. clear all

. cap noi which tabout
c:\ado\plus\t\tabout.ado
*! 2.0.8 Ian Watson 15mar2019
*! tabout version 3 (beta) available at: http://tabout.net.au

. if _rc==111 {
.         cap noi ssc install tabout
.         }

. cap noi which pathutil
c:\ado\plus\p\pathutil.ado
*! version 2.2.0 19nov2020 daniel klein

. if _rc==111 {
.         cap noi net install pathutil, from("http://fmwww.bc.edu/repec/bocode/p/") 
.         }

. cap noi which pathutil
c:\ado\plus\p\pathutil.ado
*! version 2.2.0 19nov2020 daniel klein

. if _rc==111 {
.         ssc install dirtools    
.         }

. cap noi which project
c:\ado\plus\p\project.ado
*! version 1.3.1  22dec2013  picard@netbox.com

. if _rc==111 {   
.         ssc install project
.         }

. cap noi which stipw
c:\ado\plus\s\stipw.ado
*! Version 1.0.0 17Jan2022

. if _rc==111 {   
.         ssc install stipw
.         }

. cap noi which stpm2
c:\ado\plus\s\stpm2.ado
*! version 1.7.5 May2021

. if _rc==111 {   
.         ssc install stpm2
.         }       

. cap noi which rcsgen
c:\ado\plus\r\rcsgen.ado
*! version 1.5.9 13FEB2022

. if _rc==111 {   
.         ssc install rcsgen
.         }       

. cap noi which matselrc
c:\ado\plus\m\matselrc.ado
*! NJC 1.1.0 20 Apr 2000  (STB-56: dm79)

. if _rc==111 {           
. cap noi net install dm79, from(http://www.stata.com/stb/stb56)
.         }

Exercise

Date created: 18:39:27 16 Feb 2023.

Get the folder


E:\Mi unidad\Alvacast\SISTRAT 2022 (github)


Fecha: 16 Feb 2023, considerando un SO Windows para el usuario: andre

Path data= ;

Tiempo: 16 Feb 2023, considerando un SO Windows

The file is located and named as: E:\Mi unidad\Alvacast\SISTRAT 2022 (github)fiscalia_mariel_oct_2022_match_SENDA.dta

=============================================================================

Structure database and survival

=============================================================================

We open the files

. *mariel_nov_22_2
. *fiscalia_mariel_oct_2022_match_SENDA_pris
. use "fiscalia_mariel_oct_2022_match_SENDA.dta", clear

. 
. encode escolaridad_rec, gen(esc_rec)

. encode sex, generate(sex_enc)

. encode sus_principal_mod, generate(sus_prin_mod)

. encode freq_cons_sus_prin, generate(fr_sus_prin)

. encode compromiso_biopsicosocial, generate(comp_biosoc)

. encode tenencia_de_la_vivienda_mod, generate(ten_viv)

. *encode dg_cie_10_rec, generate(dg_cie_10_mental_h) *already numeric
. encode dg_trs_cons_sus_or, generate(sud_severity_icd10)

. encode macrozona, generate(macrozone)

. gen     motivodeegreso_mod_imp_rec3 = 1

. replace motivodeegreso_mod_imp_rec3 = 2 if strpos(motivodeegreso_mod_imp_rec,"Early")>0
(15,797 real changes made)

. replace motivodeegreso_mod_imp_rec3 = 3 if strpos(motivodeegreso_mod_imp_rec,"Late")>0
(35,781 real changes made)

. 
. *encode policonsumo, generate(policon) *already numeric
. 
. *motivodeegreso_mod_imp_rec3 edad_al_ing_1 edad_ini_cons dias_treat_imp_sin_na_1 i.escolaridad_rec i.sus_principal_mod i.freq
> _cons_sus_prin i.compromiso_biopsicosocial i.tenencia_de_la_vivienda_mod i.dg_cie_10_rec i.dg_trs_cons_sus_or i.macrozona i.n
> _off_vio i.n_off_acq i.n_off_sud i.n_off_oth

Then we set the data base in surirval format and bring the urban-rural classification of municipallities from this link.

. cap qui noi frame create temp

. frame temp: import excel "Clasificacion-comunas-PNDR.xlsx", firstrow clear
(11 vars, 345 obs)

. *frame temp: browse
. frame change default

. 
. *select code of municipality
. gen str20 comuna = ustrregexs(1) if ustrregexm(comuna_residencia_cod,"([\d,]+)")
(2 missing values generated)

. 
. *recode comuna if 
. *http://www.sinim.cl/archivos/centro_descargas/modificacion_instructivo_pres_codigos.pdf
. *file:///C:/Users/CISSFO~1/AppData/Local/Temp/MicrosoftEdgeDownloads/4ef08de9-6832-4db6-8124-f69a7b256270/codigoComunas-20180
> 801%20(1).pdf
. 
. replace comuna= "16101" if strpos(strlower(comuna),"8401")>0
(434 real changes made)

. replace comuna= "16102" if strpos(strlower(comuna),"8402")>0
(10 real changes made)

. replace comuna= "16103" if strpos(strlower(comuna),"8406")>0
(32 real changes made)

. replace comuna= "16104" if strpos(strlower(comuna),"8407")>0
(2 real changes made)

. replace comuna= "16105" if strpos(strlower(comuna),"8410")>0
(1 real change made)

. replace comuna= "16106" if strpos(strlower(comuna),"8411")>0
(8 real changes made)

. replace comuna= "16107" if strpos(strlower(comuna),"8413")>0
(12 real changes made)

. replace comuna= "16108" if strpos(strlower(comuna),"8418")>0
(4 real changes made)

. replace comuna= "16109" if strpos(strlower(comuna),"8421")>0
(6 real changes made)

. replace comuna= "16201" if strpos(strlower(comuna),"8414")>0
(22 real changes made)

. replace comuna= "16202" if strpos(strlower(comuna),"8403")>0
(0 real changes made)

. replace comuna= "16203" if strpos(strlower(comuna),"8404")>0
(13 real changes made)

. replace comuna= "16204" if strpos(strlower(comuna),"8408")>0
(1 real change made)

. replace comuna= "16205" if strpos(strlower(comuna),"8412")>0
(1 real change made)

. replace comuna= "16206" if strpos(strlower(comuna),"8415")>0
(2 real changes made)

. replace comuna= "16207" if strpos(strlower(comuna),"8420")>0
(1 real change made)

. replace comuna= "16301" if strpos(strlower(comuna),"8416")>0
(12 real changes made)

. replace comuna= "16302" if strpos(strlower(comuna),"8405")>0
(6 real changes made)

. replace comuna= "16303" if strpos(strlower(comuna),"8409")>0
(0 real changes made)

. replace comuna= "16304" if strpos(strlower(comuna),"8417")>0
(0 real changes made)

. replace comuna= "16305" if strpos(strlower(comuna),"8419")>0
(0 real changes made)

. 
. destring comuna, replace
comuna: all characters numeric; replaced as int
(2 missing values generated)

. 
. *frame temp: gen str20 comuna = ustrregexs(1) if ustrregexm(cod_com,"([\d,]+)")
. 
. frlink m:1 comuna, frame(temp cod_com) //*Clasificación
  (2 observations in frame default unmatched)

. frget Clasificación, from(temp)
(2 missing values generated)
  (1 variable copied from linked frame)

. 
. encode Clasificación, generate(clas)

. *70,863

We open the files

. *si no está perdido cod_region, significa que hubo un registro (0/1) y el tiempo es el tiempo desde 
. *set the indicator
. gen event=0

. replace event=1 if !missing(offender_d)
(22,287 real changes made)

. *replace event=1 if !missing(sex)
. 
. gen diff= age_offending_imp-edad_al_egres_imp

. 
. *age time
. stset age_offending_imp, fail(event ==1) enter(edad_al_egres_imp)

     failure event:  event == 1
obs. time interval:  (0, age_offending_imp]
 enter on or after:  time edad_al_egres_imp
 exit on or before:  failure

------------------------------------------------------------------------------
     70,863  total observations
          1  observation ends on or before enter()
------------------------------------------------------------------------------
     70,862  observations remaining, representing
     22,287  failures in single-record/single-failure data
 229,620.92  total analysis time at risk and under observation
                                                at risk from t =         0
                                     earliest observed entry t =  10.95068
                                          last observed exit t =  90.65027

. 
. stdescribe, weight

         failure _d:  event == 1
   analysis time _t:  age_offending_imp
  enter on or after:  time edad_al_egres_imp

                                   |-------------- per subject --------------|
Category                   total        mean         min     median        max
------------------------------------------------------------------------------
no. of subjects            70862   
no. of records             70862           1           1          1          1

(first) entry time                  36.52205    10.95068    34.6274   88.91507
(final) exit time                   39.76244    14.84463   38.01506   90.65027

subjects with gap              0   
time on gap if gap             0   
time at risk           229620.92    3.240396    .0000449   2.665753   10.75828

failures                   22287    .3145127           0          0          1
------------------------------------------------------------------------------

We calculate the incidence rate.

. stsum, by (motivodeegreso_mod_imp_rec)

         failure _d:  event == 1
   analysis time _t:  age_offending_imp
  enter on or after:  time edad_al_egres_imp

         |               Incidence     Number of   |------ Survival time -----|
motivo~c | Time at risk       rate      subjects        25%       50%       75%
---------+---------------------------------------------------------------------
Treatmen |  63,974.7794   .0597892         19275   19.60301  22.39014   28.2026
Treatmen |  46,815.0893   .1309407         15797   18.18207  19.15674  21.12526
Treatmen |  118,806.623   .1037484         35781   16.49829  17.60986  20.97467
---------+---------------------------------------------------------------------
   Total |  229,596.491   .0970442         70853   16.67765   18.3436  21.03217

=============================================================================

Descriptives

=============================================================================

. 
. local ttl `" "Tr Completion" "Tr Disch (Early)" "Tr Disch (Late)" "' 

. forvalues i = 1/3 {
  2. cap drop sum_*
  3.         gettoken title ttl: ttl
  4. cap qui egen sum_`i' = total(_t) if motivodeegreso_mod_imp_rec3==`i'
  5. cap qui tab _d motivodeegreso_mod_imp_rec3 if _d==1 & motivodeegreso_mod_imp_rec3==`i'
  6. scalar n_eventos`i' =r(N)
  7. qui tabstat sum_`i', save
  8. scalar sum_`i'_total =r(StatTotal)[1,1]
  9. scalar ir_t = round((n_eventos`i'/sum_`i'_total)*1000,.1)
 10. *di ir_t
. *es numérico, para no generar incompatibilidad, se pasa a escalar
. global dens_inc`i' "Incidence rate ratio for `title' (`i') (x1,000 person-days):  `=scalar(ir_t)'"
 11. *di %9.1f (n_eventos`i'/sum_`i'_total)*1000
. }

Incidence rate ratio for Tr Completion (1) (x1,000 person-days): 4.600000000000001
Incidence rate ratio for Tr Disch (Early) (2) (x1,000 person-days): 10.5
Incidence rate ratio for Tr Disch (Late) (3) (x1,000 person-days): 8.800000000000001

We recode the discharge cause to contrast them

. cap noi recode motivodeegreso_mod_imp_rec3 (1=0 "Tr Completion" ) ///
>                          (2=1 "Early Disch") ///
>                          (else=.), gen(tto_2_1)
(70863 differences between motivodeegreso_mod_imp_rec3 and tto_2_1)

. cap noi recode motivodeegreso_mod_imp_rec3 (1=0 "Tr Completion" ) ///
>                          (3=1 "Late Disch") ///
>                          (else=.), gen(tto_3_1)
(70863 differences between motivodeegreso_mod_imp_rec3 and tto_3_1)

. cap noi recode motivodeegreso_mod_imp_rec3 (2=0 "Early Disch" ) ///
>                          (3=1 "Late Disch") ///
>                          (else=.), gen(tto_3_2)
(70863 differences between motivodeegreso_mod_imp_rec3 and tto_3_2)

We explored the inicidence rate ratios (IRR) of each cause of discharge.

. 
. *set trace on
. local stname `" "2_1" "3_1" "3_2" "' 

. local titl `" "Early Disch vs Tr Completion" "Late Disch vs Tr Completion" "Late vs Early Disch" "' 

. foreach s of local stname {
  2.         gettoken title titl: titl
  3. cap noi qui ir _d tto_`s' _t
  4. scalar ir_`s' =round(r(irr),.01)
  5. *di ir_`s'
. scalar ir_`s'_lb =round(r(lb_irr),.01) 
  6. *di ir_`s'_lb
. scalar ir_`s'_ub =round(r(ub_irr),.01)
  7. *di ir_`s'_ub
. local ir1= ir_`s'
  8. local ir2= ir_`s'_lb
  9. local ir3= ir_`s'_ub
 10. *di  in gr _col(13) " `title': IRR `ir1' (IC 95% `ir2' - `ir3') "
. global irr_`s' " `title': IRR `ir1' (IC 95% `ir2' - `ir3') "
 11. global ir_`s' "`ir1' (IC 95% `ir2' - `ir3')"
 12. }

We generated a log-rank test to compare the expected to the observed result, obtaining that:

Early Disch vs Tr Completion: IRR 2.3 (IC 95% 2.21 - 2.39) , so patients with an Early Discharge had a greater incidence rate than patients in Tr Completion
Late Disch vs Tr Completion: IRR 1.91 (IC 95% 1.85 - 1.99) , so patients with a Late Discharge had a greater incidence rate than patients in Tr Completion
Late vs Early Disch: IRR .8300000000000001 (IC 95% .8100000000000001 - .86) , so patients with a Late Discharge had a lower incidence rate than patients with an Early Discharge

. *set trace on
. local stname `" "2_1" "3_1" "3_2" "' 

. local titl `" "Early Disch vs Tr Completion" "Late Disch vs Tr Completion" "Late vs Early Disch" "' 

. foreach s of local stname {
  2.         gettoken title titl: titl
  3. cap noi qui sts test tto_`s', logrank
  4. scalar logrank_chi`s'= round(r(chi2),.01)
  5. scalar logrank_df`s'= r(df)
  6. scalar logrank_p_`s'= round(chiprob(r(df),r(chi2)),.001)
  7. local lr1= logrank_chi`s'
  8. local lr2= logrank_df`s'
  9. local lr3= logrank_p_`s'
 10. global logrank_`s' " Chi^2(`lr2')=`lr1',p=`lr3'"
 11. }

. *set trace off

With a value of Chi^2(1)=699.0500000000001,p=0, survival curves between patients that had an Early Disch y Tr Completion were significantly different.
With a value of Chi^2(1)=486.13,p=0, survival curves between patients that had an Late Disch y Tr Completion were significantly different.
With a value of Chi^2(1)=87.27,p=0, survival curves between patients that had an Late y Early Disch were significantly different.

=============================================================================

Graph

=============================================================================

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 (end with 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 of age") 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 "L
> ate Tr Disch" )size(*.5)region(lstyle(none)) region(c(none)) nobox)

         failure _d:  event == 1
   analysis time _t:  age_offending_imp
  enter on or after:  time edad_al_egres_imp
(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.gph", replace
(note: file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\tto.gph not found)
(file E:\Mi unidad\Alvacast\SISTRAT 2022 (github)\_figs\tto.gph saved)

=============================================================================

Survival Analyses

=============================================================================

We tested the schoefeld residuals.

. *c("edad_al_ing_1", "edad_ini_cons", "dias_treat_imp_sin_na_1", "escolaridad_rec", "sus_principal_mod", "freq_cons_sus_prin",
>  "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod", "dg_cie_10_rec", "dg_trs_cons_sus_or", "macrozona", "policonsumo
> ", "n_prev_off", "n_off_vio", "n_off_acq", "n_off_sud", "n_off_oth")
. 
. global sim 1e5 //5e1 1e5 

. global boots 1e3 //5e1 2e3

. global times 0 90 365 1096 1826

. range timevar0 90 1826 90
(70,773 missing values generated)

. 
. global covs "edad_al_ing_1 edad_ini_cons dias_treat_imp_sin_na_1 sex esc_rec sus_prin_mod fr_sus_prin comp_biosoc ten_viv ori
> gen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n_off_vio n_off_acq n_off_sud clas
> "

. global covs_2 "motivodeegreso_mod_imp_rec3 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin_mod fr_sus_prin comp_biosoc t
> en_viv origen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n_off_vio n_off_acq n_of
> f_sud clas"

. 
. 
. qui stcox  $covs_2 , efron robust nolog schoenfeld(sch*) scaledsch(sca*)

. qui estat phtest, log detail

. scalar chi2_scho_test = r(chi2)

.  
. mat mat_scho_test = r(phtest)

. 
. esttab matrix(mat_scho_test) using "mat_scho_test.csv", replace
(output written to mat_scho_test.csv)

. esttab matrix(mat_scho_test) using "mat_scho_test.html", replace
(output written to mat_scho_test.html)

.


	mat_scho_test
	rho	chi2	df	p

motivodeegreso_mod_imp_rec3	.0072061	.8628438	1	.3529441
edad_al_ing_1	-.034582	25.75503	1	3.88e-07
edad_ini_cons	.0039231	.2847726	1	.59359
sex_enc	.0065353	.7693374	1	.3804221
esc_rec	-.0232456	9.550511	1	.0019989
sus_prin_mod	-.0029909	.1401456	1	.7081364
fr_sus_prin	-.0028388	.1372357	1	.7110447
comp_biosoc	-.0117667	2.517286	1	.1126042
ten_viv	.0144298	3.927334	1	.0475072
origen_ingreso_mod	.0237432	9.885705	1	.0016657
numero_de_hijos_mod	-.0159804	4.425313	1	.0354096
dg_cie_10_rec	.0097901	1.674955	1	.1955962
sud_severity_icd10	.0195916	6.667162	1	.0098205
macrozone	.001587	.0435012	1	.8347843
policonsumo	.0273983	13.59189	1	.0002272
n_off_vio	.0377649	27.35135	1	1.70e-07
n_off_acq	.0232681	10.59447	1	.0011343
n_off_sud	.0165033	5.246645	1	.0219891
clas	-.0165835	4.966375	1	.0258448

. /*
> stphplot, by(n_off_vio) adjust($covs_health) ///
>  xtitle("Log Time (days)", size(small)) ///
>         ylabel(-4(2)8, labsize(vsmall)) ///     
>         legend(pos(7) ring(0) col(1) symysize(zero) keygap(1) symxsize(large) order( 1 2) lab(1 "Outpatient") lab(2 "Resident
> ial") size(small)) ///
>         ytitle("Schoenfeld residuals", size(small)) scheme(sj) graphregion(color(white)) ///
>         note("{it:Note. Means and 95% CI's; Bandwidth=.8; Natural log of analysis time used.}",size(vsmall)) ///
>         title("Plot of −ln{−ln(survival)} vs. ln(analysis time)" "by Treatment Modality at Baseline", size(medium)) ///
>         subtitle("{it: Fourth transition}",size(small)) ///
>         name(stphplot_trans_4, replace)  ///
>         saving(stphplot_trans_4.gph, replace)
> */
.

We generated a list of parametric survival models with different distributions (Exponential, Weibull, Gompertz, Log-logistic, Log-normal & Generalized gamma). Aditionally, we defined a series of Royston-Parmar models with a function of restricted cubic splines, in which the knots (#df -1) are defined in each percentile of the distribution. We saved the estimates in the file called `parmodels_m2_nov_22’.

. *Hannah Bower, Michael J. Crowther, Mark J. Rutherford, Therese M.-L. Andersson, Mark Clements, Xing-Rong Liu, Paul W. Dickma
> n & Paul C. Lambert (2021) Capturing simple and complex time-dependent effects using flexible parametric survival models: A s
> imulation study, Communications in Statistics - Simulation and Computation, 50:11, 3777-3793, DOI: 10.1080/03610918.2019.1634
> 201
. *can be used in case of nonproportional hazards
. *Our usualstarting point is to use 5 degrees of freedom for the baseline and 3 degrees of freedomfor any time-dependent effec
> ts
. *The results presented indicate that restricted cubic splines accurately capture time-dependent effects if appropriate degree
> s of freedom are selected; these results are con-sistent with the findings of Rutherford et al. (Rutherford, Crowther, and La
> mbert2015)for proportional-hazards models
.                 // Cox w/tvc
.         forvalues j=1/7 {
  2.                 di in yellow "{bf: ***********}"
  3.                 di in yellow "{bf: family Cox tvc `j'}"
  4.                 di in yellow "{bf: ***********}"
  5.                 set seed 2125
  6.                 qui cap noi stmerlin $covs_2 , dist(exponential) tvc(motivodeegreso_mod_imp_rec3) dftvc(`j')
  7.                 estimates store m2_1_cox`j'     
  8.         }
 ***********
 family Cox tvc 1
 ***********
note; a delayed entry model is being fitted
variables created for model 1, component 20: _cmp_1_20_1 to _cmp_1_20_1

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood = -56187.558  
Iteration 2:   log likelihood = -54629.696  
Iteration 3:   log likelihood =  -53751.26  
Iteration 4:   log likelihood = -53742.417  
Iteration 5:   log likelihood = -53742.414  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53742.414
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1092164   .0100517    10.87   0.000     .0895154    .1289174
edad_al_in~1 |  -.0041688   .0019007    -2.19   0.028    -.0078941   -.0004435
edad_ini_c~s |  -.0110434   .0018701    -5.91   0.000    -.0147087   -.0073782
     sex_enc |  -.3373454   .0202534   -16.66   0.000    -.3770414   -.2976494
     esc_rec |   .0887826   .0122991     7.22   0.000     .0646769    .1128883
sus_prin_mod |   .1346638   .0081805    16.46   0.000     .1186304    .1506972
 fr_sus_prin |   .0320833   .0075659     4.24   0.000     .0172544    .0469122
 comp_biosoc |   .1973794   .0141778    13.92   0.000     .1695915    .2251674
     ten_viv |  -.0142428   .0076415    -1.86   0.062    -.0292199    .0007342
origen_ing~d |  -.0209873   .0044051    -4.76   0.000    -.0296212   -.0123534
numero_de_~d |    .075887    .006292    12.06   0.000     .0635548    .0882192
dg_cie_10_~c |   .0282162    .008753     3.22   0.001     .0110606    .0453718
sud_sever~10 |  -.0645295   .0191639    -3.37   0.001    -.1020901   -.0269689
   macrozone |    .206452    .011806    17.49   0.000     .1833128    .2295913
 policonsumo |    .103661   .0216998     4.78   0.000     .0611302    .1461918
   n_off_vio |   .3122056   .0187107    16.69   0.000     .2755333     .348878
   n_off_acq |   .6650854   .0174485    38.12   0.000     .6308869    .6992839
   n_off_sud |   .2310131   .0183747    12.57   0.000     .1949994    .2670268
        clas |   .0152261   .0128285     1.19   0.235    -.0099173    .0403695
motivodeeg~( |  -.1517037   .0075471   -20.10   0.000    -.1664957   -.1369117
       _cons |  -3.616424   .1127314   -32.08   0.000    -3.837373   -3.395474
------------------------------------------------------------------------------
 ***********
 family Cox tvc 2
 ***********
note; a delayed entry model is being fitted
variables created for model 1, component 20: _cmp_1_20_1 to _cmp_1_20_2

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood = -56173.947  
Iteration 2:   log likelihood =  -54568.57  
Iteration 3:   log likelihood =  -53723.07  
Iteration 4:   log likelihood = -53714.092  
Iteration 5:   log likelihood = -53714.089  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53714.089
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .0932363   .0103386     9.02   0.000      .072973    .1134996
edad_al_in~1 |   .0031164   .0021369     1.46   0.145    -.0010719    .0073047
edad_ini_c~s |  -.0111257   .0018804    -5.92   0.000    -.0148112   -.0074402
     sex_enc |  -.3331514   .0202686   -16.44   0.000    -.3728772   -.2934257
     esc_rec |   .0973974    .012353     7.88   0.000      .073186    .1216087
sus_prin_mod |   .1352062   .0081934    16.50   0.000     .1191474     .151265
 fr_sus_prin |   .0333389   .0075636     4.41   0.000     .0185145    .0481633
 comp_biosoc |   .1974758   .0141808    13.93   0.000      .169682    .2252696
     ten_viv |  -.0165284   .0076436    -2.16   0.031    -.0315096   -.0015472
origen_ing~d |  -.0202743   .0044049    -4.60   0.000    -.0289078   -.0116408
numero_de_~d |   .0704058   .0063457    11.09   0.000     .0579684    .0828432
dg_cie_10_~c |   .0282432   .0087495     3.23   0.001     .0110945    .0453919
sud_sever~10 |  -.0613986   .0191686    -3.20   0.001    -.0989684   -.0238288
   macrozone |    .208965   .0118081    17.70   0.000     .1858216    .2321084
 policonsumo |    .097371    .021701     4.49   0.000     .0548378    .1399041
   n_off_vio |   .3129423   .0187039    16.73   0.000     .2762834    .3496012
   n_off_acq |   .6659397   .0174346    38.20   0.000     .6317686    .7001108
   n_off_sud |   .2252714   .0183841    12.25   0.000     .1892391    .2613037
        clas |   .0164078   .0128329     1.28   0.201    -.0087443    .0415599
motivodeeg~( |  -.1982551   .0098871   -20.05   0.000    -.2176335   -.1788767
motivodeeg~( |    .030215   .0040786     7.41   0.000     .0222211     .038209
       _cons |  -3.880322   .1185042   -32.74   0.000    -4.112586   -3.648058
------------------------------------------------------------------------------
 ***********
 family Cox tvc 3
 ***********
note; a delayed entry model is being fitted
variables created for model 1, component 20: _cmp_1_20_1 to _cmp_1_20_3

