcs7 | .9991972 .0142306 -0.06 0.955 .9716914 1.027482
_rcs8 | .9838341 .0106254 -1.51 0.131 .9632275 1.004882
_rcs_tr_outcome1 | .9968057 .0895392 -0.04 0.972 .8358924 1.188696
_rcs_tr_outcome2 | .976746 .0548332 -0.42 0.675 .8749764 1.090353
_rcs_tr_outcome3 | 1.085018 .0731691 1.21 0.226 .9506827 1.238336
_rcs_tr_outcome4 | 1.013266 .0395815 0.34 0.736 .9385833 1.093892
_rcs_tr_outcome5 | .9958546 .0269688 -0.15 0.878 .9443751 1.05014
_rcs_tr_outcome6 | .9995598 .021569 -0.02 0.984 .9581668 1.042741
_rcs_tr_outcome7 | 1.022643 .0134408 1.70 0.088 .9966363 1.049329
_cons | .0458781 .0067873 -20.83 0.000 .0343301 .0613104
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11906.406
Iteration 1: log pseudolikelihood = -11899.794
Iteration 2: log pseudolikelihood = -11899.721
Iteration 3: log pseudolikelihood = -11899.721
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11899.721 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.455976 .2256648 2.42 0.015 1.074546 1.972804
_rcs1 | 2.055709 .2083024 7.11 0.000 1.685429 2.507338
_rcs2 | 1.065204 .0234798 2.87 0.004 1.020165 1.112232
_rcs3 | .9962764 .0334428 -0.11 0.912 .9328395 1.064027
_rcs4 | .9871165 .018618 -0.69 0.492 .951292 1.02429
_rcs5 | 1.008247 .0125641 0.66 0.510 .9839206 1.033176
_rcs6 | 1.009106 .0103441 0.88 0.377 .9890344 1.029585
_rcs7 | 1.008672 .0094913 0.92 0.359 .9902398 1.027447
_rcs8 | 1.001709 .0060101 0.28 0.776 .9899989 1.013559
_rcs9 | .9963226 .005743 -0.64 0.523 .9851298 1.007643
_rcs_tr_outcome1 | .9889715 .1042114 -0.11 0.916 .8044326 1.215844
_cons | .0459968 .0069124 -20.49 0.000 .0342618 .0617512
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11906.504
Iteration 1: log pseudolikelihood = -11898.917
Iteration 2: log pseudolikelihood = -11898.813
Iteration 3: log pseudolikelihood = -11898.813
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11898.813 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.45974 .2258396 2.44 0.014 1.077915 1.976819
_rcs1 | 2.048805 .2007057 7.32 0.000 1.690889 2.482484
_rcs2 | 1.05012 .0441255 1.16 0.244 .9671014 1.140266
_rcs3 | .9931231 .0377671 -0.18 0.856 .9217923 1.069974
_rcs4 | .9857435 .0191058 -0.74 0.459 .9489992 1.02391
_rcs5 | 1.008017 .0126397 0.64 0.524 .9835461 1.033098
_rcs6 | 1.009189 .0101663 0.91 0.364 .989459 1.029313
_rcs7 | 1.008781 .0092986 0.95 0.343 .9907198 1.027172
_rcs8 | 1.001948 .0057204 0.34 0.733 .9907987 1.013223
_rcs9 | .9965769 .0055654 -0.61 0.539 .9857284 1.007545
_rcs_tr_outcome1 | .9960044 .1014514 -0.04 0.969 .8157541 1.216083
_rcs_tr_outcome2 | 1.028506 .0535 0.54 0.589 .928816 1.138896
_cons | .0459412 .0069019 -20.50 0.000 .0342233 .0616711
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11906.826
Iteration 1: log pseudolikelihood = -11891.531
Iteration 2: log pseudolikelihood = -11891.192
Iteration 3: log pseudolikelihood = -11891.191
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11891.191 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.462004 .2232933 2.49 0.013 1.083786 1.972211
_rcs1 | 2.046833 .196202 7.47 0.000 1.696248 2.469878
_rcs2 | 1.067952 .0508197 1.38 0.167 .9728507 1.172349
_rcs3 | .9693554 .045688 -0.66 0.509 .8838201 1.063169
_rcs4 | .9680261 .0280483 -1.12 0.262 .9145843 1.024591
_rcs5 | .9966402 .0186875 -0.18 0.858 .9606782 1.033948
_rcs6 | 1.005341 .0118263 0.45 0.651 .9824274 1.02879
_rcs7 | 1.007924 .0095408 0.83 0.404 .9893964 1.026798
_rcs8 | 1.002453 .0053125 0.46 0.644 .9920949 1.01292
_rcs9 | .9969086 .0054108 -0.57 0.568 .9863598 1.00757
_rcs_tr_outcome1 | .9981279 .0984285 -0.02 0.985 .8227096 1.210949
_rcs_tr_outcome2 | 1.002387 .0523033 0.05 0.964 .9049418 1.110324
_rcs_tr_outcome3 | 1.062985 .0523466 1.24 0.215 .9651831 1.170697
_cons | .0458774 .0068242 -20.72 0.000 .0342755 .0614064
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11906.93
Iteration 1: log pseudolikelihood = -11886.253
Iteration 2: log pseudolikelihood = -11885.677
Iteration 3: log pseudolikelihood = -11885.676
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11885.676 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466464 .2227484 2.52 0.012 1.088875 1.974988
_rcs1 | 2.063827 .192542 7.77 0.000 1.718943 2.477907
_rcs2 | 1.081564 .0584337 1.45 0.147 .9728915 1.202375
_rcs3 | .9523364 .0485058 -0.96 0.338 .861858 1.052313
_rcs4 | .9716344 .0277754 -1.01 0.314 .9186926 1.027627
_rcs5 | 1.008066 .025125 0.32 0.747 .9600057 1.058533
_rcs6 | 1.01556 .0196213 0.80 0.424 .9778224 1.054755
_rcs7 | 1.011746 .0122631 0.96 0.335 .9879945 1.036069
_rcs8 | 1.002798 .0054513 0.51 0.607 .9921699 1.013539
_rcs9 | .997168 .0052922 -0.53 0.593 .9868492 1.007595
_rcs_tr_outcome1 | .9875214 .0931537 -0.13 0.894 .8208277 1.188067
_rcs_tr_outcome2 | .9879097 .0552564 -0.22 0.828 .8853344 1.10237
_rcs_tr_outcome3 | 1.083943 .0550811 1.59 0.113 .9811883 1.197459
_rcs_tr_outcome4 | .9793746 .0344437 -0.59 0.553 .9141403 1.049264
_cons | .0457994 .0067989 -20.77 0.000 .0342372 .0612661
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11906.542
Iteration 1: log pseudolikelihood = -11886.861
Iteration 2: log pseudolikelihood = -11886.344
Iteration 3: log pseudolikelihood = -11886.343
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11886.343 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466507 .2228644 2.52 0.012 1.088749 1.975336
_rcs1 | 2.062842 .190026 7.86 0.000 1.722084 2.471026
_rcs2 | 1.080863 .0580536 1.45 0.148 .9728647 1.200851
_rcs3 | .9519883 .0558808 -0.84 0.402 .8485293 1.068062
_rcs4 | .9711919 .0285792 -0.99 0.321 .9167624 1.028853
_rcs5 | 1.005642 .0248757 0.23 0.820 .9580491 1.055598
_rcs6 | 1.014209 .0212907 0.67 0.502 .9733269 1.056808
_rcs7 | 1.01375 .0190977 0.72 0.469 .9770015 1.05188
_rcs8 | 1.00458 .007307 0.63 0.530 .99036 1.019004
_rcs9 | .9973523 .0052062 -0.51 0.612 .9872005 1.007609
_rcs_tr_outcome1 | .9878951 .0924388 -0.13 0.896 .8223612 1.186749
_rcs_tr_outcome2 | .9860653 .0542489 -0.26 0.799 .8852713 1.098335
_rcs_tr_outcome3 | 1.085255 .0615472 1.44 0.149 .9710873 1.212845
_rcs_tr_outcome4 | .9976705 .0359737 -0.06 0.948 .9295972 1.070729
_rcs_tr_outcome5 | .9881324 .0267456 -0.44 0.659 .9370782 1.041968
_cons | .0458004 .0067979 -20.77 0.000 .0342397 .0612644
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11905.382
Iteration 1: log pseudolikelihood = -11883.954
Iteration 2: log pseudolikelihood = -11882.95
Iteration 3: log pseudolikelihood = -11882.947
Iteration 4: log pseudolikelihood = -11882.947
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11882.947 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.465991 .2229767 2.52 0.012 1.088088 1.975144
_rcs1 | 2.062271 .1901108 7.85 0.000 1.721383 2.470665
_rcs2 | 1.081459 .0579408 1.46 0.144 .9736562 1.201197
_rcs3 | .9549001 .0633713 -0.70 0.487 .8384334 1.087545
_rcs4 | .9713445 .0314557 -0.90 0.369 .9116083 1.034995
_rcs5 | 1.008733 .02555 0.34 0.731 .9598787 1.060074
_rcs6 | 1.016869 .0226394 0.75 0.452 .973451 1.062224
_rcs7 | 1.005832 .0180393 0.32 0.746 .9710899 1.041817
_rcs8 | .9963402 .0105844 -0.35 0.730 .9758097 1.017303
_rcs9 | .9959432 .0055076 -0.74 0.462 .9852067 1.006797
_rcs_tr_outcome1 | .9890415 .0928092 -0.12 0.907 .8228863 1.188746
_rcs_tr_outcome2 | .9835797 .0538695 -0.30 0.762 .8834668 1.095037
_rcs_tr_outcome3 | 1.081069 .0684716 1.23 0.218 .9548624 1.223956
_rcs_tr_outcome4 | 1.009922 .0388197 0.26 0.797 .9366321 1.088947
_rcs_tr_outcome5 | .9856552 .0282815 -0.50 0.615 .9317543 1.042674
_rcs_tr_outcome6 | 1.013292 .0168993 0.79 0.429 .9807054 1.046961
_cons | .045798 .0068005 -20.77 0.000 .0342335 .061269
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11908.55
Iteration 1: log pseudolikelihood = -11878.947
Iteration 2: log pseudolikelihood = -11877.611
Iteration 3: log pseudolikelihood = -11877.606
Iteration 4: log pseudolikelihood = -11877.606
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11877.606 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466924 .2221938 2.53 0.011 1.090126 1.97396
_rcs1 | 2.064888 .1879773 7.96 0.000 1.727457 2.468231
_rcs2 | 1.088154 .0626768 1.47 0.142 .9719905 1.218201
_rcs3 | .9473967 .0680531 -0.75 0.452 .8229788 1.090624
_rcs4 | .9772899 .0351681 -0.64 0.523 .9107363 1.048707
_rcs5 | 1.006961 .0254288 0.27 0.784 .9583351 1.058055
_rcs6 | 1.015315 .023766 0.65 0.516 .9697867 1.06298
_rcs7 | 1.007776 .019507 0.40 0.689 .970259 1.046743
_rcs8 | .9879246 .0137173 -0.87 0.382 .9614017 1.015179
_rcs9 | .9914696 .0070922 -1.20 0.231 .977666 1.005468
_rcs_tr_outcome1 | .987797 .0915326 -0.13 0.895 .8237444 1.184521
_rcs_tr_outcome2 | .9765888 .0573599 -0.40 0.687 .8703951 1.095739
_rcs_tr_outcome3 | 1.088452 .0756774 1.22 0.223 .9497891 1.247358
_rcs_tr_outcome4 | 1.009025 .0398914 0.23 0.820 .9337917 1.09032
_rcs_tr_outcome5 | .9934999 .0280171 -0.23 0.817 .9400774 1.049958
_rcs_tr_outcome6 | .9976357 .0225685 -0.10 0.917 .9543686 1.042864
_rcs_tr_outcome7 | 1.02446 .0159101 1.56 0.120 .9937462 1.056122
_cons | .0457713 .0067778 -20.83 0.000 .0342411 .0611843
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11901.128
Iteration 1: log pseudolikelihood = -11892.844
Iteration 2: log pseudolikelihood = -11892.715
Iteration 3: log pseudolikelihood = -11892.715
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11892.715 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.455458 .2252782 2.42 0.015 1.074606 1.971288
_rcs1 | 2.055532 .2061235 7.19 0.000 1.68876 2.501962
_rcs2 | 1.066434 .0246673 2.78 0.005 1.019166 1.115893
_rcs3 | .9960811 .0348246 -0.11 0.911 .9301122 1.066729
_rcs4 | .9894988 .0180928 -0.58 0.564 .9546655 1.025603
_rcs5 | 1.003875 .0123443 0.31 0.753 .9799693 1.028363
_rcs6 | 1.010666 .0100507 1.07 0.286 .991158 1.030558
_rcs7 | 1.007178 .0105674 0.68 0.495 .9866779 1.028104
_rcs8 | 1.008704 .006733 1.30 0.194 .9955936 1.021987
_rcs9 | .9961615 .0064929 -0.59 0.555 .9835166 1.008969
_rcs10 | 1.001365 .0044355 0.31 0.758 .9927095 1.010097
_rcs_tr_outcome1 | .9903712 .1030838 -0.09 0.926 .8076065 1.214496
_cons | .0460014 .0069045 -20.51 0.000 .0342777 .0617349
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11901.215
Iteration 1: log pseudolikelihood = -11892.093
Iteration 2: log pseudolikelihood = -11891.918
Iteration 3: log pseudolikelihood = -11891.918
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11891.918 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.458932 .2255083 2.44 0.015 1.077616 1.975176
_rcs1 | 2.048999 .1988584 7.39 0.000 1.69407 2.47829
_rcs2 | 1.052354 .0446335 1.20 0.229 .9684117 1.143573
_rcs3 | .9929438 .0393559 -0.18 0.858 .9187276 1.073155
_rcs4 | .9880757 .018756 -0.63 0.527 .9519902 1.025529
_rcs5 | 1.003528 .0124391 0.28 0.776 .9794416 1.028207
_rcs6 | 1.010661 .0099542 1.08 0.282 .9913381 1.03036
_rcs7 | 1.007267 .0104118 0.70 0.484 .9870655 1.027882
_rcs8 | 1.008817 .0065398 1.35 0.176 .9960802 1.021716
_rcs9 | .9964124 .0062611 -0.57 0.567 .9842161 1.00876
_rcs10 | 1.001489 .0043235 0.34 0.730 .9930505 1.009998
_rcs_tr_outcome1 | .9970157 .1003332 -0.03 0.976 .8185452 1.214399
_rcs_tr_outcome2 | 1.026703 .0540047 0.50 0.616 .926129 1.138199
_cons | .0459503 .006896 -20.52 0.000 .0342408 .0616642
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11901.637
Iteration 1: log pseudolikelihood = -11884.694
Iteration 2: log pseudolikelihood = -11884.277
Iteration 3: log pseudolikelihood = -11884.276
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11884.276 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.461466 .2229081 2.49 0.013 1.083828 1.970684
_rcs1 | 2.047787 .1944796 7.55 0.000 1.699987 2.466743
_rcs2 | 1.070771 .0511704 1.43 0.152 .9750319 1.17591
_rcs3 | .9705165 .0460024 -0.63 0.528 .8844149 1.065001
_rcs4 | .9703102 .0282186 -1.04 0.300 .9165495 1.027224
_rcs5 | .9913075 .0189084 -0.46 0.647 .9549319 1.029069
_rcs6 | 1.005132 .0127023 0.41 0.685 .980542 1.030339
_rcs7 | 1.005383 .011063 0.49 0.626 .9839317 1.027301
_rcs8 | 1.008579 .0064596 1.33 0.182 .9959972 1.021319
_rcs9 | .9967844 .0060326 -0.53 0.595 .9850306 1.008678
_rcs10 | 1.001703 .004068 0.42 0.675 .9937613 1.009708
_rcs_tr_outcome1 | .9985503 .0972639 -0.01 0.988 .8250088 1.208596
_rcs_tr_outcome2 | 1.000465 .0527412 0.01 0.993 .9022548 1.109365
_rcs_tr_outcome3 | 1.062736 .0518875 1.25 0.213 .9657526 1.169458
_cons | .0458827 .0068168 -20.74 0.000 .0342915 .0613919
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11901.667
Iteration 1: log pseudolikelihood = -11879.994
Iteration 2: log pseudolikelihood = -11879.326
Iteration 3: log pseudolikelihood = -11879.325
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11879.325 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.465181 .2225685 2.51 0.012 1.087901 1.9733
_rcs1 | 2.062974 .190425 7.85 0.000 1.721562 2.472093
_rcs2 | 1.084278 .059 1.49 0.137 .974593 1.206308
_rcs3 | .9542464 .0492852 -0.91 0.365 .8623776 1.055902
_rcs4 | .9717136 .0274235 -1.02 0.309 .9194241 1.026977
_rcs5 | 1.000249 .0233422 0.01 0.992 .9555293 1.047061
_rcs6 | 1.014934 .0199836 0.75 0.452 .9765126 1.054866
_rcs7 | 1.011393 .0156616 0.73 0.464 .9811576 1.04256
_rcs8 | 1.010195 .0075249 1.36 0.173 .9955532 1.025051
_rcs9 | .9972733 .0059311 -0.46 0.646 .985716 1.008966
_rcs10 | 1.001704 .0041416 0.41 0.681 .9936194 1.009854
_rcs_tr_outcome1 | .989232 .0921042 -0.12 0.907 .8242246 1.187273
_rcs_tr_outcome2 | .986048 .0557242 -0.25 0.804 .882662 1.101544
_rcs_tr_outcome3 | 1.083125 .0554173 1.56 0.119 .9797773 1.197374
_rcs_tr_outcome4 | .9818524 .0333368 -0.54 0.590 .9186402 1.049414
_cons | .0458162 .0067968 -20.78 0.000 .0342566 .0612766
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11901.326
Iteration 1: log pseudolikelihood = -11880.336
Iteration 2: log pseudolikelihood = -11879.708
Iteration 3: log pseudolikelihood = -11879.707
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11879.707 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.465428 .2226727 2.51 0.012 1.087988 1.973809
_rcs1 | 2.062932 .1880081 7.95 0.000 1.725479 2.466382
_rcs2 | 1.0848 .0596209 1.48 0.139 .9740186 1.208181
_rcs3 | .9526882 .0567764 -0.81 0.416 .8476617 1.070728
_rcs4 | .9718592 .0276577 -1.00 0.316 .9191353 1.027608
_rcs5 | .9989912 .0239572 -0.04 0.966 .9531225 1.047067
_rcs6 | 1.013169 .0199837 0.66 0.507 .9747495 1.053104
_rcs7 | 1.011621 .0208553 0.56 0.575 .9715603 1.053334
_rcs8 | 1.01193 .0125597 0.96 0.339 .9876101 1.036848
_rcs9 | .998193 .0063564 -0.28 0.776 .9858121 1.010729
_rcs10 | 1.00178 .0041323 0.43 0.666 .9937134 1.009912
_rcs_tr_outcome1 | .9891419 .0913206 -0.12 0.906 .8254163 1.185343
_rcs_tr_outcome2 | .9832336 .0550369 -0.30 0.763 .8810698 1.097244
_rcs_tr_outcome3 | 1.085721 .0621789 1.44 0.151 .970443 1.214692
_rcs_tr_outcome4 | .9978577 .0353381 -0.06 0.952 .9309453 1.069579
_rcs_tr_outcome5 | .9902995 .0264152 -0.37 0.715 .9398566 1.04345
_cons | .0458127 .006794 -20.79 0.000 .0342573 .0612658
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11900.458
Iteration 1: log pseudolikelihood = -11877.997
Iteration 2: log pseudolikelihood = -11876.851
Iteration 3: log pseudolikelihood = -11876.847
Iteration 4: log pseudolikelihood = -11876.847
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11876.847 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.464248 .2228125 2.51 0.012 1.086647 1.973062
_rcs1 | 2.061085 .187087 7.97 0.000 1.725168 2.46241
_rcs2 | 1.086076 .0606611 1.48 0.139 .9734589 1.211721
_rcs3 | .954779 .0658892 -0.67 0.503 .8339913 1.093061
_rcs4 | .9720236 .030077 -0.92 0.359 .9148257 1.032798
_rcs5 | 1.00038 .0247852 0.02 0.988 .9529629 1.050157
_rcs6 | 1.015919 .0214477 0.75 0.454 .9747399 1.058837
_rcs7 | 1.008579 .0198309 0.43 0.664 .970451 1.048206
_rcs8 | 1.003446 .0130081 0.27 0.791 .9782722 1.029269
_rcs9 | .9928997 .0086116 -0.82 0.411 .976164 1.009922
_rcs10 | 1.000982 .0042818 0.23 0.819 .9926249 1.009409
_rcs_tr_outcome1 | .9913193 .0914302 -0.09 0.925 .8273829 1.187738
_rcs_tr_outcome2 | .979994 .0552282 -0.36 0.720 .8775128 1.094444
_rcs_tr_outcome3 | 1.082678 .0709025 1.21 0.225 .9522603 1.230957
_rcs_tr_outcome4 | 1.010217 .0394377 0.26 0.795 .9358039 1.090548
_rcs_tr_outcome5 | .9894885 .0279833 -0.37 0.709 .9361346 1.045883
_rcs_tr_outcome6 | 1.012602 .0157691 0.80 0.421 .9821617 1.043985
_cons | .0458222 .0067982 -20.78 0.000 .0342603 .0612859
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -11901.78
Iteration 1: log pseudolikelihood = -11869.35
Iteration 2: log pseudolikelihood = -11867.713
Iteration 3: log pseudolikelihood = -11867.708
Iteration 4: log pseudolikelihood = -11867.708
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -11867.708 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.466624 .2215666 2.53 0.011 1.090751 1.972023
_rcs1 | 2.065615 .1848016 8.11 0.000 1.733389 2.461516
_rcs2 | 1.09311 .0672863 1.45 0.148 .9688765 1.233274
_rcs3 | .9447939 .0717577 -0.75 0.455 .8141187 1.096444
_rcs4 | .9770929 .0326868 -0.69 0.488 .9150831 1.043305
_rcs5 | 1.000611 .0241783 0.03 0.980 .9543272 1.04914
_rcs6 | 1.014701 .0226974 0.65 0.514 .9711762 1.060177
_rcs7 | 1.013374 .0234692 0.57 0.566 .9684031 1.060432
_rcs8 | 1.000575 .0129131 0.04 0.964 .9755832 1.026207
_rcs9 | .9826458 .0128884 -1.33 0.182 .9577069 1.008234
_rcs10 | .9977947 .00505 -0.44 0.663 .9879457 1.007742
_rcs_tr_outcome1 | .9887996 .0899404 -0.12 0.901 .8273395 1.18177
_rcs_tr_outcome2 | .9733758 .0598685 -0.44 0.661 .8628326 1.098082
_rcs_tr_outcome3 | 1.093892 .0803669 1.22 0.222 .9471916 1.263314
_rcs_tr_outcome4 | 1.008736 .0399284 0.22 0.826 .9334368 1.09011
_rcs_tr_outcome5 | .9935307 .0278698 -0.23 0.817 .9403813 1.049684