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood = -56166.773  
Iteration 2:   log likelihood =  -54541.58  
Iteration 3:   log likelihood = -53691.515  
Iteration 4:   log likelihood = -53682.846  
Iteration 5:   log likelihood = -53682.843  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53682.843
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .0843175   .0104802     8.05   0.000     .0637768    .1048582
edad_al_in~1 |   .0053004   .0021819     2.43   0.015     .0010239    .0095769
edad_ini_c~s |  -.0110592   .0018869    -5.86   0.000    -.0147574   -.0073609
     sex_enc |  -.3304964   .0202716   -16.30   0.000    -.3702279   -.2907649
     esc_rec |   .0978001   .0123418     7.92   0.000     .0736107    .1219895
sus_prin_mod |   .1340157   .0081885    16.37   0.000     .1179665    .1500649
 fr_sus_prin |   .0325646   .0075634     4.31   0.000     .0177405    .0473886
 comp_biosoc |   .1957736   .0141843    13.80   0.000      .167973    .2235743
     ten_viv |  -.0160194   .0076413    -2.10   0.036    -.0309959   -.0010428
origen_ing~d |  -.0202157   .0044049    -4.59   0.000    -.0288491   -.0115823
numero_de_~d |   .0682902   .0063486    10.76   0.000     .0558472    .0807332
dg_cie_10_~c |   .0277253   .0087499     3.17   0.002     .0105757    .0448748
sud_sever~10 |  -.0655118   .0191751    -3.42   0.001    -.1030943   -.0279293
   macrozone |   .2091102   .0118101    17.71   0.000     .1859629    .2322575
 policonsumo |   .0941474   .0216708     4.34   0.000     .0516734    .1366214
   n_off_vio |   .3136228   .0187017    16.77   0.000     .2769682    .3502775
   n_off_acq |   .6667588   .0174299    38.25   0.000     .6325967    .7009208
   n_off_sud |   .2289826   .0183859    12.45   0.000     .1929468    .2650183
        clas |   .0149013   .0128347     1.16   0.246    -.0102543    .0400569
motivodeeg~( |  -.2211225    .010576   -20.91   0.000    -.2418511   -.2003939
motivodeeg~( |   .0501226   .0049297    10.17   0.000     .0404605    .0597846
motivodeeg~( |   .0276142   .0033189     8.32   0.000     .0211093     .034119
       _cons |  -3.935748   .1192877   -32.99   0.000    -4.169547   -3.701948
------------------------------------------------------------------------------
 ***********
 family Cox tvc 4
 ***********
note; a delayed entry model is being fitted
variables created for model 1, component 20: _cmp_1_20_1 to _cmp_1_20_4

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood =   -56165.3  
Iteration 2:   log likelihood = -54539.877  
Iteration 3:   log likelihood = -53687.073  
Iteration 4:   log likelihood = -53678.355  
Iteration 5:   log likelihood = -53678.352  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53678.352
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .0814039   .0105572     7.71   0.000     .0607121    .1020957
edad_al_in~1 |   .0058916      .0022     2.68   0.007     .0015797    .0102034
edad_ini_c~s |  -.0111137   .0018883    -5.89   0.000    -.0148147   -.0074127
     sex_enc |  -.3302015   .0202738   -16.29   0.000    -.3699374   -.2904656
     esc_rec |   .0975582   .0123405     7.91   0.000     .0733712    .1217452
sus_prin_mod |   .1346374   .0081916    16.44   0.000     .1185822    .1506926
 fr_sus_prin |   .0327127   .0075645     4.32   0.000     .0178865    .0475388
 comp_biosoc |   .1957239   .0141831    13.80   0.000     .1679257    .2235222
     ten_viv |   -.016046   .0076399    -2.10   0.036    -.0310199   -.0010721
origen_ing~d |  -.0201368   .0044051    -4.57   0.000    -.0287705    -.011503
numero_de_~d |   .0683578   .0063489    10.77   0.000     .0559142    .0808014
dg_cie_10_~c |   .0275704   .0087502     3.15   0.002     .0104203    .0447204
sud_sever~10 |  -.0654316   .0191754    -3.41   0.001    -.1030147   -.0278485
   macrozone |   .2091635   .0118114    17.71   0.000     .1860135    .2323135
 policonsumo |   .0930592   .0216646     4.30   0.000     .0505975     .135521
   n_off_vio |   .3127826   .0187052    16.72   0.000      .276121    .3494441
   n_off_acq |   .6659193   .0174345    38.20   0.000     .6317483    .7000904
   n_off_sud |   .2284496   .0183872    12.42   0.000     .1924113    .2644879
        clas |   .0149571   .0128355     1.17   0.244       -.0102    .0401141
motivodeeg~( |  -.2279086   .0109371   -20.84   0.000    -.2493449   -.2064724
motivodeeg~( |   .0555866   .0054034    10.29   0.000     .0449963     .066177
motivodeeg~( |   .0319715   .0036279     8.81   0.000     .0248609    .0390821
motivodeeg~( |   .0158304   .0035752     4.43   0.000     .0088232    .0228377
       _cons |  -3.954702    .119674   -33.05   0.000    -4.189259   -3.720145
------------------------------------------------------------------------------
 ***********
 family Cox tvc 5
 ***********
note; a delayed entry model is being fitted
variables created for model 1, component 20: _cmp_1_20_1 to _cmp_1_20_5

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood = -56165.419  
Iteration 2:   log likelihood = -54538.745  
Iteration 3:   log likelihood = -53681.323  
Iteration 4:   log likelihood = -53672.782  
Iteration 5:   log likelihood = -53672.778  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53672.778
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .0786429   .0106182     7.41   0.000     .0578316    .0994543
edad_al_in~1 |    .006397    .002213     2.89   0.004     .0020596    .0107343
edad_ini_c~s |   -.011084   .0018888    -5.87   0.000    -.0147859   -.0073821
     sex_enc |  -.3304184    .020275   -16.30   0.000    -.3701567   -.2906801
     esc_rec |   .0978527    .012343     7.93   0.000     .0736609    .1220445
sus_prin_mod |   .1351006   .0081904    16.49   0.000     .1190477    .1511536
 fr_sus_prin |   .0328835   .0075635     4.35   0.000     .0180593    .0477078
 comp_biosoc |   .1954677   .0141825    13.78   0.000     .1676705     .223265
     ten_viv |   -.015998   .0076395    -2.09   0.036    -.0309711   -.0010249
origen_ing~d |  -.0200268   .0044054    -4.55   0.000    -.0286612   -.0113924
numero_de_~d |   .0682045   .0063488    10.74   0.000      .055761     .080648
dg_cie_10_~c |   .0274728   .0087504     3.14   0.002     .0103224    .0446233
sud_sever~10 |   -.065682   .0191769    -3.43   0.001    -.1032681   -.0280959
   macrozone |   .2088495   .0118129    17.68   0.000     .1856966    .2320024
 policonsumo |   .0926582   .0216576     4.28   0.000     .0502101    .1351063
   n_off_vio |   .3129508   .0187053    16.73   0.000      .276289    .3496126
   n_off_acq |     .66639   .0174351    38.22   0.000     .6322179    .7005621
   n_off_sud |   .2282182   .0183867    12.41   0.000      .192181    .2642555
        clas |   .0149893   .0128348     1.17   0.243    -.0101664     .040145
motivodeeg~( |  -.2343541     .01121   -20.91   0.000    -.2563253   -.2123829
motivodeeg~( |    .061257   .0057755    10.61   0.000     .0499373    .0725767
motivodeeg~( |   .0333847   .0035379     9.44   0.000     .0264506    .0403189
motivodeeg~( |   .0226567   .0037261     6.08   0.000     .0153537    .0299596
motivodeeg~( |    .017248   .0038079     4.53   0.000     .0097846    .0247114
       _cons |  -3.972136   .1199474   -33.12   0.000    -4.207228   -3.737043
------------------------------------------------------------------------------
 ***********
 family Cox tvc 6
 ***********
note; a delayed entry model is being fitted
variables created for model 1, component 20: _cmp_1_20_1 to _cmp_1_20_6

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood = -56164.431  
Iteration 2:   log likelihood = -54538.542  
Iteration 3:   log likelihood = -53674.702  
Iteration 4:   log likelihood = -53666.239  
Iteration 5:   log likelihood = -53666.236  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53666.236
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .0760671   .0106721     7.13   0.000     .0551502     .096984
edad_al_in~1 |   .0067226   .0022223     3.03   0.002      .002367    .0110782
edad_ini_c~s |  -.0110411   .0018891    -5.84   0.000    -.0147436   -.0073386
     sex_enc |  -.3299361   .0202743   -16.27   0.000    -.3696731   -.2901992
     esc_rec |   .0979093   .0123449     7.93   0.000     .0737137    .1221049
sus_prin_mod |   .1349929   .0081917    16.48   0.000     .1189374    .1510484
 fr_sus_prin |   .0329023   .0075642     4.35   0.000     .0180767    .0477279
 comp_biosoc |   .1956746   .0141826    13.80   0.000     .1678772     .223472
     ten_viv |  -.0160681   .0076392    -2.10   0.035    -.0310407   -.0010954
origen_ing~d |  -.0200839   .0044053    -4.56   0.000    -.0287181   -.0114497
numero_de_~d |   .0683175   .0063493    10.76   0.000     .0558731    .0807618
dg_cie_10_~c |   .0274761   .0087502     3.14   0.002      .010326    .0446262
sud_sever~10 |  -.0652447   .0191767    -3.40   0.001    -.1028303    -.027659
   macrozone |   .2087268   .0118128    17.67   0.000     .1855741    .2318794
 policonsumo |   .0915266   .0216536     4.23   0.000     .0490863    .1339668
   n_off_vio |    .312223   .0187065    16.69   0.000      .275559     .348887
   n_off_acq |   .6660891    .017436    38.20   0.000     .6319151    .7002631
   n_off_sud |    .227406   .0183878    12.37   0.000     .1913666    .2634454
        clas |    .014639   .0128344     1.14   0.254    -.0105161     .039794
motivodeeg~( |  -.2396847   .0114234   -20.98   0.000    -.2620742   -.2172953
motivodeeg~( |    .066097   .0060707    10.89   0.000     .0541986    .0779954
motivodeeg~( |   .0350118    .003552     9.86   0.000       .02805    .0419736
motivodeeg~( |   .0261548   .0036519     7.16   0.000     .0189971    .0333124
motivodeeg~( |   .0203657   .0038656     5.27   0.000     .0127893    .0279422
motivodeeg~( |   .0212003   .0039918     5.31   0.000     .0133766     .029024
       _cons |  -3.982051   .1201487   -33.14   0.000    -4.217538   -3.746564
------------------------------------------------------------------------------
 ***********
 family Cox tvc 7
 ***********
note; a delayed entry model is being fitted
variables created for model 1, component 20: _cmp_1_20_1 to _cmp_1_20_7

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood =  -56164.17  
Iteration 2:   log likelihood = -54539.614  
Iteration 3:   log likelihood = -53674.221  
Iteration 4:   log likelihood = -53665.669  
Iteration 5:   log likelihood = -53665.666  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53665.666
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .0751875   .0107052     7.02   0.000     .0542057    .0961693
edad_al_in~1 |   .0068749   .0022275     3.09   0.002     .0025091    .0112407
edad_ini_c~s |  -.0110342   .0018892    -5.84   0.000     -.014737   -.0073313
     sex_enc |  -.3298309   .0202744   -16.27   0.000    -.3695679   -.2900939
     esc_rec |   .0978733   .0123454     7.93   0.000     .0736767    .1220699
sus_prin_mod |   .1349493   .0081918    16.47   0.000     .1188937    .1510048
 fr_sus_prin |   .0329279   .0075642     4.35   0.000     .0181023    .0477535
 comp_biosoc |    .195668   .0141833    13.80   0.000     .1678693    .2234667
     ten_viv |  -.0161304   .0076392    -2.11   0.035     -.031103   -.0011579
origen_ing~d |  -.0200705   .0044053    -4.56   0.000    -.0287047   -.0114364
numero_de_~d |   .0683063   .0063494    10.76   0.000     .0558617    .0807509
dg_cie_10_~c |   .0274495   .0087501     3.14   0.002     .0102997    .0445994
sud_sever~10 |  -.0650434   .0191773    -3.39   0.001    -.1026301   -.0274567
   macrozone |   .2087722   .0118128    17.67   0.000     .1856195    .2319249
 policonsumo |   .0914344   .0216521     4.22   0.000      .048997    .1338717
   n_off_vio |   .3120758   .0187067    16.68   0.000     .2754114    .3487402
   n_off_acq |   .6660573    .017436    38.20   0.000     .6318833    .7002312
   n_off_sud |   .2273514   .0183877    12.36   0.000     .1913122    .2633906
        clas |   .0146452   .0128341     1.14   0.254    -.0105092    .0397996
motivodeeg~( |  -.2418688   .0115736   -20.90   0.000    -.2645526    -.219185
motivodeeg~( |   .0680475   .0062956    10.81   0.000     .0557084    .0803866
motivodeeg~( |   .0344204   .0035047     9.82   0.000     .0275512    .0412895
motivodeeg~( |    .027874    .003644     7.65   0.000     .0207319    .0350162
motivodeeg~( |   .0195473   .0037268     5.25   0.000      .012243    .0268517
motivodeeg~( |    .022663   .0039793     5.70   0.000     .0148638    .0304622
motivodeeg~( |   .0171387   .0041602     4.12   0.000     .0089848    .0252925
       _cons |  -3.987084   .1202594   -33.15   0.000    -4.222788    -3.75138
------------------------------------------------------------------------------

. 
.         // Gompertz
.         di in yellow "{bf: ***********}"
 ***********

.         di in yellow "{bf: family Gomp}"
 family Gomp

.         di in yellow "{bf: ***********}"
 ***********

.         set seed 2125

.         qui cap noi stmerlin $covs_2 , dist(gompertz)
note; a delayed entry model is being fitted

Fitting full model:

Iteration 0:   log likelihood =  -19759395  
Iteration 1:   log likelihood = -97290.834  
Iteration 2:   log likelihood = -56787.306  
Iteration 3:   log likelihood = -53125.796  
Iteration 4:   log likelihood =  -52688.37  
Iteration 5:   log likelihood = -52687.428  
Iteration 6:   log likelihood = -52687.428  

Survival model                                  Number of obs     =     59,220
Log likelihood = -52687.428
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |    .141407   .0099143    14.26   0.000     .1219754    .1608387
edad_al_in~1 |    .195171   .0050912    38.33   0.000     .1851924    .2051497
edad_ini_c~s |  -.0085601   .0018824    -4.55   0.000    -.0122495   -.0048707
     sex_enc |  -.3079403    .020237   -15.22   0.000     -.347604   -.2682765
     esc_rec |   .1016923   .0122694     8.29   0.000     .0776448    .1257398
sus_prin_mod |   .1396628   .0082616    16.91   0.000     .1234704    .1558552
 fr_sus_prin |   .0260699   .0075787     3.44   0.001     .0112159    .0409239
 comp_biosoc |    .195356   .0141896    13.77   0.000     .1675449    .2231672
     ten_viv |  -.0196022   .0076148    -2.57   0.010     -.034527   -.0046773
origen_ing~d |  -.0135837   .0044026    -3.09   0.002    -.0222127   -.0049546
numero_de_~d |   .0631181   .0063094    10.00   0.000     .0507519    .0754842
dg_cie_10_~c |   .0274942   .0088286     3.11   0.002     .0101903     .044798
sud_sever~10 |  -.0635471   .0191609    -3.32   0.001    -.1011018   -.0259924
   macrozone |   .2019204   .0118238    17.08   0.000     .1787461    .2250946
 policonsumo |    .136991   .0216576     6.33   0.000     .0945429    .1794392
   n_off_vio |   .2647199   .0186981    14.16   0.000     .2280722    .3013675
   n_off_acq |   .5912024   .0174501    33.88   0.000     .5570008    .6254041
   n_off_sud |   .1786645    .018373     9.72   0.000      .142654    .2146749
        clas |   .0232213   .0128167     1.81   0.070    -.0018989    .0483416
       _cons |  -2.033876   .0974164   -20.88   0.000    -2.224809   -1.842944
       gamma |  -.2301211   .0049436   -46.55   0.000    -.2398103   -.2204319
------------------------------------------------------------------------------

.         //qui cap noi merlin (_time $covs if _trans == 1, family(gompertz, fail(_status)))
.         estimates store m2_1_gom

. 
.         // Weibull
.         di in yellow "{bf: ***********}"
 ***********

.         di in yellow "{bf: family Weibull}"
 family Weibull

.         di in yellow "{bf: ***********}"
 ***********

.         set seed 2125

.         qui cap noi stmerlin $covs_2 , dist(weibull)
note; a delayed entry model is being fitted

Fitting full model:

Iteration 0:   log likelihood = -178984.61  
Iteration 1:   log likelihood = -56421.887  (not concave)
Iteration 2:   log likelihood = -54901.105  
Iteration 3:   log likelihood = -53994.002  
Iteration 4:   log likelihood = -53869.087  
Iteration 5:   log likelihood = -53850.179  
Iteration 6:   log likelihood = -53802.751  (not concave)
Iteration 7:   log likelihood = -53798.928  
Iteration 8:   log likelihood = -53792.613  
Iteration 9:   log likelihood = -53754.735  (not concave)
Iteration 10:  log likelihood = -53753.857  
Iteration 11:  log likelihood = -53741.416  
Iteration 12:  log likelihood = -53713.228  
Iteration 13:  log likelihood = -53689.449  
Iteration 14:  log likelihood = -53685.153  
Iteration 15:  log likelihood = -53674.164  (not concave)
Iteration 16:  log likelihood = -53673.914  
Iteration 17:  log likelihood = -53671.976  
Iteration 18:  log likelihood =  -53664.25  (not concave)
Iteration 19:  log likelihood = -53663.976  
Iteration 20:  log likelihood = -53661.402  
Iteration 21:  log likelihood = -53657.321  
Iteration 22:  log likelihood = -53655.025  (not concave)
Iteration 23:  log likelihood = -53654.747  
Iteration 24:  log likelihood = -53653.915  
Iteration 25:  log likelihood = -53653.063  
Iteration 26:  log likelihood = -53652.713  
Iteration 27:  log likelihood = -53652.561  
Iteration 28:  log likelihood = -53652.513  
Iteration 29:  log likelihood = -53652.496  
Iteration 30:  log likelihood = -53652.492  
Iteration 31:  log likelihood = -53652.491  
Iteration 32:  log likelihood = -53652.491  
Iteration 33:  log likelihood = -53652.491  (not concave)
Iteration 34:  log likelihood = -53652.491  (not concave)
Iteration 35:  log likelihood = -53652.491  (not concave)
Iteration 36:  log likelihood = -53652.491  (not concave)
Iteration 37:  log likelihood = -53652.491  (not concave)
Iteration 38:  log likelihood = -53652.491  (not concave)
Iteration 39:  log likelihood = -53652.491  (not concave)
Iteration 40:  log likelihood = -53652.491  (not concave)
Iteration 41:  log likelihood = -53652.491  (not concave)
Iteration 42:  log likelihood = -53652.491  (not concave)
Iteration 43:  log likelihood = -53652.491  (not concave)
Iteration 44:  log likelihood = -53652.491  (not concave)
Iteration 45:  log likelihood = -53652.491  (not concave)
Iteration 46:  log likelihood = -53652.491  (not concave)
Iteration 47:  log likelihood = -53652.491  (not concave)
Iteration 48:  log likelihood = -53652.491  (not concave)
Iteration 49:  log likelihood = -53652.491  (not concave)
Iteration 50:  log likelihood = -53652.491  (not concave)
Iteration 51:  log likelihood = -53652.491  (not concave)
Iteration 52:  log likelihood = -53652.491  (not concave)
Iteration 53:  log likelihood = -53652.491  (not concave)
Iteration 54:  log likelihood = -53652.491  (not concave)
Iteration 55:  log likelihood = -53652.491  (not concave)
Iteration 56:  log likelihood = -53652.491  (not concave)
Iteration 57:  log likelihood = -53652.491  (not concave)
Iteration 58:  log likelihood = -53652.491  (not concave)
Iteration 59:  log likelihood = -53652.491  (not concave)
Iteration 60:  log likelihood = -53652.491  (not concave)
Iteration 61:  log likelihood = -53652.491  (not concave)
Iteration 62:  log likelihood = -53652.491  (not concave)
Iteration 63:  log likelihood = -53652.491  (not concave)
Iteration 64:  log likelihood = -53652.491  (not concave)
Iteration 65:  log likelihood = -53652.491  (not concave)
Iteration 66:  log likelihood = -53652.491  (not concave)
Iteration 67:  log likelihood = -53652.491  (not concave)
Iteration 68:  log likelihood = -53652.491  (not concave)
Iteration 69:  log likelihood = -53652.491  (not concave)
Iteration 70:  log likelihood = -53652.491  (not concave)
Iteration 71:  log likelihood = -53652.491  (not concave)
Iteration 72:  log likelihood = -53652.491  (not concave)
Iteration 73:  log likelihood = -53652.491  (not concave)
Iteration 74:  log likelihood = -53652.491  (not concave)
Iteration 75:  log likelihood = -53652.491  (not concave)
Iteration 76:  log likelihood = -53652.491  (not concave)
Iteration 77:  log likelihood = -53652.491  (not concave)
Iteration 78:  log likelihood = -53652.491  (not concave)
Iteration 79:  log likelihood = -53652.491  (not concave)
Iteration 80:  log likelihood = -53652.491  (not concave)
Iteration 81:  log likelihood = -53652.491  (not concave)
Iteration 82:  log likelihood = -53652.491  (not concave)
Iteration 83:  log likelihood = -53652.491  (not concave)
Iteration 84:  log likelihood = -53652.491  (not concave)
Iteration 85:  log likelihood = -53652.491  (not concave)
Iteration 86:  log likelihood = -53652.491  (not concave)
Iteration 87:  log likelihood = -53652.491  (not concave)
Iteration 88:  log likelihood = -53652.491  (not concave)
Iteration 89:  log likelihood = -53652.491  (not concave)
Iteration 90:  log likelihood = -53652.491  (not concave)
Iteration 91:  log likelihood = -53652.491  (not concave)
Iteration 92:  log likelihood = -53652.491  (not concave)
Iteration 93:  log likelihood = -53652.491  (not concave)
Iteration 94:  log likelihood = -53652.491  (not concave)
Iteration 95:  log likelihood = -53652.491  (not concave)
Iteration 96:  log likelihood = -53652.491  (not concave)
Iteration 97:  log likelihood = -53652.491  (not concave)
Iteration 98:  log likelihood = -53652.491  (not concave)
Iteration 99:  log likelihood = -53652.491  (not concave)
Iteration 100: log likelihood = -53652.491  (not concave)
Iteration 101: log likelihood = -53652.491  (not concave)
Iteration 102: log likelihood = -53652.491  (not concave)
Iteration 103: log likelihood = -53652.491  (not concave)
Iteration 104: log likelihood = -53652.491  (not concave)
Iteration 105: log likelihood = -53652.491  (not concave)
Iteration 106: log likelihood = -53652.491  (not concave)
Iteration 107: log likelihood = -53652.491  (not concave)
Iteration 108: log likelihood = -53652.491  (not concave)
Iteration 109: log likelihood = -53652.491  (not concave)
Iteration 110: log likelihood = -53652.491  (not concave)
Iteration 111: log likelihood = -53652.491  (not concave)
Iteration 112: log likelihood = -53652.491  (not concave)
Iteration 113: log likelihood = -53652.491  (not concave)
Iteration 114: log likelihood = -53652.491  (not concave)
Iteration 115: log likelihood = -53652.491  (not concave)
Iteration 116: log likelihood = -53652.491  (not concave)
Iteration 117: log likelihood = -53652.491  (not concave)
Iteration 118: log likelihood = -53652.491  (not concave)
Iteration 119: log likelihood = -53652.491  (not concave)
Iteration 120: log likelihood = -53652.491  (not concave)
Iteration 121: log likelihood = -53652.491  (not concave)
Iteration 122: log likelihood = -53652.491  (not concave)
Iteration 123: log likelihood = -53652.491  (not concave)
Iteration 124: log likelihood = -53652.491  (not concave)
Iteration 125: log likelihood = -53652.491  (not concave)
Iteration 126: log likelihood = -53652.491  (not concave)
Iteration 127: log likelihood = -53652.491  (not concave)
Iteration 128: log likelihood = -53652.491  (not concave)
Iteration 129: log likelihood = -53652.491  (not concave)
Iteration 130: log likelihood = -53652.491  (not concave)
Iteration 131: log likelihood = -53652.491  (not concave)
Iteration 132: log likelihood = -53652.491  (not concave)
Iteration 133: log likelihood = -53652.491  (not concave)
Iteration 134: log likelihood = -53652.491  (not concave)
Iteration 135: log likelihood = -53652.491  (not concave)
Iteration 136: log likelihood = -53652.491  (not concave)
Iteration 137: log likelihood = -53652.491  (not concave)
Iteration 138: log likelihood = -53652.491  (not concave)
Iteration 139: log likelihood = -53652.491  (not concave)
Iteration 140: log likelihood = -53652.491  (not concave)
Iteration 141: log likelihood = -53652.491  (not concave)
Iteration 142: log likelihood = -53652.491  (not concave)
Iteration 143: log likelihood = -53652.491  (not concave)
Iteration 144: log likelihood = -53652.491  (not concave)
Iteration 145: log likelihood = -53652.491  (not concave)
Iteration 146: log likelihood = -53652.491  (not concave)
Iteration 147: log likelihood = -53652.491  (not concave)
Iteration 148: log likelihood = -53652.491  (not concave)
Iteration 149: log likelihood = -53652.491  (not concave)
Iteration 150: log likelihood = -53652.491  (not concave)
Iteration 151: log likelihood = -53652.491  (not concave)
Iteration 152: log likelihood = -53652.491  (not concave)
Iteration 153: log likelihood = -53652.491  (not concave)
Iteration 154: log likelihood = -53652.491  (not concave)
Iteration 155: log likelihood = -53652.491  (not concave)
Iteration 156: log likelihood = -53652.491  (not concave)
Iteration 157: log likelihood = -53652.491  (not concave)
Iteration 158: log likelihood = -53652.491  (not concave)
Iteration 159: log likelihood = -53652.491  (not concave)
Iteration 160: log likelihood = -53652.491  (not concave)
Iteration 161: log likelihood = -53652.491  (not concave)
Iteration 162: log likelihood = -53652.491  (not concave)
Iteration 163: log likelihood = -53652.491  (not concave)
Iteration 164: log likelihood = -53652.491  (not concave)
Iteration 165: log likelihood = -53652.491  (not concave)
Iteration 166: log likelihood = -53652.491  (not concave)
Iteration 167: log likelihood = -53652.491  (not concave)
Iteration 168: log likelihood = -53652.491  (not concave)
Iteration 169: log likelihood = -53652.491  (not concave)
Iteration 170: log likelihood = -53652.491  (not concave)
Iteration 171: log likelihood = -53652.491  (not concave)
Iteration 172: log likelihood = -53652.491  (not concave)
Iteration 173: log likelihood = -53652.491  (not concave)
Iteration 174: log likelihood = -53652.491  (not concave)
Iteration 175: log likelihood = -53652.491  (not concave)
Iteration 176: log likelihood = -53652.491  (not concave)
Iteration 177: log likelihood = -53652.491  (not concave)
Iteration 178: log likelihood = -53652.491  (not concave)
Iteration 179: log likelihood = -53652.491  (not concave)
Iteration 180: log likelihood = -53652.491  (not concave)
Iteration 181: log likelihood = -53652.491  (not concave)
Iteration 182: log likelihood = -53652.491  (not concave)
Iteration 183: log likelihood = -53652.491  (not concave)
Iteration 184: log likelihood = -53652.491  (not concave)
Iteration 185: log likelihood = -53652.491  (not concave)
Iteration 186: log likelihood = -53652.491  (not concave)
Iteration 187: log likelihood = -53652.491  (not concave)
Iteration 188: log likelihood = -53652.491  (not concave)
Iteration 189: log likelihood = -53652.491  (not concave)
Iteration 190: log likelihood = -53652.491  (not concave)
Iteration 191: log likelihood = -53652.491  (not concave)
Iteration 192: log likelihood = -53652.491  (not concave)
Iteration 193: log likelihood = -53652.491  (not concave)
Iteration 194: log likelihood = -53652.491  (not concave)
Iteration 195: log likelihood = -53652.491  (not concave)
Iteration 196: log likelihood = -53652.491  (not concave)
Iteration 197: log likelihood = -53652.491  (not concave)
Iteration 198: log likelihood = -53652.491  (not concave)
Iteration 199: log likelihood = -53652.491  (not concave)
Iteration 200: log likelihood = -53652.491  (not concave)
Iteration 201: log likelihood = -53652.491  (not concave)
Iteration 202: log likelihood = -53652.491  (not concave)
Iteration 203: log likelihood = -53652.491  (not concave)
Iteration 204: log likelihood = -53652.491  (not concave)
Iteration 205: log likelihood = -53652.491  (not concave)
Iteration 206: log likelihood = -53652.491  (not concave)
Iteration 207: log likelihood = -53652.491  (not concave)
Iteration 208: log likelihood = -53652.491  (not concave)
Iteration 209: log likelihood = -53652.491  (not concave)
Iteration 210: log likelihood = -53652.491  (not concave)
Iteration 211: log likelihood = -53652.491  (not concave)
Iteration 212: log likelihood = -53652.491  (not concave)
Iteration 213: log likelihood = -53652.491  (not concave)
Iteration 214: log likelihood = -53652.491  (not concave)
Iteration 215: log likelihood = -53652.491  (not concave)
Iteration 216: log likelihood = -53652.491  (not concave)
Iteration 217: log likelihood = -53652.491  (not concave)
Iteration 218: log likelihood = -53652.491  (not concave)
Iteration 219: log likelihood = -53652.491  (not concave)
Iteration 220: log likelihood = -53652.491  (not concave)
Iteration 221: log likelihood = -53652.491  (not concave)
Iteration 222: log likelihood = -53652.491  (not concave)
Iteration 223: log likelihood = -53652.491  (not concave)
Iteration 224: log likelihood = -53652.491  (not concave)
Iteration 225: log likelihood = -53652.491  (not concave)
Iteration 226: log likelihood = -53652.491  (not concave)
Iteration 227: log likelihood = -53652.491  (not concave)
Iteration 228: log likelihood = -53652.491  (not concave)
Iteration 229: log likelihood = -53652.491  (not concave)
Iteration 230: log likelihood = -53652.491  (not concave)
Iteration 231: log likelihood = -53652.491  (not concave)
Iteration 232: log likelihood = -53652.491  (not concave)
Iteration 233: log likelihood = -53652.491  (not concave)
Iteration 234: log likelihood = -53652.491  (not concave)
Iteration 235: log likelihood = -53652.491  (not concave)
Iteration 236: log likelihood = -53652.491  (not concave)
Iteration 237: log likelihood = -53652.491  (not concave)
Iteration 238: log likelihood = -53652.491  (not concave)
Iteration 239: log likelihood = -53652.491  (not concave)
Iteration 240: log likelihood = -53652.491  (not concave)
Iteration 241: log likelihood = -53652.491  (not concave)
Iteration 242: log likelihood = -53652.491  (not concave)
Iteration 243: log likelihood = -53652.491  (not concave)
Iteration 244: log likelihood = -53652.491  (not concave)
Iteration 245: log likelihood = -53652.491  (not concave)
Iteration 246: log likelihood = -53652.491  (not concave)
Iteration 247: log likelihood = -53652.491  (not concave)
Iteration 248: log likelihood = -53652.491  (not concave)
Iteration 249: log likelihood = -53652.491  (not concave)
Iteration 250: log likelihood = -53652.491  (not concave)
Iteration 251: log likelihood = -53652.491  (not concave)
Iteration 252: log likelihood = -53652.491  (not concave)
Iteration 253: log likelihood = -53652.491  (not concave)
Iteration 254: log likelihood = -53652.491  (not concave)
Iteration 255: log likelihood = -53652.491  (not concave)
Iteration 256: log likelihood = -53652.491  (not concave)
Iteration 257: log likelihood = -53652.491  (not concave)
Iteration 258: log likelihood = -53652.491  (not concave)
Iteration 259: log likelihood = -53652.491  (not concave)
Iteration 260: log likelihood = -53652.491  (not concave)
Iteration 261: log likelihood = -53652.491  (not concave)
Iteration 262: log likelihood = -53652.491  (not concave)
Iteration 263: log likelihood = -53652.491  (not concave)
Iteration 264: log likelihood = -53652.491  (not concave)
Iteration 265: log likelihood = -53652.491  (not concave)
Iteration 266: log likelihood = -53652.491  (not concave)
Iteration 267: log likelihood = -53652.491  (not concave)
Iteration 268: log likelihood = -53652.491  (not concave)
Iteration 269: log likelihood = -53652.491  (not concave)
Iteration 270: log likelihood = -53652.491  (not concave)
Iteration 271: log likelihood = -53652.491  (not concave)
Iteration 272: log likelihood = -53652.491  (not concave)
Iteration 273: log likelihood = -53652.491  (not concave)
Iteration 274: log likelihood = -53652.491  (not concave)
Iteration 275: log likelihood = -53652.491  (not concave)
Iteration 276: log likelihood = -53652.491  (not concave)
Iteration 277: log likelihood = -53652.491  (not concave)
Iteration 278: log likelihood = -53652.491  (not concave)
Iteration 279: log likelihood = -53652.491  (not concave)
Iteration 280: log likelihood = -53652.491  (not concave)
Iteration 281: log likelihood = -53652.491  (not concave)
Iteration 282: log likelihood = -53652.491  (not concave)
Iteration 283: log likelihood = -53652.491  (not concave)
Iteration 284: log likelihood = -53652.491  (not concave)
Iteration 285: log likelihood = -53652.491  (not concave)
Iteration 286: log likelihood = -53652.491  (not concave)
Iteration 287: log likelihood = -53652.491  (not concave)
Iteration 288: log likelihood = -53652.491  (not concave)
Iteration 289: log likelihood = -53652.491  (not concave)
Iteration 290: log likelihood = -53652.491  (not concave)
Iteration 291: log likelihood = -53652.491  (not concave)
Iteration 292: log likelihood = -53652.491  (not concave)
Iteration 293: log likelihood = -53652.491  (not concave)
Iteration 294: log likelihood = -53652.491  (not concave)
Iteration 295: log likelihood = -53652.491  (not concave)
Iteration 296: log likelihood = -53652.491  (not concave)
Iteration 297: log likelihood = -53652.491  (not concave)
Iteration 298: log likelihood = -53652.491  (not concave)
Iteration 299: log likelihood = -53652.491  (not concave)
Iteration 300: log likelihood = -53652.491  (not concave)
convergence not achieved