_rcs_tr_outcome6 | .9958792 .0228289 -0.18 0.857 .9521256 1.041643
_rcs_tr_outcome7 | 1.027719 .0161545 1.74 0.082 .9965398 1.059875
_cons | .0457662 .006759 -20.88 0.000 .0342637 .0611301
------------------------------------------------------------------------------------
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. }
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
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 = -12195.462
Iteration 1: log pseudolikelihood = -12159.295
Iteration 2: log pseudolikelihood = -12159.019
Iteration 3: log pseudolikelihood = -12159.019
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 35,074
Wald chi2(1) = 6.55
Log pseudolikelihood = -12159.019 Prob > chi2 = 0.0105
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.413923 .1913385 2.56 0.010 1.08452 1.843378
_cons | .0135471 .001789 -32.57 0.000 .0104578 .0175489
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
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 = -12195.462
Iteration 1: log pseudolikelihood = -11975.96
Iteration 2: log pseudolikelihood = -11971.829
Iteration 3: log pseudolikelihood = -11971.827
Iteration 4: log pseudolikelihood = -11971.827
Fitting full model:
Iteration 0: log pseudolikelihood = -11971.827
Iteration 1: log pseudolikelihood = -11931.111
Iteration 2: log pseudolikelihood = -11930.759
Iteration 3: log pseudolikelihood = -11930.759
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 35,074
Wald chi2(1) = 7.57
Log pseudolikelihood = -11930.759 Prob > chi2 = 0.0059
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.444522 .1930714 2.75 0.006 1.111616 1.877128
_cons | .0220201 .0034389 -24.43 0.000 .0162139 .0299056
-------------+----------------------------------------------------------------
/ln_p | -.3649561 .0575976 -6.34 0.000 -.4778453 -.2520668
-------------+----------------------------------------------------------------
p | .6942271 .0399858 .6201181 .7771928
1/p | 1.440451 .0829665 1.286682 1.612596
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
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 = -12201.163
Iteration 1: log pseudolikelihood = -12017.648
Iteration 2: log pseudolikelihood = -12009.081
Iteration 3: log pseudolikelihood = -12009.063
Iteration 4: log pseudolikelihood = -12009.063
Fitting full model:
Iteration 0: log pseudolikelihood = -12009.063
Iteration 1: log pseudolikelihood = -11968.128
Iteration 2: log pseudolikelihood = -11967.772
Iteration 3: log pseudolikelihood = -11967.772
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 35,074
Wald chi2(1) = 7.56
Log pseudolikelihood = -11967.772 Prob > chi2 = 0.0060
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.445974 .1939241 2.75 0.006 1.11174 1.880693
_cons | .0224557 .0036147 -23.58 0.000 .0163798 .0307853
-------------+----------------------------------------------------------------
/gamma | -.2009868 .0401107 -5.01 0.000 -.2796024 -.1223712
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
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 = -21210.503
Iteration 1: log pseudolikelihood = -12205.568
Iteration 2: log pseudolikelihood = -11982.334
Iteration 3: log pseudolikelihood = -11961.018
Iteration 4: log pseudolikelihood = -11960.602
Iteration 5: log pseudolikelihood = -11960.602
Fitting full model:
Iteration 0: log pseudolikelihood = -11960.602
Iteration 1: log pseudolikelihood = -11920.208
Iteration 2: log pseudolikelihood = -11918.929
Iteration 3: log pseudolikelihood = -11918.925
Iteration 4: log pseudolikelihood = -11918.925
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 35,074
Wald chi2(1) = 7.83
Log pseudolikelihood = -11918.925 Prob > chi2 = 0.0051
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5506274 .117431 -2.80 0.005 .3625134 .8363567
_cons | 640.1963 197.6137 20.93 0.000 349.5965 1172.355
-------------+----------------------------------------------------------------
/lnsigma | 1.169884 .0554265 21.11 0.000 1.06125 1.278517
-------------+----------------------------------------------------------------
sigma | 3.221618 .1785629 2.88998 3.591312
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -14609.374
Iteration 2: log likelihood = -14371.466
Iteration 3: log likelihood = -14365.531
Iteration 4: log likelihood = -14365.51
Iteration 5: log likelihood = -14365.51
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -24138.519
Iteration 1: log likelihood = -24138.519
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 = -12173.604
Iteration 1: log pseudolikelihood = -11993.208
Iteration 2: log pseudolikelihood = -11974.222
Iteration 3: log pseudolikelihood = -11973.934
Iteration 4: log pseudolikelihood = -11973.934
Fitting full model:
Iteration 0: log pseudolikelihood = -11973.934
Iteration 1: log pseudolikelihood = -11933.872
Iteration 2: log pseudolikelihood = -11932.2
Iteration 3: log pseudolikelihood = -11932.196
Iteration 4: log pseudolikelihood = -11932.196
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 35,074
Wald chi2(1) = 8.07
Log pseudolikelihood = -11932.196 Prob > chi2 = 0.0045
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .5808817 .1110977 -2.84 0.005 .399291 .8450569
_cons | 210.5028 54.46577 20.68 0.000 126.7701 349.5416
-------------+----------------------------------------------------------------
/lngamma | .3401062 .0578669 5.88 0.000 .2266891 .4535234
-------------+----------------------------------------------------------------
gamma | 1.405097 .0813087 1.25444 1.573848
------------------------------------------------------------------------------
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 | 2,375 . -11930.42 4 23868.85 23891.94
m2_stipw_n~2 | 2,375 . -11917.5 5 23845 23873.86
m2_stipw_n~3 | 2,375 . -11917 6 23846 23880.64
m2_stipw_n~4 | 2,375 . -11916.85 7 23847.7 23888.11
m2_stipw_n~5 | 2,375 . -11915.94 8 23847.87 23894.05
m2_stipw_n~6 | 2,375 . -11914.07 9 23846.14 23898.1
m2_stipw_n~7 | 2,375 . -11912.12 10 23844.24 23901.96
m2_stipw_n~1 | 2,375 . -11913.21 5 23836.42 23865.29
m2_stipw_n~2 | 2,375 . -11912.53 6 23837.05 23871.69
m2_stipw_n~3 | 2,375 . -11912.03 7 23838.07 23878.48
m2_stipw_n~4 | 2,375 . -11911.88 8 23839.76 23885.94
m2_stipw_n~5 | 2,375 . -11910.93 9 23839.86 23891.82
m2_stipw_n~6 | 2,375 . -11909.1 10 23838.2 23895.92
m2_stipw_n~7 | 2,375 . -11907.13 11 23836.26 23899.76
m2_stipw_n~1 | 2,375 . -11910.58 6 23833.15 23867.79
m2_stipw_n~2 | 2,375 . -11909.86 7 23833.73 23874.14
m2_stipw_n~3 | 2,375 . -11901.83 8 23819.67 23865.85
m2_stipw_n~4 | 2,375 . -11901.46 9 23820.91 23872.87
m2_stipw_n~5 | 2,375 . -11900.56 10 23821.12 23878.84
m2_stipw_n~6 | 2,375 . -11898.91 11 23819.81 23883.31
m2_stipw_n~7 | 2,375 . -11897.13 12 23818.25 23887.53
m2_stipw_n~1 | 2,375 . -11904.19 7 23822.38 23862.79
m2_stipw_n~2 | 2,375 . -11903.24 8 23822.47 23868.65
m2_stipw_n~3 | 2,375 . -11896.2 9 23810.4 23862.36
m2_stipw_n~4 | 2,375 . -11889.09 10 23798.18 23855.9
m2_stipw_n~5 | 2,375 . -11889.58 11 23801.15 23864.65
m2_stipw_n~6 | 2,375 . -11885.21 12 23794.42 23863.69
m2_stipw_n~7 | 2,375 . -11884.35 13 23794.71 23869.75
m2_stipw_n~1 | 2,375 . -11902.71 8 23821.42 23867.6
m2_stipw_n~2 | 2,375 . -11901.73 9 23821.45 23873.41
m2_stipw_n~3 | 2,375 . -11893.72 10 23807.43 23865.16
m2_stipw_n~4 | 2,375 . -11887.96 11 23797.92 23861.42
m2_stipw_n~5 | 2,375 . -11887.94 12 23799.89 23869.16
m2_stipw_n~6 | 2,375 . -11886.41 13 23798.81 23873.86
m2_stipw_n~7 | 2,375 . -11884.16 14 23796.31 23877.13
m2_stipw_n~1 | 2,375 . -11903.54 9 23825.07 23877.03
m2_stipw_n~2 | 2,375 . -11902.52 10 23825.05 23882.77
m2_stipw_n~3 | 2,375 . -11894.33 11 23810.67 23874.17
m2_stipw_n~4 | 2,375 . -11889.07 12 23802.14 23871.41
m2_stipw_n~5 | 2,375 . -11889.67 13 23805.33 23880.38
m2_stipw_n~6 | 2,375 . -11886.03 14 23800.07 23880.89
m2_stipw_n~7 | 2,375 . -11885.71 15 23801.43 23888.02
m2_stipw_n~1 | 2,375 . -11901.95 10 23823.91 23881.63
m2_stipw_n~2 | 2,375 . -11900.98 11 23823.96 23887.46
m2_stipw_n~3 | 2,375 . -11892.95 12 23809.9 23879.18
m2_stipw_n~4 | 2,375 . -11886.94 13 23799.89 23874.93
m2_stipw_n~5 | 2,375 . -11888.34 14 23804.68 23885.5
m2_stipw_n~6 | 2,375 . -11881.74 15 23793.49 23880.08
m2_stipw_n~7 | 2,375 . -11876.31 16 23784.62 23876.98
m2_stipw_n~1 | 2,375 . -11892.12 11 23806.24 23869.74
m2_stipw_n~2 | 2,375 . -11891.38 12 23806.76 23876.03
m2_stipw_n~3 | 2,375 . -11884.11 13 23794.21 23869.26
m2_stipw_n~4 | 2,375 . -11879.3 14 23786.6 23867.42
m2_stipw_n~5 | 2,375 . -11879.47 15 23788.95 23875.54
m2_stipw_n~6 | 2,375 . -11877.85 16 23787.71 23880.07
m2_stipw_n~7 | 2,375 . -11870.81 17 23775.62 23873.75
m2_stipw_n~1 | 2,375 . -11899.72 12 23823.44 23892.71
m2_stipw_n~2 | 2,375 . -11898.81 13 23823.63 23898.67
m2_stipw_n~3 | 2,375 . -11891.19 14 23810.38 23891.2
m2_stipw_n~4 | 2,375 . -11885.68 15 23801.35 23887.94
m2_stipw_n~5 | 2,375 . -11886.34 16 23804.69 23897.05
m2_stipw_n~6 | 2,375 . -11882.95 17 23799.89 23898.03
m2_stipw_n~7 | 2,375 . -11877.61 18 23791.21 23895.12
m2_stipw_n~1 | 2,375 . -11892.72 13 23811.43 23886.48
m2_stipw_n~2 | 2,375 . -11891.92 14 23811.84 23892.65
m2_stipw_n~3 | 2,375 . -11884.28 15 23798.55 23885.14
m2_stipw_n~4 | 2,375 . -11879.32 16 23790.65 23883.01
m2_stipw_n~5 | 2,375 . -11879.71 17 23793.41 23891.55
m2_stipw_n~6 | 2,375 . -11876.85 18 23789.69 23893.6
m2_stipw_n~7 | 2,375 . -11867.71 19 23773.42 23883.1
m2_stipw_n~p | 2,375 -12195.46 -12159.02 2 24322.04 24333.58
m2_stipw_n~i | 2,375 -11971.83 -11930.76 3 23867.52 23884.84
m2_stipw_n~m | 2,375 -12009.06 -11967.77 3 23941.54 23958.86
m2_stipw_n~n | 2,375 -11960.6 -11918.93 3 23843.85 23861.17
m2_stipw_n~g | 2,375 -11973.93 -11932.2 3 23870.39 23887.71
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_3=r(S)
. mata : st_sort_matrix("stats_3", 5) // 5 AIC, 6 BIC
. esttab matrix(stats_3) using "testreg_aic_bic_mrl_23_3_pris_m1.csv", replace
(output written to testreg_aic_bic_mrl_23_3_pris_m1.csv)
. esttab matrix(stats_3) using "testreg_aic_bic_mrl_23_3_pris_m1.html", replace
(output written to testreg_aic_bic_mrl_23_3_pris_m1.html)
| stats_3 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m2_stipw_nostag_rp10_tvcdf7 | 2375 | . | -11867.71 | 19 | 23773.42 | 23883.1 |
| m2_stipw_nostag_rp8_tvcdf7 | 2375 | . | -11870.81 | 17 | 23775.62 | 23873.75 |
| m2_stipw_nostag_rp7_tvcdf7 | 2375 | . | -11876.31 | 16 | 23784.62 | 23876.98 |
| m2_stipw_nostag_rp8_tvcdf4 | 2375 | . | -11879.3 | 14 | 23786.6 | 23867.42 |
| m2_stipw_nostag_rp8_tvcdf6 | 2375 | . | -11877.85 | 16 | 23787.71 | 23880.07 |
| m2_stipw_nostag_rp8_tvcdf5 | 2375 | . | -11879.47 | 15 | 23788.95 | 23875.54 |
| m2_stipw_nostag_rp10_tvcdf6 | 2375 | . | -11876.85 | 18 | 23789.69 | 23893.6 |
| m2_stipw_nostag_rp10_tvcdf4 | 2375 | . | -11879.32 | 16 | 23790.65 | 23883.01 |
| m2_stipw_nostag_rp9_tvcdf7 | 2375 | . | -11877.61 | 18 | 23791.21 | 23895.12 |
| m2_stipw_nostag_rp10_tvcdf5 | 2375 | . | -11879.71 | 17 | 23793.41 | 23891.55 |
| m2_stipw_nostag_rp7_tvcdf6 | 2375 | . | -11881.74 | 15 | 23793.49 | 23880.08 |
| m2_stipw_nostag_rp8_tvcdf3 | 2375 | . | -11884.11 | 13 | 23794.21 | 23869.26 |
| m2_stipw_nostag_rp4_tvcdf6 | 2375 | . | -11885.21 | 12 | 23794.42 | 23863.69 |
| m2_stipw_nostag_rp4_tvcdf7 | 2375 | . | -11884.35 | 13 | 23794.71 | 23869.75 |
| m2_stipw_nostag_rp5_tvcdf7 | 2375 | . | -11884.16 | 14 | 23796.31 | 23877.13 |
| m2_stipw_nostag_rp5_tvcdf4 | 2375 | . | -11887.96 | 11 | 23797.92 | 23861.42 |
| m2_stipw_nostag_rp4_tvcdf4 | 2375 | . | -11889.09 | 10 | 23798.18 | 23855.9 |
| m2_stipw_nostag_rp10_tvcdf3 | 2375 | . | -11884.28 | 15 | 23798.55 | 23885.14 |
| m2_stipw_nostag_rp5_tvcdf6 | 2375 | . | -11886.41 | 13 | 23798.81 | 23873.86 |
| m2_stipw_nostag_rp5_tvcdf5 | 2375 | . | -11887.94 | 12 | 23799.89 | 23869.16 |
| m2_stipw_nostag_rp7_tvcdf4 | 2375 | . | -11886.94 | 13 | 23799.89 | 23874.93 |
| m2_stipw_nostag_rp9_tvcdf6 | 2375 | . | -11882.95 | 17 | 23799.89 | 23898.03 |
| m2_stipw_nostag_rp6_tvcdf6 | 2375 | . | -11886.03 | 14 | 23800.07 | 23880.89 |
| m2_stipw_nostag_rp4_tvcdf5 | 2375 | . | -11889.58 | 11 | 23801.15 | 23864.65 |
| m2_stipw_nostag_rp9_tvcdf4 | 2375 | . | -11885.68 | 15 | 23801.35 | 23887.94 |
| m2_stipw_nostag_rp6_tvcdf7 | 2375 | . | -11885.71 | 15 | 23801.43 | 23888.02 |
| m2_stipw_nostag_rp6_tvcdf4 | 2375 | . | -11889.07 | 12 | 23802.14 | 23871.41 |
| m2_stipw_nostag_rp7_tvcdf5 | 2375 | . | -11888.34 | 14 | 23804.68 | 23885.5 |
| m2_stipw_nostag_rp9_tvcdf5 | 2375 | . | -11886.34 | 16 | 23804.69 | 23897.05 |
| m2_stipw_nostag_rp6_tvcdf5 | 2375 | . | -11889.67 | 13 | 23805.33 | 23880.38 |
| m2_stipw_nostag_rp8_tvcdf1 | 2375 | . | -11892.12 | 11 | 23806.24 | 23869.74 |
| m2_stipw_nostag_rp8_tvcdf2 | 2375 | . | -11891.38 | 12 | 23806.76 | 23876.03 |
| m2_stipw_nostag_rp5_tvcdf3 | 2375 | . | -11893.72 | 10 | 23807.43 | 23865.16 |
| m2_stipw_nostag_rp7_tvcdf3 | 2375 | . | -11892.95 | 12 | 23809.9 | 23879.18 |
| m2_stipw_nostag_rp9_tvcdf3 | 2375 | . | -11891.19 | 14 | 23810.38 | 23891.2 |
| m2_stipw_nostag_rp4_tvcdf3 | 2375 | . | -11896.2 | 9 | 23810.4 | 23862.36 |
| m2_stipw_nostag_rp6_tvcdf3 | 2375 | . | -11894.33 | 11 | 23810.67 | 23874.17 |
| m2_stipw_nostag_rp10_tvcdf1 | 2375 | . | -11892.72 | 13 | 23811.43 | 23886.48 |
| m2_stipw_nostag_rp10_tvcdf2 | 2375 | . | -11891.92 | 14 | 23811.84 | 23892.65 |
| m2_stipw_nostag_rp3_tvcdf7 | 2375 | . | -11897.13 | 12 | 23818.25 | 23887.53 |
| m2_stipw_nostag_rp3_tvcdf3 | 2375 | . | -11901.83 | 8 | 23819.67 | 23865.85 |
| m2_stipw_nostag_rp3_tvcdf6 | 2375 | . | -11898.91 | 11 | 23819.81 | 23883.31 |
| m2_stipw_nostag_rp3_tvcdf4 | 2375 | . | -11901.46 | 9 | 23820.91 | 23872.87 |
| m2_stipw_nostag_rp3_tvcdf5 | 2375 | . | -11900.56 | 10 | 23821.12 | 23878.84 |
| m2_stipw_nostag_rp5_tvcdf1 | 2375 | . | -11902.71 | 8 | 23821.42 | 23867.6 |
| m2_stipw_nostag_rp5_tvcdf2 | 2375 | . | -11901.73 | 9 | 23821.45 | 23873.41 |
| m2_stipw_nostag_rp4_tvcdf1 | 2375 | . | -11904.19 | 7 | 23822.38 | 23862.79 |
| m2_stipw_nostag_rp4_tvcdf2 | 2375 | . | -11903.24 | 8 | 23822.47 | 23868.65 |
| m2_stipw_nostag_rp9_tvcdf1 | 2375 | . | -11899.72 | 12 | 23823.44 | 23892.71 |
| m2_stipw_nostag_rp9_tvcdf2 | 2375 | . | -11898.81 | 13 | 23823.63 | 23898.67 |
| m2_stipw_nostag_rp7_tvcdf1 | 2375 | . | -11901.95 | 10 | 23823.91 | 23881.63 |
| m2_stipw_nostag_rp7_tvcdf2 | 2375 | . | -11900.98 | 11 | 23823.96 | 23887.46 |
| m2_stipw_nostag_rp6_tvcdf2 | 2375 | . | -11902.52 | 10 | 23825.05 | 23882.77 |
| m2_stipw_nostag_rp6_tvcdf1 | 2375 | . | -11903.54 | 9 | 23825.07 | 23877.03 |
| m2_stipw_nostag_rp3_tvcdf1 | 2375 | . | -11910.58 | 6 | 23833.15 | 23867.79 |
| m2_stipw_nostag_rp3_tvcdf2 | 2375 | . | -11909.86 | 7 | 23833.73 | 23874.14 |
| m2_stipw_nostag_rp2_tvcdf7 | 2375 | . | -11907.13 | 11 | 23836.26 | 23899.76 |
| m2_stipw_nostag_rp2_tvcdf1 | 2375 | . | -11913.21 | 5 | 23836.42 | 23865.29 |
| m2_stipw_nostag_rp2_tvcdf2 | 2375 | . | -11912.53 | 6 | 23837.05 | 23871.69 |
| m2_stipw_nostag_rp2_tvcdf3 | 2375 | . | -11912.03 | 7 | 23838.07 | 23878.48 |
| m2_stipw_nostag_rp2_tvcdf6 | 2375 | . | -11909.1 | 10 | 23838.2 | 23895.92 |
| m2_stipw_nostag_rp2_tvcdf4 | 2375 | . | -11911.88 | 8 | 23839.76 | 23885.94 |
| m2_stipw_nostag_rp2_tvcdf5 | 2375 | . | -11910.93 | 9 | 23839.86 | 23891.82 |
| m2_stipw_nostag_logn | 2375 | -11960.6 | -11918.93 | 3 | 23843.85 | 23861.17 |
| m2_stipw_nostag_rp1_tvcdf7 | 2375 | . | -11912.12 | 10 | 23844.24 | 23901.96 |
| m2_stipw_nostag_rp1_tvcdf2 | 2375 | . | -11917.5 | 5 | 23845 | 23873.86 |
| m2_stipw_nostag_rp1_tvcdf3 | 2375 | . | -11917 | 6 | 23846 | 23880.64 |
| m2_stipw_nostag_rp1_tvcdf6 | 2375 | . | -11914.07 | 9 | 23846.14 | 23898.1 |
| m2_stipw_nostag_rp1_tvcdf4 | 2375 | . | -11916.85 | 7 | 23847.7 | 23888.11 |
| m2_stipw_nostag_rp1_tvcdf5 | 2375 | . | -11915.94 | 8 | 23847.87 | 23894.05 |
| m2_stipw_nostag_wei | 2375 | -11971.83 | -11930.76 | 3 | 23867.52 | 23884.84 |
| m2_stipw_nostag_rp1_tvcdf1 | 2375 | . | -11930.42 | 4 | 23868.85 | 23891.94 |
| m2_stipw_nostag_llog | 2375 | -11973.93 | -11932.2 | 3 | 23870.39 | 23887.71 |
| m2_stipw_nostag_gom | 2375 | -12009.06 | -11967.77 | 3 | 23941.54 | 23958.86 |
| m2_stipw_nostag_exp | 2375 | -12195.46 | -12159.02 | 2 | 24322.04 | 24333.58 |
. estimates replay m2_stipw_nostag_rp8_tvcdf7, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m2_stipw_nostag_rp8_tvcdf7
------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -11870.809 Number of obs = 35,074
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.460691 .2214945 2.50 0.012 1.085138 1.966219
_rcs1 | 2.053068 .1830502 8.07 0.000 1.723895 2.445096
_rcs2 | 1.08582 .0597213 1.50 0.134 .9748564 1.209413
_rcs3 | .9476994 .0650697 -0.78 0.434 .8283741 1.084213
_rcs4 | .9792856 .0365874 -0.56 0.575 .9101382 1.053686
_rcs5 | 1.011993 .025804 0.47 0.640 .9626607 1.063853
_rcs6 | 1.012112 .0240902 0.51 0.613 .9659802 1.060446
_rcs7 | .9991972 .0142306 -0.06 0.955 .9716914 1.027482
_rcs8 | .9838341 .0106254 -1.51 0.131 .9632275 1.004882
_rcs_tr_outcome1 | .9968057 .0895392 -0.04 0.972 .8358924 1.188696
_rcs_tr_outcome2 | .976746 .0548332 -0.42 0.675 .8749764 1.090353
_rcs_tr_outcome3 | 1.085018 .0731691 1.21 0.226 .9506827 1.238336
_rcs_tr_outcome4 | 1.013266 .0395815 0.34 0.736 .9385833 1.093892
_rcs_tr_outcome5 | .9958546 .0269688 -0.15 0.878 .9443751 1.05014
_rcs_tr_outcome6 | .9995598 .021569 -0.02 0.984 .9581668 1.042741
_rcs_tr_outcome7 | 1.022643 .0134408 1.70 0.088 .9966363 1.049329
_cons | .0458781 .0067873 -20.83 0.000 .0343301 .0613104
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m2_stipw_nostag_rp8_tvcdf7
(results m2_stipw_nostag_rp8_tvcdf7 are active now)
.