Survival model                                  Number of obs     =     59,220
Log likelihood = -53652.491
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1518671   .0097575    15.56   0.000     .1327427    .1709915
edad_al_in~1 |  -.0098832    .001013    -9.76   0.000    -.0118686   -.0078979
edad_ini_c~s |  -.0108262   .0018795    -5.76   0.000      -.01451   -.0071423
     sex_enc |  -.3339387   .0202562   -16.49   0.000    -.3736402   -.2942373
     esc_rec |   .0938296   .0122827     7.64   0.000     .0697559    .1179032
sus_prin_mod |   .1341104   .0081841    16.39   0.000     .1180698     .150151
 fr_sus_prin |    .032481   .0075686     4.29   0.000     .0176468    .0473152
 comp_biosoc |    .194934   .0141677    13.76   0.000     .1671657    .2227022
     ten_viv |  -.0151608   .0076355    -1.99   0.047     -.030126   -.0001956
origen_ing~d |   -.020781   .0044046    -4.72   0.000     -.029414    -.012148
numero_de_~d |   .0715694   .0062779    11.40   0.000      .059265    .0838738
dg_cie_10_~c |   .0271661   .0087695     3.10   0.002     .0099782     .044354
sud_sever~10 |   -.063452   .0191568    -3.31   0.001    -.1009987   -.0259052
   macrozone |   .2082406    .011805    17.64   0.000     .1851033    .2313779
 policonsumo |   .0966545   .0216183     4.47   0.000     .0542833    .1390257
   n_off_vio |   .3169266   .0186861    16.96   0.000     .2803026    .3535506
   n_off_acq |   .6691237   .0173954    38.47   0.000     .6350294    .7032181
   n_off_sud |   .2317407   .0183709    12.61   0.000     .1957344    .2677471
        clas |   .0129129   .0128281     1.01   0.314    -.0122297    .0380555
       _cons |   16.96054          .        .       .            .           .
  log(gamma) |  -16.80384   .0967911  -173.61   0.000    -16.99355   -16.61414
------------------------------------------------------------------------------

.         //qui cap noi merlin (_time $covs if _trans == 1, family(gompertz, fail(_status)))
.         estimates store m2_1_wei

.         
.         // Log logistic
.         di in yellow "{bf: ***********}"
 ***********

.         di in yellow "{bf: family Logl}"
 family Logl

.         di in yellow "{bf: ***********}"
 ***********

.         set seed 2125

.         qui cap noi stmerlin $covs_2 , dist(loglogistic)
note; a delayed entry model is being fitted

Fitting full model:

Iteration 0:   log likelihood = -66283.404  
Iteration 1:   log likelihood = -58198.331  (not concave)
Iteration 2:   log likelihood = -55945.056  (not concave)
Iteration 3:   log likelihood = -55835.288  (not concave)
Iteration 4:   log likelihood = -55771.602  (not concave)
Iteration 5:   log likelihood = -55754.263  (not concave)
Iteration 6:   log likelihood = -55741.317  (not concave)
Iteration 7:   log likelihood =  -55729.43  (not concave)
Iteration 8:   log likelihood = -55715.568  (not concave)
Iteration 9:   log likelihood = -55698.854  (not concave)
Iteration 10:  log likelihood =  -55689.87  (not concave)
Iteration 11:  log likelihood = -55671.963  (not concave)
Iteration 12:  log likelihood = -55659.828  (not concave)
Iteration 13:  log likelihood = -55650.477  (not concave)
Iteration 14:  log likelihood = -55641.091  (not concave)
Iteration 15:  log likelihood = -55630.169  (not concave)
Iteration 16:  log likelihood = -55612.726  (not concave)
Iteration 17:  log likelihood =   -55602.7  (not concave)
Iteration 18:  log likelihood =  -55587.04  (not concave)
Iteration 19:  log likelihood =  -55577.32  (not concave)
Iteration 20:  log likelihood = -55564.706  (not concave)
Iteration 21:  log likelihood = -55555.128  (not concave)
Iteration 22:  log likelihood = -55545.559  (not concave)
Iteration 23:  log likelihood =  -55535.84  (not concave)
Iteration 24:  log likelihood = -55526.063  (not concave)
Iteration 25:  log likelihood = -55516.599  (not concave)
Iteration 26:  log likelihood = -55506.897  (not concave)
Iteration 27:  log likelihood =  -55497.25  (not concave)
Iteration 28:  log likelihood = -55487.485  (not concave)
Iteration 29:  log likelihood = -55477.903  (not concave)
Iteration 30:  log likelihood = -55468.141  (not concave)
Iteration 31:  log likelihood = -55458.492  (not concave)
Iteration 32:  log likelihood = -55448.703  (not concave)
Iteration 33:  log likelihood = -55439.065  (not concave)
Iteration 34:  log likelihood = -55429.274  (not concave)
Iteration 35:  log likelihood = -55419.614  (not concave)
Iteration 36:  log likelihood = -55409.819  (not concave)
Iteration 37:  log likelihood = -55400.168  (not concave)
Iteration 38:  log likelihood = -55390.383  (not concave)
Iteration 39:  log likelihood = -55380.738  (not concave)
Iteration 40:  log likelihood = -55370.969  (not concave)
Iteration 41:  log likelihood = -55361.344  (not concave)
Iteration 42:  log likelihood = -55351.602  (not concave)
Iteration 43:  log likelihood = -55342.003  (not concave)
Iteration 44:  log likelihood = -55332.295  (not concave)
Iteration 45:  log likelihood = -55322.731  (not concave)
Iteration 46:  log likelihood = -55313.064  (not concave)
Iteration 47:  log likelihood = -55303.543  (not concave)
Iteration 48:  log likelihood = -55293.924  (not concave)
Iteration 49:  log likelihood = -55284.452  (not concave)
Iteration 50:  log likelihood = -55274.889  (not concave)
Iteration 51:  log likelihood = -55265.472  (not concave)
Iteration 52:  log likelihood =  -55255.97  (not concave)
Iteration 53:  log likelihood = -55246.614  (not concave)
Iteration 54:  log likelihood = -55237.179  (not concave)
Iteration 55:  log likelihood =  -55227.89  (not concave)
Iteration 56:  log likelihood = -55218.528  (not concave)
Iteration 57:  log likelihood = -55209.311  (not concave)
Iteration 58:  log likelihood = -55200.027  (not concave)
Iteration 59:  log likelihood = -55190.888  (not concave)
Iteration 60:  log likelihood = -55181.687  (not concave)
Iteration 61:  log likelihood = -55172.629  (not concave)
Iteration 62:  log likelihood = -55163.515  (not concave)
Iteration 63:  log likelihood = -55154.544  (not concave)
Iteration 64:  log likelihood = -55145.522  (not concave)
Iteration 65:  log likelihood = -55136.644  (not concave)
Iteration 66:  log likelihood = -55127.718  (not concave)
Iteration 67:  log likelihood = -55118.934  (not concave)
Iteration 68:  log likelihood = -55110.108  (not concave)
Iteration 69:  log likelihood = -55101.424  (not concave)
Iteration 70:  log likelihood = -55092.702  (not concave)
Iteration 71:  log likelihood =  -55084.12  (not concave)
Iteration 72:  log likelihood = -55075.506  (not concave)
Iteration 73:  log likelihood = -55067.031  (not concave)
Iteration 74:  log likelihood = -55058.529  (not concave)
Iteration 75:  log likelihood = -55050.163  (not concave)
Iteration 76:  log likelihood = -55041.775  (not concave)
Iteration 77:  log likelihood = -55033.522  (not concave)
Iteration 78:  log likelihood = -55025.251  (not concave)
Iteration 79:  log likelihood = -55017.115  (not concave)
Iteration 80:  log likelihood = -55008.964  (not concave)
Iteration 81:  log likelihood = -55000.946  (not concave)
Iteration 82:  log likelihood = -54992.918  (not concave)
Iteration 83:  log likelihood = -54985.022  (not concave)
Iteration 84:  log likelihood = -54977.119  (not concave)
Iteration 85:  log likelihood = -54969.346  (not concave)
Iteration 86:  log likelihood = -54961.571  (not concave)
Iteration 87:  log likelihood = -54953.924  (not concave)
Iteration 88:  log likelihood = -54946.278  (not concave)
Iteration 89:  log likelihood = -54938.759  (not concave)
Iteration 90:  log likelihood = -54931.244  (not concave)
Iteration 91:  log likelihood = -54923.855  (not concave)
Iteration 92:  log likelihood = -54916.473  (not concave)
Iteration 93:  log likelihood = -54909.215  (not concave)
Iteration 94:  log likelihood = -54901.967  (not concave)
Iteration 95:  log likelihood = -54894.842  (not concave)
Iteration 96:  log likelihood = -54887.731  (not concave)
Iteration 97:  log likelihood = -54880.739  (not concave)
Iteration 98:  log likelihood = -54873.765  (not concave)
Iteration 99:  log likelihood = -54866.909  (not concave)
Iteration 100: log likelihood = -54860.072  (not concave)
Iteration 101: log likelihood = -54853.352  (not concave)
Iteration 102: log likelihood = -54846.654  (not concave)
Iteration 103: log likelihood = -54840.071  (not concave)
Iteration 104: log likelihood = -54833.513  (not concave)
Iteration 105: log likelihood = -54827.067  (not concave)
Iteration 106: log likelihood = -54820.648  (not concave)
Iteration 107: log likelihood = -54814.341  (not concave)
Iteration 108: log likelihood = -54808.062  (not concave)
Iteration 109: log likelihood = -54801.893  (not concave)
Iteration 110: log likelihood = -54795.754  (not concave)
Iteration 111: log likelihood = -54789.723  (not concave)
Iteration 112: log likelihood = -54783.724  
Iteration 113: log likelihood = -54751.561  (backed up)
Iteration 114: log likelihood =  -54464.96  (not concave)
Iteration 115: log likelihood = -54464.933  
Iteration 116: log likelihood = -54431.981  
Iteration 117: log likelihood = -54430.129  
Iteration 118: log likelihood = -54430.067  
Iteration 119: log likelihood = -54430.064  
Iteration 120: log likelihood = -54430.064  

Survival model                                  Number of obs     =     59,220
Log likelihood = -54430.064
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |  -.0694756   .0050388   -13.79   0.000    -.0793515   -.0595998
edad_al_in~1 |   .0320229   .0004846    66.09   0.000     .0310731    .0329726
edad_ini_c~s |   .0036966   .0007891     4.68   0.000       .00215    .0052432
     sex_enc |   .1559605    .009951    15.67   0.000     .1364569    .1754641
     esc_rec |  -.0564166   .0064404    -8.76   0.000    -.0690397   -.0437936
sus_prin_mod |  -.0641715   .0047693   -13.46   0.000    -.0735191    -.054824
 fr_sus_prin |   -.016021   .0038344    -4.18   0.000    -.0235364   -.0085056
 comp_biosoc |  -.0927651   .0075242   -12.33   0.000    -.1075123   -.0780179
     ten_viv |   .0049484   .0041658     1.19   0.235    -.0032164    .0131133
origen_ing~d |   .0068123   .0023115     2.95   0.003     .0022819    .0113427
numero_de_~d |  -.0311215   .0036661    -8.49   0.000    -.0383069    -.023936
dg_cie_10_~c |  -.0069559   .0047283    -1.47   0.141    -.0162232    .0023114
sud_sever~10 |   .0130713   .0095135     1.37   0.169    -.0055749    .0317174
   macrozone |  -.0896473   .0075911   -11.81   0.000    -.1045255    -.074769
 policonsumo |  -.0306024   .0099349    -3.08   0.002    -.0500744   -.0111303
   n_off_vio |  -.2117419   .0173208   -12.22   0.000    -.2456901   -.1777937
   n_off_acq |   -.949405   .2861069    -3.32   0.001    -1.510164   -.3886458
   n_off_sud |  -.1875736   .0167457   -11.20   0.000    -.2203946   -.1547526
        clas |   .0075946   .0070463     1.08   0.281     -.006216    .0214051
       _cons |   3.026698   .0482567    62.72   0.000     2.932116    3.121279
       dap:1 |  -1.723149   .0113198  -152.22   0.000    -1.745336   -1.700963
------------------------------------------------------------------------------

.         //qui cap noi merlin (_time $covs if _trans == 1, family(loglogistic, fail(_status)))
.         estimates store m2_1_logl

. 
.         // Log normal
.         di in yellow "{bf: ***********}"
 ***********

.         di in yellow "{bf: family Logn}"
 family Logn

.         di in yellow "{bf: ***********}"
 ***********

.         set seed 2125

.         qui cap noi stmerlin $covs_2 , dist(lognormal)
note; a delayed entry model is being fitted

Fitting full model:

Iteration 0:   log likelihood = -56595.733  (not concave)
Iteration 1:   log likelihood = -54886.386  (not concave)
Iteration 2:   log likelihood = -54861.727  (not concave)
Iteration 3:   log likelihood = -54857.082  (not concave)
Iteration 4:   log likelihood = -54778.083  (not concave)
Iteration 5:   log likelihood =  -54773.48  (not concave)
Iteration 6:   log likelihood = -54764.709  (not concave)
Iteration 7:   log likelihood = -54754.991  (not concave)
Iteration 8:   log likelihood = -54744.532  (not concave)
Iteration 9:   log likelihood = -54718.158  (not concave)
Iteration 10:  log likelihood =   -54706.3  (not concave)
Iteration 11:  log likelihood = -54634.141  (not concave)
Iteration 12:  log likelihood = -54630.127  (not concave)
Iteration 13:  log likelihood = -54629.897  (not concave)
Iteration 14:  log likelihood =  -54609.33  (not concave)
Iteration 15:  log likelihood = -54576.971  (not concave)
Iteration 16:  log likelihood = -54515.979  (not concave)
Iteration 17:  log likelihood = -54497.765  (not concave)
Iteration 18:  log likelihood = -54497.632  (not concave)
Iteration 19:  log likelihood = -54497.628  (not concave)
Iteration 20:  log likelihood = -54497.628  (not concave)
Iteration 21:  log likelihood = -54497.622  (not concave)
Iteration 22:  log likelihood = -54497.621  (not concave)
Iteration 23:  log likelihood = -54497.621  (not concave)
Iteration 24:  log likelihood = -54497.621  (not concave)
Iteration 25:  log likelihood = -54497.621  (not concave)
Iteration 26:  log likelihood = -54497.621  (not concave)
Iteration 27:  log likelihood = -54497.621  (not concave)
Iteration 28:  log likelihood = -54497.621  (not concave)
Iteration 29:  log likelihood = -54497.621  (not concave)
Iteration 30:  log likelihood = -54497.621  (not concave)
Iteration 31:  log likelihood = -54497.621  (not concave)
Iteration 32:  log likelihood = -54497.621  (not concave)
Iteration 33:  log likelihood = -54497.621  (not concave)
Iteration 34:  log likelihood = -54497.621  (not concave)
Iteration 35:  log likelihood = -54497.621  (not concave)
Iteration 36:  log likelihood = -54497.621  (not concave)
Iteration 37:  log likelihood = -54497.621  (not concave)
Iteration 38:  log likelihood = -54497.621  (not concave)
Iteration 39:  log likelihood = -54497.621  (not concave)
Iteration 40:  log likelihood = -54497.621  (not concave)
Iteration 41:  log likelihood = -54497.621  (not concave)
Iteration 42:  log likelihood = -54497.621  (not concave)
Iteration 43:  log likelihood = -54497.621  (not concave)
Iteration 44:  log likelihood = -54497.621  (not concave)
Iteration 45:  log likelihood = -54497.621  (not concave)
Iteration 46:  log likelihood = -54497.621  (not concave)
Iteration 47:  log likelihood = -54497.621  (not concave)
Iteration 48:  log likelihood = -54497.621  (not concave)
Iteration 49:  log likelihood = -54497.621  (not concave)
Iteration 50:  log likelihood = -54497.621  (not concave)
Iteration 51:  log likelihood = -54497.621  (not concave)
Iteration 52:  log likelihood = -54497.621  (not concave)
Iteration 53:  log likelihood = -54497.621  (not concave)
Iteration 54:  log likelihood = -54497.621  (not concave)
Iteration 55:  log likelihood = -54497.621  (not concave)
Iteration 56:  log likelihood = -54497.621  (not concave)
Iteration 57:  log likelihood = -54497.621  (not concave)
Iteration 58:  log likelihood = -54497.621  (not concave)
Iteration 59:  log likelihood = -54497.621  (not concave)
Iteration 60:  log likelihood = -54497.621  (not concave)
Iteration 61:  log likelihood = -54497.621  (not concave)
Iteration 62:  log likelihood = -54497.621  (not concave)
Iteration 63:  log likelihood = -54497.621  (not concave)
Iteration 64:  log likelihood = -54497.621  (not concave)
Iteration 65:  log likelihood = -54497.621  (not concave)
Iteration 66:  log likelihood = -54497.621  (not concave)
Iteration 67:  log likelihood = -54497.621  (not concave)
Iteration 68:  log likelihood = -54497.621  (not concave)
Iteration 69:  log likelihood = -54497.621  (not concave)
Iteration 70:  log likelihood = -54497.621  (not concave)
Iteration 71:  log likelihood = -54497.621  (not concave)
Iteration 72:  log likelihood = -54497.621  (not concave)
Iteration 73:  log likelihood = -54497.621  (not concave)
Iteration 74:  log likelihood = -54497.621  (not concave)
Iteration 75:  log likelihood = -54497.621  (not concave)
Iteration 76:  log likelihood = -54497.621  (not concave)
Iteration 77:  log likelihood = -54497.621  (not concave)
Iteration 78:  log likelihood = -54497.621  (not concave)
Iteration 79:  log likelihood = -54497.621  (not concave)
Iteration 80:  log likelihood = -54497.621  (not concave)
Iteration 81:  log likelihood = -54497.621  (not concave)
Iteration 82:  log likelihood = -54497.621  (not concave)
Iteration 83:  log likelihood = -54497.621  (not concave)
Iteration 84:  log likelihood = -54497.621  (not concave)
Iteration 85:  log likelihood = -54497.621  (not concave)
Iteration 86:  log likelihood = -54497.621  (not concave)
Iteration 87:  log likelihood = -54497.621  (not concave)
Iteration 88:  log likelihood = -54497.621  (not concave)
Iteration 89:  log likelihood = -54497.621  (not concave)
Iteration 90:  log likelihood = -54497.621  (not concave)
Iteration 91:  log likelihood = -54497.621  (not concave)
Iteration 92:  log likelihood = -54497.621  (not concave)
Iteration 93:  log likelihood = -54497.621  (not concave)
Iteration 94:  log likelihood = -54497.621  (not concave)
Iteration 95:  log likelihood = -54497.621  (not concave)
Iteration 96:  log likelihood = -54497.621  (not concave)
Iteration 97:  log likelihood = -54497.621  (not concave)
Iteration 98:  log likelihood = -54497.621  (not concave)
Iteration 99:  log likelihood = -54497.621  (not concave)
Iteration 100: log likelihood = -54497.621  (not concave)
Iteration 101: log likelihood = -54497.621  (not concave)
Iteration 102: log likelihood = -54497.621  (not concave)
Iteration 103: log likelihood = -54497.621  (not concave)
Iteration 104: log likelihood = -54497.621  (not concave)
Iteration 105: log likelihood = -54497.621  (not concave)
Iteration 106: log likelihood = -54497.621  (not concave)
Iteration 107: log likelihood = -54497.621  (not concave)
Iteration 108: log likelihood = -54497.621  (not concave)
Iteration 109: log likelihood = -54497.621  (not concave)
Iteration 110: log likelihood = -54497.621  (not concave)
Iteration 111: log likelihood = -54497.621  (not concave)
Iteration 112: log likelihood = -54497.621  (not concave)
Iteration 113: log likelihood = -54497.621  (not concave)
Iteration 114: log likelihood = -54497.621  (not concave)
Iteration 115: log likelihood = -54497.621  (not concave)
Iteration 116: log likelihood = -54497.621  (not concave)
Iteration 117: log likelihood = -54497.621  (not concave)
Iteration 118: log likelihood = -54497.621  (not concave)
Iteration 119: log likelihood = -54497.621  (not concave)
Iteration 120: log likelihood = -54497.621  (not concave)
Iteration 121: log likelihood = -54497.621  (not concave)
Iteration 122: log likelihood = -54497.621  (not concave)
Iteration 123: log likelihood = -54497.621  (not concave)
Iteration 124: log likelihood = -54497.621  (not concave)
Iteration 125: log likelihood = -54497.621  (not concave)
Iteration 126: log likelihood = -54497.621  (not concave)
Iteration 127: log likelihood = -54497.621  (not concave)
Iteration 128: log likelihood = -54497.621  (not concave)
Iteration 129: log likelihood = -54497.621  (not concave)
Iteration 130: log likelihood = -54497.621  (not concave)
Iteration 131: log likelihood = -54497.621  (not concave)
Iteration 132: log likelihood = -54497.621  (not concave)
Iteration 133: log likelihood = -54497.621  (not concave)
Iteration 134: log likelihood = -54497.621  (not concave)
Iteration 135: log likelihood = -54497.621  (not concave)
Iteration 136: log likelihood = -54497.621  (not concave)
Iteration 137: log likelihood = -54497.621  (not concave)
Iteration 138: log likelihood = -54497.621  (not concave)
Iteration 139: log likelihood = -54497.621  (not concave)
Iteration 140: log likelihood = -54497.621  (not concave)
Iteration 141: log likelihood = -54497.621  (not concave)
Iteration 142: log likelihood = -54497.621  (not concave)
Iteration 143: log likelihood = -54497.621  (not concave)
Iteration 144: log likelihood = -54497.621  (not concave)
Iteration 145: log likelihood = -54497.621  (not concave)
Iteration 146: log likelihood = -54497.621  (not concave)
Iteration 147: log likelihood = -54497.621  (not concave)
Iteration 148: log likelihood = -54497.621  (not concave)
Iteration 149: log likelihood = -54497.621  (not concave)
Iteration 150: log likelihood = -54497.621  (not concave)
Iteration 151: log likelihood = -54497.621  (not concave)
Iteration 152: log likelihood = -54497.621  (not concave)
Iteration 153: log likelihood = -54497.621  (not concave)
Iteration 154: log likelihood = -54497.621  (not concave)
Iteration 155: log likelihood = -54497.621  (not concave)
Iteration 156: log likelihood = -54497.621  (not concave)
Iteration 157: log likelihood = -54497.621  (not concave)
Iteration 158: log likelihood = -54497.621  (not concave)
Iteration 159: log likelihood = -54497.621  (not concave)
Iteration 160: log likelihood = -54497.621  (not concave)
Iteration 161: log likelihood = -54497.621  (not concave)
Iteration 162: log likelihood = -54497.621  (not concave)
Iteration 163: log likelihood = -54497.621  (not concave)
Iteration 164: log likelihood = -54497.621  (not concave)
Iteration 165: log likelihood = -54497.621  (not concave)
Iteration 166: log likelihood = -54497.621  (not concave)
Iteration 167: log likelihood = -54497.621  (not concave)
Iteration 168: log likelihood = -54497.621  (not concave)
Iteration 169: log likelihood = -54497.621  (not concave)
Iteration 170: log likelihood = -54497.621  (not concave)
Iteration 171: log likelihood = -54497.621  (not concave)
Iteration 172: log likelihood = -54497.621  (not concave)
Iteration 173: log likelihood = -54497.621  (not concave)
Iteration 174: log likelihood = -54497.621  (not concave)
Iteration 175: log likelihood = -54497.621  (not concave)
Iteration 176: log likelihood = -54497.621  (not concave)
Iteration 177: log likelihood = -54497.621  (not concave)
Iteration 178: log likelihood = -54497.621  (not concave)
Iteration 179: log likelihood = -54497.621  (not concave)
Iteration 180: log likelihood = -54497.621  (not concave)
Iteration 181: log likelihood = -54497.621  (not concave)
Iteration 182: log likelihood = -54497.621  (not concave)
Iteration 183: log likelihood = -54497.621  (not concave)
Iteration 184: log likelihood = -54497.621  (not concave)
Iteration 185: log likelihood = -54497.621  (not concave)
Iteration 186: log likelihood = -54497.621  (not concave)
Iteration 187: log likelihood = -54497.621  (not concave)
Iteration 188: log likelihood = -54497.621  (not concave)
Iteration 189: log likelihood = -54497.621  (not concave)
Iteration 190: log likelihood = -54497.621  (not concave)
Iteration 191: log likelihood = -54497.621  (not concave)
Iteration 192: log likelihood = -54497.621  (not concave)
Iteration 193: log likelihood = -54497.621  (not concave)
Iteration 194: log likelihood = -54497.621  (not concave)
Iteration 195: log likelihood = -54497.621  (not concave)
Iteration 196: log likelihood = -54497.621  (not concave)
Iteration 197: log likelihood = -54497.621  (not concave)
Iteration 198: log likelihood = -54497.621  (not concave)
Iteration 199: log likelihood = -54497.621  (not concave)
Iteration 200: log likelihood = -54497.621  (not concave)
Iteration 201: log likelihood = -54497.621  (not concave)
Iteration 202: log likelihood = -54497.621  (not concave)
Iteration 203: log likelihood = -54497.621  (not concave)
Iteration 204: log likelihood = -54497.621  (not concave)
Iteration 205: log likelihood = -54497.621  (not concave)
Iteration 206: log likelihood = -54497.621  (not concave)
Iteration 207: log likelihood = -54497.621  (not concave)
Iteration 208: log likelihood = -54497.621  (not concave)
Iteration 209: log likelihood = -54497.621  (not concave)
Iteration 210: log likelihood = -54497.621  (not concave)
Iteration 211: log likelihood = -54497.621  (not concave)
Iteration 212: log likelihood = -54497.621  (not concave)
Iteration 213: log likelihood = -54497.621  (not concave)
Iteration 214: log likelihood = -54497.621  (not concave)
Iteration 215: log likelihood = -54497.621  (not concave)
Iteration 216: log likelihood = -54497.621  (not concave)
Iteration 217: log likelihood = -54497.621  (not concave)
Iteration 218: log likelihood = -54497.621  (not concave)
Iteration 219: log likelihood = -54497.621  (not concave)
Iteration 220: log likelihood = -54497.621  (not concave)
Iteration 221: log likelihood = -54497.621  (not concave)
Iteration 222: log likelihood = -54497.621  (not concave)
Iteration 223: log likelihood = -54497.621  (not concave)
Iteration 224: log likelihood = -54497.621  (not concave)
Iteration 225: log likelihood = -54497.621  (not concave)
Iteration 226: log likelihood = -54497.621  (not concave)
Iteration 227: log likelihood = -54497.621  (not concave)
Iteration 228: log likelihood = -54497.621  (not concave)
Iteration 229: log likelihood = -54497.621  (not concave)
Iteration 230: log likelihood = -54497.621  (not concave)
Iteration 231: log likelihood = -54497.621  (not concave)
Iteration 232: log likelihood = -54497.621  (not concave)
Iteration 233: log likelihood = -54497.621  (not concave)
Iteration 234: log likelihood = -54497.621  (not concave)
Iteration 235: log likelihood = -54497.621  (not concave)
Iteration 236: log likelihood = -54497.621  (not concave)
Iteration 237: log likelihood = -54497.621  (not concave)
Iteration 238: log likelihood = -54497.621  (not concave)
Iteration 239: log likelihood = -54497.621  (not concave)
Iteration 240: log likelihood = -54497.621  (not concave)
Iteration 241: log likelihood = -54497.621  (not concave)
Iteration 242: log likelihood = -54497.621  (not concave)
Iteration 243: log likelihood = -54497.621  (not concave)
Iteration 244: log likelihood = -54497.621  (not concave)
Iteration 245: log likelihood = -54497.621  (not concave)
Iteration 246: log likelihood = -54497.621  (not concave)
Iteration 247: log likelihood = -54497.621  (not concave)
Iteration 248: log likelihood = -54497.621  (not concave)
Iteration 249: log likelihood = -54497.621  (not concave)
Iteration 250: log likelihood = -54497.621  (not concave)
Iteration 251: log likelihood = -54497.621  (not concave)
Iteration 252: log likelihood = -54497.621  (not concave)
Iteration 253: log likelihood = -54497.621  (not concave)
Iteration 254: log likelihood = -54497.621  (not concave)
Iteration 255: log likelihood = -54497.621  (not concave)
Iteration 256: log likelihood = -54497.621  (not concave)
Iteration 257: log likelihood = -54497.621  (not concave)
Iteration 258: log likelihood = -54497.621  (not concave)
Iteration 259: log likelihood = -54497.621  (not concave)
Iteration 260: log likelihood = -54497.621  (not concave)
Iteration 261: log likelihood = -54497.621  (not concave)
Iteration 262: log likelihood = -54497.621  (not concave)
Iteration 263: log likelihood = -54497.621  (not concave)
Iteration 264: log likelihood = -54497.621  (not concave)
Iteration 265: log likelihood = -54497.621  (not concave)
Iteration 266: log likelihood = -54497.621  (not concave)
Iteration 267: log likelihood = -54497.621  (not concave)
Iteration 268: log likelihood = -54497.621  (not concave)
Iteration 269: log likelihood = -54497.621  (not concave)
Iteration 270: log likelihood = -54497.621  (not concave)
Iteration 271: log likelihood = -54497.621  (not concave)
Iteration 272: log likelihood = -54497.621  (not concave)
Iteration 273: log likelihood = -54497.621  (not concave)
Iteration 274: log likelihood = -54497.621  (not concave)
Iteration 275: log likelihood = -54497.621  (not concave)
Iteration 276: log likelihood = -54497.621  (not concave)
Iteration 277: log likelihood = -54497.621  (not concave)
Iteration 278: log likelihood = -54497.621  (not concave)
Iteration 279: log likelihood = -54497.621  (not concave)
Iteration 280: log likelihood = -54497.621  (not concave)
Iteration 281: log likelihood = -54497.621  (not concave)
Iteration 282: log likelihood = -54497.621  (not concave)
Iteration 283: log likelihood = -54497.621  (not concave)
Iteration 284: log likelihood = -54497.621  (not concave)
Iteration 285: log likelihood = -54497.621  (not concave)
Iteration 286: log likelihood = -54497.621  (not concave)
Iteration 287: log likelihood = -54497.621  (not concave)
Iteration 288: log likelihood = -54497.621  (not concave)
Iteration 289: log likelihood = -54497.621  (not concave)
Iteration 290: log likelihood = -54497.621  (not concave)
Iteration 291: log likelihood = -54497.621  (not concave)
Iteration 292: log likelihood = -54497.621  (not concave)
Iteration 293: log likelihood = -54497.621  (not concave)
Iteration 294: log likelihood = -54497.621  (not concave)
Iteration 295: log likelihood = -54497.621  (not concave)
Iteration 296: log likelihood = -54497.621  (not concave)
Iteration 297: log likelihood = -54497.621  (not concave)
Iteration 298: log likelihood = -54497.621  (not concave)
Iteration 299: log likelihood = -54497.621  (not concave)
Iteration 300: log likelihood = -54497.621  (not concave)
convergence not achieved

Survival model                                  Number of obs     =     59,220
Log likelihood = -54497.621
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |  -.1324127          .        .       .            .           .
edad_al_in~1 |   .0234693   3.19e-06  7347.40   0.000      .023463    .0234755
edad_ini_c~s |   .0288506   6.30e-06  4579.61   0.000     .0288382    .0288629
     sex_enc |   .2588283          .        .       .            .           .
     esc_rec |  -.1543121   .0000253 -6106.85   0.000    -.1543616   -.1542626
sus_prin_mod |  -.1644768   .0000231 -7108.81   0.000    -.1645221   -.1644314
 fr_sus_prin |   .0607371   .0000474  1281.22   0.000     .0606442      .06083
 comp_biosoc |  -.0907099   .0000546 -1660.59   0.000     -.090817   -.0906029
     ten_viv |  -.0706943   .0000148 -4775.09   0.000    -.0707233   -.0706652
origen_ing~d |   .1354098          .        .       .            .           .
numero_de_~d |   .0841148   .0001312   641.05   0.000     .0838576     .084372
dg_cie_10_~c |   .0270841          .        .       .            .           .
sud_sever~10 |   .3312617   .0000732  4522.40   0.000     .3311181    .3314052
   macrozone |  -.3324599   .0000575 -5786.38   0.000    -.3325725   -.3323473
 policonsumo |  -.3487598          .        .       .            .           .
   n_off_vio |  -1.039847   .0000994 -1.0e+04   0.000    -1.040042   -1.039653
   n_off_acq |   -1.60836          .        .       .            .           .
   n_off_sud |  -1.138595   .0000894 -1.3e+04   0.000     -1.13877    -1.13842
        clas |    .219755          .        .       .            .           .
       _cons |   .1303322          .        .       .            .           .
       dap:1 |  -.0481346          .        .       .            .           .
------------------------------------------------------------------------------

.         //qui cap noi merlin (_time $covs if _trans == 1, family(lognormal, fail(_status)))
.         estimates store m2_1_logn

.         
.         // Generalised gamma
.         di in yellow "{bf: ***********}"
 ***********

.         di in yellow "{bf: family Ggam}"
 family Ggam

.         di in yellow "{bf: ***********}"
 ***********

.         set seed 2125

.         qui cap noi stmerlin $covs_2 , dist(ggamma)
note; a delayed entry model is being fitted

Fitting full model:

Iteration 0:   log likelihood = -62692.817  (not concave)
Iteration 1:   log likelihood = -54687.677  (not concave)
Iteration 2:   log likelihood = -54658.186  (not concave)
Iteration 3:   log likelihood = -54658.184  (not concave)
Iteration 4:   log likelihood = -54658.184  (not concave)
Iteration 5:   log likelihood = -54658.184  (not concave)
Iteration 6:   log likelihood = -54658.184  (not concave)
Iteration 7:   log likelihood = -54658.184  (not concave)
Iteration 8:   log likelihood = -54658.184  (not concave)
Iteration 9:   log likelihood = -54658.184  (not concave)
Iteration 10:  log likelihood = -54658.184  (not concave)
Iteration 11:  log likelihood = -54658.184  (not concave)
Iteration 12:  log likelihood = -54658.184  (not concave)
Iteration 13:  log likelihood = -54658.184  (not concave)
Iteration 14:  log likelihood = -54658.184  (not concave)
Iteration 15:  log likelihood = -54658.184  (not concave)
Iteration 16:  log likelihood = -54658.184  (not concave)
Iteration 17:  log likelihood = -54658.184  (not concave)
Iteration 18:  log likelihood = -54658.184  (not concave)
Iteration 19:  log likelihood = -54658.184  (not concave)
Iteration 20:  log likelihood = -54658.184  (not concave)
Iteration 21:  log likelihood = -54658.184  (not concave)
Iteration 22:  log likelihood = -54658.184  (not concave)
Iteration 23:  log likelihood = -54658.184  (not concave)
Iteration 24:  log likelihood = -54658.184  (not concave)
Iteration 25:  log likelihood = -54658.184  (not concave)
Iteration 26:  log likelihood = -54658.184  (not concave)
Iteration 27:  log likelihood = -54658.184  (not concave)
Iteration 28:  log likelihood = -54658.184  (not concave)
Iteration 29:  log likelihood = -54658.184  (not concave)
Iteration 30:  log likelihood = -54658.184  (not concave)
Iteration 31:  log likelihood = -54658.184  (not concave)
Iteration 32:  log likelihood = -54658.184  (not concave)
Iteration 33:  log likelihood = -54658.184  (not concave)
Iteration 34:  log likelihood = -54658.184  (not concave)
Iteration 35:  log likelihood = -54658.184  (not concave)
Iteration 36:  log likelihood = -54658.184  (not concave)
Iteration 37:  log likelihood = -54658.184  (not concave)
Iteration 38:  log likelihood = -54658.184  (not concave)
Iteration 39:  log likelihood = -54658.184  (not concave)
Iteration 40:  log likelihood = -54658.184  (not concave)
Iteration 41:  log likelihood = -54658.184  (not concave)
Iteration 42:  log likelihood = -54658.184  (not concave)
Iteration 43:  log likelihood = -54658.184  (not concave)
Iteration 44:  log likelihood = -54658.184  (not concave)
Iteration 45:  log likelihood = -54658.184  (not concave)
Iteration 46:  log likelihood = -54658.184  (not concave)
Iteration 47:  log likelihood = -54658.184  (not concave)
Iteration 48:  log likelihood = -54658.184  (not concave)
Iteration 49:  log likelihood = -54658.184  (not concave)
Iteration 50:  log likelihood = -54658.184  (not concave)
Iteration 51:  log likelihood = -54658.184  (not concave)
Iteration 52:  log likelihood = -54658.184  (not concave)
Iteration 53:  log likelihood = -54658.184  (not concave)
Iteration 54:  log likelihood = -54658.184  (not concave)
Iteration 55:  log likelihood = -54658.184  (not concave)
Iteration 56:  log likelihood = -54658.184  (not concave)
Iteration 57:  log likelihood = -54658.184  (not concave)
Iteration 58:  log likelihood = -54658.184  (not concave)
Iteration 59:  log likelihood = -54658.184  (not concave)
Iteration 60:  log likelihood = -54658.184  (not concave)
Iteration 61:  log likelihood = -54658.184  (not concave)
Iteration 62:  log likelihood = -54658.184  (not concave)
Iteration 63:  log likelihood = -54658.184  (not concave)
Iteration 64:  log likelihood = -54658.184  (not concave)
Iteration 65:  log likelihood = -54658.184  (not concave)
Iteration 66:  log likelihood = -54658.184  (not concave)
Iteration 67:  log likelihood = -54658.184  (not concave)
Iteration 68:  log likelihood = -54658.184  (not concave)
Iteration 69:  log likelihood = -54658.184  (not concave)
Iteration 70:  log likelihood = -54658.184  (not concave)
Iteration 71:  log likelihood = -54658.184  (not concave)
Iteration 72:  log likelihood = -54658.184  (not concave)
Iteration 73:  log likelihood = -54658.184  (not concave)
Iteration 74:  log likelihood = -54658.184  (not concave)
Iteration 75:  log likelihood = -54658.184  (not concave)
Iteration 76:  log likelihood = -54658.184  (not concave)
Iteration 77:  log likelihood = -54658.184  (not concave)
Iteration 78:  log likelihood = -54658.184  (not concave)
Iteration 79:  log likelihood = -54658.184  (not concave)
Iteration 80:  log likelihood = -54658.184  (not concave)
Iteration 81:  log likelihood = -54658.184  (not concave)
Iteration 82:  log likelihood = -54658.184  (not concave)
Iteration 83:  log likelihood = -54658.184  (not concave)
Iteration 84:  log likelihood = -54658.184  (not concave)
Iteration 85:  log likelihood = -54658.184  (not concave)
Iteration 86:  log likelihood = -54658.184  (not concave)
Iteration 87:  log likelihood = -54658.184  (not concave)
Iteration 88:  log likelihood = -54658.184  (not concave)
Iteration 89:  log likelihood = -54658.184  (not concave)
Iteration 90:  log likelihood = -54658.184  (not concave)
Iteration 91:  log likelihood = -54658.184  (not concave)
Iteration 92:  log likelihood = -54658.184  (not concave)
Iteration 93:  log likelihood = -54658.184  (not concave)
Iteration 94:  log likelihood = -54658.184  (not concave)
Iteration 95:  log likelihood = -54658.184  (not concave)
Iteration 96:  log likelihood = -54658.184  (not concave)
Iteration 97:  log likelihood = -54658.184  (not concave)
Iteration 98:  log likelihood = -54658.184  (not concave)
Iteration 99:  log likelihood = -54658.184  (not concave)
Iteration 100: log likelihood = -54658.184  (not concave)
Iteration 101: log likelihood = -54658.184  (not concave)
Iteration 102: log likelihood = -54658.184  (not concave)
Iteration 103: log likelihood = -54658.184  (not concave)
Iteration 104: log likelihood = -54658.184  (not concave)
Iteration 105: log likelihood = -54658.184  (not concave)
Iteration 106: log likelihood = -54658.184  (not concave)
Iteration 107: log likelihood = -54658.184  (not concave)
Iteration 108: log likelihood = -54658.184  (not concave)
Iteration 109: log likelihood = -54658.184  (not concave)
Iteration 110: log likelihood = -54658.184  (not concave)
Iteration 111: log likelihood = -54658.184  (not concave)
Iteration 112: log likelihood = -54658.184  (not concave)
Iteration 113: log likelihood = -54658.184  (not concave)
Iteration 114: log likelihood = -54658.184  (not concave)
Iteration 115: log likelihood = -54658.184  (not concave)
Iteration 116: log likelihood = -54658.184  (not concave)
Iteration 117: log likelihood = -54658.184  (not concave)
Iteration 118: log likelihood = -54658.184  (not concave)
Iteration 119: log likelihood = -54658.184  (not concave)
Iteration 120: log likelihood = -54658.184  (not concave)
Iteration 121: log likelihood = -54658.184  (not concave)
Iteration 122: log likelihood = -54658.184  (not concave)
Iteration 123: log likelihood = -54658.184  (not concave)
Iteration 124: log likelihood = -54658.184  (not concave)
Iteration 125: log likelihood = -54658.184  (not concave)
Iteration 126: log likelihood = -54658.184  (not concave)
Iteration 127: log likelihood = -54658.184  (not concave)
Iteration 128: log likelihood = -54658.184  (not concave)
Iteration 129: log likelihood = -54658.184  (not concave)
Iteration 130: log likelihood = -54658.184  (not concave)
Iteration 131: log likelihood = -54658.184  (not concave)
Iteration 132: log likelihood = -54658.184  (not concave)
Iteration 133: log likelihood = -54658.184  (not concave)
Iteration 134: log likelihood = -54658.184  (not concave)
Iteration 135: log likelihood = -54658.184  (not concave)
Iteration 136: log likelihood = -54658.184  (not concave)
Iteration 137: log likelihood = -54658.184  (not concave)
Iteration 138: log likelihood = -54658.184  (not concave)
Iteration 139: log likelihood = -54658.184  (not concave)
Iteration 140: log likelihood = -54658.184  (not concave)
Iteration 141: log likelihood = -54658.184  (not concave)
Iteration 142: log likelihood = -54658.184  (not concave)
Iteration 143: log likelihood = -54658.184  (not concave)
Iteration 144: log likelihood = -54658.184  (not concave)
Iteration 145: log likelihood = -54658.184  (not concave)
Iteration 146: log likelihood = -54658.184  (not concave)
Iteration 147: log likelihood = -54658.184  (not concave)
Iteration 148: log likelihood = -54658.184  (not concave)
Iteration 149: log likelihood = -54658.184  (not concave)
Iteration 150: log likelihood = -54658.184  (not concave)
Iteration 151: log likelihood = -54658.184  (not concave)
Iteration 152: log likelihood = -54658.184  (not concave)
Iteration 153: log likelihood = -54658.184  (not concave)
Iteration 154: log likelihood = -54658.184  (not concave)
Iteration 155: log likelihood = -54658.184  (not concave)
Iteration 156: log likelihood = -54658.184  (not concave)
Iteration 157: log likelihood = -54658.184  (not concave)
Iteration 158: log likelihood = -54658.184  (not concave)
Iteration 159: log likelihood = -54658.184  (not concave)
Iteration 160: log likelihood = -54658.184  (not concave)
Iteration 161: log likelihood = -54658.184  (not concave)
Iteration 162: log likelihood = -54658.184  (not concave)
Iteration 163: log likelihood = -54658.184  (not concave)
Iteration 164: log likelihood = -54658.184  (not concave)
Iteration 165: log likelihood = -54658.184  (not concave)
Iteration 166: log likelihood = -54658.184  (not concave)
Iteration 167: log likelihood = -54658.184  (not concave)
Iteration 168: log likelihood = -54658.184  (not concave)
Iteration 169: log likelihood = -54658.184  (not concave)
Iteration 170: log likelihood = -54658.184  (not concave)
Iteration 171: log likelihood = -54658.184  (not concave)
Iteration 172: log likelihood = -54658.184  (not concave)
Iteration 173: log likelihood = -54658.184  (not concave)
Iteration 174: log likelihood = -54658.184  (not concave)
Iteration 175: log likelihood = -54658.184  (not concave)
Iteration 176: log likelihood = -54658.184  (not concave)
Iteration 177: log likelihood = -54658.184  (not concave)
Iteration 178: log likelihood = -54658.184  (not concave)
Iteration 179: log likelihood = -54658.184  (not concave)
Iteration 180: log likelihood = -54658.184  (not concave)
Iteration 181: log likelihood = -54658.184  (not concave)
Iteration 182: log likelihood = -54658.184  (not concave)
Iteration 183: log likelihood = -54658.184  (not concave)
Iteration 184: log likelihood = -54658.184  (not concave)
Iteration 185: log likelihood = -54658.184  (not concave)
Iteration 186: log likelihood = -54658.184  (not concave)
Iteration 187: log likelihood = -54658.184  (not concave)
Iteration 188: log likelihood = -54658.184  (not concave)
Iteration 189: log likelihood = -54658.184  (not concave)
Iteration 190: log likelihood = -54658.184  (not concave)
Iteration 191: log likelihood = -54658.184  (not concave)
Iteration 192: log likelihood = -54658.184  (not concave)
Iteration 193: log likelihood = -54658.184  (not concave)
Iteration 194: log likelihood = -54658.184  (not concave)
Iteration 195: log likelihood = -54658.184  (not concave)
Iteration 196: log likelihood = -54658.184  (not concave)
Iteration 197: log likelihood = -54658.184  (not concave)
Iteration 198: log likelihood = -54658.184  (not concave)
Iteration 199: log likelihood = -54658.184  (not concave)
Iteration 200: log likelihood = -54658.184  (not concave)
Iteration 201: log likelihood = -54658.184  (not concave)
Iteration 202: log likelihood = -54658.184  (not concave)
Iteration 203: log likelihood = -54658.184  (not concave)
Iteration 204: log likelihood = -54658.184  (not concave)
Iteration 205: log likelihood = -54658.184  (not concave)
Iteration 206: log likelihood = -54658.184  (not concave)
Iteration 207: log likelihood = -54658.184  (not concave)
Iteration 208: log likelihood = -54658.184  (not concave)
Iteration 209: log likelihood = -54658.184  (not concave)
Iteration 210: log likelihood = -54658.184  (not concave)
Iteration 211: log likelihood = -54658.184  (not concave)
Iteration 212: log likelihood = -54658.184  (not concave)
Iteration 213: log likelihood = -54658.184  (not concave)
Iteration 214: log likelihood = -54658.184  (not concave)
Iteration 215: log likelihood = -54658.184  (not concave)
Iteration 216: log likelihood = -54658.184  (not concave)
Iteration 217: log likelihood = -54658.184  (not concave)
Iteration 218: log likelihood = -54658.184  (not concave)
Iteration 219: log likelihood = -54658.184  (not concave)
Iteration 220: log likelihood = -54658.184  (not concave)
Iteration 221: log likelihood = -54658.184  (not concave)
Iteration 222: log likelihood = -54658.184  (not concave)
Iteration 223: log likelihood = -54658.184  (not concave)
Iteration 224: log likelihood = -54658.184  (not concave)
Iteration 225: log likelihood = -54658.184  (not concave)
Iteration 226: log likelihood = -54658.184  (not concave)
Iteration 227: log likelihood = -54658.184  (not concave)
Iteration 228: log likelihood = -54658.184  (not concave)
Iteration 229: log likelihood = -54658.184  (not concave)
Iteration 230: log likelihood = -54658.184  (not concave)
Iteration 231: log likelihood = -54658.184  (not concave)
Iteration 232: log likelihood = -54658.184  (not concave)
Iteration 233: log likelihood = -54658.184  (not concave)
Iteration 234: log likelihood = -54658.184  (not concave)
Iteration 235: log likelihood = -54658.184  (not concave)
Iteration 236: log likelihood = -54658.184  (not concave)
Iteration 237: log likelihood = -54658.184  (not concave)
Iteration 238: log likelihood = -54658.184  (not concave)
Iteration 239: log likelihood = -54658.184  (not concave)
Iteration 240: log likelihood = -54658.184  (not concave)
Iteration 241: log likelihood = -54658.184  (not concave)
Iteration 242: log likelihood = -54658.184  (not concave)
Iteration 243: log likelihood = -54658.184  (not concave)
Iteration 244: log likelihood = -54658.184  (not concave)
Iteration 245: log likelihood = -54658.184  (not concave)
Iteration 246: log likelihood = -54658.184  (not concave)
Iteration 247: log likelihood = -54658.184  (not concave)
Iteration 248: log likelihood = -54658.184  (not concave)
Iteration 249: log likelihood = -54658.184  (not concave)
Iteration 250: log likelihood = -54658.184  (not concave)
Iteration 251: log likelihood = -54658.184  (not concave)
Iteration 252: log likelihood = -54658.184  (not concave)
Iteration 253: log likelihood = -54658.184  (not concave)
Iteration 254: log likelihood = -54658.184  (not concave)
Iteration 255: log likelihood = -54658.184  (not concave)
Iteration 256: log likelihood = -54658.184  (not concave)
Iteration 257: log likelihood = -54658.184  (not concave)
Iteration 258: log likelihood = -54658.184  (not concave)
Iteration 259: log likelihood = -54658.184  (not concave)
Iteration 260: log likelihood = -54658.184  (not concave)
Iteration 261: log likelihood = -54658.184  (not concave)
Iteration 262: log likelihood = -54658.184  (not concave)
Iteration 263: log likelihood = -54658.184  (not concave)
Iteration 264: log likelihood = -54658.184  (not concave)
Iteration 265: log likelihood = -54658.184  (not concave)
Iteration 266: log likelihood = -54658.184  (not concave)
Iteration 267: log likelihood = -54658.184  (not concave)
Iteration 268: log likelihood = -54658.184  (not concave)
Iteration 269: log likelihood = -54658.184  (not concave)
Iteration 270: log likelihood = -54658.184  (not concave)
Iteration 271: log likelihood = -54658.184  (not concave)
Iteration 272: log likelihood = -54658.184  (not concave)
Iteration 273: log likelihood = -54658.184  (not concave)
Iteration 274: log likelihood = -54658.184  (not concave)
Iteration 275: log likelihood = -54658.184  (not concave)
Iteration 276: log likelihood = -54658.184  (not concave)
Iteration 277: log likelihood = -54658.184  (not concave)
Iteration 278: log likelihood = -54658.184  (not concave)
Iteration 279: log likelihood = -54658.184  (not concave)
Iteration 280: log likelihood = -54658.184  (not concave)
Iteration 281: log likelihood = -54658.184  (not concave)
Iteration 282: log likelihood = -54658.184  (not concave)
Iteration 283: log likelihood = -54658.184  (not concave)
Iteration 284: log likelihood = -54658.184  (not concave)
Iteration 285: log likelihood = -54658.184  (not concave)
Iteration 286: log likelihood = -54658.184  (not concave)
Iteration 287: log likelihood = -54658.184  (not concave)
Iteration 288: log likelihood = -54658.184  (not concave)
Iteration 289: log likelihood = -54658.184  (not concave)
Iteration 290: log likelihood = -54658.184  (not concave)
Iteration 291: log likelihood = -54658.184  (not concave)
Iteration 292: log likelihood = -54658.184  (not concave)
Iteration 293: log likelihood = -54658.184  (not concave)
Iteration 294: log likelihood = -54658.184  (not concave)
Iteration 295: log likelihood = -54658.184  (not concave)
Iteration 296: log likelihood = -54658.184  (not concave)
Iteration 297: log likelihood = -54658.184  (not concave)
Iteration 298: log likelihood = -54658.184  (not concave)
Iteration 299: log likelihood = -54658.184  (not concave)
Iteration 300: log likelihood = -54658.184  (not concave)
convergence not achieved

Survival model                                  Number of obs     =     59,220
Log likelihood = -54658.184
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |  -.0496178          .        .       .            .           .
edad_al_in~1 |   .0050326   5.35e-06   940.96   0.000     .0050221    .0050431
edad_ini_c~s |   .0069927          .        .       .            .           .
     sex_enc |   .0706737   .0000909   777.18   0.000     .0704955     .070852
     esc_rec |  -.0236083          .        .       .            .           .
sus_prin_mod |  -.0869265    .000011 -7884.54   0.000    -.0869481   -.0869049
 fr_sus_prin |  -.0174927          .        .       .            .           .
 comp_biosoc |  -.0443151   .0000902  -491.56   0.000    -.0444918   -.0441384
     ten_viv |  -.0000833   .0000225    -3.71   0.000    -.0001274   -.0000393
origen_ing~d |   .0104863          .        .       .            .           .
numero_de_~d |   .0192015          .        .       .            .           .
dg_cie_10_~c |  -.0532259          .        .       .            .           .
sud_sever~10 |   .0589787          .        .       .            .           .
   macrozone |  -.0753613          .        .       .            .           .
 policonsumo |  -.1704786    .000036 -4741.04   0.000    -.1705491   -.1704081
   n_off_vio |  -.7775847          .        .       .            .           .
   n_off_acq |  -1.567608          .        .       .            .           .
   n_off_sud |   -.695196          .        .       .            .           .
        clas |   .0023144          .        .       .            .           .
       _cons |  -.0147522          .        .       .            .           .
  log(sigma) |   .9306374          .        .       .            .           .
       kappa |   1.389548    .000043  3.2e+04   0.000     1.389464    1.389633
------------------------------------------------------------------------------

.         //qui cap noi merlin (_time $covs if _trans == 1, family(ggamma, fail(_status)))
.         estimates store m2_1_ggam

. 
.         // Royston Parmar models
.         forvalues j=1/10 {
  2.                 di in yellow "{bf: ***********}"
  3.                 di in yellow "{bf: family RP`j'}"
  4.                 di in yellow "{bf: ***********}"
  5.                 set seed 2125
  6.                 qui cap noi stmerlin $covs_2, dist(rp) df(`j')
  7.                 //qui cap noi merlin (_time $covs if _trans == 1, family(rp, df(`j') fail(_status)))
.                 estimates store m2_1_rp`j'
  8.                 *estimates save "${pathdata2}parmodels.ster", append    
.         }       
 ***********
 family RP1
 ***********
note; a delayed entry model is being fitted
variables created: _rcs1_1 to _rcs1_1

Fitting full model:

Iteration 0:   log likelihood = -68543.374  (not concave)
Iteration 1:   log likelihood = -59414.609  (not concave)
Iteration 2:   log likelihood = -56469.943  
Iteration 3:   log likelihood = -53919.856  
Iteration 4:   log likelihood = -53781.764  (not concave)
Iteration 5:   log likelihood = -53767.857  
Iteration 6:   log likelihood = -53749.701  
Iteration 7:   log likelihood = -53728.341  
Iteration 8:   log likelihood = -53697.379  (not concave)
Iteration 9:   log likelihood = -53696.741  
Iteration 10:  log likelihood = -53692.459  
Iteration 11:  log likelihood = -53678.383  
Iteration 12:  log likelihood = -53676.365  
Iteration 13:  log likelihood = -53669.221  
Iteration 14:  log likelihood = -53667.775  (backed up)
Iteration 15:  log likelihood = -53665.297  
Iteration 16:  log likelihood =   -53662.5  
Iteration 17:  log likelihood = -53661.096  
Iteration 18:  log likelihood = -53660.131  
Iteration 19:  log likelihood =  -53658.95  
Iteration 20:  log likelihood = -53658.606  
Iteration 21:  log likelihood = -53657.078  (not concave)
Iteration 22:  log likelihood = -53656.929  
Iteration 23:  log likelihood = -53656.682  
Iteration 24:  log likelihood = -53656.429  
Iteration 25:  log likelihood = -53655.745  
Iteration 26:  log likelihood = -53655.595  
Iteration 27:  log likelihood = -53655.292  
Iteration 28:  log likelihood = -53654.919  
Iteration 29:  log likelihood = -53654.611  
Iteration 30:  log likelihood = -53654.348  
Iteration 31:  log likelihood = -53654.163  
Iteration 32:  log likelihood = -53654.151  
Iteration 33:  log likelihood = -53653.971  (not concave)
Iteration 34:  log likelihood = -53653.971  
Iteration 35:  log likelihood = -53653.946  
Iteration 36:  log likelihood = -53653.816  (not concave)
Iteration 37:  log likelihood = -53653.813  
Iteration 38:  log likelihood = -53653.763  
Iteration 39:  log likelihood = -53653.713  
Iteration 40:  log likelihood = -53653.566  
Iteration 41:  log likelihood = -53653.527  (backed up)
Iteration 42:  log likelihood = -53653.478  
Iteration 43:  log likelihood = -53653.401  
Iteration 44:  log likelihood = -53653.362  
Iteration 45:  log likelihood = -53653.298  
Iteration 46:  log likelihood = -53653.261  
Iteration 47:  log likelihood = -53653.216  
Iteration 48:  log likelihood = -53653.143  
Iteration 49:  log likelihood = -53653.125  
Iteration 50:  log likelihood = -53653.081  
Iteration 51:  log likelihood = -53653.065  
Iteration 52:  log likelihood = -53652.991  
Iteration 53:  log likelihood = -53652.974  
Iteration 54:  log likelihood = -53652.943  
Iteration 55:  log likelihood =  -53652.93  
Iteration 56:  log likelihood = -53652.892  
Iteration 57:  log likelihood = -53652.883  
Iteration 58:  log likelihood =  -53652.86  
Iteration 59:  log likelihood = -53652.849  
Iteration 60:  log likelihood = -53652.822  
Iteration 61:  log likelihood = -53652.811  
Iteration 62:  log likelihood = -53652.797  
Iteration 63:  log likelihood = -53652.774  
Iteration 64:  log likelihood = -53652.773  
Iteration 65:  log likelihood = -53652.756  (not concave)
Iteration 66:  log likelihood = -53652.755  
Iteration 67:  log likelihood =  -53652.75  (backed up)
Iteration 68:  log likelihood = -53652.746  
Iteration 69:  log likelihood = -53652.733  (not concave)
Iteration 70:  log likelihood = -53652.731  
Iteration 71:  log likelihood = -53652.731  
Iteration 72:  log likelihood = -53652.717  (not concave)
Iteration 73:  log likelihood = -53652.716  (not concave)
Iteration 74:  log likelihood = -53652.716  
Iteration 75:  log likelihood = -53652.711  
Iteration 76:  log likelihood = -53652.705  
Iteration 77:  log likelihood = -53652.696  
Iteration 78:  log likelihood = -53652.686  
Iteration 79:  log likelihood = -53652.683  
Iteration 80:  log likelihood = -53652.667  (not concave)
Iteration 81:  log likelihood = -53652.665  (not concave)
Iteration 82:  log likelihood = -53652.665  
Iteration 83:  log likelihood = -53652.662  
Iteration 84:  log likelihood = -53652.651  
Iteration 85:  log likelihood = -53652.649  
Iteration 86:  log likelihood = -53652.642  
Iteration 87:  log likelihood = -53652.637  
Iteration 88:  log likelihood = -53652.633  
Iteration 89:  log likelihood = -53652.627  
Iteration 90:  log likelihood = -53652.624  
Iteration 91:  log likelihood = -53652.619  
Iteration 92:  log likelihood = -53652.615  
Iteration 93:  log likelihood = -53652.611  
Iteration 94:  log likelihood = -53652.608  
Iteration 95:  log likelihood = -53652.603  
Iteration 96:  log likelihood = -53652.601  
Iteration 97:  log likelihood = -53652.597  
Iteration 98:  log likelihood = -53652.594  
Iteration 99:  log likelihood =  -53652.59  
Iteration 100: log likelihood = -53652.584  (not concave)
Iteration 101: log likelihood = -53652.584  (backed up)
Iteration 102: log likelihood = -53652.583  
Iteration 103: log likelihood = -53652.578  
Iteration 104: log likelihood = -53652.575  (not concave)
Iteration 105: log likelihood = -53652.575  
Iteration 106: log likelihood = -53652.574  
Iteration 107: log likelihood = -53652.573  
Iteration 108: log likelihood = -53652.567  
Iteration 109: log likelihood = -53652.566  
Iteration 110: log likelihood = -53652.561  (not concave)
Iteration 111: log likelihood = -53652.561  
Iteration 112: log likelihood =  -53652.56  
Iteration 113: log likelihood = -53652.555  
Iteration 114: log likelihood = -53652.555  
Iteration 115: log likelihood = -53652.554  
Iteration 116: log likelihood = -53652.551  
Iteration 117: log likelihood =  -53652.55  
Iteration 118: log likelihood = -53652.548  
Iteration 119: log likelihood = -53652.547  
Iteration 120: log likelihood = -53652.545  
Iteration 121: log likelihood = -53652.544  
Iteration 122: log likelihood = -53652.541  
Iteration 123: log likelihood = -53652.541  
Iteration 124: log likelihood =  -53652.54  (not concave)
Iteration 125: log likelihood =  -53652.54  
Iteration 126: log likelihood = -53652.539  
Iteration 127: log likelihood = -53652.537  
Iteration 128: log likelihood = -53652.537  
Iteration 129: log likelihood = -53652.535  (not concave)
Iteration 130: log likelihood = -53652.535  
Iteration 131: log likelihood = -53652.535  
Iteration 132: log likelihood = -53652.534  (not concave)
Iteration 133: log likelihood = -53652.534  (backed up)
Iteration 134: log likelihood = -53652.533  (backed up)
Iteration 135: log likelihood = -53652.532  
Iteration 136: log likelihood =  -53652.53  
Iteration 137: log likelihood = -53652.529  
Iteration 138: log likelihood = -53652.528  (not concave)
Iteration 139: log likelihood = -53652.528  (backed up)
Iteration 140: log likelihood = -53652.527  
Iteration 141: log likelihood = -53652.527  
Iteration 142: log likelihood = -53652.526  
Iteration 143: log likelihood = -53652.525  
Iteration 144: log likelihood = -53652.524  
Iteration 145: log likelihood = -53652.524  
Iteration 146: log likelihood = -53652.523  
Iteration 147: log likelihood = -53652.522  
Iteration 148: log likelihood = -53652.521  
Iteration 149: log likelihood = -53652.521  
Iteration 150: log likelihood = -53652.519  
Iteration 151: log likelihood = -53652.519  
Iteration 152: log likelihood = -53652.518  
Iteration 153: log likelihood = -53652.518  
Iteration 154: log likelihood = -53652.517  
Iteration 155: log likelihood = -53652.517  
Iteration 156: log likelihood = -53652.516  
Iteration 157: log likelihood = -53652.516  
Iteration 158: log likelihood = -53652.515  
Iteration 159: log likelihood = -53652.514  (not concave)
Iteration 160: log likelihood = -53652.514  (backed up)
Iteration 161: log likelihood = -53652.514  
Iteration 162: log likelihood = -53652.513  
Iteration 163: log likelihood = -53652.513  
Iteration 164: log likelihood = -53652.513  (not concave)
Iteration 165: log likelihood = -53652.513  
Iteration 166: log likelihood = -53652.513  
Iteration 167: log likelihood = -53652.512  (not concave)
Iteration 168: log likelihood = -53652.512  (backed up)
Iteration 169: log likelihood = -53652.512  
Iteration 170: log likelihood = -53652.511  
Iteration 171: log likelihood = -53652.511  
Iteration 172: log likelihood =  -53652.51  (not concave)
Iteration 173: log likelihood =  -53652.51  
Iteration 174: log likelihood =  -53652.51  
Iteration 175: log likelihood =  -53652.51  
Iteration 176: log likelihood =  -53652.51  
Iteration 177: log likelihood = -53652.509  (not concave)
Iteration 178: log likelihood = -53652.509  
Iteration 179: log likelihood = -53652.509  
Iteration 180: log likelihood = -53652.508  
Iteration 181: log likelihood = -53652.508  
Iteration 182: log likelihood = -53652.508  
Iteration 183: log likelihood = -53652.508  
Iteration 184: log likelihood = -53652.507  (not concave)
Iteration 185: log likelihood = -53652.507  
Iteration 186: log likelihood = -53652.507  
Iteration 187: log likelihood = -53652.507  
Iteration 188: log likelihood = -53652.506  
Iteration 189: log likelihood = -53652.506  
Iteration 190: log likelihood = -53652.506  
Iteration 191: log likelihood = -53652.506  
Iteration 192: log likelihood = -53652.506  
Iteration 193: log likelihood = -53652.505  
Iteration 194: log likelihood = -53652.505  
Iteration 195: log likelihood = -53652.505  
Iteration 196: log likelihood = -53652.505  (not concave)
Iteration 197: log likelihood = -53652.505  
Iteration 198: log likelihood = -53652.504  
Iteration 199: log likelihood = -53652.504  (not concave)
Iteration 200: log likelihood = -53652.504  
Iteration 201: log likelihood = -53652.504  
Iteration 202: log likelihood = -53652.504  (not concave)
Iteration 203: log likelihood = -53652.504  
Iteration 204: log likelihood = -53652.504  
Iteration 205: log likelihood = -53652.504  (not concave)
Iteration 206: log likelihood = -53652.504  
Iteration 207: log likelihood = -53652.503  
Iteration 208: log likelihood = -53652.503  
Iteration 209: log likelihood = -53652.503  
Iteration 210: log likelihood = -53652.503  
Iteration 211: log likelihood = -53652.502  
Iteration 212: log likelihood = -53652.502  
Iteration 213: log likelihood = -53652.502  
Iteration 214: log likelihood = -53652.502  
Iteration 215: log likelihood = -53652.502  
Iteration 216: log likelihood = -53652.502  
Iteration 217: log likelihood = -53652.501  
Iteration 218: log likelihood = -53652.501  
Iteration 219: log likelihood = -53652.501  (not concave)
Iteration 220: log likelihood = -53652.501  (not concave)
Iteration 221: log likelihood = -53652.501  (not concave)
Iteration 222: log likelihood = -53652.501  (not concave)
Iteration 223: log likelihood = -53652.501  (not concave)
Iteration 224: log likelihood = -53652.501  (not concave)
Iteration 225: log likelihood = -53652.501  (not concave)
Iteration 226: log likelihood = -53652.501  (not concave)
Iteration 227: log likelihood = -53652.501  (not concave)
Iteration 228: log likelihood = -53652.501  (not concave)
Iteration 229: log likelihood = -53652.501  (not concave)
Iteration 230: log likelihood = -53652.501  (not concave)
Iteration 231: log likelihood = -53652.501  (not concave)
Iteration 232: log likelihood = -53652.501  (not concave)
Iteration 233: log likelihood = -53652.501  (not concave)
Iteration 234: log likelihood = -53652.501  (not concave)
Iteration 235: log likelihood = -53652.501  (not concave)
Iteration 236: log likelihood = -53652.501  (not concave)
Iteration 237: log likelihood = -53652.501  (not concave)
Iteration 238: log likelihood = -53652.501  (not concave)
Iteration 239: log likelihood = -53652.501  (not concave)
Iteration 240: log likelihood = -53652.501  (not concave)
Iteration 241: log likelihood = -53652.501  (not concave)
Iteration 242: log likelihood = -53652.501  (not concave)
Iteration 243: log likelihood = -53652.501  (not concave)
Iteration 244: log likelihood = -53652.501  (not concave)
Iteration 245: log likelihood = -53652.501  (not concave)
Iteration 246: log likelihood = -53652.501  (not concave)
Iteration 247: log likelihood = -53652.501  (not concave)
Iteration 248: log likelihood = -53652.501  (not concave)
Iteration 249: log likelihood = -53652.501  (not concave)
Iteration 250: log likelihood = -53652.501  (not concave)
Iteration 251: log likelihood = -53652.501  (not concave)
Iteration 252: log likelihood = -53652.501  (not concave)
Iteration 253: log likelihood = -53652.501  (not concave)
Iteration 254: log likelihood = -53652.501  (not concave)
Iteration 255: log likelihood = -53652.501  (not concave)
Iteration 256: log likelihood = -53652.501  (not concave)
Iteration 257: log likelihood = -53652.501  (not concave)
Iteration 258: log likelihood = -53652.501  (not concave)
Iteration 259: log likelihood = -53652.501  (not concave)
Iteration 260: log likelihood = -53652.501  (not concave)
Iteration 261: log likelihood = -53652.501  (not concave)
Iteration 262: log likelihood = -53652.501  (not concave)
Iteration 263: log likelihood = -53652.501  (not concave)
Iteration 264: log likelihood = -53652.501  (not concave)
Iteration 265: log likelihood = -53652.501  (not concave)
Iteration 266: log likelihood = -53652.501  (not concave)
Iteration 267: log likelihood = -53652.501  (not concave)
Iteration 268: log likelihood = -53652.501  (not concave)
Iteration 269: log likelihood = -53652.501  (not concave)
Iteration 270: log likelihood = -53652.501  (not concave)
Iteration 271: log likelihood = -53652.501  (not concave)
Iteration 272: log likelihood = -53652.501  (not concave)
Iteration 273: log likelihood = -53652.501  (not concave)
Iteration 274: log likelihood = -53652.501  (not concave)
Iteration 275: log likelihood = -53652.501  (not concave)
Iteration 276: log likelihood = -53652.501  (not concave)
Iteration 277: log likelihood = -53652.501  (not concave)
Iteration 278: log likelihood = -53652.501  (not concave)
Iteration 279: log likelihood = -53652.501  (not concave)
Iteration 280: log likelihood = -53652.501  (not concave)
Iteration 281: log likelihood = -53652.501  (not concave)
Iteration 282: log likelihood = -53652.501  (not concave)
Iteration 283: log likelihood = -53652.501  (not concave)
Iteration 284: log likelihood = -53652.501  (not concave)
Iteration 285: log likelihood = -53652.501  (not concave)
Iteration 286: log likelihood = -53652.501  (not concave)
Iteration 287: log likelihood = -53652.501  (not concave)
Iteration 288: log likelihood = -53652.501  (not concave)
Iteration 289: log likelihood = -53652.501  (not concave)
Iteration 290: log likelihood = -53652.501  (not concave)
Iteration 291: log likelihood = -53652.501  (not concave)
Iteration 292: log likelihood = -53652.501  (not concave)
Iteration 293: log likelihood = -53652.501  (not concave)
Iteration 294: log likelihood = -53652.501  (not concave)
Iteration 295: log likelihood = -53652.501  (not concave)
Iteration 296: log likelihood = -53652.501  (not concave)
Iteration 297: log likelihood = -53652.501  (not concave)
Iteration 298: log likelihood = -53652.501  (not concave)
Iteration 299: log likelihood = -53652.501  (not concave)
Iteration 300: log likelihood = -53652.501  (not concave)
convergence not achieved