. sts gen km_b=s, by(tr_outcome)
.
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) ci contrast(difference) ///
> atvar(s_comp_b s_early_b) contrastvar(sdiff_comp_vs_early)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_comp_b rmst_early_b) contrastvar(rmstdiff_comp_vs_early)
.
. * s_tr_comp_early_b s_tr_comp_early_b_lci s_tr_comp_early_b_uci s_late_drop_b s_late_drop_b_lci s_late_drop_b_uci sdiff_tr_comp_early_vs_late sdiff_
> tr_comp_early_vs_late_lci sdiff_tr_comp_early_vs_late_uci
.
. twoway (rarea s_comp_b_lci s_comp_b_uci tt, color(gs7%35)) ///
> (rarea s_early_b_lci s_early_b_uci tt, color(gs2%35)) ///
> (line km_b _t if tr_outcome==0 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs7%50)) ///
> (line km_b _t if tr_outcome==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%50)) ///
> (line s_comp_b tt, lcolor(gs7) lwidth(thick)) ///
> (line s_early_b tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Probibability of avoiding sentence (standardized)") ///
> legend(order(5 "Tr. completion" 6 "Early dropout") ring(0) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(km_vs_standsurv_fin_b, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_22_b_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_22_b_pris_m1.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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_b_pris_m1.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
(19,277 observations deleted)
.
. recode motivodeegreso_mod_imp_rec (3=0 "Late dropout") (2=1 "Early dropout"), gen(tr_outcome)
(51586 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. }
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -21130.836
Iteration 1: log pseudolikelihood = -21040.689
Iteration 2: log pseudolikelihood = -21039.671
Iteration 3: log pseudolikelihood = -21039.67
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -21039.67 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.211284 .0474685 4.89 0.000 1.121731 1.307987
_rcs1 | 2.014463 .0243557 57.93 0.000 1.967288 2.06277
_rcs_tr_outcome1 | .9631886 .0197347 -1.83 0.067 .9252756 1.002655
_cons | .0680874 .0015996 -114.37 0.000 .0650234 .0712958
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -21070.371
Iteration 1: log pseudolikelihood = -21018.938
Iteration 2: log pseudolikelihood = -21018.632
Iteration 3: log pseudolikelihood = -21018.632
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -21018.632 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.226276 .0481771 5.19 0.000 1.135394 1.324432
_rcs1 | 2.014463 .0243557 57.93 0.000 1.967288 2.06277
_rcs_tr_outcome1 | .9774338 .0221196 -1.01 0.313 .9350277 1.021763
_rcs_tr_outcome2 | 1.079584 .0169757 4.87 0.000 1.04682 1.113374
_cons | .0680874 .0015996 -114.37 0.000 .0650234 .0712958
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -21061.925
Iteration 1: log pseudolikelihood = -21018.524
Iteration 2: log pseudolikelihood = -21018.293
Iteration 3: log pseudolikelihood = -21018.293
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -21018.293 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.226865 .0481803 5.21 0.000 1.135976 1.325026
_rcs1 | 2.014463 .0243557 57.93 0.000 1.967288 2.06277
_rcs_tr_outcome1 | .978173 .0220636 -0.98 0.328 .9358712 1.022387
_rcs_tr_outcome2 | 1.076288 .0165222 4.79 0.000 1.044387 1.109163
_rcs_tr_outcome3 | 1.012379 .0119788 1.04 0.298 .9891707 1.036131
_cons | .0680874 .0015996 -114.37 0.000 .0650234 .0712958
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -21065.025
Iteration 1: log pseudolikelihood = -21018.504
Iteration 2: log pseudolikelihood = -21018.236
Iteration 3: log pseudolikelihood = -21018.236
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -21018.236 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.226931 .0481693 5.21 0.000 1.136062 1.325069
_rcs1 | 2.014463 .0243557 57.93 0.000 1.967288 2.06277
_rcs_tr_outcome1 | .9782452 .0220607 -0.98 0.329 .9359486 1.022453
_rcs_tr_outcome2 | 1.075764 .0167773 4.68 0.000 1.043378 1.109155
_rcs_tr_outcome3 | 1.01508 .0125074 1.21 0.224 .9908598 1.039893
_rcs_tr_outcome4 | 1.002713 .0085009 0.32 0.749 .986189 1.019513
_cons | .0680874 .0015996 -114.37 0.000 .0650234 .0712958
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -21062.212
Iteration 1: log pseudolikelihood = -21018.222
Iteration 2: log pseudolikelihood = -21017.987
Iteration 3: log pseudolikelihood = -21017.987
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -21017.987 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.227171 .048176 5.21 0.000 1.136289 1.325322
_rcs1 | 2.014463 .0243557 57.93 0.000 1.967288 2.06277
_rcs_tr_outcome1 | .9785639 .0220576 -0.96 0.336 .9362729 1.022765
_rcs_tr_outcome2 | 1.074634 .0166997 4.63 0.000 1.042397 1.107868
_rcs_tr_outcome3 | 1.017877 .0127675 1.41 0.158 .9931581 1.043211
_rcs_tr_outcome4 | 1.003885 .0085999 0.45 0.651 .9871702 1.020883
_rcs_tr_outcome5 | 1.003877 .0063754 0.61 0.542 .9914592 1.016451
_cons | .0680874 .0015996 -114.37 0.000 .0650234 .0712958
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -21063.825
Iteration 1: log pseudolikelihood = -21017.245
Iteration 2: log pseudolikelihood = -21016.971
Iteration 3: log pseudolikelihood = -21016.971
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -21016.971 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.227287 .0481817 5.22 0.000 1.136395 1.32545
_rcs1 | 2.014463 .0243557 57.93 0.000 1.967288 2.06277
_rcs_tr_outcome1 | .9788531 .0220816 -0.95 0.343 .9365168 1.023103
_rcs_tr_outcome2 | 1.074533 .0171063 4.52 0.000 1.041522 1.108589
_rcs_tr_outcome3 | 1.017822 .0128222 1.40 0.161 .9929983 1.043266
_rcs_tr_outcome4 | 1.005944 .0084714 0.70 0.482 .9894762 1.022685
_rcs_tr_outcome5 | 1.002341 .0066073 0.35 0.723 .989474 1.015375
_rcs_tr_outcome6 | 1.006196 .0053576 1.16 0.246 .9957499 1.016752
_cons | .0680874 .0015996 -114.37 0.000 .0650234 .0712958
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -21062.206
Iteration 1: log pseudolikelihood = -21017.164
Iteration 2: log pseudolikelihood = -21016.906
Iteration 3: log pseudolikelihood = -21016.906
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -21016.906 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.227318 .0481843 5.22 0.000 1.136421 1.325486
_rcs1 | 2.014463 .0243557 57.93 0.000 1.967288 2.06277
_rcs_tr_outcome1 | .9789534 .0220562 -0.94 0.345 .9366646 1.023151
_rcs_tr_outcome2 | 1.072546 .0162626 4.62 0.000 1.041141 1.104899
_rcs_tr_outcome3 | 1.02201 .0124065 1.79 0.073 .9979811 1.046618
_rcs_tr_outcome4 | 1.003862 .0085379 0.45 0.650 .9872663 1.020736
_rcs_tr_outcome5 | 1.003653 .0066834 0.55 0.584 .9906386 1.016838
_rcs_tr_outcome6 | 1.005212 .0055762 0.94 0.349 .9943421 1.016201
_rcs_tr_outcome7 | 1.00464 .0046694 1.00 0.319 .9955296 1.013833
_cons | .0680874 .0015996 -114.37 0.000 .0650234 .0712958
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20997.619
Iteration 1: log pseudolikelihood = -20981.688
Iteration 2: log pseudolikelihood = -20981.621
Iteration 3: log pseudolikelihood = -20981.621
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20981.621 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.219497 .0479478 5.05 0.000 1.129051 1.317189
_rcs1 | 2.047254 .0291275 50.36 0.000 1.990954 2.105146
_rcs2 | 1.084129 .0102885 8.51 0.000 1.064151 1.104483
_rcs_tr_outcome1 | .9632472 .0229917 -1.57 0.117 .9192221 1.009381
_cons | .0684849 .0016191 -113.41 0.000 .065384 .0717329
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20998.211
Iteration 1: log pseudolikelihood = -20981.624
Iteration 2: log pseudolikelihood = -20981.526
Iteration 3: log pseudolikelihood = -20981.526
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20981.526 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.21917 .0479995 5.03 0.000 1.12863 1.316972
_rcs1 | 2.049302 .0295847 49.70 0.000 1.99213 2.108116
_rcs2 | 1.087179 .0128444 7.07 0.000 1.062294 1.112647
_rcs_tr_outcome1 | .9608171 .0230284 -1.67 0.095 .9167259 1.007029
_rcs_tr_outcome2 | .9930143 .0195236 -0.36 0.721 .9554766 1.032027
_cons | .0684842 .0016189 -113.42 0.000 .0653836 .0717319
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20989.638
Iteration 1: log pseudolikelihood = -20981.176
Iteration 2: log pseudolikelihood = -20981.154
Iteration 3: log pseudolikelihood = -20981.154
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20981.154 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.219688 .0480015 5.05 0.000 1.129144 1.317493
_rcs1 | 2.049325 .0295879 49.70 0.000 1.992147 2.108144
_rcs2 | 1.087212 .0128451 7.08 0.000 1.062326 1.112682
_rcs_tr_outcome1 | .961479 .022971 -1.64 0.100 .9174945 1.007572
_rcs_tr_outcome2 | .9899823 .0191096 -0.52 0.602 .9532279 1.028154
_rcs_tr_outcome3 | 1.007133 .0119416 0.60 0.549 .9839976 1.030812
_cons | .0684842 .0016189 -113.42 0.000 .0653836 .0717319
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20992.865
Iteration 1: log pseudolikelihood = -20981.19
Iteration 2: log pseudolikelihood = -20981.13
Iteration 3: log pseudolikelihood = -20981.13
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20981.13 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.219821 .0479919 5.05 0.000 1.129294 1.317606
_rcs1 | 2.049302 .0295847 49.70 0.000 1.99213 2.108116
_rcs2 | 1.087179 .0128444 7.07 0.000 1.062294 1.112647
_rcs_tr_outcome1 | .9616147 .0229758 -1.64 0.101 .9176211 1.007717
_rcs_tr_outcome2 | .9899556 .0193228 -0.52 0.605 .9527988 1.028561
_rcs_tr_outcome3 | 1.006227 .0124607 0.50 0.616 .982099 1.030949
_rcs_tr_outcome4 | 1.002713 .0085009 0.32 0.749 .986189 1.019513
_cons | .0684842 .0016189 -113.42 0.000 .0653836 .0717319
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20990.034
Iteration 1: log pseudolikelihood = -20980.865
Iteration 2: log pseudolikelihood = -20980.839
Iteration 3: log pseudolikelihood = -20980.839
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20980.839 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22007 .0479989 5.06 0.000 1.129529 1.317868
_rcs1 | 2.049342 .0295904 49.69 0.000 1.992159 2.108167
_rcs2 | 1.087237 .0128479 7.08 0.000 1.062345 1.112713
_rcs_tr_outcome1 | .9618967 .022975 -1.63 0.104 .9179043 1.007997
_rcs_tr_outcome2 | .9891865 .0192432 -0.56 0.576 .9521805 1.027631
_rcs_tr_outcome3 | 1.006385 .012729 0.50 0.615 .9817437 1.031646
_rcs_tr_outcome4 | 1.002942 .0085902 0.34 0.732 .9862458 1.019921
_rcs_tr_outcome5 | 1.003998 .0063775 0.63 0.530 .9915753 1.016575
_cons | .0684842 .0016189 -113.42 0.000 .0653836 .0717319
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20991.665
Iteration 1: log pseudolikelihood = -20979.931
Iteration 2: log pseudolikelihood = -20979.866
Iteration 3: log pseudolikelihood = -20979.866
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20979.866 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220176 .0480042 5.06 0.000 1.129625 1.317985
_rcs1 | 2.049302 .0295847 49.70 0.000 1.99213 2.108116
_rcs2 | 1.087179 .0128444 7.07 0.000 1.062294 1.112647
_rcs_tr_outcome1 | .9622122 .0229967 -1.61 0.107 .9181789 1.008357
_rcs_tr_outcome2 | .9893112 .0195261 -0.54 0.586 .9517715 1.028332
_rcs_tr_outcome3 | 1.005234 .0127865 0.41 0.682 .9804824 1.03061
_rcs_tr_outcome4 | 1.003997 .0084593 0.47 0.636 .9875531 1.020715
_rcs_tr_outcome5 | 1.002341 .0066073 0.35 0.723 .989474 1.015375
_rcs_tr_outcome6 | 1.006196 .0053576 1.16 0.246 .9957499 1.016752
_cons | .0684842 .0016189 -113.42 0.000 .0653836 .0717319
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20990.032
Iteration 1: log pseudolikelihood = -20979.834
Iteration 2: log pseudolikelihood = -20979.785
Iteration 3: log pseudolikelihood = -20979.785
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20979.785 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220209 .0480069 5.06 0.000 1.129654 1.318024
_rcs1 | 2.049317 .0295868 49.70 0.000 1.992141 2.108135
_rcs2 | 1.087201 .0128457 7.08 0.000 1.062313 1.112672
_rcs_tr_outcome1 | .9622971 .0229739 -1.61 0.107 .9183064 1.008395
_rcs_tr_outcome2 | .9877104 .0188744 -0.65 0.518 .9514014 1.025405
_rcs_tr_outcome3 | 1.00791 .0123936 0.64 0.522 .9839098 1.032497
_rcs_tr_outcome4 | 1.00114 .0085235 0.13 0.894 .9845729 1.017986
_rcs_tr_outcome5 | 1.003411 .0066813 0.51 0.609 .9904007 1.016592
_rcs_tr_outcome6 | 1.005244 .0055767 0.94 0.346 .9943735 1.016234
_rcs_tr_outcome7 | 1.004628 .0046693 0.99 0.320 .9955179 1.013821
_cons | .0684842 .0016189 -113.42 0.000 .0653836 .0717319
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20982.306
Iteration 1: log pseudolikelihood = -20979.318
Iteration 2: log pseudolikelihood = -20979.315
Iteration 3: log pseudolikelihood = -20979.315
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20979.315 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220404 .0479789 5.07 0.000 1.129899 1.318159
_rcs1 | 2.046918 .0289514 50.65 0.000 1.990954 2.104456
_rcs2 | 1.079292 .009815 8.39 0.000 1.060225 1.098702
_rcs3 | 1.016752 .0070246 2.40 0.016 1.003077 1.030614
_rcs_tr_outcome1 | .9647907 .0230713 -1.50 0.134 .9206152 1.011086
_cons | .0684749 .0016196 -113.36 0.000 .065373 .071724
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20982.659
Iteration 1: log pseudolikelihood = -20979.218
Iteration 2: log pseudolikelihood = -20979.212
Iteration 3: log pseudolikelihood = -20979.212
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20979.212 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220119 .0480281 5.05 0.000 1.129525 1.317979
_rcs1 | 2.049029 .0292346 50.28 0.000 1.992524 2.107136
_rcs2 | 1.082381 .0122322 7.00 0.000 1.05867 1.106624
_rcs3 | 1.016939 .0070168 2.43 0.015 1.003279 1.030785
_rcs_tr_outcome1 | .9622827 .0227742 -1.62 0.104 .9186655 1.007971
_rcs_tr_outcome2 | .992904 .0183871 -0.38 0.701 .9575121 1.029604
_cons | .0684734 .0016193 -113.38 0.000 .0653721 .0717218
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20982.479
Iteration 1: log pseudolikelihood = -20979.045
Iteration 2: log pseudolikelihood = -20979.031
Iteration 3: log pseudolikelihood = -20979.031
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20979.031 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22008 .04803 5.05 0.000 1.129483 1.317944
_rcs1 | 2.049312 .029254 50.26 0.000 1.99277 2.107458
_rcs2 | 1.081335 .0120894 6.99 0.000 1.057898 1.105291
_rcs3 | 1.019655 .0085666 2.32 0.021 1.003003 1.036584
_rcs_tr_outcome1 | .9615393 .0228837 -1.65 0.099 .917718 1.007453
_rcs_tr_outcome2 | .9953326 .0188945 -0.25 0.805 .9589804 1.033063
_rcs_tr_outcome3 | .9928637 .0144062 -0.49 0.622 .9650258 1.021505
_cons | .068466 .0016199 -113.33 0.000 .0653635 .0717158
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.12
Iteration 1: log pseudolikelihood = -20979.108
Iteration 2: log pseudolikelihood = -20979.049
Iteration 3: log pseudolikelihood = -20979.049
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20979.049 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220093 .0480176 5.05 0.000 1.129518 1.31793
_rcs1 | 2.049254 .0292482 50.27 0.000 1.992723 2.107389
_rcs2 | 1.081325 .0120946 6.99 0.000 1.057877 1.105291
_rcs3 | 1.019521 .0085471 2.31 0.021 1.002906 1.036412
_rcs_tr_outcome1 | .961617 .0228821 -1.64 0.100 .9177986 1.007527
_rcs_tr_outcome2 | .9957544 .0191939 -0.22 0.825 .9588367 1.034093
_rcs_tr_outcome3 | .9928906 .0147442 -0.48 0.631 .964409 1.022213
_rcs_tr_outcome4 | .9987354 .0086891 -0.15 0.884 .9818495 1.015912
_cons | .0684665 .0016199 -113.33 0.000 .065364 .0717162
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20982.782
Iteration 1: log pseudolikelihood = -20978.741
Iteration 2: log pseudolikelihood = -20978.725
Iteration 3: log pseudolikelihood = -20978.725
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20978.725 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220386 .0480267 5.06 0.000 1.129794 1.318242
_rcs1 | 2.049295 .0292494 50.27 0.000 1.992762 2.107432
_rcs2 | 1.081242 .0120756 6.99 0.000 1.057831 1.105171
_rcs3 | 1.019774 .0085644 2.33 0.020 1.003126 1.036699
_rcs_tr_outcome1 | .9619264 .0228784 -1.63 0.103 .9181147 1.007829
_rcs_tr_outcome2 | .9953771 .0190895 -0.24 0.809 .9586568 1.033504
_rcs_tr_outcome3 | .9940058 .0146301 -0.41 0.683 .9657411 1.023098
_rcs_tr_outcome4 | .9968514 .0091108 -0.35 0.730 .9791537 1.014869
_rcs_tr_outcome5 | 1.003335 .0063758 0.52 0.600 .9909165 1.01591
_cons | .0684657 .00162 -113.33 0.000 .0653631 .0717155
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20984.378
Iteration 1: log pseudolikelihood = -20977.765
Iteration 2: log pseudolikelihood = -20977.71
Iteration 3: log pseudolikelihood = -20977.71
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20977.71 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.2205 .0480315 5.06 0.000 1.129899 1.318366
_rcs1 | 2.049312 .029254 50.26 0.000 1.99277 2.107458
_rcs2 | 1.081335 .0120894 6.99 0.000 1.057898 1.105291
_rcs3 | 1.019655 .0085666 2.32 0.021 1.003003 1.036584
_rcs_tr_outcome1 | .9622078 .0229021 -1.62 0.106 .9183514 1.008159
_rcs_tr_outcome2 | .9955143 .0193951 -0.23 0.818 .9582172 1.034263
_rcs_tr_outcome3 | .9936679 .0144834 -0.44 0.663 .9656827 1.022464
_rcs_tr_outcome4 | .9970778 .0092192 -0.32 0.752 .9791712 1.015312
_rcs_tr_outcome5 | 1.000297 .0066525 0.04 0.964 .9873426 1.013421
_rcs_tr_outcome6 | 1.006196 .0053576 1.16 0.246 .9957499 1.016752
_cons | .068466 .0016199 -113.33 0.000 .0653635 .0717158
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20982.676
Iteration 1: log pseudolikelihood = -20977.638
Iteration 2: log pseudolikelihood = -20977.599
Iteration 3: log pseudolikelihood = -20977.599
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20977.599 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22055 .048036 5.06 0.000 1.129941 1.318426
_rcs1 | 2.049323 .0292511 50.27 0.000 1.992786 2.107463