Survival model                                  Number of obs     =     59,220
Log likelihood = -53652.501
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1518669   .0097576    15.56   0.000     .1327424    .1709914
edad_al_in~1 |  -.0098844    .001013    -9.76   0.000    -.0118698   -.0078989
edad_ini_c~s |  -.0108262   .0018795    -5.76   0.000      -.01451   -.0071424
     sex_enc |  -.3339391   .0202562   -16.49   0.000    -.3736406   -.2942376
     esc_rec |   .0938296   .0122827     7.64   0.000     .0697559    .1179033
sus_prin_mod |   .1341102   .0081842    16.39   0.000     .1180696    .1501509
 fr_sus_prin |    .032481   .0075686     4.29   0.000     .0176468    .0473152
 comp_biosoc |   .1949337   .0141678    13.76   0.000     .1671653     .222702
     ten_viv |   -.015161   .0076355    -1.99   0.047    -.0301263   -.0001957
origen_ing~d |  -.0207811   .0044047    -4.72   0.000    -.0294141   -.0121482
numero_de_~d |    .071569   .0062779    11.40   0.000     .0592646    .0838734
dg_cie_10_~c |   .0271659   .0087695     3.10   0.002      .009978    .0443538
sud_sever~10 |  -.0634523    .019157    -3.31   0.001    -.1009993   -.0259053
   macrozone |   .2082405    .011805    17.64   0.000     .1851031    .2313779
 policonsumo |   .0966533   .0216184     4.47   0.000     .0542821    .1390246
   n_off_vio |   .3169269   .0186861    16.96   0.000     .2803029    .3535509
   n_off_acq |   .6691243   .0173954    38.47   0.000     .6350299    .7032187
   n_off_sud |   .2317407   .0183709    12.61   0.000     .1957344    .2677471
        clas |   .0129126   .0128282     1.01   0.314    -.0122302    .0380554
       _cons |   10.22557          .        .       .            .           .
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP2
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_2

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53629.527  (not concave)
Iteration 2:   log likelihood = -53617.571  (not concave)
Iteration 3:   log likelihood = -53608.436  (not concave)
Iteration 4:   log likelihood = -53601.244  (not concave)
Iteration 5:   log likelihood = -53595.667  (not concave)
Iteration 6:   log likelihood = -53591.598  
Iteration 7:   log likelihood = -53453.856  (not concave)
Iteration 8:   log likelihood = -53432.743  (not concave)
Iteration 9:   log likelihood =  -53426.07  (not concave)
Iteration 10:  log likelihood = -53420.228  (not concave)
Iteration 11:  log likelihood = -53414.247  (not concave)
Iteration 12:  log likelihood = -53408.434  (not concave)
Iteration 13:  log likelihood = -53402.646  (not concave)
Iteration 14:  log likelihood = -53396.729  (not concave)
Iteration 15:  log likelihood = -53390.752  (not concave)
Iteration 16:  log likelihood = -53384.753  (not concave)
Iteration 17:  log likelihood = -53378.711  (not concave)
Iteration 18:  log likelihood = -53372.599  (not concave)
Iteration 19:  log likelihood = -53366.434  (not concave)
Iteration 20:  log likelihood =  -53360.22  (not concave)
Iteration 21:  log likelihood = -53353.959  (not concave)
Iteration 22:  log likelihood = -53347.645  (not concave)
Iteration 23:  log likelihood = -53341.291  (not concave)
Iteration 24:  log likelihood = -53334.896  (not concave)
Iteration 25:  log likelihood = -53328.471  (not concave)
Iteration 26:  log likelihood = -53322.017  (not concave)
Iteration 27:  log likelihood = -53315.547  (not concave)
Iteration 28:  log likelihood = -53309.063  (not concave)
Iteration 29:  log likelihood = -53302.582  (not concave)
Iteration 30:  log likelihood = -53296.108  (not concave)
Iteration 31:  log likelihood = -53289.656  
Iteration 32:  log likelihood = -53225.669  (backed up)
Iteration 33:  log likelihood = -53175.755  
Iteration 34:  log likelihood =  -53141.52  
Iteration 35:  log likelihood = -53118.039  
Iteration 36:  log likelihood =  -53111.06  (not concave)
Iteration 37:  log likelihood = -53111.059  (backed up)
Iteration 38:  log likelihood = -53111.057  
Iteration 39:  log likelihood = -53111.057  
Iteration 40:  log likelihood = -53111.057  
Iteration 41:  log likelihood = -53111.057  
Iteration 42:  log likelihood = -53111.057  
Iteration 43:  log likelihood = -53111.056  
Iteration 44:  log likelihood = -53111.056  
Iteration 45:  log likelihood = -53111.056  
Iteration 46:  log likelihood = -53111.056  
Iteration 47:  log likelihood = -53111.056  
Iteration 48:  log likelihood = -53111.056  
Iteration 49:  log likelihood = -53111.056  
Iteration 50:  log likelihood = -53111.056  
Iteration 51:  log likelihood = -53111.055  
Iteration 52:  log likelihood = -53111.055  
Iteration 53:  log likelihood = -53111.055  
Iteration 54:  log likelihood = -53111.055  
Iteration 55:  log likelihood = -53111.055  
Iteration 56:  log likelihood = -53111.055  
Iteration 57:  log likelihood = -53111.055  
Iteration 58:  log likelihood = -53111.054  
Iteration 59:  log likelihood = -53111.054  
Iteration 60:  log likelihood = -53111.054  
Iteration 61:  log likelihood = -53111.054  
Iteration 62:  log likelihood = -53111.054  
Iteration 63:  log likelihood = -53111.054  
Iteration 64:  log likelihood = -53111.054  
Iteration 65:  log likelihood = -53111.054  
Iteration 66:  log likelihood = -53111.054  
Iteration 67:  log likelihood = -53111.054  
Iteration 68:  log likelihood = -53111.054  (not concave)
Iteration 69:  log likelihood = -53111.054  
Iteration 70:  log likelihood = -53111.054  
Iteration 71:  log likelihood = -53111.053  (not concave)
Iteration 72:  log likelihood = -53111.053  (backed up)
Iteration 73:  log likelihood = -53111.053  
Iteration 74:  log likelihood = -53111.053  (not concave)
Iteration 75:  log likelihood = -53111.053  (backed up)
Iteration 76:  log likelihood = -53111.053  
Iteration 77:  log likelihood = -53111.053  (not concave)
Iteration 78:  log likelihood = -53111.053  (backed up)
Iteration 79:  log likelihood = -53111.053  
Iteration 80:  log likelihood = -53111.053  
Iteration 81:  log likelihood = -53111.053  
Iteration 82:  log likelihood = -53111.053  
Iteration 83:  log likelihood = -53111.053  
Iteration 84:  log likelihood = -53111.053  
Iteration 85:  log likelihood = -53111.053  (not concave)
Iteration 86:  log likelihood = -53111.053  (not concave)
Iteration 87:  log likelihood = -53111.053  (not concave)
Iteration 88:  log likelihood = -53111.053  (not concave)
Iteration 89:  log likelihood = -53111.053  (not concave)
Iteration 90:  log likelihood = -53111.053  (not concave)
Iteration 91:  log likelihood = -53111.053  (not concave)
Iteration 92:  log likelihood = -53111.053  (not concave)
Iteration 93:  log likelihood = -53111.053  (not concave)
Iteration 94:  log likelihood = -53111.053  (not concave)
Iteration 95:  log likelihood = -53111.053  (not concave)
Iteration 96:  log likelihood = -53111.053  (not concave)
Iteration 97:  log likelihood = -53111.053  (not concave)
Iteration 98:  log likelihood = -53111.053  (not concave)
Iteration 99:  log likelihood = -53111.053  (not concave)
Iteration 100: log likelihood = -53111.053  (not concave)
Iteration 101: log likelihood = -53111.053  (not concave)
Iteration 102: log likelihood = -53111.053  (not concave)
Iteration 103: log likelihood = -53111.053  (not concave)
Iteration 104: log likelihood = -53111.053  (not concave)
Iteration 105: log likelihood = -53111.053  (not concave)
Iteration 106: log likelihood = -53111.053  (not concave)
Iteration 107: log likelihood = -53111.053  (not concave)
Iteration 108: log likelihood = -53111.053  (not concave)
Iteration 109: log likelihood = -53111.053  (not concave)
Iteration 110: log likelihood = -53111.053  (not concave)
Iteration 111: log likelihood = -53111.053  (not concave)
Iteration 112: log likelihood = -53111.053  (not concave)
Iteration 113: log likelihood = -53111.053  (not concave)
Iteration 114: log likelihood = -53111.053  (not concave)
Iteration 115: log likelihood = -53111.053  (not concave)
Iteration 116: log likelihood = -53111.053  (not concave)
Iteration 117: log likelihood = -53111.053  (not concave)
Iteration 118: log likelihood = -53111.053  (not concave)
Iteration 119: log likelihood = -53111.053  (not concave)
Iteration 120: log likelihood = -53111.053  (not concave)
Iteration 121: log likelihood = -53111.053  (not concave)
Iteration 122: log likelihood = -53111.053  (not concave)
Iteration 123: log likelihood = -53111.053  (not concave)
Iteration 124: log likelihood = -53111.053  (not concave)
Iteration 125: log likelihood = -53111.053  (not concave)
Iteration 126: log likelihood = -53111.053  (not concave)
Iteration 127: log likelihood = -53111.053  (not concave)
Iteration 128: log likelihood = -53111.053  (not concave)
Iteration 129: log likelihood = -53111.053  (not concave)
Iteration 130: log likelihood = -53111.053  (not concave)
Iteration 131: log likelihood = -53111.053  (not concave)
Iteration 132: log likelihood = -53111.053  (not concave)
Iteration 133: log likelihood = -53111.053  (not concave)
Iteration 134: log likelihood = -53111.053  (not concave)
Iteration 135: log likelihood = -53111.053  (not concave)
Iteration 136: log likelihood = -53111.053  (not concave)
Iteration 137: log likelihood = -53111.053  (not concave)
Iteration 138: log likelihood = -53111.053  (not concave)
Iteration 139: log likelihood = -53111.053  (not concave)
Iteration 140: log likelihood = -53111.053  (not concave)
Iteration 141: log likelihood = -53111.053  (not concave)
Iteration 142: log likelihood = -53111.053  (not concave)
Iteration 143: log likelihood = -53111.053  (not concave)
Iteration 144: log likelihood = -53111.053  (not concave)
Iteration 145: log likelihood = -53111.053  (not concave)
Iteration 146: log likelihood = -53111.053  (not concave)
Iteration 147: log likelihood = -53111.053  (not concave)
Iteration 148: log likelihood = -53111.053  (not concave)
Iteration 149: log likelihood = -53111.053  (not concave)
Iteration 150: log likelihood = -53111.053  (not concave)
Iteration 151: log likelihood = -53111.053  (not concave)
Iteration 152: log likelihood = -53111.053  (not concave)
Iteration 153: log likelihood = -53111.053  (not concave)
Iteration 154: log likelihood = -53111.053  (not concave)
Iteration 155: log likelihood = -53111.053  (not concave)
Iteration 156: log likelihood = -53111.053  (not concave)
Iteration 157: log likelihood = -53111.053  (not concave)
Iteration 158: log likelihood = -53111.053  (not concave)
Iteration 159: log likelihood = -53111.053  (not concave)
Iteration 160: log likelihood = -53111.053  (not concave)
Iteration 161: log likelihood = -53111.053  (not concave)
Iteration 162: log likelihood = -53111.053  (not concave)
Iteration 163: log likelihood = -53111.053  (not concave)
Iteration 164: log likelihood = -53111.053  (not concave)
Iteration 165: log likelihood = -53111.053  (not concave)
Iteration 166: log likelihood = -53111.053  (not concave)
Iteration 167: log likelihood = -53111.053  (not concave)
Iteration 168: log likelihood = -53111.053  (not concave)
Iteration 169: log likelihood = -53111.053  (not concave)
Iteration 170: log likelihood = -53111.053  (not concave)
Iteration 171: log likelihood = -53111.053  (not concave)
Iteration 172: log likelihood = -53111.053  (not concave)
Iteration 173: log likelihood = -53111.053  (not concave)
Iteration 174: log likelihood = -53111.053  (not concave)
Iteration 175: log likelihood = -53111.053  (not concave)
Iteration 176: log likelihood = -53111.053  (not concave)
Iteration 177: log likelihood = -53111.053  (not concave)
Iteration 178: log likelihood = -53111.053  (not concave)
Iteration 179: log likelihood = -53111.053  (not concave)
Iteration 180: log likelihood = -53111.053  (not concave)
Iteration 181: log likelihood = -53111.053  (not concave)
Iteration 182: log likelihood = -53111.053  (not concave)
Iteration 183: log likelihood = -53111.053  (not concave)
Iteration 184: log likelihood = -53111.053  (not concave)
Iteration 185: log likelihood = -53111.053  (not concave)
Iteration 186: log likelihood = -53111.053  (not concave)
Iteration 187: log likelihood = -53111.053  (not concave)
Iteration 188: log likelihood = -53111.053  (not concave)
Iteration 189: log likelihood = -53111.053  (not concave)
Iteration 190: log likelihood = -53111.053  (not concave)
Iteration 191: log likelihood = -53111.053  (not concave)
Iteration 192: log likelihood = -53111.053  (not concave)
Iteration 193: log likelihood = -53111.053  (not concave)
Iteration 194: log likelihood = -53111.053  (not concave)
Iteration 195: log likelihood = -53111.053  (not concave)
Iteration 196: log likelihood = -53111.053  (not concave)
Iteration 197: log likelihood = -53111.053  (not concave)
Iteration 198: log likelihood = -53111.053  (not concave)
Iteration 199: log likelihood = -53111.053  (not concave)
Iteration 200: log likelihood = -53111.053  (not concave)
Iteration 201: log likelihood = -53111.053  (not concave)
Iteration 202: log likelihood = -53111.053  (not concave)
Iteration 203: log likelihood = -53111.053  (not concave)
Iteration 204: log likelihood = -53111.053  (not concave)
Iteration 205: log likelihood = -53111.053  (not concave)
Iteration 206: log likelihood = -53111.053  (not concave)
Iteration 207: log likelihood = -53111.053  (not concave)
Iteration 208: log likelihood = -53111.053  (not concave)
Iteration 209: log likelihood = -53111.053  (not concave)
Iteration 210: log likelihood = -53111.053  (not concave)
Iteration 211: log likelihood = -53111.053  (not concave)
Iteration 212: log likelihood = -53111.053  (not concave)
Iteration 213: log likelihood = -53111.053  (not concave)
Iteration 214: log likelihood = -53111.053  (not concave)
Iteration 215: log likelihood = -53111.053  (not concave)
Iteration 216: log likelihood = -53111.053  (not concave)
Iteration 217: log likelihood = -53111.053  (not concave)
Iteration 218: log likelihood = -53111.053  (not concave)
Iteration 219: log likelihood = -53111.053  (not concave)
Iteration 220: log likelihood = -53111.053  (not concave)
Iteration 221: log likelihood = -53111.053  (not concave)
Iteration 222: log likelihood = -53111.053  (not concave)
Iteration 223: log likelihood = -53111.053  (not concave)
Iteration 224: log likelihood = -53111.053  (not concave)
Iteration 225: log likelihood = -53111.053  (not concave)
Iteration 226: log likelihood = -53111.053  (not concave)
Iteration 227: log likelihood = -53111.053  (not concave)
Iteration 228: log likelihood = -53111.053  (not concave)
Iteration 229: log likelihood = -53111.053  (not concave)
Iteration 230: log likelihood = -53111.053  (not concave)
Iteration 231: log likelihood = -53111.053  (not concave)
Iteration 232: log likelihood = -53111.053  (not concave)
Iteration 233: log likelihood = -53111.053  (not concave)
Iteration 234: log likelihood = -53111.053  (not concave)
Iteration 235: log likelihood = -53111.053  (not concave)
Iteration 236: log likelihood = -53111.053  (not concave)
Iteration 237: log likelihood = -53111.053  (not concave)
Iteration 238: log likelihood = -53111.053  (not concave)
Iteration 239: log likelihood = -53111.053  (not concave)
Iteration 240: log likelihood = -53111.053  (not concave)
Iteration 241: log likelihood = -53111.053  (not concave)
Iteration 242: log likelihood = -53111.053  (not concave)
Iteration 243: log likelihood = -53111.053  (not concave)
Iteration 244: log likelihood = -53111.053  (not concave)
Iteration 245: log likelihood = -53111.053  (not concave)
Iteration 246: log likelihood = -53111.053  (not concave)
Iteration 247: log likelihood = -53111.053  (not concave)
Iteration 248: log likelihood = -53111.053  (not concave)
Iteration 249: log likelihood = -53111.053  (not concave)
Iteration 250: log likelihood = -53111.053  (not concave)
Iteration 251: log likelihood = -53111.053  (not concave)
Iteration 252: log likelihood = -53111.053  (not concave)
Iteration 253: log likelihood = -53111.053  (not concave)
Iteration 254: log likelihood = -53111.053  (not concave)
Iteration 255: log likelihood = -53111.053  (not concave)
Iteration 256: log likelihood = -53111.053  (not concave)
Iteration 257: log likelihood = -53111.053  (not concave)
Iteration 258: log likelihood = -53111.053  (not concave)
Iteration 259: log likelihood = -53111.053  (not concave)
Iteration 260: log likelihood = -53111.053  (not concave)
Iteration 261: log likelihood = -53111.053  (not concave)
Iteration 262: log likelihood = -53111.053  (not concave)
Iteration 263: log likelihood = -53111.053  (not concave)
Iteration 264: log likelihood = -53111.053  (not concave)
Iteration 265: log likelihood = -53111.053  (not concave)
Iteration 266: log likelihood = -53111.053  (not concave)
Iteration 267: log likelihood = -53111.053  (not concave)
Iteration 268: log likelihood = -53111.053  (not concave)
Iteration 269: log likelihood = -53111.053  (not concave)
Iteration 270: log likelihood = -53111.053  (not concave)
Iteration 271: log likelihood = -53111.053  (not concave)
Iteration 272: log likelihood = -53111.053  (not concave)
Iteration 273: log likelihood = -53111.053  (not concave)
Iteration 274: log likelihood = -53111.053  (not concave)
Iteration 275: log likelihood = -53111.053  (not concave)
Iteration 276: log likelihood = -53111.053  (not concave)
Iteration 277: log likelihood = -53111.053  (not concave)
Iteration 278: log likelihood = -53111.053  (not concave)
Iteration 279: log likelihood = -53111.053  (not concave)
Iteration 280: log likelihood = -53111.053  (not concave)
Iteration 281: log likelihood = -53111.053  (not concave)
Iteration 282: log likelihood = -53111.053  (not concave)
Iteration 283: log likelihood = -53111.053  (not concave)
Iteration 284: log likelihood = -53111.053  (not concave)
Iteration 285: log likelihood = -53111.053  (not concave)
Iteration 286: log likelihood = -53111.053  (not concave)
Iteration 287: log likelihood = -53111.053  (not concave)
Iteration 288: log likelihood = -53111.053  (not concave)
Iteration 289: log likelihood = -53111.053  (not concave)
Iteration 290: log likelihood = -53111.053  (not concave)
Iteration 291: log likelihood = -53111.053  (not concave)
Iteration 292: log likelihood = -53111.053  (not concave)
Iteration 293: log likelihood = -53111.053  (not concave)
Iteration 294: log likelihood = -53111.053  (not concave)
Iteration 295: log likelihood = -53111.053  (not concave)
Iteration 296: log likelihood = -53111.053  (not concave)
Iteration 297: log likelihood = -53111.053  (not concave)
Iteration 298: log likelihood = -53111.053  (not concave)
Iteration 299: log likelihood = -53111.053  (not concave)
Iteration 300: log likelihood = -53111.053  (not concave)
convergence not achieved

Survival model                                  Number of obs     =     59,220
Log likelihood = -53111.053
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1459788   .0097978    14.90   0.000     .1267754    .1651821
edad_al_in~1 |   .0579899   .0010924    53.08   0.000     .0558488    .0601309
edad_ini_c~s |  -.0100049   .0019023    -5.26   0.000    -.0137333   -.0062765
     sex_enc |  -.3181101   .0202639   -15.70   0.000    -.3578265   -.2783936
     esc_rec |   .1104302    .012287     8.99   0.000     .0863481    .1345123
sus_prin_mod |   .1372873   .0082228    16.70   0.000     .1211709    .1534037
 fr_sus_prin |   .0320189   .0075659     4.23   0.000       .01719    .0468479
 comp_biosoc |   .1935622   .0141767    13.65   0.000     .1657765    .2213479
     ten_viv |   -.019892   .0076206    -2.61   0.009     -.034828    -.004956
origen_ing~d |  -.0171595   .0044018    -3.90   0.000    -.0257869   -.0085321
numero_de_~d |   .0596908   .0063137     9.45   0.000     .0473162    .0720654
dg_cie_10_~c |   .0266199   .0087863     3.03   0.002      .009399    .0438408
sud_sever~10 |  -.0603811   .0191584    -3.15   0.002    -.0979309   -.0228313
   macrozone |   .2105694   .0118113    17.83   0.000     .1874196    .2337191
 policonsumo |   .0967329   .0215629     4.49   0.000     .0544703    .1389954
   n_off_vio |   .3014908   .0186673    16.15   0.000     .2649036    .3380781
   n_off_acq |   .6467834   .0173584    37.26   0.000     .6127617    .6808052
   n_off_sud |   .2068538   .0183534    11.27   0.000     .1708818    .2428257
        clas |   .0167362   .0128289     1.30   0.192     -.008408    .0418804
       _cons |   8.796722          .        .       .            .           .
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP3
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_3