_rcs2 | 1.081224 .0120689 7.00 0.000 1.057826 1.105139
_rcs3 | 1.019877 .0085667 2.34 0.019 1.003224 1.036806
_rcs_tr_outcome1 | .9622854 .0228786 -1.62 0.106 .9184728 1.008188
_rcs_tr_outcome2 | .9942402 .0187511 -0.31 0.759 .9581598 1.031679
_rcs_tr_outcome3 | .9968075 .013976 -0.23 0.820 .969788 1.02458
_rcs_tr_outcome4 | .9936252 .009433 -0.67 0.501 .9753078 1.012287
_rcs_tr_outcome5 | 1.000374 .0068104 0.05 0.956 .987115 1.013812
_rcs_tr_outcome6 | 1.004632 .0055765 0.83 0.405 .9937616 1.015621
_rcs_tr_outcome7 | 1.004697 .0046704 1.01 0.313 .9955851 1.013893
_cons | .0684654 .00162 -113.33 0.000 .0653628 .0717152
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20992.714
Iteration 1: log pseudolikelihood = -20978.704
Iteration 2: log pseudolikelihood = -20978.606
Iteration 3: log pseudolikelihood = -20978.606
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20978.606 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220679 .0479793 5.07 0.000 1.130172 1.318434
_rcs1 | 2.046746 .028959 50.62 0.000 1.990767 2.104298
_rcs2 | 1.079254 .010035 8.20 0.000 1.059764 1.099103
_rcs3 | 1.018378 .0073451 2.52 0.012 1.004083 1.032877
_rcs4 | 1.00678 .0049578 1.37 0.170 .9971094 1.016544
_rcs_tr_outcome1 | .9654059 .0231002 -1.47 0.141 .9211756 1.01176
_cons | .0684767 .0016195 -113.37 0.000 .065375 .0717254
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20992.978
Iteration 1: log pseudolikelihood = -20978.592
Iteration 2: log pseudolikelihood = -20978.491
Iteration 3: log pseudolikelihood = -20978.491
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20978.491 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220382 .0480279 5.06 0.000 1.129788 1.31824
_rcs1 | 2.048976 .0292717 50.21 0.000 1.992401 2.107158
_rcs2 | 1.082526 .0123709 6.94 0.000 1.058549 1.107046
_rcs3 | 1.018676 .0073654 2.56 0.010 1.004341 1.033214
_rcs4 | 1.006845 .004944 1.39 0.165 .997202 1.016582
_rcs_tr_outcome1 | .9627551 .0228314 -1.60 0.109 .9190304 1.00856
_rcs_tr_outcome2 | .9924858 .0185395 -0.40 0.686 .9568061 1.029496
_cons | .068475 .0016191 -113.40 0.000 .065374 .0717231
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20992.841
Iteration 1: log pseudolikelihood = -20978.331
Iteration 2: log pseudolikelihood = -20978.243
Iteration 3: log pseudolikelihood = -20978.243
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20978.243 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220385 .048034 5.06 0.000 1.12978 1.318256
_rcs1 | 2.049344 .0292807 50.22 0.000 1.992751 2.107545
_rcs2 | 1.081194 .0122178 6.91 0.000 1.057511 1.105408
_rcs3 | 1.021728 .0087627 2.51 0.012 1.004697 1.039048
_rcs4 | 1.007589 .0050303 1.51 0.130 .9977776 1.017496
_rcs_tr_outcome1 | .9618594 .0229251 -1.63 0.103 .9179604 1.007858
_rcs_tr_outcome2 | .995247 .0191547 -0.25 0.804 .9584037 1.033507
_rcs_tr_outcome3 | .9918135 .0142503 -0.57 0.567 .9642729 1.020141
_cons | .0684656 .0016198 -113.34 0.000 .0653633 .0717152
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20992.619
Iteration 1: log pseudolikelihood = -20978.141
Iteration 2: log pseudolikelihood = -20978.041
Iteration 3: log pseudolikelihood = -20978.041
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20978.041 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220183 .048021 5.06 0.000 1.129602 1.318027
_rcs1 | 2.049459 .0292993 50.19 0.000 1.992831 2.107697
_rcs2 | 1.081831 .0123595 6.88 0.000 1.057876 1.106328
_rcs3 | 1.020628 .00897 2.32 0.020 1.003198 1.038362
_rcs4 | 1.009548 .0059965 1.60 0.110 .9978633 1.02137
_rcs_tr_outcome1 | .9615411 .0228919 -1.65 0.099 .9177046 1.007472
_rcs_tr_outcome2 | .9943922 .0192172 -0.29 0.771 .9574316 1.03278
_rcs_tr_outcome3 | .994564 .0150501 -0.36 0.719 .9654994 1.024504
_rcs_tr_outcome4 | .9932293 .0102782 -0.66 0.511 .9732872 1.01358
_cons | .068464 .0016199 -113.33 0.000 .0653615 .0717137
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20989.077
Iteration 1: log pseudolikelihood = -20978.048
Iteration 2: log pseudolikelihood = -20977.992
Iteration 3: log pseudolikelihood = -20977.992
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20977.992 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220342 .0480218 5.06 0.000 1.129759 1.318188
_rcs1 | 2.049369 .0292893 50.21 0.000 1.99276 2.107586
_rcs2 | 1.081613 .012291 6.90 0.000 1.057789 1.105973
_rcs3 | 1.020966 .0089637 2.36 0.018 1.003547 1.038686
_rcs4 | 1.008637 .0059311 1.46 0.144 .997079 1.020329
_rcs_tr_outcome1 | .9619351 .0228876 -1.63 0.103 .9181061 1.007856
_rcs_tr_outcome2 | .9940922 .0191284 -0.31 0.758 .9572994 1.032299
_rcs_tr_outcome3 | .9959959 .015203 -0.26 0.793 .9666398 1.026243
_rcs_tr_outcome4 | .9931494 .0101597 -0.67 0.502 .9734351 1.013263
_rcs_tr_outcome5 | 1.000984 .0067662 0.15 0.884 .9878097 1.014333
_cons | .0684655 .0016198 -113.33 0.000 .0653631 .0717151
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20991.697
Iteration 1: log pseudolikelihood = -20976.853
Iteration 2: log pseudolikelihood = -20976.742
Iteration 3: log pseudolikelihood = -20976.742
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20976.742 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220533 .0480331 5.06 0.000 1.129928 1.318402
_rcs1 | 2.049502 .0293133 50.17 0.000 1.992847 2.107768
_rcs2 | 1.082184 .0124432 6.87 0.000 1.058069 1.106849
_rcs3 | 1.02005 .0089639 2.26 0.024 1.002631 1.037771
_rcs4 | 1.00995 .0059981 1.67 0.096 .9982618 1.021774
_rcs_tr_outcome1 | .9620919 .0229155 -1.62 0.105 .9182105 1.00807
_rcs_tr_outcome2 | .9937711 .0195309 -0.32 0.751 .9562192 1.032798
_rcs_tr_outcome3 | .9966828 .0151687 -0.22 0.827 .9673918 1.026861
_rcs_tr_outcome4 | .9936393 .0097006 -0.65 0.513 .9748073 1.012835
_rcs_tr_outcome5 | .996305 .0075308 -0.49 0.624 .9816537 1.011175
_rcs_tr_outcome6 | 1.005308 .0053802 0.99 0.323 .9948178 1.015908
_cons | .0684643 .0016199 -113.33 0.000 .0653619 .071714
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20989.898
Iteration 1: log pseudolikelihood = -20976.839
Iteration 2: log pseudolikelihood = -20976.749
Iteration 3: log pseudolikelihood = -20976.749
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20976.749 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220543 .0480336 5.06 0.000 1.129938 1.318413
_rcs1 | 2.049463 .0293068 50.18 0.000 1.99282 2.107716
_rcs2 | 1.082042 .0124033 6.88 0.000 1.058003 1.106627
_rcs3 | 1.020282 .008969 2.28 0.022 1.002854 1.038013
_rcs4 | 1.009602 .0059955 1.61 0.108 .9979196 1.021422
_rcs_tr_outcome1 | .9622462 .0228912 -1.62 0.106 .9184103 1.008175
_rcs_tr_outcome2 | .9924784 .0188909 -0.40 0.692 .9561351 1.030203
_rcs_tr_outcome3 | .9998335 .0147377 -0.01 0.991 .9713614 1.02914
_rcs_tr_outcome4 | .9914042 .0095888 -0.89 0.372 .9727874 1.010377
_rcs_tr_outcome5 | .9965255 .0078064 -0.44 0.657 .9813422 1.011944
_rcs_tr_outcome6 | 1.002672 .0057938 0.46 0.644 .9913804 1.014092
_rcs_tr_outcome7 | 1.004443 .0046692 0.95 0.340 .995333 1.013636
_cons | .0684647 .0016199 -113.33 0.000 .0653623 .0717144
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20983.084
Iteration 1: log pseudolikelihood = -20976.792
Iteration 2: log pseudolikelihood = -20976.775
Iteration 3: log pseudolikelihood = -20976.775
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20976.775 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220923 .0479803 5.08 0.000 1.130414 1.31868
_rcs1 | 2.046367 .0289349 50.64 0.000 1.990434 2.103872
_rcs2 | 1.077998 .009859 8.21 0.000 1.058847 1.097495
_rcs3 | 1.021176 .0075098 2.85 0.004 1.006562 1.036001
_rcs4 | 1.008119 .0051795 1.57 0.116 .9980183 1.018322
_rcs5 | 1.006876 .0037553 1.84 0.066 .9995424 1.014263
_rcs_tr_outcome1 | .9664182 .0231502 -1.43 0.154 .9220934 1.012874
_cons | .0684758 .0016193 -113.38 0.000 .0653744 .0717243
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20983.255
Iteration 1: log pseudolikelihood = -20976.663
Iteration 2: log pseudolikelihood = -20976.643
Iteration 3: log pseudolikelihood = -20976.643
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20976.643 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220614 .0480274 5.07 0.000 1.13002 1.318471
_rcs1 | 2.048758 .029253 50.23 0.000 1.992218 2.106902
_rcs2 | 1.081487 .0121428 6.98 0.000 1.057948 1.105551
_rcs3 | 1.021593 .0075621 2.89 0.004 1.006879 1.036523
_rcs4 | 1.008218 .0051578 1.60 0.110 .9981591 1.018378
_rcs5 | 1.006921 .0037509 1.85 0.064 .9995963 1.014299
_rcs_tr_outcome1 | .9635758 .0228312 -1.57 0.117 .9198506 1.009379
_rcs_tr_outcome2 | .99195 .0183865 -0.44 0.663 .9565598 1.02865
_cons | .0684739 .0016189 -113.41 0.000 .0653732 .0717216
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20983.194
Iteration 1: log pseudolikelihood = -20976.417
Iteration 2: log pseudolikelihood = -20976.396
Iteration 3: log pseudolikelihood = -20976.396
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20976.396 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220617 .048033 5.07 0.000 1.130013 1.318486
_rcs1 | 2.049131 .0292635 50.24 0.000 1.992571 2.107297
_rcs2 | 1.08016 .011974 6.96 0.000 1.056944 1.103885
_rcs3 | 1.024358 .0086654 2.84 0.004 1.007514 1.041483
_rcs4 | 1.009377 .005476 1.72 0.085 .9987015 1.020168
_rcs5 | 1.007068 .0037338 1.90 0.057 .9997763 1.014413
_rcs_tr_outcome1 | .9626785 .0229231 -1.60 0.110 .9187824 1.008672
_rcs_tr_outcome2 | .9945388 .0188917 -0.29 0.773 .9581925 1.032264
_rcs_tr_outcome3 | .992 .0140644 -0.57 0.571 .9648138 1.019952
_cons | .0684647 .0016196 -113.35 0.000 .0653629 .0717137
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20983.344
Iteration 1: log pseudolikelihood = -20975.838
Iteration 2: log pseudolikelihood = -20975.813
Iteration 3: log pseudolikelihood = -20975.813
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20975.813 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220502 .0480316 5.06 0.000 1.129901 1.318368
_rcs1 | 2.049478 .0292935 50.20 0.000 1.992861 2.107704
_rcs2 | 1.08094 .0121748 6.91 0.000 1.057339 1.105067
_rcs3 | 1.022673 .0090727 2.53 0.012 1.005044 1.04061
_rcs4 | 1.012077 .006157 1.97 0.048 1.000081 1.024217
_rcs5 | 1.008438 .0038989 2.17 0.030 1.000825 1.016109
_rcs_tr_outcome1 | .9619577 .022883 -1.63 0.103 .9181373 1.007869
_rcs_tr_outcome2 | .9937551 .0187782 -0.33 0.740 .9576238 1.03125
_rcs_tr_outcome3 | .9952003 .0149002 -0.32 0.748 .9664208 1.024837
_rcs_tr_outcome4 | .9903174 .010111 -0.95 0.341 .9706971 1.010334
_cons | .0684576 .0016198 -113.33 0.000 .0653553 .0717072
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20983.28
Iteration 1: log pseudolikelihood = -20975.99
Iteration 2: log pseudolikelihood = -20975.957
Iteration 3: log pseudolikelihood = -20975.957
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20975.957 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220556 .0480337 5.06 0.000 1.129951 1.318427
_rcs1 | 2.049513 .0292941 50.21 0.000 1.992894 2.10774
_rcs2 | 1.080548 .0120109 6.97 0.000 1.057262 1.104347
_rcs3 | 1.023539 .0091664 2.60 0.009 1.00573 1.041664
_rcs4 | 1.011053 .0064103 1.73 0.083 .9985673 1.023696
_rcs5 | 1.008991 .0045735 1.97 0.048 1.000067 1.017995
_rcs_tr_outcome1 | .9618293 .0228873 -1.64 0.102 .9180009 1.00775
_rcs_tr_outcome2 | .9945267 .0189951 -0.29 0.774 .9579853 1.032462
_rcs_tr_outcome3 | .9944677 .0153256 -0.36 0.719 .9648791 1.024964
_rcs_tr_outcome4 | .9929099 .0105792 -0.67 0.504 .9723901 1.013863
_rcs_tr_outcome5 | .9949317 .0077634 -0.65 0.515 .9798314 1.010265
_cons | .0684564 .0016198 -113.33 0.000 .0653542 .0717058
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.53
Iteration 1: log pseudolikelihood = -20975.222
Iteration 2: log pseudolikelihood = -20975.131
Iteration 3: log pseudolikelihood = -20975.131
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20975.131 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220584 .0480343 5.07 0.000 1.129978 1.318456
_rcs1 | 2.049436 .0292967 50.20 0.000 1.992813 2.107669
_rcs2 | 1.080934 .0121283 6.94 0.000 1.057423 1.104968
_rcs3 | 1.022772 .0091652 2.51 0.012 1.004965 1.040894
_rcs4 | 1.011553 .0064021 1.81 0.070 .9990824 1.024179
_rcs5 | 1.008497 .0045057 1.89 0.058 .9997045 1.017367
_rcs_tr_outcome1 | .9622158 .0229133 -1.62 0.106 .9183385 1.00819
_rcs_tr_outcome2 | .994545 .0194195 -0.28 0.779 .9572026 1.033344
_rcs_tr_outcome3 | .9947748 .0154073 -0.34 0.735 .9650308 1.025436
_rcs_tr_outcome4 | .9946946 .0103461 -0.51 0.609 .9746219 1.015181
_rcs_tr_outcome5 | .992502 .0078236 -0.95 0.340 .9772858 1.007955
_rcs_tr_outcome6 | 1.001943 .0058992 0.33 0.742 .9904469 1.013572
_cons | .0684588 .0016197 -113.34 0.000 .0653566 .0717082
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20984.679
Iteration 1: log pseudolikelihood = -20975.394
Iteration 2: log pseudolikelihood = -20975.326
Iteration 3: log pseudolikelihood = -20975.326
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20975.326 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220544 .0480327 5.06 0.000 1.12994 1.318412
_rcs1 | 2.04938 .0293018 50.18 0.000 1.992747 2.107622
_rcs2 | 1.081257 .0122272 6.91 0.000 1.057556 1.10549
_rcs3 | 1.022135 .0091691 2.44 0.015 1.004321 1.040265
_rcs4 | 1.01202 .0064341 1.88 0.060 .9994877 1.02471
_rcs5 | 1.007884 .0045549 1.74 0.082 .9989964 1.016851
_rcs_tr_outcome1 | .9623879 .0228938 -1.61 0.107 .9185469 1.008321
_rcs_tr_outcome2 | .9926461 .0187788 -0.39 0.696 .9565143 1.030143
_rcs_tr_outcome3 | .998937 .0150447 -0.07 0.944 .9698808 1.028864
_rcs_tr_outcome4 | .9918738 .0101887 -0.79 0.427 .9721039 1.012046
_rcs_tr_outcome5 | .9943236 .0076743 -0.74 0.461 .9793955 1.009479
_rcs_tr_outcome6 | .9988347 .0066235 -0.18 0.860 .9859369 1.011901
_rcs_tr_outcome7 | 1.003094 .0047562 0.65 0.515 .9938155 1.01246
_cons | .0684609 .0016197 -113.34 0.000 .0653588 .0717103
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.538
Iteration 1: log pseudolikelihood = -20973.318
Iteration 2: log pseudolikelihood = -20973.231
Iteration 3: log pseudolikelihood = -20973.231
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20973.231 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220889 .0479801 5.08 0.000 1.13038 1.318644
_rcs1 | 2.046163 .0289142 50.67 0.000 1.99027 2.103626
_rcs2 | 1.078013 .0099994 8.10 0.000 1.058592 1.097791
_rcs3 | 1.020981 .0076616 2.77 0.006 1.006075 1.036109
_rcs4 | 1.009639 .0052996 1.83 0.068 .9993056 1.02008
_rcs5 | 1.006299 .0038483 1.64 0.101 .9987843 1.01387
_rcs6 | 1.008434 .0031364 2.70 0.007 1.002305 1.0146
_rcs_tr_outcome1 | .9672266 .0231644 -1.39 0.164 .9228743 1.013711
_cons | .0684799 .0016192 -113.39 0.000 .0653787 .0717282
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.621
Iteration 1: log pseudolikelihood = -20973.154
Iteration 2: log pseudolikelihood = -20973.066
Iteration 3: log pseudolikelihood = -20973.066
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20973.066 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220554 .0480251 5.07 0.000 1.129965 1.318406
_rcs1 | 2.048848 .0292762 50.20 0.000 1.992264 2.107039
_rcs2 | 1.081917 .0122198 6.97 0.000 1.058229 1.106134
_rcs3 | 1.021489 .0077456 2.80 0.005 1.00642 1.036783
_rcs4 | 1.009782 .0052671 1.87 0.062 .9995109 1.020158
_rcs5 | 1.006361 .0038431 1.66 0.097 .998857 1.013922
_rcs6 | 1.008484 .0031293 2.72 0.006 1.00237 1.014636
_rcs_tr_outcome1 | .9640343 .0228376 -1.55 0.122 .9202966 1.009851
_rcs_tr_outcome2 | .9909971 .0184307 -0.49 0.627 .9555241 1.027787
_cons | .0684775 .0016188 -113.42 0.000 .0653771 .0717249
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.568
Iteration 1: log pseudolikelihood = -20972.866
Iteration 2: log pseudolikelihood = -20972.779
Iteration 3: log pseudolikelihood = -20972.779
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20972.779 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220579 .0480335 5.07 0.000 1.129974 1.318449
_rcs1 | 2.049183 .029271 50.23 0.000 1.992609 2.107364
_rcs2 | 1.080317 .0120511 6.93 0.000 1.056954 1.104197
_rcs3 | 1.024314 .0086953 2.83 0.005 1.007412 1.041499
_rcs4 | 1.011316 .0057728 1.97 0.049 1.000064 1.022694
_rcs5 | 1.006771 .0038297 1.77 0.076 .9992933 1.014306
_rcs6 | 1.008566 .0031209 2.76 0.006 1.002467 1.014701
_rcs_tr_outcome1 | .9631398 .0229261 -1.58 0.115 .9192375 1.009139
_rcs_tr_outcome2 | .9940946 .0190718 -0.31 0.758 .9574086 1.032186
_rcs_tr_outcome3 | .9912318 .0141681 -0.62 0.538 .9638483 1.019393
_cons | .0684673 .0016195 -113.36 0.000 .0653656 .0717162
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.621
Iteration 1: log pseudolikelihood = -20972.581
Iteration 2: log pseudolikelihood = -20972.485
Iteration 3: log pseudolikelihood = -20972.485
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20972.485 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220465 .0480279 5.06 0.000 1.129871 1.318324
_rcs1 | 2.049317 .0292803 50.22 0.000 1.992725 2.107517
_rcs2 | 1.08066 .0121401 6.91 0.000 1.057126 1.104718
_rcs3 | 1.023401 .0092152 2.57 0.010 1.005498 1.041623
_rcs4 | 1.012546 .0060301 2.09 0.036 1.000796 1.024434
_rcs5 | 1.008112 .0042479 1.92 0.055 .9998204 1.016472
_rcs6 | 1.008823 .0031218 2.84 0.005 1.002723 1.01496
_rcs_tr_outcome1 | .9627487 .0228986 -1.60 0.110 .9188983 1.008692
_rcs_tr_outcome2 | .9939782 .0191255 -0.31 0.754 .957191 1.032179
_rcs_tr_outcome3 | .9928541 .0151255 -0.47 0.638 .9636469 1.022946
_rcs_tr_outcome4 | .993056 .0099492 -0.70 0.487 .9737461 1.012749
_cons | .0684631 .0016196 -113.35 0.000 .0653613 .0717122
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.263
Iteration 1: log pseudolikelihood = -20972.226
Iteration 2: log pseudolikelihood = -20972.136
Iteration 3: log pseudolikelihood = -20972.136
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20972.136 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220709 .0480387 5.07 0.000 1.130094 1.318589
_rcs1 | 2.049679 .0292916 50.22 0.000 1.993065 2.107901