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53620.874  (not concave)
Iteration 2:   log likelihood = -53610.546  (not concave)
Iteration 3:   log likelihood = -53599.263  (not concave)
Iteration 4:   log likelihood = -53589.962  (not concave)
Iteration 5:   log likelihood = -53582.702  (not concave)
Iteration 6:   log likelihood = -53576.348  
Iteration 7:   log likelihood = -53487.005  
Iteration 8:   log likelihood = -53264.819  
Iteration 9:   log likelihood = -53109.687  
Iteration 10:  log likelihood = -53068.319  
Iteration 11:  log likelihood =  -53052.36  
Iteration 12:  log likelihood = -53052.318  
Iteration 13:  log likelihood = -53052.318  
Iteration 14:  log likelihood = -53052.318  
Iteration 15:  log likelihood = -53052.318  
Iteration 16:  log likelihood = -53052.318  
Iteration 17:  log likelihood = -53052.318  
Iteration 18:  log likelihood = -53052.318  
Iteration 19:  log likelihood = -53052.318  
Iteration 20:  log likelihood = -53052.318  
Iteration 21:  log likelihood = -53052.317  
Iteration 22:  log likelihood = -53052.317  
Iteration 23:  log likelihood = -53052.317  
Iteration 24:  log likelihood = -53052.317  
Iteration 25:  log likelihood = -53052.317  
Iteration 26:  log likelihood = -53052.317  
Iteration 27:  log likelihood = -53052.317  
Iteration 28:  log likelihood = -53052.317  
Iteration 29:  log likelihood = -53052.317  
Iteration 30:  log likelihood = -53052.317  
Iteration 31:  log likelihood = -53052.317  
Iteration 32:  log likelihood = -53052.317  
Iteration 33:  log likelihood = -53052.317  
Iteration 34:  log likelihood = -53052.317  
Iteration 35:  log likelihood = -53052.317  
Iteration 36:  log likelihood = -53052.317  
Iteration 37:  log likelihood = -53052.317  
Iteration 38:  log likelihood = -53052.317  (not concave)
Iteration 39:  log likelihood = -53052.317  
Iteration 40:  log likelihood = -53052.317  
Iteration 41:  log likelihood = -53052.317  
Iteration 42:  log likelihood = -53052.317  
Iteration 43:  log likelihood = -53052.317  
Iteration 44:  log likelihood = -53052.317  
Iteration 45:  log likelihood = -53052.317  
Iteration 46:  log likelihood = -53052.317  
Iteration 47:  log likelihood = -53052.317  
Iteration 48:  log likelihood = -53052.317  (not concave)
Iteration 49:  log likelihood = -53052.317  
Iteration 50:  log likelihood = -53052.317  
Iteration 51:  log likelihood = -53052.316  
Iteration 52:  log likelihood = -53052.316  
Iteration 53:  log likelihood = -53052.316  
Iteration 54:  log likelihood = -53052.316  
Iteration 55:  log likelihood = -53052.316  
Iteration 56:  log likelihood = -53052.316  (not concave)
Iteration 57:  log likelihood = -53052.316  
Iteration 58:  log likelihood = -53052.316  
Iteration 59:  log likelihood = -53052.316  
Iteration 60:  log likelihood = -53052.316  
Iteration 61:  log likelihood = -53052.316  
Iteration 62:  log likelihood = -53052.316  
Iteration 63:  log likelihood = -53052.316  
Iteration 64:  log likelihood = -53052.316  
Iteration 65:  log likelihood = -53052.316  
Iteration 66:  log likelihood = -53052.316  
Iteration 67:  log likelihood = -53052.316  
Iteration 68:  log likelihood = -53052.316  
Iteration 69:  log likelihood = -53052.316  
Iteration 70:  log likelihood = -53052.316  
Iteration 71:  log likelihood = -53052.316  (not concave)
Iteration 72:  log likelihood = -53052.316  (backed up)
Iteration 73:  log likelihood = -53052.316  
Iteration 74:  log likelihood = -53052.316  
Iteration 75:  log likelihood = -53052.316  
Iteration 76:  log likelihood = -53052.316  
Iteration 77:  log likelihood = -53052.316  
Iteration 78:  log likelihood = -53052.316  
Iteration 79:  log likelihood = -53052.316  
Iteration 80:  log likelihood = -53052.316  
Iteration 81:  log likelihood = -53052.316  
Iteration 82:  log likelihood = -53052.316  
Iteration 83:  log likelihood = -53052.316  
Iteration 84:  log likelihood = -53052.316  
Iteration 85:  log likelihood = -53052.316  (not concave)
Iteration 86:  log likelihood = -53052.316  (not concave)
Iteration 87:  log likelihood = -53052.316  (backed up)
Iteration 88:  log likelihood = -53052.316  
Iteration 89:  log likelihood = -53052.316  
Iteration 90:  log likelihood = -53052.316  
Iteration 91:  log likelihood = -53052.316  
Iteration 92:  log likelihood = -53052.316  
Iteration 93:  log likelihood = -53052.316  
Iteration 94:  log likelihood = -53052.316  
Iteration 95:  log likelihood = -53052.316  
Iteration 96:  log likelihood = -53052.316  
Iteration 97:  log likelihood = -53052.316  
Iteration 98:  log likelihood = -53052.316  
Iteration 99:  log likelihood = -53052.316  
Iteration 100: log likelihood = -53052.316  
Iteration 101: log likelihood = -53052.316  
Iteration 102: log likelihood = -53052.315  
Iteration 103: log likelihood = -53052.315  
Iteration 104: log likelihood = -53052.315  (not concave)
Iteration 105: log likelihood = -53052.315  (backed up)
Iteration 106: log likelihood = -53052.315  
Iteration 107: log likelihood = -53052.315  
Iteration 108: log likelihood = -53052.315  
Iteration 109: log likelihood = -53052.315  
Iteration 110: log likelihood = -53052.315  
Iteration 111: log likelihood = -53052.315  
Iteration 112: log likelihood = -53052.315  
Iteration 113: log likelihood = -53052.315  
Iteration 114: log likelihood = -53052.315  (not concave)
Iteration 115: log likelihood = -53052.315  (backed up)
Iteration 116: log likelihood = -53052.315  (backed up)
Iteration 117: log likelihood = -53052.315  (not concave)
Iteration 118: log likelihood = -53052.315  (backed up)
Iteration 119: log likelihood = -53052.315  
Iteration 120: log likelihood = -53052.315  
Iteration 121: log likelihood = -53052.315  
Iteration 122: log likelihood = -53052.315  
Iteration 123: log likelihood = -53052.315  (not concave)
Iteration 124: log likelihood = -53052.315  (backed up)
Iteration 125: log likelihood = -53052.315  
Iteration 126: log likelihood = -53052.315  
Iteration 127: log likelihood = -53052.315  
Iteration 128: log likelihood = -53052.315  
Iteration 129: log likelihood = -53052.315  
Iteration 130: log likelihood = -53052.315  
Iteration 131: log likelihood = -53052.315  
Iteration 132: log likelihood = -53052.315  (not concave)
Iteration 133: log likelihood = -53052.315  (backed up)
Iteration 134: log likelihood = -53052.315  
Iteration 135: log likelihood = -53052.315  (not concave)
Iteration 136: log likelihood = -53052.315  (backed up)
Iteration 137: log likelihood = -53052.315  
Iteration 138: log likelihood = -53052.315  
Iteration 139: log likelihood = -53052.315  
Iteration 140: log likelihood = -53052.315  
Iteration 141: log likelihood = -53052.315  
Iteration 142: log likelihood = -53052.315  
Iteration 143: log likelihood = -53052.315  
Iteration 144: log likelihood = -53052.315  
Iteration 145: log likelihood = -53052.315  
Iteration 146: log likelihood = -53052.315  
Iteration 147: log likelihood = -53052.315  (not concave)
Iteration 148: log likelihood = -53052.315  (backed up)
Iteration 149: log likelihood = -53052.315  
Iteration 150: log likelihood = -53052.315  
Iteration 151: log likelihood = -53052.315  (backed up)
Iteration 152: log likelihood = -53052.315  
Iteration 153: log likelihood = -53052.315  
Iteration 154: log likelihood = -53052.315  
Iteration 155: log likelihood = -53052.315  
Iteration 156: log likelihood = -53052.315  
Iteration 157: log likelihood = -53052.315  
Iteration 158: log likelihood = -53052.315  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53052.315
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |    .146983   .0098142    14.98   0.000     .1277475    .1662185
edad_al_in~1 |   .0697298   .0015541    44.87   0.000     .0666838    .0727759
edad_ini_c~s |  -.0098602   .0018844    -5.23   0.000    -.0135537   -.0061668
     sex_enc |  -.3233888   .0202588   -15.96   0.000    -.3630953   -.2836823
     esc_rec |   .0973893    .012335     7.90   0.000     .0732132    .1215653
sus_prin_mod |   .1387388   .0082144    16.89   0.000     .1226389    .1548388
 fr_sus_prin |   .0303541   .0075739     4.01   0.000     .0155096    .0451986
 comp_biosoc |   .1955806   .0141775    13.80   0.000     .1677932    .2233681
     ten_viv |  -.0173284   .0076268    -2.27   0.023    -.0322766   -.0023801
origen_ing~d |  -.0175866   .0044031    -3.99   0.000    -.0262165   -.0089566
numero_de_~d |   .0685213   .0063491    10.79   0.000     .0560773    .0809653
dg_cie_10_~c |   .0267894   .0087918     3.05   0.002     .0095578     .044021
sud_sever~10 |  -.0626237   .0191611    -3.27   0.001    -.1001787   -.0250687
   macrozone |    .205667   .0118232    17.40   0.000     .1824939    .2288401
 policonsumo |   .1120804   .0216514     5.18   0.000     .0696443    .1545164
   n_off_vio |   .2927909   .0186932    15.66   0.000     .2561529    .3294288
   n_off_acq |   .6350328   .0174167    36.46   0.000     .6008968    .6691689
   n_off_sud |   .2064549   .0183614    11.24   0.000     .1704672    .2424425
        clas |   .0177979   .0128237     1.39   0.165    -.0073361     .042932
       _cons |   8.986626    2.09379     4.29   0.000     4.882873    13.09038
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP4
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_4

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53631.291  (not concave)
Iteration 2:   log likelihood = -53613.089  (not concave)
Iteration 3:   log likelihood = -53580.869  (not concave)
Iteration 4:   log likelihood = -53543.937  
Iteration 5:   log likelihood = -53414.579  (backed up)
Iteration 6:   log likelihood = -53325.221  
Iteration 7:   log likelihood = -53224.027  
Iteration 8:   log likelihood = -53141.452  
Iteration 9:   log likelihood = -53040.802  
Iteration 10:  log likelihood = -53039.693  
Iteration 11:  log likelihood = -53039.519  (not concave)
Iteration 12:  log likelihood = -53039.517  
Iteration 13:  log likelihood = -53039.514  
Iteration 14:  log likelihood = -53039.514  
Iteration 15:  log likelihood = -53039.514  
Iteration 16:  log likelihood = -53039.513  
Iteration 17:  log likelihood = -53039.513  
Iteration 18:  log likelihood = -53039.513  
Iteration 19:  log likelihood = -53039.513  
Iteration 20:  log likelihood = -53039.513  
Iteration 21:  log likelihood = -53039.513  
Iteration 22:  log likelihood = -53039.513  
Iteration 23:  log likelihood = -53039.513  
Iteration 24:  log likelihood = -53039.513  
Iteration 25:  log likelihood = -53039.513  
Iteration 26:  log likelihood = -53039.513  
Iteration 27:  log likelihood = -53039.513  
Iteration 28:  log likelihood = -53039.513  
Iteration 29:  log likelihood = -53039.513  
Iteration 30:  log likelihood = -53039.513  
Iteration 31:  log likelihood = -53039.513  
Iteration 32:  log likelihood = -53039.513  (not concave)
Iteration 33:  log likelihood = -53039.513  
Iteration 34:  log likelihood = -53039.513  
Iteration 35:  log likelihood = -53039.513  
Iteration 36:  log likelihood = -53039.513  
Iteration 37:  log likelihood = -53039.513  (not concave)
Iteration 38:  log likelihood = -53039.513  (backed up)
Iteration 39:  log likelihood = -53039.513  
Iteration 40:  log likelihood = -53039.512  (not concave)
Iteration 41:  log likelihood = -53039.512  (backed up)
Iteration 42:  log likelihood = -53039.512  
Iteration 43:  log likelihood = -53039.512  (not concave)
Iteration 44:  log likelihood = -53039.512  (backed up)
Iteration 45:  log likelihood = -53039.512  
Iteration 46:  log likelihood = -53039.512  
Iteration 47:  log likelihood = -53039.512  
Iteration 48:  log likelihood = -53039.512  
Iteration 49:  log likelihood = -53039.512  
Iteration 50:  log likelihood = -53039.512  
Iteration 51:  log likelihood = -53039.512  
Iteration 52:  log likelihood = -53039.512  
Iteration 53:  log likelihood = -53039.512  
Iteration 54:  log likelihood = -53039.512  
Iteration 55:  log likelihood = -53039.512  
Iteration 56:  log likelihood = -53039.512  (not concave)
Iteration 57:  log likelihood = -53039.512  (backed up)
Iteration 58:  log likelihood = -53039.512  
Iteration 59:  log likelihood = -53039.512  
Iteration 60:  log likelihood = -53039.512  
Iteration 61:  log likelihood = -53039.512  
Iteration 62:  log likelihood = -53039.512  (not concave)
Iteration 63:  log likelihood = -53039.512  
Iteration 64:  log likelihood = -53039.512  
Iteration 65:  log likelihood = -53039.512  
Iteration 66:  log likelihood = -53039.512  
Iteration 67:  log likelihood = -53039.512  
Iteration 68:  log likelihood = -53039.512  
Iteration 69:  log likelihood = -53039.512  
Iteration 70:  log likelihood = -53039.512  
Iteration 71:  log likelihood = -53039.512  
Iteration 72:  log likelihood = -53039.512  
Iteration 73:  log likelihood = -53039.512  
Iteration 74:  log likelihood = -53039.512  
Iteration 75:  log likelihood = -53039.512  
Iteration 76:  log likelihood = -53039.512  
Iteration 77:  log likelihood = -53039.512  (not concave)
Iteration 78:  log likelihood = -53039.512  (not concave)
Iteration 79:  log likelihood = -53039.512  (not concave)
Iteration 80:  log likelihood = -53039.512  (not concave)
Iteration 81:  log likelihood = -53039.512  (not concave)
Iteration 82:  log likelihood = -53039.512  (not concave)
Iteration 83:  log likelihood = -53039.512  (not concave)
Iteration 84:  log likelihood = -53039.512  (not concave)
Iteration 85:  log likelihood = -53039.512  (not concave)
Iteration 86:  log likelihood = -53039.512  (not concave)
Iteration 87:  log likelihood = -53039.512  (not concave)
Iteration 88:  log likelihood = -53039.512  (not concave)
Iteration 89:  log likelihood = -53039.512  (not concave)
Iteration 90:  log likelihood = -53039.512  (not concave)
Iteration 91:  log likelihood = -53039.512  (not concave)
Iteration 92:  log likelihood = -53039.512  (not concave)
Iteration 93:  log likelihood = -53039.512  (not concave)
Iteration 94:  log likelihood = -53039.512  (not concave)
Iteration 95:  log likelihood = -53039.512  (not concave)
Iteration 96:  log likelihood = -53039.512  (not concave)
Iteration 97:  log likelihood = -53039.512  (not concave)
Iteration 98:  log likelihood = -53039.512  (not concave)
Iteration 99:  log likelihood = -53039.512  (not concave)
Iteration 100: log likelihood = -53039.512  (not concave)
Iteration 101: log likelihood = -53039.512  (not concave)
Iteration 102: log likelihood = -53039.512  (not concave)
Iteration 103: log likelihood = -53039.512  (not concave)
Iteration 104: log likelihood = -53039.512  (not concave)
Iteration 105: log likelihood = -53039.512  (not concave)
Iteration 106: log likelihood = -53039.512  (not concave)
Iteration 107: log likelihood = -53039.512  (not concave)
Iteration 108: log likelihood = -53039.512  (not concave)
Iteration 109: log likelihood = -53039.512  (not concave)
Iteration 110: log likelihood = -53039.512  (not concave)
Iteration 111: log likelihood = -53039.512  (not concave)
Iteration 112: log likelihood = -53039.512  (not concave)
Iteration 113: log likelihood = -53039.512  (not concave)
Iteration 114: log likelihood = -53039.512  (not concave)
Iteration 115: log likelihood = -53039.512  (not concave)
Iteration 116: log likelihood = -53039.512  (not concave)
Iteration 117: log likelihood = -53039.512  (not concave)
Iteration 118: log likelihood = -53039.512  (not concave)
Iteration 119: log likelihood = -53039.512  (not concave)
Iteration 120: log likelihood = -53039.512  (not concave)
Iteration 121: log likelihood = -53039.512  (not concave)
Iteration 122: log likelihood = -53039.512  (not concave)
Iteration 123: log likelihood = -53039.512  (not concave)
Iteration 124: log likelihood = -53039.512  (not concave)
Iteration 125: log likelihood = -53039.512  (not concave)
Iteration 126: log likelihood = -53039.512  (not concave)
Iteration 127: log likelihood = -53039.512  (not concave)
Iteration 128: log likelihood = -53039.512  (not concave)
Iteration 129: log likelihood = -53039.512  (not concave)
Iteration 130: log likelihood = -53039.512  (not concave)
Iteration 131: log likelihood = -53039.512  (not concave)
Iteration 132: log likelihood = -53039.512  (not concave)
Iteration 133: log likelihood = -53039.512  (not concave)
Iteration 134: log likelihood = -53039.512  (not concave)
Iteration 135: log likelihood = -53039.512  (not concave)
Iteration 136: log likelihood = -53039.512  (not concave)
Iteration 137: log likelihood = -53039.512  (not concave)
Iteration 138: log likelihood = -53039.512  (not concave)
Iteration 139: log likelihood = -53039.512  (not concave)
Iteration 140: log likelihood = -53039.512  (not concave)
Iteration 141: log likelihood = -53039.512  (not concave)
Iteration 142: log likelihood = -53039.512  (not concave)
Iteration 143: log likelihood = -53039.512  (not concave)
Iteration 144: log likelihood = -53039.512  (not concave)
Iteration 145: log likelihood = -53039.512  (not concave)
Iteration 146: log likelihood = -53039.512  (not concave)
Iteration 147: log likelihood = -53039.512  (not concave)
Iteration 148: log likelihood = -53039.512  (not concave)
Iteration 149: log likelihood = -53039.512  (not concave)
Iteration 150: log likelihood = -53039.512  (not concave)
Iteration 151: log likelihood = -53039.512  (not concave)
Iteration 152: log likelihood = -53039.512  (not concave)
Iteration 153: log likelihood = -53039.512  (not concave)
Iteration 154: log likelihood = -53039.512  (not concave)
Iteration 155: log likelihood = -53039.512  (not concave)
Iteration 156: log likelihood = -53039.512  (not concave)
Iteration 157: log likelihood = -53039.512  (not concave)
Iteration 158: log likelihood = -53039.512  (not concave)
Iteration 159: log likelihood = -53039.512  (not concave)
Iteration 160: log likelihood = -53039.512  (not concave)
Iteration 161: log likelihood = -53039.512  (not concave)
Iteration 162: log likelihood = -53039.512  (not concave)
Iteration 163: log likelihood = -53039.512  (not concave)
Iteration 164: log likelihood = -53039.512  (not concave)
Iteration 165: log likelihood = -53039.512  (not concave)
Iteration 166: log likelihood = -53039.512  (not concave)
Iteration 167: log likelihood = -53039.512  (not concave)
Iteration 168: log likelihood = -53039.512  (not concave)
Iteration 169: log likelihood = -53039.512  (not concave)
Iteration 170: log likelihood = -53039.512  (not concave)
Iteration 171: log likelihood = -53039.512  (not concave)
Iteration 172: log likelihood = -53039.512  (not concave)
Iteration 173: log likelihood = -53039.512  (not concave)
Iteration 174: log likelihood = -53039.512  (not concave)
Iteration 175: log likelihood = -53039.512  (not concave)
Iteration 176: log likelihood = -53039.512  (not concave)
Iteration 177: log likelihood = -53039.512  (not concave)
Iteration 178: log likelihood = -53039.512  (not concave)
Iteration 179: log likelihood = -53039.512  (not concave)
Iteration 180: log likelihood = -53039.512  (not concave)
Iteration 181: log likelihood = -53039.512  (not concave)
Iteration 182: log likelihood = -53039.512  (not concave)
Iteration 183: log likelihood = -53039.512  (not concave)
Iteration 184: log likelihood = -53039.512  (not concave)
Iteration 185: log likelihood = -53039.512  (not concave)
Iteration 186: log likelihood = -53039.512  (not concave)
Iteration 187: log likelihood = -53039.512  (not concave)
Iteration 188: log likelihood = -53039.512  (not concave)
Iteration 189: log likelihood = -53039.512  (not concave)
Iteration 190: log likelihood = -53039.512  (not concave)
Iteration 191: log likelihood = -53039.512  (not concave)
Iteration 192: log likelihood = -53039.512  (not concave)
Iteration 193: log likelihood = -53039.512  (not concave)
Iteration 194: log likelihood = -53039.512  (not concave)
Iteration 195: log likelihood = -53039.512  (not concave)
Iteration 196: log likelihood = -53039.512  (not concave)
Iteration 197: log likelihood = -53039.512  (not concave)
Iteration 198: log likelihood = -53039.512  (not concave)
Iteration 199: log likelihood = -53039.512  (not concave)
Iteration 200: log likelihood = -53039.512  (not concave)
Iteration 201: log likelihood = -53039.512  (not concave)
Iteration 202: log likelihood = -53039.512  (not concave)
Iteration 203: log likelihood = -53039.512  (not concave)
Iteration 204: log likelihood = -53039.512  (not concave)
Iteration 205: log likelihood = -53039.512  (not concave)
Iteration 206: log likelihood = -53039.512  (not concave)
Iteration 207: log likelihood = -53039.512  (not concave)
Iteration 208: log likelihood = -53039.512  (not concave)
Iteration 209: log likelihood = -53039.512  (not concave)
Iteration 210: log likelihood = -53039.512  (not concave)
Iteration 211: log likelihood = -53039.512  (not concave)
Iteration 212: log likelihood = -53039.512  (not concave)
Iteration 213: log likelihood = -53039.512  (not concave)
Iteration 214: log likelihood = -53039.512  (not concave)
Iteration 215: log likelihood = -53039.512  (not concave)
Iteration 216: log likelihood = -53039.512  (not concave)
Iteration 217: log likelihood = -53039.512  (not concave)
Iteration 218: log likelihood = -53039.512  (not concave)
Iteration 219: log likelihood = -53039.512  (not concave)
Iteration 220: log likelihood = -53039.512  (not concave)
Iteration 221: log likelihood = -53039.512  (not concave)
Iteration 222: log likelihood = -53039.512  (not concave)
Iteration 223: log likelihood = -53039.512  (not concave)
Iteration 224: log likelihood = -53039.512  (not concave)
Iteration 225: log likelihood = -53039.512  (not concave)
Iteration 226: log likelihood = -53039.512  (not concave)
Iteration 227: log likelihood = -53039.512  (not concave)
Iteration 228: log likelihood = -53039.512  (not concave)
Iteration 229: log likelihood = -53039.512  (not concave)
Iteration 230: log likelihood = -53039.512  (not concave)
Iteration 231: log likelihood = -53039.512  (not concave)
Iteration 232: log likelihood = -53039.512  (not concave)
Iteration 233: log likelihood = -53039.512  (not concave)
Iteration 234: log likelihood = -53039.512  (not concave)
Iteration 235: log likelihood = -53039.512  (not concave)
Iteration 236: log likelihood = -53039.512  (not concave)
Iteration 237: log likelihood = -53039.512  (not concave)
Iteration 238: log likelihood = -53039.512  (not concave)
Iteration 239: log likelihood = -53039.512  (not concave)
Iteration 240: log likelihood = -53039.512  (not concave)
Iteration 241: log likelihood = -53039.512  (not concave)
Iteration 242: log likelihood = -53039.512  (not concave)
Iteration 243: log likelihood = -53039.512  (not concave)
Iteration 244: log likelihood = -53039.512  (not concave)
Iteration 245: log likelihood = -53039.512  (not concave)
Iteration 246: log likelihood = -53039.512  (not concave)
Iteration 247: log likelihood = -53039.512  (not concave)
Iteration 248: log likelihood = -53039.512  (not concave)
Iteration 249: log likelihood = -53039.512  (not concave)
Iteration 250: log likelihood = -53039.512  (not concave)
Iteration 251: log likelihood = -53039.512  (not concave)
Iteration 252: log likelihood = -53039.512  (not concave)
Iteration 253: log likelihood = -53039.512  (not concave)
Iteration 254: log likelihood = -53039.512  (not concave)
Iteration 255: log likelihood = -53039.512  (not concave)
Iteration 256: log likelihood = -53039.512  (not concave)
Iteration 257: log likelihood = -53039.512  (not concave)
Iteration 258: log likelihood = -53039.512  (not concave)
Iteration 259: log likelihood = -53039.512  (not concave)
Iteration 260: log likelihood = -53039.512  (not concave)
Iteration 261: log likelihood = -53039.512  (not concave)
Iteration 262: log likelihood = -53039.512  (not concave)
Iteration 263: log likelihood = -53039.512  (not concave)
Iteration 264: log likelihood = -53039.512  (not concave)
Iteration 265: log likelihood = -53039.512  (not concave)
Iteration 266: log likelihood = -53039.512  (not concave)
Iteration 267: log likelihood = -53039.512  (not concave)
Iteration 268: log likelihood = -53039.512  (not concave)
Iteration 269: log likelihood = -53039.512  (not concave)
Iteration 270: log likelihood = -53039.512  (not concave)
Iteration 271: log likelihood = -53039.512  (not concave)
Iteration 272: log likelihood = -53039.512  (not concave)
Iteration 273: log likelihood = -53039.512  (not concave)
Iteration 274: log likelihood = -53039.512  (not concave)
Iteration 275: log likelihood = -53039.512  (not concave)
Iteration 276: log likelihood = -53039.512  (not concave)
Iteration 277: log likelihood = -53039.512  (not concave)
Iteration 278: log likelihood = -53039.512  (not concave)
Iteration 279: log likelihood = -53039.512  (not concave)
Iteration 280: log likelihood = -53039.512  (not concave)
Iteration 281: log likelihood = -53039.512  (not concave)
Iteration 282: log likelihood = -53039.512  (not concave)
Iteration 283: log likelihood = -53039.512  (not concave)
Iteration 284: log likelihood = -53039.512  (not concave)
Iteration 285: log likelihood = -53039.512  (not concave)
Iteration 286: log likelihood = -53039.512  (not concave)
Iteration 287: log likelihood = -53039.512  (not concave)
Iteration 288: log likelihood = -53039.512  (not concave)
Iteration 289: log likelihood = -53039.512  (not concave)
Iteration 290: log likelihood = -53039.512  (not concave)
Iteration 291: log likelihood = -53039.512  (not concave)
Iteration 292: log likelihood = -53039.512  (not concave)
Iteration 293: log likelihood = -53039.512  (not concave)
Iteration 294: log likelihood = -53039.512  (not concave)
Iteration 295: log likelihood = -53039.512  (not concave)
Iteration 296: log likelihood = -53039.512  (not concave)
Iteration 297: log likelihood = -53039.512  (not concave)
Iteration 298: log likelihood = -53039.512  (not concave)
Iteration 299: log likelihood = -53039.512  (not concave)
Iteration 300: log likelihood = -53039.512  (not concave)
convergence not achieved