_rcs2 | 1.080296 .0119886 6.96 0.000 1.057052 1.10405
_rcs3 | 1.024049 .0094017 2.59 0.010 1.005787 1.042643
_rcs4 | 1.011782 .0065109 1.82 0.069 .9991011 1.024624
_rcs5 | 1.009265 .0044792 2.08 0.038 1.000524 1.018082
_rcs6 | 1.009939 .0033968 2.94 0.003 1.003303 1.016618
_rcs_tr_outcome1 | .9620596 .0228881 -1.63 0.104 .9182296 1.007982
_rcs_tr_outcome2 | .9949582 .0193805 -0.26 0.795 .957689 1.033678
_rcs_tr_outcome3 | .9925299 .0155061 -0.48 0.631 .962599 1.023391
_rcs_tr_outcome4 | .9939999 .0104121 -0.57 0.566 .9738005 1.014618
_rcs_tr_outcome5 | .9932847 .0076178 -0.88 0.380 .9784658 1.008328
_cons | .0684548 .0016194 -113.36 0.000 .0653534 .0717035
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20986.619
Iteration 1: log pseudolikelihood = -20972.308
Iteration 2: log pseudolikelihood = -20972.187
Iteration 3: log pseudolikelihood = -20972.187
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20972.187 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220695 .0480389 5.07 0.000 1.13008 1.318576
_rcs1 | 2.049742 .0293327 50.15 0.000 1.99305 2.108047
_rcs2 | 1.08071 .0120794 6.94 0.000 1.057292 1.104646
_rcs3 | 1.023277 .0094683 2.49 0.013 1.004886 1.042003
_rcs4 | 1.012289 .0067433 1.83 0.067 .9991583 1.025592
_rcs5 | 1.009094 .0046289 1.97 0.048 1.000062 1.018208
_rcs6 | 1.010096 .0037949 2.67 0.007 1.002686 1.017562
_rcs_tr_outcome1 | .9620057 .0229175 -1.63 0.104 .9181207 1.007988
_rcs_tr_outcome2 | .994284 .0193346 -0.29 0.768 .957102 1.032911
_rcs_tr_outcome3 | .9946692 .0155463 -0.34 0.732 .9646609 1.025611
_rcs_tr_outcome4 | .9937316 .0106674 -0.59 0.558 .9730423 1.014861
_rcs_tr_outcome5 | .9933074 .0079759 -0.84 0.403 .9777974 1.009063
_rcs_tr_outcome6 | .9961385 .0064906 -0.59 0.553 .9834981 1.008941
_cons | .0684551 .0016196 -113.34 0.000 .0653533 .0717042
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20985.635
Iteration 1: log pseudolikelihood = -20972.035
Iteration 2: log pseudolikelihood = -20971.917
Iteration 3: log pseudolikelihood = -20971.917
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20971.917 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220674 .048048 5.07 0.000 1.130043 1.318574
_rcs1 | 2.049958 .029383 50.08 0.000 1.99317 2.108364
_rcs2 | 1.08163 .0123624 6.87 0.000 1.05767 1.106133
_rcs3 | 1.021746 .0094869 2.32 0.021 1.00332 1.04051
_rcs4 | 1.013874 .0067478 2.07 0.038 1.000734 1.027186
_rcs5 | 1.007737 .0046023 1.69 0.091 .9987568 1.016798
_rcs6 | 1.010147 .0037327 2.73 0.006 1.002857 1.017489
_rcs_tr_outcome1 | .9618557 .0229243 -1.63 0.103 .9179581 1.007852
_rcs_tr_outcome2 | .9916571 .0187133 -0.44 0.657 .9556496 1.029021
_rcs_tr_outcome3 | .9998607 .0152058 -0.01 0.993 .9704977 1.030112
_rcs_tr_outcome4 | .9891118 .0107375 -1.01 0.313 .9682891 1.010382
_rcs_tr_outcome5 | .9971131 .0078564 -0.37 0.714 .9818331 1.012631
_rcs_tr_outcome6 | .9954212 .0066564 -0.69 0.493 .9824601 1.008553
_rcs_tr_outcome7 | .9982715 .00525 -0.33 0.742 .9880345 1.008614
_cons | .0684568 .0016197 -113.33 0.000 .0653547 .0717062
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20979.964
Iteration 1: log pseudolikelihood = -20971.782
Iteration 2: log pseudolikelihood = -20971.749
Iteration 3: log pseudolikelihood = -20971.749
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20971.749 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220597 .0479895 5.07 0.000 1.130072 1.318373
_rcs1 | 2.045521 .0288437 50.75 0.000 1.989762 2.102842
_rcs2 | 1.076529 .0097037 8.18 0.000 1.057677 1.095717
_rcs3 | 1.023438 .0076536 3.10 0.002 1.008547 1.038549
_rcs4 | 1.008513 .0054482 1.57 0.117 .9978913 1.019248
_rcs5 | 1.007251 .0038382 1.90 0.058 .9997561 1.014802
_rcs6 | 1.007487 .003247 2.31 0.021 1.001143 1.013871
_rcs7 | 1.007526 .0026701 2.83 0.005 1.002307 1.012773
_rcs_tr_outcome1 | .9679959 .0231798 -1.36 0.174 .9236139 1.014511
_cons | .0684875 .0016199 -113.35 0.000 .065385 .0717372
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20980.015
Iteration 1: log pseudolikelihood = -20971.606
Iteration 2: log pseudolikelihood = -20971.57
Iteration 3: log pseudolikelihood = -20971.57
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20971.57 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220256 .0480337 5.06 0.000 1.129652 1.318127
_rcs1 | 2.048318 .0292086 50.28 0.000 1.991863 2.106373
_rcs2 | 1.080566 .0119168 7.03 0.000 1.05746 1.104177
_rcs3 | 1.02404 .0077608 3.13 0.002 1.008942 1.039365
_rcs4 | 1.008673 .0054129 1.61 0.108 .9981195 1.019338
_rcs5 | 1.007331 .0038335 1.92 0.055 .9998457 1.014873
_rcs6 | 1.007544 .0032382 2.34 0.019 1.001217 1.01391
_rcs7 | 1.007572 .0026657 2.85 0.004 1.002361 1.01281
_rcs_tr_outcome1 | .9646647 .0228126 -1.52 0.128 .9209731 1.010429
_rcs_tr_outcome2 | .9906363 .0182387 -0.51 0.609 .9555264 1.027036
_cons | .0684849 .0016194 -113.38 0.000 .0653833 .0717336
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20979.992
Iteration 1: log pseudolikelihood = -20971.327
Iteration 2: log pseudolikelihood = -20971.288
Iteration 3: log pseudolikelihood = -20971.288
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20971.288 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220282 .0480412 5.06 0.000 1.129664 1.31817
_rcs1 | 2.048675 .0292117 50.30 0.000 1.992214 2.106736
_rcs2 | 1.078987 .0117383 6.99 0.000 1.056224 1.10224
_rcs3 | 1.026671 .0086312 3.13 0.002 1.009893 1.043728
_rcs4 | 1.010318 .0059771 1.74 0.083 .998671 1.022101
_rcs5 | 1.007918 .0038697 2.05 0.040 1.000362 1.015531
_rcs6 | 1.00773 .0032196 2.41 0.016 1.00144 1.014061
_rcs7 | 1.00762 .0026611 2.87 0.004 1.002418 1.012849
_rcs_tr_outcome1 | .9637549 .0229076 -1.55 0.120 .9198867 1.009715
_rcs_tr_outcome2 | .9935735 .0187315 -0.34 0.732 .9575304 1.030973
_rcs_tr_outcome3 | .991381 .0139986 -0.61 0.540 .9643203 1.019201
_cons | .0684747 .0016201 -113.32 0.000 .0653719 .0717249
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20979.98
Iteration 1: log pseudolikelihood = -20970.959
Iteration 2: log pseudolikelihood = -20970.918
Iteration 3: log pseudolikelihood = -20970.918
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20970.918 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220189 .0480386 5.05 0.000 1.129576 1.318071
_rcs1 | 2.048843 .0292193 50.30 0.000 1.992367 2.10692
_rcs2 | 1.07943 .0118496 6.96 0.000 1.056453 1.102906
_rcs3 | 1.025459 .0091694 2.81 0.005 1.007644 1.043589
_rcs4 | 1.011372 .0060565 1.89 0.059 .9995709 1.023312
_rcs5 | 1.009531 .0043514 2.20 0.028 1.001038 1.018095
_rcs6 | 1.00848 .0033046 2.58 0.010 1.002024 1.014978
_rcs7 | 1.007717 .0026576 2.91 0.004 1.002522 1.012939
_rcs_tr_outcome1 | .9632964 .0228736 -1.57 0.115 .9194922 1.009187
_rcs_tr_outcome2 | .9933677 .0186555 -0.35 0.723 .9574682 1.030613
_rcs_tr_outcome3 | .9934315 .0148357 -0.44 0.659 .9647756 1.022939
_rcs_tr_outcome4 | .9923181 .0098354 -0.78 0.437 .9732272 1.011784
_cons | .0684695 .0016203 -113.31 0.000 .0653664 .07172
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20979.953
Iteration 1: log pseudolikelihood = -20970.671
Iteration 2: log pseudolikelihood = -20970.63
Iteration 3: log pseudolikelihood = -20970.63
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20970.63 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220454 .0480625 5.06 0.000 1.129797 1.318386
_rcs1 | 2.049215 .0292389 50.28 0.000 1.992702 2.107331
_rcs2 | 1.078971 .0116605 7.03 0.000 1.056357 1.102069
_rcs3 | 1.026384 .0094438 2.83 0.005 1.008041 1.045062
_rcs4 | 1.010604 .0065458 1.63 0.103 .9978557 1.023515
_rcs5 | 1.009845 .004281 2.31 0.021 1.001489 1.018271
_rcs6 | 1.00971 .0037765 2.58 0.010 1.002336 1.017139
_rcs7 | 1.008199 .0026861 3.06 0.002 1.002948 1.013478
_rcs_tr_outcome1 | .9626311 .0228789 -1.60 0.109 .9188176 1.008534
_rcs_tr_outcome2 | .9945175 .0188796 -0.29 0.772 .9581941 1.032218
_rcs_tr_outcome3 | .9925747 .0152292 -0.49 0.627 .9631703 1.022877
_rcs_tr_outcome4 | .9938318 .0104334 -0.59 0.556 .9735917 1.014493
_rcs_tr_outcome5 | .9934595 .0075793 -0.86 0.390 .9787149 1.008426
_cons | .0684614 .0016203 -113.30 0.000 .0653581 .071712
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20980.266
Iteration 1: log pseudolikelihood = -20970.434
Iteration 2: log pseudolikelihood = -20970.388
Iteration 3: log pseudolikelihood = -20970.388
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20970.388 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220548 .0480592 5.06 0.000 1.129897 1.318472
_rcs1 | 2.04936 .0292598 50.26 0.000 1.992806 2.107518
_rcs2 | 1.078935 .0115827 7.08 0.000 1.05647 1.101877
_rcs3 | 1.026505 .0095876 2.80 0.005 1.007885 1.04547
_rcs4 | 1.010041 .0068968 1.46 0.143 .9966141 1.02365
_rcs5 | 1.010494 .0044569 2.37 0.018 1.001797 1.019267
_rcs6 | 1.009939 .0037977 2.63 0.009 1.002523 1.01741
_rcs7 | 1.008442 .0029404 2.88 0.004 1.002695 1.014222
_rcs_tr_outcome1 | .9623808 .0228916 -1.61 0.107 .9185439 1.00831
_rcs_tr_outcome2 | .9947086 .0189248 -0.28 0.780 .9582998 1.032501
_rcs_tr_outcome3 | .9929398 .0153663 -0.46 0.647 .9632746 1.023519
_rcs_tr_outcome4 | .995619 .0106707 -0.41 0.682 .974923 1.016754
_rcs_tr_outcome5 | .9913824 .0077597 -1.11 0.269 .9762898 1.006708
_rcs_tr_outcome6 | .9963481 .0063182 -0.58 0.564 .9840413 1.008809
_cons | .0684585 .0016201 -113.31 0.000 .0653556 .0717087
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20980.035
Iteration 1: log pseudolikelihood = -20970.355
Iteration 2: log pseudolikelihood = -20970.283
Iteration 3: log pseudolikelihood = -20970.283
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20970.283 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22067 .0480401 5.07 0.000 1.130053 1.318553
_rcs1 | 2.04958 .0292947 50.21 0.000 1.99296 2.107808
_rcs2 | 1.079674 .0118494 6.98 0.000 1.056697 1.10315
_rcs3 | 1.024646 .0096696 2.58 0.010 1.005868 1.043775
_rcs4 | 1.011782 .007052 1.68 0.093 .9980539 1.025698
_rcs5 | 1.009811 .004574 2.16 0.031 1.000885 1.018815
_rcs6 | 1.009363 .0038893 2.42 0.016 1.001769 1.017015
_rcs7 | 1.009627 .0031498 3.07 0.002 1.003473 1.01582
_rcs_tr_outcome1 | .9621805 .0228858 -1.62 0.105 .9183547 1.008098
_rcs_tr_outcome2 | .9933983 .018588 -0.35 0.723 .9576264 1.030506
_rcs_tr_outcome3 | .9974277 .0153363 -0.17 0.867 .9678174 1.027944
_rcs_tr_outcome4 | .9921721 .0109083 -0.71 0.475 .9710209 1.013784
_rcs_tr_outcome5 | .993902 .0080029 -0.76 0.447 .9783396 1.009712
_rcs_tr_outcome6 | .9958871 .0067276 -0.61 0.542 .9827881 1.009161
_rcs_tr_outcome7 | .9950601 .0055667 -0.89 0.376 .9842093 1.006031
_cons | .0684582 .0016197 -113.34 0.000 .0653561 .0717076
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20974.002
Iteration 1: log pseudolikelihood = -20968.32
Iteration 2: log pseudolikelihood = -20968.297
Iteration 3: log pseudolikelihood = -20968.297
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20968.297 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22097 .048016 5.08 0.000 1.130395 1.318801
_rcs1 | 2.045976 .0288436 50.78 0.000 1.990217 2.103296
_rcs2 | 1.075438 .0093932 8.33 0.000 1.057184 1.094007
_rcs3 | 1.025468 .0075682 3.41 0.001 1.010742 1.040409
_rcs4 | 1.007102 .0055985 1.27 0.203 .9961887 1.018135
_rcs5 | 1.008859 .00381 2.34 0.020 1.00142 1.016355
_rcs6 | 1.004465 .0031612 1.42 0.157 .9982879 1.01068
_rcs7 | 1.009747 .0029055 3.37 0.001 1.004069 1.015458
_rcs8 | 1.005239 .0024189 2.17 0.030 1.000509 1.009991
_rcs_tr_outcome1 | .9673992 .0231905 -1.38 0.167 .9229979 1.013936
_cons | .0684806 .0016196 -113.37 0.000 .0653787 .0717297
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20974.041
Iteration 1: log pseudolikelihood = -20968.104
Iteration 2: log pseudolikelihood = -20968.08
Iteration 3: log pseudolikelihood = -20968.08
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20968.08 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220603 .0480572 5.06 0.000 1.129955 1.318523
_rcs1 | 2.049061 .0292439 50.27 0.000 1.992538 2.107187
_rcs2 | 1.079868 .0116984 7.09 0.000 1.057181 1.103041
_rcs3 | 1.026174 .0076765 3.45 0.001 1.011238 1.04133
_rcs4 | 1.00729 .0055649 1.31 0.189 .9964419 1.018256
_rcs5 | 1.008973 .0038016 2.37 0.018 1.001549 1.016452
_rcs6 | 1.004518 .003154 1.44 0.151 .9983553 1.010719
_rcs7 | 1.009814 .0028973 3.40 0.001 1.004152 1.015509
_rcs8 | 1.005275 .0024148 2.19 0.029 1.000553 1.010019
_rcs_tr_outcome1 | .963732 .0227871 -1.56 0.118 .9200892 1.009445
_rcs_tr_outcome2 | .9897185 .0180016 -0.57 0.570 .9550575 1.025638
_cons | .0684777 .0016191 -113.40 0.000 .0653766 .0717258
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20974.033
Iteration 1: log pseudolikelihood = -20967.782
Iteration 2: log pseudolikelihood = -20967.759
Iteration 3: log pseudolikelihood = -20967.759
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20967.759 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220629 .048064 5.06 0.000 1.129969 1.318563
_rcs1 | 2.049414 .0292446 50.29 0.000 1.99289 2.107542
_rcs2 | 1.078094 .011465 7.07 0.000 1.055856 1.100801
_rcs3 | 1.028874 .0085299 3.43 0.001 1.01229 1.045729
_rcs4 | 1.009124 .0061315 1.49 0.135 .9971773 1.021213
_rcs5 | 1.009807 .0039267 2.51 0.012 1.00214 1.017532
_rcs6 | 1.004827 .0031338 1.54 0.123 .9987031 1.010988
_rcs7 | 1.00993 .0028854 3.46 0.001 1.00429 1.015601
_rcs8 | 1.005324 .0024097 2.22 0.027 1.000612 1.010058
_rcs_tr_outcome1 | .9627854 .0228955 -1.59 0.111 .9189408 1.008722
_rcs_tr_outcome2 | .9929168 .0183677 -0.38 0.701 .9575615 1.029577
_rcs_tr_outcome3 | .9907551 .0138741 -0.66 0.507 .9639322 1.018324
_cons | .0684668 .0016198 -113.34 0.000 .0653645 .0717164
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20974.035
Iteration 1: log pseudolikelihood = -20967.5
Iteration 2: log pseudolikelihood = -20967.476
Iteration 3: log pseudolikelihood = -20967.476
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20967.476 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22054 .0480606 5.06 0.000 1.129886 1.318468
_rcs1 | 2.049547 .0292497 50.28 0.000 1.993013 2.107685
_rcs2 | 1.078523 .0115345 7.07 0.000 1.056151 1.101369
_rcs3 | 1.027768 .009081 3.10 0.002 1.010123 1.045721
_rcs4 | 1.009738 .0061215 1.60 0.110 .9978112 1.021808
_rcs5 | 1.01107 .0043662 2.55 0.011 1.002549 1.019664
_rcs6 | 1.005789 .0033743 1.72 0.085 .9991969 1.012424
_rcs7 | 1.010241 .0028889 3.56 0.000 1.004595 1.015919
_rcs8 | 1.005374 .0024069 2.24 0.025 1.000668 1.010102
_rcs_tr_outcome1 | .9624161 .0228625 -1.61 0.107 .9186336 1.008285
_rcs_tr_outcome2 | .9927536 .0182325 -0.40 0.692 .957654 1.02914
_rcs_tr_outcome3 | .9925826 .0145581 -0.51 0.612 .9644555 1.02153
_rcs_tr_outcome4 | .9930583 .0097202 -0.71 0.477 .9741886 1.012293
_cons | .0684627 .0016199 -113.33 0.000 .0653602 .0717124
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20974.064
Iteration 1: log pseudolikelihood = -20967.237
Iteration 2: log pseudolikelihood = -20967.209
Iteration 3: log pseudolikelihood = -20967.209
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20967.209 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220733 .0480755 5.06 0.000 1.130052 1.318691
_rcs1 | 2.049827 .0292616 50.28 0.000 1.99327 2.107989
_rcs2 | 1.078236 .011394 7.13 0.000 1.056134 1.100801
_rcs3 | 1.028253 .0093634 3.06 0.002 1.010064 1.046769
_rcs4 | 1.009357 .0065164 1.44 0.149 .9966657 1.02221
_rcs5 | 1.011184 .0043268 2.60 0.009 1.002739 1.0197
_rcs6 | 1.006697 .0037057 1.81 0.070 .9994601 1.013986
_rcs7 | 1.011064 .0031282 3.56 0.000 1.004952 1.017214
_rcs8 | 1.005541 .0024016 2.31 0.021 1.000845 1.010259
_rcs_tr_outcome1 | .9618749 .0228556 -1.64 0.102 .9181058 1.007731
_rcs_tr_outcome2 | .99362 .0183017 -0.35 0.728 .9583891 1.030146
_rcs_tr_outcome3 | .9925844 .0148182 -0.50 0.618 .963962 1.022057
_rcs_tr_outcome4 | .9932447 .0103551 -0.65 0.516 .973155 1.013749
_rcs_tr_outcome5 | .9942346 .0076494 -0.75 0.452 .9793546 1.009341
_cons | .0684557 .0016199 -113.32 0.000 .0653532 .0717055
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20974.155
Iteration 1: log pseudolikelihood = -20966.911
Iteration 2: log pseudolikelihood = -20966.874
Iteration 3: log pseudolikelihood = -20966.874
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20966.874 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.221068 .0480884 5.07 0.000 1.130362 1.319052
_rcs1 | 2.050278 .0293038 50.23 0.000 1.993641 2.108525
_rcs2 | 1.078207 .0113567 7.15 0.000 1.056176 1.100697
_rcs3 | 1.028175 .0095614 2.99 0.003 1.009605 1.047087
_rcs4 | 1.009188 .0069086 1.34 0.182 .9957377 1.02282
_rcs5 | 1.011199 .0043201 2.61 0.009 1.002767 1.019701
_rcs6 | 1.007095 .0036811 1.93 0.053 .9999055 1.014335
_rcs7 | 1.012005 .0034352 3.52 0.000 1.005294 1.01876
_rcs8 | 1.006047 .0024464 2.48 0.013 1.001263 1.010853
_rcs_tr_outcome1 | .9611952 .0228707 -1.66 0.096 .9173986 1.007083
_rcs_tr_outcome2 | .9939139 .0182828 -0.33 0.740 .9587185 1.030401
_rcs_tr_outcome3 | .9933821 .0149164 -0.44 0.658 .9645725 1.023052
_rcs_tr_outcome4 | .9943947 .0107162 -0.52 0.602 .9736116 1.015622
_rcs_tr_outcome5 | .9926906 .0077804 -0.94 0.349 .9775578 1.008058
_rcs_tr_outcome6 | .9945615 .0063496 -0.85 0.393 .982194 1.007085
_cons | .068448 .00162 -113.31 0.000 .0653454 .0716979
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20973.41
Iteration 1: log pseudolikelihood = -20966.233
Iteration 2: log pseudolikelihood = -20966.196
Iteration 3: log pseudolikelihood = -20966.196
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20966.196 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.221519 .0481072 5.08 0.000 1.130778 1.319542
_rcs1 | 2.050978 .0293767 50.15 0.000 1.994201 2.109371
_rcs2 | 1.07854 .0114616 7.11 0.000 1.056308 1.10124
_rcs3 | 1.027263 .009702 2.85 0.004 1.008423 1.046456
_rcs4 | 1.009896 .0071817 1.38 0.166 .9959176 1.02407
_rcs5 | 1.011679 .0044741 2.63 0.009 1.002948 1.020487
_rcs6 | 1.006008 .00367 1.64 0.101 .9988401 1.013227
_rcs7 | 1.012311 .0033912 3.65 0.000 1.005687 1.01898
_rcs8 | 1.007398 .0026713 2.78 0.005 1.002176 1.012647
_rcs_tr_outcome1 | .9603829 .0229003 -1.70 0.090 .9165318 1.006332
_rcs_tr_outcome2 | .9934341 .0180578 -0.36 0.717 .9586644 1.029465
_rcs_tr_outcome3 | .9958884 .0149929 -0.27 0.784 .9669321 1.025712
_rcs_tr_outcome4 | .9924572 .010992 -0.68 0.494 .9711455 1.014237
_rcs_tr_outcome5 | .9938174 .0078613 -0.78 0.433 .9785284 1.009345
_rcs_tr_outcome6 | .9960213 .0066355 -0.60 0.550 .9831006 1.009112
_rcs_tr_outcome7 | .9932733 .005442 -1.23 0.218 .9826643 1.003997
_cons | .0684414 .0016196 -113.33 0.000 .0653395 .0716906
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20971.824
Iteration 1: log pseudolikelihood = -20967.954
Iteration 2: log pseudolikelihood = -20967.942
Iteration 3: log pseudolikelihood = -20967.942
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20967.942 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.221253 .0480785 5.08 0.000 1.130565 1.319216
_rcs1 | 2.046564 .0288948 50.72 0.000 1.990708 2.103988
_rcs2 | 1.074807 .0092277 8.40 0.000 1.056872 1.093046
_rcs3 | 1.026952 .007514 3.63 0.000 1.01233 1.041785
_rcs4 | 1.006317 .0056835 1.12 0.265 .9952392 1.017519
_rcs5 | 1.009639 .0038695 2.50 0.012 1.002083 1.017252
_rcs6 | 1.003953 .0031464 1.26 0.208 .9978049 1.010139
_rcs7 | 1.007475 .0028409 2.64 0.008 1.001922 1.013058
_rcs8 | 1.00795 .0026242 3.04 0.002 1.00282 1.013107
_rcs9 | 1.005313 .0022404 2.38 0.017 1.000931 1.009714
_rcs_tr_outcome1 | .9668788 .0232421 -1.40 0.161 .9223816 1.013523
_cons | .0684764 .0016202 -113.32 0.000 .0653733 .0717268
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20971.859
Iteration 1: log pseudolikelihood = -20967.721
Iteration 2: log pseudolikelihood = -20967.708
Iteration 3: log pseudolikelihood = -20967.708
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20967.708 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220882 .048117 5.06 0.000 1.130125 1.318928
_rcs1 | 2.049777 .0293045 50.20 0.000 1.993138 2.108025
_rcs2 | 1.079395 .0115799 7.12 0.000 1.056936 1.102332
_rcs3 | 1.027732 .0076244 3.69 0.000 1.012897 1.042785
_rcs4 | 1.006528 .0056546 1.16 0.247 .995506 1.017672
_rcs5 | 1.00978 .0038541 2.55 0.011 1.002254 1.017362
_rcs6 | 1.004007 .0031413 1.28 0.201 .9978694 1.010183
_rcs7 | 1.007545 .0028311 2.67 0.007 1.002011 1.013109
_rcs8 | 1.008008 .0026179 3.07 0.002 1.00289 1.013152
_rcs9 | 1.005346 .0022362 2.40 0.017 1.000973 1.009739
_rcs_tr_outcome1 | .9630701 .0228135 -1.59 0.112 .9193787 1.008838
_rcs_tr_outcome2 | .9893475 .0178574 -0.59 0.553 .9549596 1.024974
_cons | .0684732 .0016197 -113.35 0.000 .065371 .0717226
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20971.858
Iteration 1: log pseudolikelihood = -20967.337
Iteration 2: log pseudolikelihood = -20967.324
Iteration 3: log pseudolikelihood = -20967.324
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20967.324 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220944 .0481275 5.06 0.000 1.130168 1.319012
_rcs1 | 2.050227 .029317 50.21 0.000 1.993564 2.1085
_rcs2 | 1.077431 .0113069 7.11 0.000 1.055496 1.099821
_rcs3 | 1.03057 .0084526 3.67 0.000 1.014136 1.047271
_rcs4 | 1.00856 .0062002 1.39 0.166 .996481 1.020786
_rcs5 | 1.010892 .0040783 2.69 0.007 1.00293 1.018917
_rcs6 | 1.004453 .0031325 1.42 0.154 .9983317 1.010611
_rcs7 | 1.007764 .0028133 2.77 0.006 1.002265 1.013293
_rcs8 | 1.008086 .0026094 3.11 0.002 1.002985 1.013214
_rcs9 | 1.005412 .0022298 2.43 0.015 1.001051 1.009792
_rcs_tr_outcome1 | .9619648 .0229385 -1.63 0.104 .9180405 1.007991
_rcs_tr_outcome2 | .9927973 .0181928 -0.39 0.693 .9577727 1.029103
_rcs_tr_outcome3 | .98994 .0137993 -0.73 0.468 .96326 1.017359
_cons | .0684607 .0016205 -113.28 0.000 .0653571 .0717117
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20971.899
Iteration 1: log pseudolikelihood = -20967.02
Iteration 2: log pseudolikelihood = -20967.005
Iteration 3: log pseudolikelihood = -20967.005
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20967.005 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220855 .0481247 5.06 0.000 1.130084 1.318917
_rcs1 | 2.050355 .029319 50.21 0.000 1.993688 2.108632
_rcs2 | 1.077945 .0113896 7.10 0.000 1.055851 1.100501
_rcs3 | 1.029253 .0090144 3.29 0.001 1.011736 1.047073
_rcs4 | 1.008961 .0061393 1.47 0.143 .9969996 1.021066
_rcs5 | 1.01209 .0044337 2.74 0.006 1.003437 1.020817
_rcs6 | 1.005653 .0034737 1.63 0.103 .998868 1.012485
_rcs7 | 1.008383 .0028871 2.92 0.004 1.00274 1.014058
_rcs8 | 1.008273 .0026034 3.19 0.001 1.003184 1.013389
_rcs9 | 1.005447 .0022263 2.45 0.014 1.001093 1.00982
_rcs_tr_outcome1 | .9615903 .0229 -1.64 0.100 .9177384 1.007537
_rcs_tr_outcome2 | .9925798 .0179963 -0.41 0.681 .957927 1.028486
_rcs_tr_outcome3 | .9921623 .0144235 -0.54 0.588 .9642916 1.020838
_rcs_tr_outcome4 | .9924434 .0097242 -0.77 0.439 .9735662 1.011687
_cons | .0684563 .0016206 -113.27 0.000 .0653525 .0717075
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20971.9
Iteration 1: log pseudolikelihood = -20966.809
Iteration 2: log pseudolikelihood = -20966.791
Iteration 3: log pseudolikelihood = -20966.791
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20966.791 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22103 .0481367 5.07 0.000 1.130237 1.319117
_rcs1 | 2.050607 .0293315 50.21 0.000 1.993917 2.10891
_rcs2 | 1.077677 .0112387 7.17 0.000 1.055874 1.099931
_rcs3 | 1.029773 .0093084 3.25 0.001 1.01169 1.04818
_rcs4 | 1.008638 .0064116 1.35 0.176 .9961498 1.021284
_rcs5 | 1.011928 .0045391 2.64 0.008 1.00307 1.020863
_rcs6 | 1.006151 .0035513 1.74 0.082 .9992148 1.013136
_rcs7 | 1.009222 .0032731 2.83 0.005 1.002828 1.015658
_rcs8 | 1.008766 .0026673 3.30 0.001 1.003552 1.014008
_rcs9 | 1.005521 .0022219 2.49 0.013 1.001176 1.009885
_rcs_tr_outcome1 | .9611154 .0228941 -1.67 0.096 .9172752 1.007051
_rcs_tr_outcome2 | .9933657 .0180235 -0.37 0.714 .958661 1.029327
_rcs_tr_outcome3 | .9921909 .0146123 -0.53 0.595 .9639607 1.021248
_rcs_tr_outcome4 | .9928359 .010316 -0.69 0.489 .9728214 1.013262
_rcs_tr_outcome5 | .9944043 .0075066 -0.74 0.457 .9797999 1.009226
_cons | .0684503 .0016206 -113.27 0.000 .0653466 .0717015
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20971.948
Iteration 1: log pseudolikelihood = -20966.588
Iteration 2: log pseudolikelihood = -20966.565
Iteration 3: log pseudolikelihood = -20966.565
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20966.565 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.221186 .0481383 5.07 0.000 1.13039 1.319276
_rcs1 | 2.050816 .0293599 50.17 0.000 1.994072 2.109175
_rcs2 | 1.077676 .0111896 7.20 0.000 1.055966 1.099832
_rcs3 | 1.029733 .0095087 3.17 0.002 1.011264 1.048539
_rcs4 | 1.008354 .0067486 1.24 0.214 .9952134 1.021668
_rcs5 | 1.011995 .0044733 2.70 0.007 1.003266 1.020801
_rcs6 | 1.006472 .0037034 1.75 0.080 .9992395 1.013757
_rcs7 | 1.009643 .003309 2.93 0.003 1.003179 1.01615
_rcs8 | 1.009278 .0029077 3.21 0.001 1.003595 1.014993
_rcs9 | 1.005717 .0022158 2.59 0.010 1.001383 1.010069
_rcs_tr_outcome1 | .9607685 .0229096 -1.68 0.093 .9168996 1.006736
_rcs_tr_outcome2 | .9935606 .0179841 -0.36 0.721 .9589303 1.029442
_rcs_tr_outcome3 | .9930014 .0146646 -0.48 0.634 .9646713 1.022164
_rcs_tr_outcome4 | .993969 .0106843 -0.56 0.574 .9732472 1.015132
_rcs_tr_outcome5 | .9924382 .0077672 -0.97 0.332 .977331 1.007779
_rcs_tr_outcome6 | .9958343 .0063126 -0.66 0.510 .9835384 1.008284
_cons | .0684462 .0016206 -113.26 0.000 .0653425 .0716973
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20971.781
Iteration 1: log pseudolikelihood = -20966.058
Iteration 2: log pseudolikelihood = -20966.031
Iteration 3: log pseudolikelihood = -20966.031
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20966.031 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.221605 .0481424 5.08 0.000 1.1308 1.319702
_rcs1 | 2.051322 .0293957 50.14 0.000 1.994509 2.109753
_rcs2 | 1.078007 .0113241 7.15 0.000 1.056039 1.100432
_rcs3 | 1.028459 .0096978 2.98 0.003 1.009626 1.047642
_rcs4 | 1.009144 .0070751 1.30 0.194 .9953718 1.023107
_rcs5 | 1.01239 .0045339 2.75 0.006 1.003543 1.021316
_rcs6 | 1.005786 .0036139 1.61 0.108 .9987274 1.012894
_rcs7 | 1.009345 .003366 2.79 0.005 1.00277 1.015964
_rcs8 | 1.010538 .003033 3.49 0.000 1.004611 1.0165
_rcs9 | 1.006539 .0022892 2.87 0.004 1.002062 1.011035
_rcs_tr_outcome1 | .9601421 .0229078 -1.70 0.088 .9162773 1.006107
_rcs_tr_outcome2 | .9932104 .0177396 -0.38 0.703 .9590431 1.028595
_rcs_tr_outcome3 | .9960026 .0147368 -0.27 0.787 .9675337 1.025309
_rcs_tr_outcome4 | .9919613 .011003 -0.73 0.467 .9706284 1.013763
_rcs_tr_outcome5 | .9940758 .0079446 -0.74 0.457 .9786259 1.00977
_rcs_tr_outcome6 | .9954826 .0065499 -0.69 0.491 .9827274 1.008403
_rcs_tr_outcome7 | .9939319 .0054548 -1.11 0.267 .983298 1.004681
_cons | .0684402 .0016202 -113.29 0.000 .0653373 .0716905
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20969.336
Iteration 1: log pseudolikelihood = -20965.668
Iteration 2: log pseudolikelihood = -20965.657
Iteration 3: log pseudolikelihood = -20965.657
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20965.657 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220962 .0480575 5.07 0.000 1.130312 1.318881
_rcs1 | 2.046067 .0288781 50.72 0.000 1.990243 2.103458
_rcs2 | 1.074514 .0092144 8.38 0.000 1.056605 1.092727
_rcs3 | 1.027274 .0075099 3.68 0.000 1.01266 1.0421
_rcs4 | 1.006518 .0056571 1.16 0.248 .9954907 1.017667
_rcs5 | 1.009374 .0039395 2.39 0.017 1.001682 1.017125
_rcs6 | 1.004702 .0030908 1.52 0.127 .9986622 1.010778
_rcs7 | 1.004667 .0027891 1.68 0.094 .9992154 1.010148
_rcs8 | 1.008841 .0025883 3.43 0.001 1.003781 1.013927
_rcs9 | 1.00623 .0024593 2.54 0.011 1.001421 1.011062
_rcs10 | 1.005194 .0021544 2.42 0.016 1.00098 1.009425
_rcs_tr_outcome1 | .9674648 .0232334 -1.38 0.168 .9229833 1.01409
_cons | .0684833 .0016201 -113.34 0.000 .0653806 .0717334
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20969.361
Iteration 1: log pseudolikelihood = -20965.436
Iteration 2: log pseudolikelihood = -20965.424
Iteration 3: log pseudolikelihood = -20965.424
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20965.424 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220591 .0480961 5.06 0.000 1.129872 1.318593
_rcs1 | 2.049277 .029292 50.20 0.000 1.992662 2.1075
_rcs2 | 1.07909 .0115569 7.11 0.000 1.056675 1.10198
_rcs3 | 1.028084 .0076267 3.73 0.000 1.013244 1.043142
_rcs4 | 1.006744 .0056339 1.20 0.230 .995762 1.017847
_rcs5 | 1.009528 .0039187 2.44 0.015 1.001877 1.017238
_rcs6 | 1.004767 .0030867 1.55 0.122 .9987356 1.010835
_rcs7 | 1.00473 .0027804 1.71 0.088 .9992951 1.010194
_rcs8 | 1.008907 .0025806 3.47 0.001 1.003862 1.013978
_rcs9 | 1.006284 .0024529 2.57 0.010 1.001488 1.011103
_rcs10 | 1.005221 .0021509 2.43 0.015 1.001015 1.009446
_rcs_tr_outcome1 | .9636555 .0228123 -1.56 0.118 .9199656 1.00942
_rcs_tr_outcome2 | .98935 .0178587 -0.59 0.553 .9549595 1.024979
_cons | .0684801 .0016196 -113.37 0.000 .0653783 .0717292
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20969.36
Iteration 1: log pseudolikelihood = -20965.065
Iteration 2: log pseudolikelihood = -20965.053
Iteration 3: log pseudolikelihood = -20965.053
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20965.053 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220642 .0481057 5.06 0.000 1.129906 1.318664
_rcs1 | 2.049692 .0293016 50.20 0.000 1.993059 2.107934
_rcs2 | 1.077123 .0112957 7.08 0.000 1.05521 1.099492
_rcs3 | 1.030762 .0084223 3.71 0.000 1.014386 1.047403
_rcs4 | 1.008752 .0061597 1.43 0.154 .9967513 1.020897
_rcs5 | 1.010737 .0042278 2.55 0.011 1.002485 1.019058
_rcs6 | 1.005332 .0031074 1.72 0.085 .9992603 1.011441
_rcs7 | 1.005024 .002764 1.82 0.068 .999621 1.010456
_rcs8 | 1.009035 .0025661 3.54 0.000 1.004018 1.014077
_rcs9 | 1.006365 .0024448 2.61 0.009 1.001585 1.011168
_rcs10 | 1.005281 .002144 2.47 0.014 1.001088 1.009492
_rcs_tr_outcome1 | .9626026 .0229373 -1.60 0.110 .9186799 1.008625
_rcs_tr_outcome2 | .9927668 .0181865 -0.40 0.692 .9577543 1.029059
_rcs_tr_outcome3 | .9901088 .0137824 -0.71 0.475 .963461 1.017494
_cons | .0684681 .0016203 -113.30 0.000 .0653648 .0717187
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20969.407
Iteration 1: log pseudolikelihood = -20964.738
Iteration 2: log pseudolikelihood = -20964.723
Iteration 3: log pseudolikelihood = -20964.723
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20964.723 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220556 .0481029 5.06 0.000 1.129826 1.318573
_rcs1 | 2.049831 .0293056 50.20 0.000 1.99319 2.108081
_rcs2 | 1.077647 .0113801 7.08 0.000 1.055572 1.100184
_rcs3 | 1.029453 .0089915 3.32 0.001 1.01198 1.047227
_rcs4 | 1.009008 .0060804 1.49 0.137 .9971608 1.020996
_rcs5 | 1.011773 .0044788 2.64 0.008 1.003033 1.020589
_rcs6 | 1.006547 .0034785 1.89 0.059 .9997527 1.013388
_rcs7 | 1.005879 .0029493 2.00 0.046 1.000115 1.011677
_rcs8 | 1.009407 .0025727 3.67 0.000 1.004377 1.014462
_rcs9 | 1.006481 .0024392 2.67 0.008 1.001712 1.011274
_rcs10 | 1.005326 .0021383 2.50 0.013 1.001144 1.009526
_rcs_tr_outcome1 | .9622145 .0229004 -1.62 0.106 .9183613 1.008162
_rcs_tr_outcome2 | .9925551 .0179913 -0.41 0.680 .9579118 1.028451
_rcs_tr_outcome3 | .9922776 .0144255 -0.53 0.594 .9644032 1.020958
_rcs_tr_outcome4 | .9924469 .0096911 -0.78 0.437 .9736332 1.011624
_cons | .0684636 .0016204 -113.29 0.000 .0653602 .0717143
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20969.398
Iteration 1: log pseudolikelihood = -20964.434
Iteration 2: log pseudolikelihood = -20964.417
Iteration 3: log pseudolikelihood = -20964.417
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20964.417 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220778 .0481164 5.06 0.000 1.130022 1.318822
_rcs1 | 2.050144 .0293185 50.20 0.000 1.993479 2.10842
_rcs2 | 1.077356 .0112256 7.15 0.000 1.055578 1.099584
_rcs3 | 1.029972 .0092988 3.27 0.001 1.011907 1.04836
_rcs4 | 1.008679 .0062673 1.39 0.164 .9964703 1.021038
_rcs5 | 1.011531 .004688 2.47 0.013 1.002384 1.020761
_rcs6 | 1.006914 .0034252 2.03 0.043 1.000223 1.01365
_rcs7 | 1.006776 .0032638 2.08 0.037 1.0004 1.013193
_rcs8 | 1.010254 .0028396 3.63 0.000 1.004704 1.015835
_rcs9 | 1.00682 .0024432 2.80 0.005 1.002043 1.01162
_rcs10 | 1.005394 .0021325 2.54 0.011 1.001223 1.009583
_rcs_tr_outcome1 | .9616275 .0228891 -1.64 0.100 .917796 1.007552
_rcs_tr_outcome2 | .9934083 .0180175 -0.36 0.715 .9587149 1.029357
_rcs_tr_outcome3 | .9924117 .0146072 -0.52 0.605 .9641911 1.021458
_rcs_tr_outcome4 | .9927925 .0102895 -0.70 0.485 .9728288 1.013166
_rcs_tr_outcome5 | .9937635 .0075499 -0.82 0.410 .9790756 1.008672
_cons | .0684561 .0016204 -113.29 0.000 .0653527 .0717068
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20969.452
Iteration 1: log pseudolikelihood = -20964.216
Iteration 2: log pseudolikelihood = -20964.194
Iteration 3: log pseudolikelihood = -20964.194
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20964.194 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.220992 .0481189 5.07 0.000 1.130231 1.319041
_rcs1 | 2.050429 .0293526 50.16 0.000 1.993699 2.108774
_rcs2 | 1.077396 .0111953 7.17 0.000 1.055676 1.099564
_rcs3 | 1.029803 .0095284 3.17 0.002 1.011295 1.048648
_rcs4 | 1.008612 .0065583 1.32 0.187 .9958392 1.021548
_rcs5 | 1.011524 .0046749 2.48 0.013 1.002402 1.020728
_rcs6 | 1.006925 .0035819 1.94 0.052 .9999289 1.01397
_rcs7 | 1.00699 .0032044 2.19 0.029 1.000729 1.01329
_rcs8 | 1.010846 .0030593 3.56 0.000 1.004868 1.01686
_rcs9 | 1.007345 .0025455 2.90 0.004 1.002368 1.012346
_rcs10 | 1.005525 .002123 2.61 0.009 1.001373 1.009695
_rcs_tr_outcome1 | .9611973 .0229046 -1.66 0.097 .9173373 1.007154
_rcs_tr_outcome2 | .9935334 .0179505 -0.36 0.720 .9589667 1.029346
_rcs_tr_outcome3 | .9933713 .0146596 -0.45 0.652 .9650506 1.022523
_rcs_tr_outcome4 | .9937185 .0107146 -0.58 0.559 .9729385 1.014942
_rcs_tr_outcome5 | .9926615 .0077725 -0.94 0.347 .977544 1.008013
_rcs_tr_outcome6 | .9950229 .0061791 -0.80 0.422 .9829856 1.007208
_cons | .0684513 .0016204 -113.28 0.000 .065348 .071702
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
Fitting weighted survival model to obtain point estimates
Iteration 0: log pseudolikelihood = -20969.424
Iteration 1: log pseudolikelihood = -20963.852
Iteration 2: log pseudolikelihood = -20963.824
Iteration 3: log pseudolikelihood = -20963.824
Displaying weighted survival model with M-estimation standard errors
Log pseudolikelihood = -20963.824 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22122 .048115 5.07 0.000 1.130466 1.319261
_rcs1 | 2.050737 .0293825 50.13 0.000 1.993949 2.109141
_rcs2 | 1.07778 .0113192 7.13 0.000 1.055821 1.100195
_rcs3 | 1.028655 .0097365 2.98 0.003 1.009748 1.047916
_rcs4 | 1.009154 .0068451 1.34 0.179 .995827 1.02266
_rcs5 | 1.012047 .004643 2.61 0.009 1.002988 1.021188
_rcs6 | 1.006725 .0035929 1.88 0.060 .999708 1.013792
_rcs7 | 1.006259 .0032671 1.92 0.055 .9998756 1.012683
_rcs8 | 1.011027 .0029967 3.70 0.000 1.005171 1.016918
_rcs9 | 1.008346 .0027481 3.05 0.002 1.002974 1.013747
_rcs10 | 1.005936 .0021281 2.80 0.005 1.001774 1.010116
_rcs_tr_outcome1 | .9608485 .0229072 -1.68 0.094 .916984 1.006811
_rcs_tr_outcome2 | .9930366 .0177297 -0.39 0.696 .958888 1.028401
_rcs_tr_outcome3 | .9960543 .0147459 -0.27 0.789 .9675681 1.025379
_rcs_tr_outcome4 | .9919881 .0109979 -0.73 0.468 .970665 1.013779
_rcs_tr_outcome5 | .994013 .0078425 -0.76 0.447 .9787601 1.009504
_rcs_tr_outcome6 | .9953834 .0065151 -0.71 0.480 .9826956 1.008235
_rcs_tr_outcome7 | .9943039 .0054199 -1.05 0.295 .9837377 1.004984
_cons | .0684487 .00162 -113.31 0.000 .0653462 .0716986
------------------------------------------------------------------------------------
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. }
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
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 = -21442.776
Iteration 1: log pseudolikelihood = -21431.411
Iteration 2: log pseudolikelihood = -21431.389
Iteration 3: log pseudolikelihood = -21431.389
Displaying weighted survival model with M-estimation standard errors
Exponential PH regression Number of obs = 51,586
Wald chi2(1) = 14.12
Log pseudolikelihood = -21431.389 Prob > chi2 = 0.0002
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.160561 .0459854 3.76 0.000 1.073842 1.254283
_cons | .0185564 .0004272 -173.18 0.000 .0177376 .0194129
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
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 = -21442.776
Iteration 1: log pseudolikelihood = -21064.375
Iteration 2: log pseudolikelihood = -21057.02
Iteration 3: log pseudolikelihood = -21057.016
Iteration 4: log pseudolikelihood = -21057.016
Fitting full model:
Iteration 0: log pseudolikelihood = -21057.016
Iteration 1: log pseudolikelihood = -21041.585
Iteration 2: log pseudolikelihood = -21041.543
Iteration 3: log pseudolikelihood = -21041.543
Displaying weighted survival model with M-estimation standard errors
Weibull PH regression Number of obs = 51,586
Wald chi2(1) = 20.02
Log pseudolikelihood = -21041.543 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.189888 .0462369 4.47 0.000 1.102631 1.284051
_cons | .0302299 .000841 -125.77 0.000 .0286257 .031924
-------------+----------------------------------------------------------------
/ln_p | -.3553182 .0142658 -24.91 0.000 -.3832786 -.3273578
-------------+----------------------------------------------------------------
p | .7009504 .0099996 .681623 .7208258
1/p | 1.426635 .020352 1.387298 1.467087
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
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 = -21442.437
Iteration 1: log pseudolikelihood = -21050.257
Iteration 2: log pseudolikelihood = -21028.924
Iteration 3: log pseudolikelihood = -21028.863
Iteration 4: log pseudolikelihood = -21028.863
Fitting full model:
Iteration 0: log pseudolikelihood = -21028.863
Iteration 1: log pseudolikelihood = -21010.778
Iteration 2: log pseudolikelihood = -21010.719
Iteration 3: log pseudolikelihood = -21010.719
Displaying weighted survival model with M-estimation standard errors
Gompertz PH regression Number of obs = 51,586
Wald chi2(1) = 23.84
Log pseudolikelihood = -21010.719 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | 1.207378 .0465965 4.88 0.000 1.11942 1.302249
_cons | .0321903 .0010816 -102.26 0.000 .0301387 .0343815
-------------+----------------------------------------------------------------
/gamma | -.2185929 .0110709 -19.74 0.000 -.2402915 -.1968942
------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
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 = -34528.881
Iteration 1: log pseudolikelihood = -22187.361
Iteration 2: log pseudolikelihood = -21096.857
Iteration 3: log pseudolikelihood = -20999.398
Iteration 4: log pseudolikelihood = -20999.253
Iteration 5: log pseudolikelihood = -20999.253
Fitting full model:
Iteration 0: log pseudolikelihood = -20999.253
Iteration 1: log pseudolikelihood = -20980.907
Iteration 2: log pseudolikelihood = -20980.776
Iteration 3: log pseudolikelihood = -20980.776
Displaying weighted survival model with M-estimation standard errors
Lognormal AFT regression Number of obs = 51,586
Wald chi2(1) = 24.62
Log pseudolikelihood = -20980.776 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .7364073 .0454074 -4.96 0.000 .6525779 .8310054
_cons | 302.7045 23.12375 74.78 0.000 260.6125 351.595
-------------+----------------------------------------------------------------
/lnsigma | 1.118728 .014647 76.38 0.000 1.090021 1.147436
-------------+----------------------------------------------------------------
sigma | 3.06096 .044834 2.974336 3.150106
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
Fitting logistic regression to obtain denominator for weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -23737.54
Iteration 2: log likelihood = -23522.537
Iteration 3: log likelihood = -23521.67
Iteration 4: log likelihood = -23521.67
Fitting second logistic regression with no confounders to obtain numerator for stabilised weights
Iteration 0: log likelihood = -31779.455
Iteration 1: log likelihood = -31779.455
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 = -21405.044
Iteration 1: log pseudolikelihood = -21047.393
Iteration 2: log pseudolikelihood = -21043.515
Iteration 3: log pseudolikelihood = -21043.506
Iteration 4: log pseudolikelihood = -21043.506
Fitting full model:
Iteration 0: log pseudolikelihood = -21043.506
Iteration 1: log pseudolikelihood = -21027.542
Iteration 2: log pseudolikelihood = -21027.405
Iteration 3: log pseudolikelihood = -21027.405
Displaying weighted survival model with M-estimation standard errors
Loglogistic AFT regression Number of obs = 51,586
Wald chi2(1) = 20.79
Log pseudolikelihood = -21027.405 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| M-estimation
_t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tr_outcome | .7717785 .0438507 -4.56 0.000 .6904455 .8626924
_cons | 123.4825 7.132068 83.38 0.000 110.2661 138.2831
-------------+----------------------------------------------------------------
/lngamma | .3243963 .0143813 22.56 0.000 .2962095 .3525831
-------------+----------------------------------------------------------------
gamma | 1.383195 .0198921 1.344752 1.422738
------------------------------------------------------------------------------
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 | 4,480 . -21039.67 4 42087.34 42112.97
m3_stipw_n~2 | 4,480 . -21018.63 5 42047.26 42079.3
m3_stipw_n~3 | 4,480 . -21018.29 6 42048.59 42087.03
m3_stipw_n~4 | 4,480 . -21018.24 7 42050.47 42095.32
m3_stipw_n~5 | 4,480 . -21017.99 8 42051.97 42103.23
m3_stipw_n~6 | 4,480 . -21016.97 9 42051.94 42109.61
m3_stipw_n~7 | 4,480 . -21016.91 10 42053.81 42117.89
m3_stipw_n~1 | 4,480 . -20981.62 5 41973.24 42005.28
m3_stipw_n~2 | 4,480 . -20981.53 6 41975.05 42013.5
m3_stipw_n~3 | 4,480 . -20981.15 7 41976.31 42021.16
m3_stipw_n~4 | 4,480 . -20981.13 8 41978.26 42029.52
m3_stipw_n~5 | 4,480 . -20980.84 9 41979.68 42037.34
m3_stipw_n~6 | 4,480 . -20979.87 10 41979.73 42043.81
m3_stipw_n~7 | 4,480 . -20979.78 11 41981.57 42052.05
m3_stipw_n~1 | 4,480 . -20979.31 6 41970.63 42009.07
m3_stipw_n~2 | 4,480 . -20979.21 7 41972.42 42017.28
m3_stipw_n~3 | 4,480 . -20979.03 8 41974.06 42025.32
m3_stipw_n~4 | 4,480 . -20979.05 9 41976.1 42033.76
m3_stipw_n~5 | 4,480 . -20978.72 10 41977.45 42041.52
m3_stipw_n~6 | 4,480 . -20977.71 11 41977.42 42047.9
m3_stipw_n~7 | 4,480 . -20977.6 12 41979.2 42056.09
m3_stipw_n~1 | 4,480 . -20978.61 7 41971.21 42016.06
m3_stipw_n~2 | 4,480 . -20978.49 8 41972.98 42024.24
m3_stipw_n~3 | 4,480 . -20978.24 9 41974.49 42032.15
m3_stipw_n~4 | 4,480 . -20978.04 10 41976.08 42040.16
m3_stipw_n~5 | 4,480 . -20977.99 11 41977.98 42048.46
m3_stipw_n~6 | 4,480 . -20976.74 12 41977.48 42054.37
m3_stipw_n~7 | 4,480 . -20976.75 13 41979.5 42062.79
m3_stipw_n~1 | 4,480 . -20976.77 8 41969.55 42020.81
m3_stipw_n~2 | 4,480 . -20976.64 9 41971.29 42028.95
m3_stipw_n~3 | 4,480 . -20976.4 10 41972.79 42036.87
m3_stipw_n~4 | 4,480 . -20975.81 11 41973.63 42044.11
m3_stipw_n~5 | 4,480 . -20975.96 12 41975.91 42052.8
m3_stipw_n~6 | 4,480 . -20975.13 13 41976.26 42059.56
m3_stipw_n~7 | 4,480 . -20975.33 14 41978.65 42068.36
m3_stipw_n~1 | 4,480 . -20973.23 9 41964.46 42022.13
m3_stipw_n~2 | 4,480 . -20973.07 10 41966.13 42030.21
m3_stipw_n~3 | 4,480 . -20972.78 11 41967.56 42038.04
m3_stipw_n~4 | 4,480 . -20972.49 12 41968.97 42045.86
m3_stipw_n~5 | 4,480 . -20972.14 13 41970.27 42053.57
m3_stipw_n~6 | 4,480 . -20972.19 14 41972.37 42062.08
m3_stipw_n~7 | 4,480 . -20971.92 15 41973.83 42069.95
m3_stipw_n~1 | 4,480 . -20971.75 10 41963.5 42027.57
m3_stipw_n~2 | 4,480 . -20971.57 11 41965.14 42035.62
m3_stipw_n~3 | 4,480 . -20971.29 12 41966.58 42043.46
m3_stipw_n~4 | 4,480 . -20970.92 13 41967.84 42051.13
m3_stipw_n~5 | 4,480 . -20970.63 14 41969.26 42058.96
m3_stipw_n~6 | 4,480 . -20970.39 15 41970.78 42066.89
m3_stipw_n~7 | 4,480 . -20970.28 16 41972.57 42075.08
m3_stipw_n~1 | 4,480 . -20968.3 11 41958.59 42029.08
m3_stipw_n~2 | 4,480 . -20968.08 12 41960.16 42037.05
m3_stipw_n~3 | 4,480 . -20967.76 13 41961.52 42044.81
m3_stipw_n~4 | 4,480 . -20967.48 14 41962.95 42052.66
m3_stipw_n~5 | 4,480 . -20967.21 15 41964.42 42060.53
m3_stipw_n~6 | 4,480 . -20966.87 16 41965.75 42068.27
m3_stipw_n~7 | 4,480 . -20966.2 17 41966.39 42075.32
m3_stipw_n~1 | 4,480 . -20967.94 12 41959.88 42036.77
m3_stipw_n~2 | 4,480 . -20967.71 13 41961.42 42044.71
m3_stipw_n~3 | 4,480 . -20967.32 14 41962.65 42052.35
m3_stipw_n~4 | 4,480 . -20967.01 15 41964.01 42060.12
m3_stipw_n~5 | 4,480 . -20966.79 16 41965.58 42068.1
m3_stipw_n~6 | 4,480 . -20966.57 17 41967.13 42076.06
m3_stipw_n~7 | 4,480 . -20966.03 18 41968.06 42083.39
m3_stipw_n~1 | 4,480 . -20965.66 13 41957.31 42040.61
m3_stipw_n~2 | 4,480 . -20965.42 14 41958.85 42048.55
m3_stipw_n~3 | 4,480 . -20965.05 15 41960.11 42056.22
m3_stipw_n~4 | 4,480 . -20964.72 16 41961.45 42063.96
m3_stipw_n~5 | 4,480 . -20964.42 17 41962.83 42071.76
m3_stipw_n~6 | 4,480 . -20964.19 18 41964.39 42079.72
m3_stipw_n~7 | 4,480 . -20963.82 19 41965.65 42087.39
m3_stipw_n~p | 4,480 -21442.78 -21431.39 2 42866.78 42879.59
m3_stipw_n~i | 4,480 -21057.02 -21041.54 3 42089.09 42108.31
m3_stipw_n~m | 4,480 -21028.86 -21010.72 3 42027.44 42046.66
m3_stipw_n~n | 4,480 -20999.25 -20980.78 3 41967.55 41986.77
m3_stipw_n~g | 4,480 -21043.51 -21027.4 3 42060.81 42080.03
-----------------------------------------------------------------------------
. //we store in a matrix de survival
. matrix stats_4=r(S)
. mata : st_sort_matrix("stats_4", 5) // 5 AIC, 6 BIC
. esttab matrix(stats_4) using "testreg_aic_bic_mrl_23_4_pris_m1.csv", replace
(output written to testreg_aic_bic_mrl_23_4_pris_m1.csv)
. esttab matrix(stats_4) using "testreg_aic_bic_mrl_23_4_pris_m1.html", replace
(output written to testreg_aic_bic_mrl_23_4_pris_m1.html)
.
| stats_4 | ||||||
| N | ll0 | ll | df | AIC | BIC | |
| m3_stipw_nostag_rp10_tvcdf1 | 4480 | . | -20965.66 | 13 | 41957.31 | 42040.61 |
| m3_stipw_nostag_rp8_tvcdf1 | 4480 | . | -20968.3 | 11 | 41958.59 | 42029.08 |
| m3_stipw_nostag_rp10_tvcdf2 | 4480 | . | -20965.42 | 14 | 41958.85 | 42048.55 |
| m3_stipw_nostag_rp9_tvcdf1 | 4480 | . | -20967.94 | 12 | 41959.88 | 42036.77 |
| m3_stipw_nostag_rp10_tvcdf3 | 4480 | . | -20965.05 | 15 | 41960.11 | 42056.22 |
| m3_stipw_nostag_rp8_tvcdf2 | 4480 | . | -20968.08 | 12 | 41960.16 | 42037.05 |
| m3_stipw_nostag_rp9_tvcdf2 | 4480 | . | -20967.71 | 13 | 41961.42 | 42044.71 |
| m3_stipw_nostag_rp10_tvcdf4 | 4480 | . | -20964.72 | 16 | 41961.45 | 42063.96 |
| m3_stipw_nostag_rp8_tvcdf3 | 4480 | . | -20967.76 | 13 | 41961.52 | 42044.81 |
| m3_stipw_nostag_rp9_tvcdf3 | 4480 | . | -20967.32 | 14 | 41962.65 | 42052.35 |
| m3_stipw_nostag_rp10_tvcdf5 | 4480 | . | -20964.42 | 17 | 41962.83 | 42071.76 |
| m3_stipw_nostag_rp8_tvcdf4 | 4480 | . | -20967.48 | 14 | 41962.95 | 42052.66 |
| m3_stipw_nostag_rp7_tvcdf1 | 4480 | . | -20971.75 | 10 | 41963.5 | 42027.57 |
| m3_stipw_nostag_rp9_tvcdf4 | 4480 | . | -20967.01 | 15 | 41964.01 | 42060.12 |
| m3_stipw_nostag_rp10_tvcdf6 | 4480 | . | -20964.19 | 18 | 41964.39 | 42079.72 |
| m3_stipw_nostag_rp8_tvcdf5 | 4480 | . | -20967.21 | 15 | 41964.42 | 42060.53 |
| m3_stipw_nostag_rp6_tvcdf1 | 4480 | . | -20973.23 | 9 | 41964.46 | 42022.13 |
| m3_stipw_nostag_rp7_tvcdf2 | 4480 | . | -20971.57 | 11 | 41965.14 | 42035.62 |
| m3_stipw_nostag_rp9_tvcdf5 | 4480 | . | -20966.79 | 16 | 41965.58 | 42068.1 |
| m3_stipw_nostag_rp10_tvcdf7 | 4480 | . | -20963.82 | 19 | 41965.65 | 42087.39 |
| m3_stipw_nostag_rp8_tvcdf6 | 4480 | . | -20966.87 | 16 | 41965.75 | 42068.27 |
| m3_stipw_nostag_rp6_tvcdf2 | 4480 | . | -20973.07 | 10 | 41966.13 | 42030.21 |
| m3_stipw_nostag_rp8_tvcdf7 | 4480 | . | -20966.2 | 17 | 41966.39 | 42075.32 |
| m3_stipw_nostag_rp7_tvcdf3 | 4480 | . | -20971.29 | 12 | 41966.58 | 42043.46 |
| m3_stipw_nostag_rp9_tvcdf6 | 4480 | . | -20966.57 | 17 | 41967.13 | 42076.06 |
| m3_stipw_nostag_logn | 4480 | -20999.25 | -20980.78 | 3 | 41967.55 | 41986.77 |
| m3_stipw_nostag_rp6_tvcdf3 | 4480 | . | -20972.78 | 11 | 41967.56 | 42038.04 |
| m3_stipw_nostag_rp7_tvcdf4 | 4480 | . | -20970.92 | 13 | 41967.84 | 42051.13 |
| m3_stipw_nostag_rp9_tvcdf7 | 4480 | . | -20966.03 | 18 | 41968.06 | 42083.39 |
| m3_stipw_nostag_rp6_tvcdf4 | 4480 | . | -20972.49 | 12 | 41968.97 | 42045.86 |
| m3_stipw_nostag_rp7_tvcdf5 | 4480 | . | -20970.63 | 14 | 41969.26 | 42058.96 |
| m3_stipw_nostag_rp5_tvcdf1 | 4480 | . | -20976.77 | 8 | 41969.55 | 42020.81 |
| m3_stipw_nostag_rp6_tvcdf5 | 4480 | . | -20972.14 | 13 | 41970.27 | 42053.57 |
| m3_stipw_nostag_rp3_tvcdf1 | 4480 | . | -20979.31 | 6 | 41970.63 | 42009.07 |
| m3_stipw_nostag_rp7_tvcdf6 | 4480 | . | -20970.39 | 15 | 41970.78 | 42066.89 |
| m3_stipw_nostag_rp4_tvcdf1 | 4480 | . | -20978.61 | 7 | 41971.21 | 42016.06 |
| m3_stipw_nostag_rp5_tvcdf2 | 4480 | . | -20976.64 | 9 | 41971.29 | 42028.95 |
| m3_stipw_nostag_rp6_tvcdf6 | 4480 | . | -20972.19 | 14 | 41972.37 | 42062.08 |
| m3_stipw_nostag_rp3_tvcdf2 | 4480 | . | -20979.21 | 7 | 41972.42 | 42017.28 |
| m3_stipw_nostag_rp7_tvcdf7 | 4480 | . | -20970.28 | 16 | 41972.57 | 42075.08 |
| m3_stipw_nostag_rp5_tvcdf3 | 4480 | . | -20976.4 | 10 | 41972.79 | 42036.87 |
| m3_stipw_nostag_rp4_tvcdf2 | 4480 | . | -20978.49 | 8 | 41972.98 | 42024.24 |
| m3_stipw_nostag_rp2_tvcdf1 | 4480 | . | -20981.62 | 5 | 41973.24 | 42005.28 |
| m3_stipw_nostag_rp5_tvcdf4 | 4480 | . | -20975.81 | 11 | 41973.63 | 42044.11 |
| m3_stipw_nostag_rp6_tvcdf7 | 4480 | . | -20971.92 | 15 | 41973.83 | 42069.95 |
| m3_stipw_nostag_rp3_tvcdf3 | 4480 | . | -20979.03 | 8 | 41974.06 | 42025.32 |
| m3_stipw_nostag_rp4_tvcdf3 | 4480 | . | -20978.24 | 9 | 41974.49 | 42032.15 |
| m3_stipw_nostag_rp2_tvcdf2 | 4480 | . | -20981.53 | 6 | 41975.05 | 42013.5 |
| m3_stipw_nostag_rp5_tvcdf5 | 4480 | . | -20975.96 | 12 | 41975.91 | 42052.8 |
| m3_stipw_nostag_rp4_tvcdf4 | 4480 | . | -20978.04 | 10 | 41976.08 | 42040.16 |
| m3_stipw_nostag_rp3_tvcdf4 | 4480 | . | -20979.05 | 9 | 41976.1 | 42033.76 |
| m3_stipw_nostag_rp5_tvcdf6 | 4480 | . | -20975.13 | 13 | 41976.26 | 42059.56 |
| m3_stipw_nostag_rp2_tvcdf3 | 4480 | . | -20981.15 | 7 | 41976.31 | 42021.16 |
| m3_stipw_nostag_rp3_tvcdf6 | 4480 | . | -20977.71 | 11 | 41977.42 | 42047.9 |
| m3_stipw_nostag_rp3_tvcdf5 | 4480 | . | -20978.72 | 10 | 41977.45 | 42041.52 |
| m3_stipw_nostag_rp4_tvcdf6 | 4480 | . | -20976.74 | 12 | 41977.48 | 42054.37 |
| m3_stipw_nostag_rp4_tvcdf5 | 4480 | . | -20977.99 | 11 | 41977.98 | 42048.46 |
| m3_stipw_nostag_rp2_tvcdf4 | 4480 | . | -20981.13 | 8 | 41978.26 | 42029.52 |
| m3_stipw_nostag_rp5_tvcdf7 | 4480 | . | -20975.33 | 14 | 41978.65 | 42068.36 |
| m3_stipw_nostag_rp3_tvcdf7 | 4480 | . | -20977.6 | 12 | 41979.2 | 42056.09 |
| m3_stipw_nostag_rp4_tvcdf7 | 4480 | . | -20976.75 | 13 | 41979.5 | 42062.79 |
| m3_stipw_nostag_rp2_tvcdf5 | 4480 | . | -20980.84 | 9 | 41979.68 | 42037.34 |
| m3_stipw_nostag_rp2_tvcdf6 | 4480 | . | -20979.87 | 10 | 41979.73 | 42043.81 |
| m3_stipw_nostag_rp2_tvcdf7 | 4480 | . | -20979.78 | 11 | 41981.57 | 42052.05 |
| m3_stipw_nostag_gom | 4480 | -21028.86 | -21010.72 | 3 | 42027.44 | 42046.66 |
| m3_stipw_nostag_rp1_tvcdf2 | 4480 | . | -21018.63 | 5 | 42047.26 | 42079.3 |
| m3_stipw_nostag_rp1_tvcdf3 | 4480 | . | -21018.29 | 6 | 42048.59 | 42087.03 |
| m3_stipw_nostag_rp1_tvcdf4 | 4480 | . | -21018.24 | 7 | 42050.47 | 42095.32 |
| m3_stipw_nostag_rp1_tvcdf6 | 4480 | . | -21016.97 | 9 | 42051.94 | 42109.61 |
| m3_stipw_nostag_rp1_tvcdf5 | 4480 | . | -21017.99 | 8 | 42051.97 | 42103.23 |
| m3_stipw_nostag_rp1_tvcdf7 | 4480 | . | -21016.91 | 10 | 42053.81 | 42117.89 |
| m3_stipw_nostag_llog | 4480 | -21043.51 | -21027.4 | 3 | 42060.81 | 42080.03 |
| m3_stipw_nostag_rp1_tvcdf1 | 4480 | . | -21039.67 | 4 | 42087.34 | 42112.97 |
| m3_stipw_nostag_wei | 4480 | -21057.02 | -21041.54 | 3 | 42089.09 | 42108.31 |
| m3_stipw_nostag_exp | 4480 | -21442.78 | -21431.39 | 2 | 42866.78 | 42879.59 |
.
. estimates replay m3_stipw_nostag_rp8_tvcdf1, eform
------------------------------------------------------------------------------------------------------------------------------------------------------
Model m3_stipw_nostag_rp8_tvcdf1
------------------------------------------------------------------------------------------------------------------------------------------------------
Log pseudolikelihood = -20968.297 Number of obs = 51,586
------------------------------------------------------------------------------------
| M-estimation
| exp(b) Std. Err. z P>|z| [95% Conf. Interval]
-------------------+----------------------------------------------------------------
xb |
tr_outcome | 1.22097 .048016 5.08 0.000 1.130395 1.318801
_rcs1 | 2.045976 .0288436 50.78 0.000 1.990217 2.103296
_rcs2 | 1.075438 .0093932 8.33 0.000 1.057184 1.094007
_rcs3 | 1.025468 .0075682 3.41 0.001 1.010742 1.040409
_rcs4 | 1.007102 .0055985 1.27 0.203 .9961887 1.018135
_rcs5 | 1.008859 .00381 2.34 0.020 1.00142 1.016355
_rcs6 | 1.004465 .0031612 1.42 0.157 .9982879 1.01068
_rcs7 | 1.009747 .0029055 3.37 0.001 1.004069 1.015458
_rcs8 | 1.005239 .0024189 2.17 0.030 1.000509 1.009991
_rcs_tr_outcome1 | .9673992 .0231905 -1.38 0.167 .9229979 1.013936
_cons | .0684806 .0016196 -113.37 0.000 .0653787 .0717297
------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
. estimates restore m3_stipw_nostag_rp8_tvcdf1 // m3_stipw_nostag_rp5_tvcdf1
(results m3_stipw_nostag_rp8_tvcdf1 are active now)
.
. sts gen km_c=s, by(tr_outcome)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) ci contrast(difference) ///
> atvar(s_late_c s_early_c) contrastvar(sdiff_late_vs_early)
.
. * s_tr_comp_early_b s_tr_comp_early_b_lci s_tr_comp_early_b_uci s_late_drop_b s_late_drop_b_lci s_late_drop_b_uci sdiff_tr_comp_early_vs_late sdiff_
> tr_comp_early_vs_late_lci sdiff_tr_comp_early_vs_late_uci
.
. twoway (rarea s_late_c_lci s_late_c_uci tt, color(gs7%35)) ///
> (rarea s_early_c_lci s_early_c_uci tt, color(gs2%35)) ///
> (line km_c _t if tr_outcome==0 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs7%50)) ///
> (line km_c _t if tr_outcome==1 , sort connect(stairstep) lpattern(dash) lwidth(medthick) lcolor(gs2%50)) ///
> (line s_late_c tt, lcolor(gs7) lwidth(thick)) ///
> (line s_early_c tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Probibability of avoiding sentence (standardized)") ///
> legend(order(5 "Late dropout" 6 "Early dropout") ring(0) pos(1) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(km_vs_standsurv_fin_c, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_22_c_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_22_c_pris_m1.gph saved)
.
. estimates restore m3_stipw_nostag_rp8_tvcdf1
(results m3_stipw_nostag_rp8_tvcdf1 are active now)
.
. stpm2_standsurv, at1(tr_outcome 0 ) at2(tr_outcome 1 ) timevar(tt) rmst ci contrast(difference) ///
> atvar(rmst_late_c rmst_early_c) contrastvar(rmstdiff_late_vs_early)
.
. twoway (rarea rmst_late_c_lci rmst_late_c_uci tt, color(gs7%35)) ///
> (rarea rmst_early_c_lci rmst_early_c_uci tt, color(gs2%35)) ///
> (line rmst_late_c tt, lcolor(gs7) lwidth(thick)) ///
> (line rmst_early_c tt, lcolor(gs2) lwidth(thick)) ///
> ,xtitle("Years from treatment outcome") ///
> ytitle("Restricted Mean Survival Times (standardized)") ///
> legend(order(3 "Late dropout" 4 "Early dropout") ring(0) pos(5) cols(1) region(lstyle(none)) region(c(none)) nobox) ///
> graphregion(color(white) lwidth(large)) bgcolor(white) ///
> plotregion(fcolor(white)) graphregion(fcolor(white) ) /// //text(.5 1 "IR = <0.001") ///
> name(rmst_std_fin_c, replace)
(note: named style large not found in class linewidth, default attributes used)
(note: linewidth not found in scheme, default attributes used)
. graph save "`c(pwd)'\_figs\h_m_ns_rp5_stdif_rmst_c_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_c_pris_m1.gph saved)
Summary
. frame change default
. cap gen tt2= round(tt,.01)
.
. frame late: cap gen tt2= round(tt,.01)
. frame late: drop if missing(tt)
(55,019 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)
(70,816 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)
(70,816 missing values generated)
(70,816 missing values generated)
(70,816 missing values generated)
(70,816 missing values generated)
(70,817 missing values generated)
(70,817 missing values generated)
(6 variables copied from linked frame)
.
. frame early: cap gen tt2= round(tt,.01)
. frame early: drop if missing(tt)
(35,050 observations deleted)
. frlink m:1 tt2, frame(early)
(70,839 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)
(70,839 missing values generated)
(70,839 missing values generated)
(70,839 missing values generated)
(70,839 missing values generated)
(70,840 missing values generated)
(70,840 missing values generated)
(6 variables copied from linked frame)
.
. frame early_late: cap gen tt2= round(tt,.01)
. frame early_late: drop if missing(tt)
(51,545 observations deleted)
. frlink m:1 tt2, frame(early_late)
(70,822 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)
(70,822 missing values generated)
(70,822 missing values generated)
(70,822 missing values generated)
(70,822 missing values generated)
(70,822 missing values generated)
(70,822 missing values generated)
(6 variables copied from linked frame)
.
. twoway (rarea sdiff_comp_vs_late_lci sdiff_comp_vs_late_uci tt, color(gs2%35)) ///
> (line sdiff_comp_vs_late tt, lcolor(gs2)) ///
> (rarea sdiff_comp_vs_early_lci sdiff_comp_vs_early_uci tt, color(gs6%35)) ///
> (line sdiff_comp_vs_early tt, lcolor(gs6)) ///
> (rarea sdiff_late_vs_early_lci sdiff_late_vs_early_uci tt, color(gs10%35)) ///
> (line sdiff_late_vs_early tt, lcolor(gs10)) ///
> (line zero tt, lcolor(black%20) lwidth(thick)) ///
> , ylabel(, format(%3.1f)) ///
> ytitle("Difference in Survival (years)") ///
> xtitle("Years from baseline treatment outcome") ///
> legend(order( 1 "Late dropout vs. Tr. completion" 3 "Early dropout vs. Tr. completion" 5 "Early vs. late dropout") ring(0) pos(7) 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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_s_abc_pris_m1.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_pris_m1.gph", replace
(file C:\Users\CISS Fondecyt\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\h_m_ns_rp5_stdif_rmst_abc_pris_m1.gph saved)
Saved at= 05:01:17 8 Apr 2023
. estwrite _all using "mariel_feb_23_2_m1.sters", replace
(saving full_spline)
(saving linear_term)
(saving m_nostag_rp1_tvc_1)
(saving m_nostag_rp1_tvc_2)
(saving m_nostag_rp1_tvc_3)
(saving m_nostag_rp1_tvc_4)
(saving m_nostag_rp1_tvc_5)
(saving m_nostag_rp1_tvc_6)
(saving m_nostag_rp1_tvc_7)
(saving m_nostag_rp2_tvc_1)
(saving m_nostag_rp2_tvc_2)
(saving m_nostag_rp2_tvc_3)
(saving m_nostag_rp2_tvc_4)
(saving m_nostag_rp2_tvc_5)
(saving m_nostag_rp2_tvc_6)
(saving m_nostag_rp2_tvc_7)
(saving m_nostag_rp3_tvc_1)
(saving m_nostag_rp3_tvc_2)
(saving m_nostag_rp3_tvc_3)
(saving m_nostag_rp3_tvc_4)
(saving m_nostag_rp3_tvc_5)
(saving m_nostag_rp3_tvc_6)
(saving m_nostag_rp3_tvc_7)
(saving m_nostag_rp4_tvc_1)
(saving m_nostag_rp4_tvc_2)
(saving m_nostag_rp4_tvc_3)
(saving m_nostag_rp4_tvc_4)
(saving m_nostag_rp4_tvc_5)
(saving m_nostag_rp4_tvc_6)
(saving m_nostag_rp4_tvc_7)
(saving m_nostag_rp5_tvc_1)
(saving m_nostag_rp5_tvc_2)
(saving m_nostag_rp5_tvc_3)
(saving m_nostag_rp5_tvc_4)
(saving m_nostag_rp5_tvc_5)
(saving m_nostag_rp5_tvc_6)
(saving m_nostag_rp5_tvc_7)
(saving m_nostag_rp6_tvc_1)
(saving m_nostag_rp6_tvc_2)
(saving m_nostag_rp6_tvc_3)
(saving m_nostag_rp6_tvc_4)
(saving m_nostag_rp6_tvc_5)
(saving m_nostag_rp6_tvc_6)
(saving m_nostag_rp6_tvc_7)
(saving m_nostag_rp7_tvc_1)
(saving m_nostag_rp7_tvc_2)
(saving m_nostag_rp7_tvc_3)
(saving m_nostag_rp7_tvc_4)
(saving m_nostag_rp7_tvc_5)
(saving m_nostag_rp7_tvc_6)
(saving m_nostag_rp7_tvc_7)
(saving m_nostag_rp8_tvc_1)
(saving m_nostag_rp8_tvc_2)
(saving m_nostag_rp8_tvc_3)
(saving m_nostag_rp8_tvc_4)
(saving m_nostag_rp8_tvc_5)
(saving m_nostag_rp8_tvc_6)
(saving m_nostag_rp8_tvc_7)
(saving m_nostag_rp9_tvc_1)
(saving m_nostag_rp9_tvc_2)
(saving m_nostag_rp9_tvc_3)
(saving m_nostag_rp9_tvc_4)
(saving m_nostag_rp9_tvc_5)
(saving m_nostag_rp9_tvc_6)
(saving m_nostag_rp9_tvc_7)
(saving m_nostag_rp10_tvc_1)
(saving m_nostag_rp10_tvc_2)
(saving m_nostag_rp10_tvc_3)
(saving m_nostag_rp10_tvc_4)
(saving m_nostag_rp10_tvc_5)
(saving m_nostag_rp10_tvc_6)
(saving m_nostag_rp10_tvc_7)
(saving m_stipw_nostag_rp1_tvcdf1)
(saving m_stipw_nostag_rp1_tvcdf2)
(saving m_stipw_nostag_rp1_tvcdf3)
(saving m_stipw_nostag_rp1_tvcdf4)
(saving m_stipw_nostag_rp1_tvcdf5)
(saving m_stipw_nostag_rp1_tvcdf6)
(saving m_stipw_nostag_rp1_tvcdf7)
(saving m_stipw_nostag_rp2_tvcdf1)
(saving m_stipw_nostag_rp2_tvcdf2)
(saving m_stipw_nostag_rp2_tvcdf3)
(saving m_stipw_nostag_rp2_tvcdf4)
(saving m_stipw_nostag_rp2_tvcdf5)
(saving m_stipw_nostag_rp2_tvcdf6)
(saving m_stipw_nostag_rp2_tvcdf7)
(saving m_stipw_nostag_rp3_tvcdf1)
(saving m_stipw_nostag_rp3_tvcdf2)
(saving m_stipw_nostag_rp3_tvcdf3)
(saving m_stipw_nostag_rp3_tvcdf4)
(saving m_stipw_nostag_rp3_tvcdf5)
(saving m_stipw_nostag_rp3_tvcdf6)
(saving m_stipw_nostag_rp3_tvcdf7)
(saving m_stipw_nostag_rp4_tvcdf1)
(saving m_stipw_nostag_rp4_tvcdf2)
(saving m_stipw_nostag_rp4_tvcdf3)
(saving m_stipw_nostag_rp4_tvcdf4)
(saving m_stipw_nostag_rp4_tvcdf5)
(saving m_stipw_nostag_rp4_tvcdf6)
(saving m_stipw_nostag_rp4_tvcdf7)
(saving m_stipw_nostag_rp5_tvcdf1)
(saving m_stipw_nostag_rp5_tvcdf2)
(saving m_stipw_nostag_rp5_tvcdf3)
(saving m_stipw_nostag_rp5_tvcdf4)
(saving m_stipw_nostag_rp5_tvcdf5)
(saving m_stipw_nostag_rp5_tvcdf6)
(saving m_stipw_nostag_rp5_tvcdf7)
(saving m_stipw_nostag_rp6_tvcdf1)
(saving m_stipw_nostag_rp6_tvcdf2)
(saving m_stipw_nostag_rp6_tvcdf3)
(saving m_stipw_nostag_rp6_tvcdf4)
(saving m_stipw_nostag_rp6_tvcdf5)
(saving m_stipw_nostag_rp6_tvcdf6)
(saving m_stipw_nostag_rp6_tvcdf7)
(saving m_stipw_nostag_rp7_tvcdf1)
(saving m_stipw_nostag_rp7_tvcdf2)
(saving m_stipw_nostag_rp7_tvcdf3)
(saving m_stipw_nostag_rp7_tvcdf4)
(saving m_stipw_nostag_rp7_tvcdf5)
(saving m_stipw_nostag_rp7_tvcdf6)
(saving m_stipw_nostag_rp7_tvcdf7)
(saving m_stipw_nostag_rp8_tvcdf1)
(saving m_stipw_nostag_rp8_tvcdf2)
(saving m_stipw_nostag_rp8_tvcdf3)
(saving m_stipw_nostag_rp8_tvcdf4)
(saving m_stipw_nostag_rp8_tvcdf5)
(saving m_stipw_nostag_rp8_tvcdf6)
(saving m_stipw_nostag_rp8_tvcdf7)
(saving m_stipw_nostag_rp9_tvcdf1)
(saving m_stipw_nostag_rp9_tvcdf2)
(saving m_stipw_nostag_rp9_tvcdf3)
(saving m_stipw_nostag_rp9_tvcdf4)
(saving m_stipw_nostag_rp9_tvcdf5)
(saving m_stipw_nostag_rp9_tvcdf6)
(saving m_stipw_nostag_rp9_tvcdf7)
(saving m_stipw_nostag_rp10_tvcdf1)
(saving m_stipw_nostag_rp10_tvcdf2)
(saving m_stipw_nostag_rp10_tvcdf3)
(saving m_stipw_nostag_rp10_tvcdf4)
(saving m_stipw_nostag_rp10_tvcdf5)
(saving m_stipw_nostag_rp10_tvcdf6)
(saving m_stipw_nostag_rp10_tvcdf7)
(saving m_stipw_nostag_exp)
(saving m_stipw_nostag_wei)
(saving m_stipw_nostag_gom)
(saving m_stipw_nostag_logn)
(saving m_stipw_nostag_llog)
(saving m2_stipw_nostag_rp1_tvcdf1)
(saving m2_stipw_nostag_rp1_tvcdf2)
(saving m2_stipw_nostag_rp1_tvcdf3)
(saving m2_stipw_nostag_rp1_tvcdf4)
(saving m2_stipw_nostag_rp1_tvcdf5)
(saving m2_stipw_nostag_rp1_tvcdf6)
(saving m2_stipw_nostag_rp1_tvcdf7)
(saving m2_stipw_nostag_rp2_tvcdf1)
(saving m2_stipw_nostag_rp2_tvcdf2)
(saving m2_stipw_nostag_rp2_tvcdf3)
(saving m2_stipw_nostag_rp2_tvcdf4)
(saving m2_stipw_nostag_rp2_tvcdf5)
(saving m2_stipw_nostag_rp2_tvcdf6)
(saving m2_stipw_nostag_rp2_tvcdf7)
(saving m2_stipw_nostag_rp3_tvcdf1)
(saving m2_stipw_nostag_rp3_tvcdf2)
(saving m2_stipw_nostag_rp3_tvcdf3)
(saving m2_stipw_nostag_rp3_tvcdf4)
(saving m2_stipw_nostag_rp3_tvcdf5)
(saving m2_stipw_nostag_rp3_tvcdf6)
(saving m2_stipw_nostag_rp3_tvcdf7)
(saving m2_stipw_nostag_rp4_tvcdf1)
(saving m2_stipw_nostag_rp4_tvcdf2)
(saving m2_stipw_nostag_rp4_tvcdf3)
(saving m2_stipw_nostag_rp4_tvcdf4)
(saving m2_stipw_nostag_rp4_tvcdf5)
(saving m2_stipw_nostag_rp4_tvcdf6)
(saving m2_stipw_nostag_rp4_tvcdf7)
(saving m2_stipw_nostag_rp5_tvcdf1)
(saving m2_stipw_nostag_rp5_tvcdf2)
(saving m2_stipw_nostag_rp5_tvcdf3)
(saving m2_stipw_nostag_rp5_tvcdf4)
(saving m2_stipw_nostag_rp5_tvcdf5)
(saving m2_stipw_nostag_rp5_tvcdf6)
(saving m2_stipw_nostag_rp5_tvcdf7)
(saving m2_stipw_nostag_rp6_tvcdf1)
(saving m2_stipw_nostag_rp6_tvcdf2)
(saving m2_stipw_nostag_rp6_tvcdf3)
(saving m2_stipw_nostag_rp6_tvcdf4)
(saving m2_stipw_nostag_rp6_tvcdf5)
(saving m2_stipw_nostag_rp6_tvcdf6)
(saving m2_stipw_nostag_rp6_tvcdf7)
(saving m2_stipw_nostag_rp7_tvcdf1)
(saving m2_stipw_nostag_rp7_tvcdf2)
(saving m2_stipw_nostag_rp7_tvcdf3)
(saving m2_stipw_nostag_rp7_tvcdf4)
(saving m2_stipw_nostag_rp7_tvcdf5)
(saving m2_stipw_nostag_rp7_tvcdf6)
(saving m2_stipw_nostag_rp7_tvcdf7)
(saving m2_stipw_nostag_rp8_tvcdf1)
(saving m2_stipw_nostag_rp8_tvcdf2)
(saving m2_stipw_nostag_rp8_tvcdf3)
(saving m2_stipw_nostag_rp8_tvcdf4)
(saving m2_stipw_nostag_rp8_tvcdf5)
(saving m2_stipw_nostag_rp8_tvcdf6)
(saving m2_stipw_nostag_rp8_tvcdf7)
(saving m2_stipw_nostag_rp9_tvcdf1)
(saving m2_stipw_nostag_rp9_tvcdf2)
(saving m2_stipw_nostag_rp9_tvcdf3)
(saving m2_stipw_nostag_rp9_tvcdf4)
(saving m2_stipw_nostag_rp9_tvcdf5)
(saving m2_stipw_nostag_rp9_tvcdf6)
(saving m2_stipw_nostag_rp9_tvcdf7)
(saving m2_stipw_nostag_rp10_tvcdf1)
(saving m2_stipw_nostag_rp10_tvcdf2)
(saving m2_stipw_nostag_rp10_tvcdf3)
(saving m2_stipw_nostag_rp10_tvcdf4)
(saving m2_stipw_nostag_rp10_tvcdf5)
(saving m2_stipw_nostag_rp10_tvcdf6)
(saving m2_stipw_nostag_rp10_tvcdf7)
(saving m2_stipw_nostag_exp)
(saving m2_stipw_nostag_wei)
(saving m2_stipw_nostag_gom)
(saving m2_stipw_nostag_logn)
(saving m2_stipw_nostag_llog)
(saving m3_stipw_nostag_rp1_tvcdf1)
(saving m3_stipw_nostag_rp1_tvcdf2)
(saving m3_stipw_nostag_rp1_tvcdf3)
(saving m3_stipw_nostag_rp1_tvcdf4)
(saving m3_stipw_nostag_rp1_tvcdf5)
(saving m3_stipw_nostag_rp1_tvcdf6)
(saving m3_stipw_nostag_rp1_tvcdf7)
(saving m3_stipw_nostag_rp2_tvcdf1)
(saving m3_stipw_nostag_rp2_tvcdf2)
(saving m3_stipw_nostag_rp2_tvcdf3)
(saving m3_stipw_nostag_rp2_tvcdf4)
(saving m3_stipw_nostag_rp2_tvcdf5)
(saving m3_stipw_nostag_rp2_tvcdf6)
(saving m3_stipw_nostag_rp2_tvcdf7)
(saving m3_stipw_nostag_rp3_tvcdf1)
(saving m3_stipw_nostag_rp3_tvcdf2)
(saving m3_stipw_nostag_rp3_tvcdf3)
(saving m3_stipw_nostag_rp3_tvcdf4)
(saving m3_stipw_nostag_rp3_tvcdf5)
(saving m3_stipw_nostag_rp3_tvcdf6)
(saving m3_stipw_nostag_rp3_tvcdf7)
(saving m3_stipw_nostag_rp4_tvcdf1)
(saving m3_stipw_nostag_rp4_tvcdf2)
(saving m3_stipw_nostag_rp4_tvcdf3)
(saving m3_stipw_nostag_rp4_tvcdf4)
(saving m3_stipw_nostag_rp4_tvcdf5)
(saving m3_stipw_nostag_rp4_tvcdf6)
(saving m3_stipw_nostag_rp4_tvcdf7)
(saving m3_stipw_nostag_rp5_tvcdf1)
(saving m3_stipw_nostag_rp5_tvcdf2)
(saving m3_stipw_nostag_rp5_tvcdf3)
(saving m3_stipw_nostag_rp5_tvcdf4)
(saving m3_stipw_nostag_rp5_tvcdf5)
(saving m3_stipw_nostag_rp5_tvcdf6)
(saving m3_stipw_nostag_rp5_tvcdf7)
(saving m3_stipw_nostag_rp6_tvcdf1)
(saving m3_stipw_nostag_rp6_tvcdf2)
(saving m3_stipw_nostag_rp6_tvcdf3)
(saving m3_stipw_nostag_rp6_tvcdf4)
(saving m3_stipw_nostag_rp6_tvcdf5)
(saving m3_stipw_nostag_rp6_tvcdf6)
(saving m3_stipw_nostag_rp6_tvcdf7)
(saving m3_stipw_nostag_rp7_tvcdf1)
(saving m3_stipw_nostag_rp7_tvcdf2)
(saving m3_stipw_nostag_rp7_tvcdf3)
(saving m3_stipw_nostag_rp7_tvcdf4)
(saving m3_stipw_nostag_rp7_tvcdf5)
(saving m3_stipw_nostag_rp7_tvcdf6)
(saving m3_stipw_nostag_rp7_tvcdf7)
(saving m3_stipw_nostag_rp8_tvcdf1)
(saving m3_stipw_nostag_rp8_tvcdf2)
(saving m3_stipw_nostag_rp8_tvcdf3)
(saving m3_stipw_nostag_rp8_tvcdf4)
(saving m3_stipw_nostag_rp8_tvcdf5)
(saving m3_stipw_nostag_rp8_tvcdf6)
(saving m3_stipw_nostag_rp8_tvcdf7)
(saving m3_stipw_nostag_rp9_tvcdf1)
(saving m3_stipw_nostag_rp9_tvcdf2)
(saving m3_stipw_nostag_rp9_tvcdf3)
(saving m3_stipw_nostag_rp9_tvcdf4)
(saving m3_stipw_nostag_rp9_tvcdf5)
(saving m3_stipw_nostag_rp9_tvcdf6)
(saving m3_stipw_nostag_rp9_tvcdf7)
(saving m3_stipw_nostag_rp10_tvcdf1)
(saving m3_stipw_nostag_rp10_tvcdf2)
(saving m3_stipw_nostag_rp10_tvcdf3)
(saving m3_stipw_nostag_rp10_tvcdf4)
(saving m3_stipw_nostag_rp10_tvcdf5)
(saving m3_stipw_nostag_rp10_tvcdf6)
(saving m3_stipw_nostag_rp10_tvcdf7)
(saving m3_stipw_nostag_exp)
(saving m3_stipw_nostag_wei)
(saving m3_stipw_nostag_gom)
(saving m3_stipw_nostag_logn)
(saving m3_stipw_nostag_llog)
(file mariel_feb_23_2_m1.sters saved)
.
. frame late: cap qui save "mariel_feb_23_2_late_m1.dta", all replace emptyok
. frame early: cap qui save "mariel_feb_23_2_early_m1.dta", all replace emptyok
. frame early_late: cap qui save "mariel_feb_23_2_early_late_m1.dta", all replace emptyok