Survival model                                  Number of obs     =     59,220
Log likelihood = -53039.512
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1469545   .0098183    14.97   0.000     .1277109    .1661981
edad_al_in~1 |   .0737812   .0018596    39.68   0.000     .0701364     .077426
edad_ini_c~s |  -.0097569   .0018817    -5.19   0.000    -.0134449   -.0060689
     sex_enc |  -.3238906   .0202555   -15.99   0.000    -.3635907   -.2841906
     esc_rec |   .0967795   .0123511     7.84   0.000     .0725718    .1209871
sus_prin_mod |   .1387568    .008217    16.89   0.000     .1226519    .1548618
 fr_sus_prin |   .0301653   .0075748     3.98   0.000      .015319    .0450117
 comp_biosoc |   .1961442   .0141792    13.83   0.000     .1683535    .2239349
     ten_viv |  -.0173418   .0076299    -2.27   0.023    -.0322962   -.0023875
origen_ing~d |  -.0175365   .0044035    -3.98   0.000    -.0261673   -.0089058
numero_de_~d |   .0693253   .0063516    10.91   0.000     .0568764    .0817743
dg_cie_10_~c |   .0269912   .0087931     3.07   0.002     .0097571    .0442253
sud_sever~10 |  -.0620479   .0191672    -3.24   0.001    -.0996149    -.024481
   macrozone |   .2050865   .0118233    17.35   0.000     .1819132    .2282597
 policonsumo |   .1150045   .0216722     5.31   0.000     .0725277    .1574813
   n_off_vio |   .2913796   .0186954    15.59   0.000     .2547372     .328022
   n_off_acq |   .6331456    .017422    36.34   0.000      .598999    .6672921
   n_off_sud |   .2048003   .0183804    11.14   0.000     .1687753    .2408253
        clas |   .0182451   .0128241     1.42   0.155    -.0068896    .0433798
       _cons |   8.070257          .        .       .            .           .
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP5
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_5

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53635.154  (not concave)
Iteration 2:   log likelihood = -53601.706  (not concave)
Iteration 3:   log likelihood = -53578.731  (not concave)
Iteration 4:   log likelihood =  -53568.11  (not concave)
Iteration 5:   log likelihood = -53558.782  (not concave)
Iteration 6:   log likelihood = -53551.072  
Iteration 7:   log likelihood = -53449.894  
Iteration 8:   log likelihood = -53334.597  
Iteration 9:   log likelihood = -53252.781  
Iteration 10:  log likelihood = -53191.722  
Iteration 11:  log likelihood = -53147.721  
Iteration 12:  log likelihood = -53089.223  
Iteration 13:  log likelihood = -53012.771  
Iteration 14:  log likelihood = -53007.688  (not concave)
Iteration 15:  log likelihood = -53006.792  
Iteration 16:  log likelihood = -53006.769  
Iteration 17:  log likelihood = -53006.769  (backed up)
Iteration 18:  log likelihood = -53006.769  
Iteration 19:  log likelihood = -53006.769  
Iteration 20:  log likelihood = -53006.769  (not concave)
Iteration 21:  log likelihood = -53006.769  (backed up)
Iteration 22:  log likelihood = -53006.769  
Iteration 23:  log likelihood = -53006.769  
Iteration 24:  log likelihood = -53006.769  
Iteration 25:  log likelihood = -53006.768  
Iteration 26:  log likelihood = -53006.768  
Iteration 27:  log likelihood = -53006.768  
Iteration 28:  log likelihood = -53006.768  
Iteration 29:  log likelihood = -53006.768  
Iteration 30:  log likelihood = -53006.768  
Iteration 31:  log likelihood = -53006.768  
Iteration 32:  log likelihood = -53006.768  
Iteration 33:  log likelihood = -53006.768  
Iteration 34:  log likelihood = -53006.768  
Iteration 35:  log likelihood = -53006.768  
Iteration 36:  log likelihood = -53006.768  (not concave)
Iteration 37:  log likelihood = -53006.768  (backed up)
Iteration 38:  log likelihood = -53006.768  
Iteration 39:  log likelihood = -53006.768  
Iteration 40:  log likelihood = -53006.768  
Iteration 41:  log likelihood = -53006.768  
Iteration 42:  log likelihood = -53006.768  
Iteration 43:  log likelihood = -53006.768  
Iteration 44:  log likelihood = -53006.768  
Iteration 45:  log likelihood = -53006.768  
Iteration 46:  log likelihood = -53006.768  
Iteration 47:  log likelihood = -53006.768  
Iteration 48:  log likelihood = -53006.768  
Iteration 49:  log likelihood = -53006.768  
Iteration 50:  log likelihood = -53006.768  
Iteration 51:  log likelihood = -53006.768  
Iteration 52:  log likelihood = -53006.768  
Iteration 53:  log likelihood = -53006.768  
Iteration 54:  log likelihood = -53006.768  
Iteration 55:  log likelihood = -53006.768  
Iteration 56:  log likelihood = -53006.768  
Iteration 57:  log likelihood = -53006.768  
Iteration 58:  log likelihood = -53006.768  
Iteration 59:  log likelihood = -53006.768  
Iteration 60:  log likelihood = -53006.768  
Iteration 61:  log likelihood = -53006.768  
Iteration 62:  log likelihood = -53006.768  

Survival model                                  Number of obs     =     59,220
Log likelihood = -53006.768
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1469505   .0098272    14.95   0.000     .1276895    .1662116
edad_al_in~1 |   .0816383   .0021239    38.44   0.000     .0774755     .085801
edad_ini_c~s |   -.009479   .0018792    -5.04   0.000    -.0131622   -.0057958
     sex_enc |  -.3225755   .0202475   -15.93   0.000      -.36226    -.282891
     esc_rec |    .096346   .0123579     7.80   0.000     .0721249    .1205671
sus_prin_mod |   .1370627    .008226    16.66   0.000     .1209402    .1531853
 fr_sus_prin |   .0291331   .0075772     3.84   0.000      .014282    .0439841
 comp_biosoc |   .1964727   .0141838    13.85   0.000     .1686729    .2242725
     ten_viv |  -.0171967   .0076329    -2.25   0.024    -.0321569   -.0022366
origen_ing~d |  -.0175985   .0044032    -4.00   0.000    -.0262285   -.0089685
numero_de_~d |   .0694244   .0063541    10.93   0.000     .0569707    .0818782
dg_cie_10_~c |   .0272959   .0087951     3.10   0.002     .0100578    .0445341
sud_sever~10 |  -.0623583   .0191673    -3.25   0.001    -.0999256    -.024791
   macrozone |   .2045608   .0118219    17.30   0.000     .1813903    .2277312
 policonsumo |    .119059   .0217004     5.49   0.000     .0765269    .1615911
   n_off_vio |   .2897753   .0186986    15.50   0.000     .2531267    .3264239
   n_off_acq |   .6304362   .0174293    36.17   0.000     .5962753     .664597
   n_off_sud |   .2039463   .0183822    11.09   0.000     .1679179    .2399747
        clas |   .0180863    .012823     1.41   0.158    -.0070462    .0432189
       _cons |   7.793442   1.966307     3.96   0.000     3.939551    11.64733
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP6
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_6

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53632.162  (not concave)
Iteration 2:   log likelihood = -53599.409  (not concave)
Iteration 3:   log likelihood = -53573.574  (not concave)
Iteration 4:   log likelihood = -53555.358  
Iteration 5:   log likelihood = -53451.353  
Iteration 6:   log likelihood = -53297.687  
Iteration 7:   log likelihood = -53188.707  
Iteration 8:   log likelihood = -53117.844  
Iteration 9:   log likelihood = -53039.309  
Iteration 10:  log likelihood = -52997.725  
Iteration 11:  log likelihood = -52996.105  
Iteration 12:  log likelihood = -52996.099  
Iteration 13:  log likelihood = -52996.054  (not concave)
Iteration 14:  log likelihood = -52996.054  
Iteration 15:  log likelihood = -52996.054  
Iteration 16:  log likelihood = -52996.054  (not concave)
Iteration 17:  log likelihood = -52996.054  (backed up)
Iteration 18:  log likelihood = -52996.054  
Iteration 19:  log likelihood = -52996.054  
Iteration 20:  log likelihood = -52996.054  
Iteration 21:  log likelihood = -52996.054  

Survival model                                  Number of obs     =     59,220
Log likelihood = -52996.054
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1468405    .009831    14.94   0.000     .1275721    .1661089
edad_al_in~1 |   .0855397   .0023072    37.08   0.000     .0810177    .0900617
edad_ini_c~s |  -.0094211   .0018788    -5.01   0.000    -.0131035   -.0057386
     sex_enc |  -.3222529    .020247   -15.92   0.000    -.3619363   -.2825696
     esc_rec |   .0964252   .0123594     7.80   0.000     .0722012    .1206491
sus_prin_mod |    .136815   .0082288    16.63   0.000     .1206868    .1529432
 fr_sus_prin |   .0288938   .0075774     3.81   0.000     .0140424    .0437453
 comp_biosoc |   .1963864   .0141854    13.84   0.000     .1685836    .2241893
     ten_viv |  -.0172791   .0076339    -2.26   0.024    -.0322414   -.0023169
origen_ing~d |  -.0174714   .0044032    -3.97   0.000    -.0261015   -.0088412
numero_de_~d |   .0692175   .0063558    10.89   0.000     .0567603    .0816747
dg_cie_10_~c |   .0273674    .008796     3.11   0.002     .0101276    .0446073
sud_sever~10 |  -.0622738   .0191675    -3.25   0.001    -.0998413   -.0247063
   macrozone |    .204528   .0118228    17.30   0.000     .1813557    .2277004
 policonsumo |   .1209119   .0217121     5.57   0.000      .078357    .1634668
   n_off_vio |   .2891115   .0186992    15.46   0.000     .2524617    .3257613
   n_off_acq |   .6291435   .0174327    36.09   0.000      .594976     .663311
   n_off_sud |   .2035275   .0183846    11.07   0.000     .1674943    .2395606
        clas |   .0184281   .0128236     1.44   0.151    -.0067056    .0435619
       _cons |   7.469041   1.911195     3.91   0.000     3.723168    11.21491
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP7
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_7

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53628.206  (not concave)
Iteration 2:   log likelihood = -53600.295  (not concave)
Iteration 3:   log likelihood = -53570.622  (not concave)
Iteration 4:   log likelihood = -53556.355  (not concave)
Iteration 5:   log likelihood = -53532.405  (not concave)
Iteration 6:   log likelihood = -53516.673  
Iteration 7:   log likelihood = -53377.035  
Iteration 8:   log likelihood = -53131.487  
Iteration 9:   log likelihood = -53077.821  
Iteration 10:  log likelihood = -53018.232  
Iteration 11:  log likelihood = -52983.731  
Iteration 12:  log likelihood = -52982.321  
Iteration 13:  log likelihood = -52982.318  
Iteration 14:  log likelihood = -52982.312  
Iteration 15:  log likelihood = -52982.312  
Iteration 16:  log likelihood = -52982.312  

Survival model                                  Number of obs     =     59,220
Log likelihood = -52982.312
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1466938   .0098354    14.91   0.000     .1274168    .1659707
edad_al_in~1 |   .0906316    .002527    35.86   0.000     .0856787    .0955845
edad_ini_c~s |  -.0093569   .0018786    -4.98   0.000    -.0130389   -.0056749
     sex_enc |  -.3219959   .0202465   -15.90   0.000    -.3616784   -.2823135
     esc_rec |   .0966812   .0123595     7.82   0.000      .072457    .1209054
sus_prin_mod |   .1367415   .0082321    16.61   0.000     .1206069     .152876
 fr_sus_prin |   .0286124   .0075776     3.78   0.000     .0137606    .0434643
 comp_biosoc |   .1961593   .0141864    13.83   0.000     .1683545    .2239642
     ten_viv |   -.017332   .0076342    -2.27   0.023    -.0322948   -.0023693
origen_ing~d |  -.0172873   .0044033    -3.93   0.000    -.0259177    -.008657
numero_de_~d |   .0688859   .0063577    10.84   0.000     .0564251    .0813468
dg_cie_10_~c |   .0274564   .0087973     3.12   0.002      .010214    .0446989
sud_sever~10 |  -.0623705   .0191672    -3.25   0.001    -.0999375   -.0248035
   macrozone |   .2044048   .0118234    17.29   0.000     .1812313    .2275782
 policonsumo |   .1230147   .0217247     5.66   0.000      .080435    .1655944
   n_off_vio |   .2883716   .0186995    15.42   0.000     .2517213    .3250219
   n_off_acq |   .6274611   .0174349    35.99   0.000     .5932893    .6616328
   n_off_sud |   .2029805   .0183862    11.04   0.000     .1669442    .2390169
        clas |   .0188509   .0128245     1.47   0.142    -.0062846    .0439864
       _cons |   7.157363   2.404712     2.98   0.003     2.444215    11.87051
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP8
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_8

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53626.277  (not concave)
Iteration 2:   log likelihood = -53607.161  (not concave)
Iteration 3:   log likelihood = -53569.518  (not concave)
Iteration 4:   log likelihood = -53550.223  
Iteration 5:   log likelihood = -53392.855  (backed up)
Iteration 6:   log likelihood = -53275.332  
Iteration 7:   log likelihood = -53210.865  
Iteration 8:   log likelihood = -53160.203  
Iteration 9:   log likelihood = -53088.031  
Iteration 10:  log likelihood = -53002.652  
Iteration 11:  log likelihood = -52977.904  
Iteration 12:  log likelihood =  -52970.39  (not concave)
Iteration 13:  log likelihood =  -52970.32  
Iteration 14:  log likelihood = -52970.284  
Iteration 15:  log likelihood = -52970.281  
Iteration 16:  log likelihood = -52970.281  
Iteration 17:  log likelihood = -52970.281  
Iteration 18:  log likelihood = -52970.281  

Survival model                                  Number of obs     =     59,220
Log likelihood = -52970.281
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1465263   .0098388    14.89   0.000     .1272426      .16581
edad_al_in~1 |   .0950365   .0026822    35.43   0.000     .0897794    .1002935
edad_ini_c~s |  -.0093167   .0018789    -4.96   0.000    -.0129993   -.0056342
     sex_enc |  -.3217155    .020247   -15.89   0.000    -.3613989   -.2820321
     esc_rec |   .0970857   .0123586     7.86   0.000     .0728632    .1213082
sus_prin_mod |   .1368623   .0082351    16.62   0.000     .1207218    .1530028
 fr_sus_prin |   .0283863   .0075779     3.75   0.000     .0135339    .0432387
 comp_biosoc |   .1959212   .0141865    13.81   0.000     .1681162    .2237262
     ten_viv |  -.0173513    .007634    -2.27   0.023    -.0323137    -.002389
origen_ing~d |  -.0171212   .0044034    -3.89   0.000    -.0257517   -.0084906
numero_de_~d |   .0685046   .0063593    10.77   0.000     .0560406    .0809686
dg_cie_10_~c |   .0275313   .0087988     3.13   0.002     .0102859    .0447767
sud_sever~10 |  -.0626385    .019167    -3.27   0.001     -.100205   -.0250719
   macrozone |   .2043429    .011824    17.28   0.000     .1811683    .2275174
 policonsumo |   .1243802   .0217316     5.72   0.000      .081787    .1669734
   n_off_vio |   .2877424   .0186998    15.39   0.000     .2510915    .3243934
   n_off_acq |   .6259495   .0174365    35.90   0.000     .5917746    .6601244
   n_off_sud |   .2023764   .0183872    11.01   0.000     .1663382    .2384147
        clas |   .0191279   .0128251     1.49   0.136    -.0060089    .0442647
       _cons |   6.812623   2.043988     3.33   0.001      2.80648    10.81877
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP9
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_9

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53628.816  (not concave)
Iteration 2:   log likelihood = -53610.071  (not concave)
Iteration 3:   log likelihood = -53572.756  (not concave)
Iteration 4:   log likelihood = -53552.832  (not concave)
Iteration 5:   log likelihood = -53527.929  (not concave)
Iteration 6:   log likelihood = -53517.691  
Iteration 7:   log likelihood =  -53386.21  
Iteration 8:   log likelihood = -53225.131  
Iteration 9:   log likelihood = -53075.422  
Iteration 10:  log likelihood = -52969.602  
Iteration 11:  log likelihood = -52950.855  
Iteration 12:  log likelihood = -52950.264  
Iteration 13:  log likelihood = -52950.263  
Iteration 14:  log likelihood = -52950.263  

Survival model                                  Number of obs     =     59,220
Log likelihood = -52950.263
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1462809   .0098436    14.86   0.000     .1269878    .1655739
edad_al_in~1 |   .1011524   .0028535    35.45   0.000     .0955597    .1067451
edad_ini_c~s |  -.0092443   .0018795    -4.92   0.000     -.012928   -.0055606
     sex_enc |  -.3211348   .0202474   -15.86   0.000     -.360819   -.2814507
     esc_rec |   .0977877   .0123567     7.91   0.000     .0735691    .1220063
sus_prin_mod |    .137126    .008239    16.64   0.000     .1209779    .1532742
 fr_sus_prin |   .0280768   .0075785     3.70   0.000     .0132233    .0429304
 comp_biosoc |   .1956123   .0141859    13.79   0.000     .1678084    .2234161
     ten_viv |  -.0173486    .007633    -2.27   0.023     -.032309   -.0023881
origen_ing~d |  -.0169035   .0044036    -3.84   0.000    -.0255343   -.0082727
numero_de_~d |   .0679605   .0063618    10.68   0.000     .0554916    .0804295
dg_cie_10_~c |   .0275365   .0088012     3.13   0.002     .0102866    .0447865
sud_sever~10 |   -.063453   .0191667    -3.31   0.001    -.1010192   -.0258869
   macrozone |   .2041995   .0118248    17.27   0.000     .1810233    .2273756
 policonsumo |    .125693   .0217379     5.78   0.000     .0830876    .1682984
   n_off_vio |   .2870158   .0186998    15.35   0.000     .2503648    .3236668
   n_off_acq |    .623454   .0174395    35.75   0.000     .5892732    .6576348
   n_off_sud |    .201105   .0183887    10.94   0.000     .1650639    .2371461
        clas |   .0193284   .0128261     1.51   0.132    -.0058104    .0444672
       _cons |   6.695181   2.863885     2.34   0.019      1.08207    12.30829
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display
 ***********
 family RP10
 ***********
note; a delayed entry model is being fitted
Obtaining initial values
convergence not achieved
variables created: _rcs1_1 to _rcs1_10

Fitting full model:

Iteration 0:   log likelihood = -53652.501  (not concave)
Iteration 1:   log likelihood = -53634.078  (not concave)
Iteration 2:   log likelihood = -53607.389  (not concave)
Iteration 3:   log likelihood = -53573.701  (not concave)
Iteration 4:   log likelihood = -53558.992  (not concave)
Iteration 5:   log likelihood = -53545.812  (not concave)
Iteration 6:   log likelihood = -53535.557  (not concave)
Iteration 7:   log likelihood = -53526.943  (not concave)
Iteration 8:   log likelihood = -53519.129  
Iteration 9:   log likelihood = -53412.121  
Iteration 10:  log likelihood = -53350.101  (not concave)
Iteration 11:  log likelihood = -53261.081  (not concave)
Iteration 12:  log likelihood = -53125.245  (not concave)
Iteration 13:  log likelihood = -53098.344  (not concave)
Iteration 14:  log likelihood = -53084.729  (not concave)
Iteration 15:  log likelihood = -53060.446  
Iteration 16:  log likelihood = -53033.812  (backed up)
Iteration 17:  log likelihood = -52937.635  
Iteration 18:  log likelihood =  -52934.59  
Iteration 19:  log likelihood = -52932.291  
Iteration 20:  log likelihood = -52932.282  
Iteration 21:  log likelihood = -52932.282  
Iteration 22:  log likelihood = -52932.282  

Survival model                                  Number of obs     =     59,220
Log likelihood = -52932.282
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_t:          |            
motivodeeg~3 |   .1461614   .0098475    14.84   0.000     .1268606    .1654621
edad_al_in~1 |   .1062914   .0029767    35.71   0.000     .1004571    .1121257
edad_ini_c~s |   -.009196   .0018803    -4.89   0.000    -.0128812   -.0055107
     sex_enc |  -.3204837   .0202482   -15.83   0.000    -.3601694   -.2807979
     esc_rec |   .0983569   .0123551     7.96   0.000     .0741415    .1225724
sus_prin_mod |   .1373741   .0082418    16.67   0.000     .1212206    .1535277
 fr_sus_prin |   .0278984   .0075788     3.68   0.000     .0130443    .0427526
 comp_biosoc |    .195431   .0141859    13.78   0.000     .1676272    .2232348
     ten_viv |  -.0174646   .0076323    -2.29   0.022    -.0324236   -.0025056
origen_ing~d |  -.0167016   .0044035    -3.79   0.000    -.0253324   -.0080709
numero_de_~d |   .0674779   .0063645    10.60   0.000     .0550036    .0799521
dg_cie_10_~c |   .0274563    .008803     3.12   0.002     .0102028    .0447099
sud_sever~10 |  -.0640959   .0191671    -3.34   0.001    -.1016628    -.026529
   macrozone |    .204194   .0118255    17.27   0.000     .1810164    .2273715
 policonsumo |   .1263612   .0217419     5.81   0.000      .083748    .1689745
   n_off_vio |   .2862623   .0187002    15.31   0.000     .2496107     .322914
   n_off_acq |   .6213778   .0174412    35.63   0.000     .5871936     .655562
   n_off_sud |   .1997368   .0183901    10.86   0.000     .1636928    .2357807
        clas |   .0195736   .0128269     1.53   0.127    -.0055667    .0447138
       _cons |   6.793827   4.307681     1.58   0.115    -1.649072    15.23673
------------------------------------------------------------------------------
    Warning: Baseline spline coefficients not shown - use ml display

. 
. *rcs(time, df(3) orthog)
. estwrite _all using "${pathdata2}parmodels_m2_nov_22.sters", replace
(saving m2_1_cox1)
(saving m2_1_cox2)
(saving m2_1_cox3)
(saving m2_1_cox4)
(saving m2_1_cox5)
(saving m2_1_cox6)
(saving m2_1_cox7)
(saving m2_1_gom)
(saving m2_1_wei)
(saving m2_1_logl)
(saving m2_1_logn)
(saving m2_1_ggam)
(saving m2_1_rp1)
(saving m2_1_rp2)
(saving m2_1_rp3)
(saving m2_1_rp4)
(saving m2_1_rp5)
(saving m2_1_rp6)
(saving m2_1_rp7)
(saving m2_1_rp8)
(saving m2_1_rp9)
(saving m2_1_rp10)
(file parmodels_m2_nov_22.sters saved)

We obtained a summary of distributions by AICs and BICs.

. *estread "${pathdata2}parmodels_m2_nov_22.sters"
. 
. *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 m2_1_*, n(`r(N)')

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
   m2_1_cox1 |     22,287          .  -53742.41      21   107526.8   107695.1
   m2_1_cox2 |     22,287          .  -53714.09      22   107472.2   107648.4
   m2_1_cox3 |     22,287          .  -53682.84      23   107411.7     107596
   m2_1_cox4 |     22,287          .  -53678.35      24   107404.7     107597
   m2_1_cox5 |     22,287          .  -53672.78      25   107395.6   107595.9
   m2_1_cox6 |     22,287          .  -53666.24      26   107384.5   107592.8
   m2_1_cox7 |     22,287          .  -53665.67      27   107385.3   107601.6
    m2_1_gom |     22,287          .  -52687.43      21   105416.9   105585.1
    m2_1_wei |     22,287          .  -53652.49      20     107345   107505.2
   m2_1_logl |     22,287          .  -54430.06      21   108902.1   109070.4
   m2_1_logn |     22,287          .  -54497.62      12   109019.2   109115.4
   m2_1_ggam |     22,287          .  -54658.18       7   109330.4   109386.4
    m2_1_rp1 |     22,287          .   -53652.5      20     107345   107505.2
    m2_1_rp2 |     22,287          .  -53111.05      21   106264.1   106432.4
    m2_1_rp3 |     22,287          .  -53052.31      23   106150.6   106334.9
    m2_1_rp4 |     22,287          .  -53039.51      23     106125   106309.3
    m2_1_rp5 |     22,287          .  -53006.77      25   106063.5   106263.8
    m2_1_rp6 |     22,287          .  -52996.05      26   106044.1   106252.4
    m2_1_rp7 |     22,287          .  -52982.31      27   106018.6   106234.9
    m2_1_rp8 |     22,287          .  -52970.28      28   105996.6   106220.9
    m2_1_rp9 |     22,287          .  -52950.26      29   105958.5   106190.9
   m2_1_rp10 |     22,287          .  -52932.28      29   105922.6   106154.9
-----------------------------------------------------------------------------

.         //we store in a matrix de survival
. matrix stats_1=r(S)

. 
. 
. estimates clear

. 
. ** to order AICs
. *https://www.statalist.org/forums/forum/general-stata-discussion/general/1665263-sorting-matr
> ix-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", 6)

. esttab matrix(stats_1) using "testreg_aic_bic_22.csv", replace
(output written to testreg_aic_bic_22.csv)

. esttab matrix(stats_1) using "testreg_aic_bic_22.html", replace
(output written to testreg_aic_bic_22.html)

.


	stats_1
	N	ll0	ll	df	AIC	BIC

m2_1_gom	22287	.	-52687.43	21	105416.9	105585.1
m2_1_rp10	22287	.	-52932.28	29	105922.6	106154.9
m2_1_rp9	22287	.	-52950.26	29	105958.5	106190.9
m2_1_rp8	22287	.	-52970.28	28	105996.6	106220.9
m2_1_rp7	22287	.	-52982.31	27	106018.6	106234.9
m2_1_rp6	22287	.	-52996.05	26	106044.1	106252.4
m2_1_rp5	22287	.	-53006.77	25	106063.5	106263.8
m2_1_rp4	22287	.	-53039.51	23	106125	106309.3
m2_1_rp3	22287	.	-53052.31	23	106150.6	106334.9
m2_1_rp2	22287	.	-53111.05	21	106264.1	106432.4
m2_1_wei	22287	.	-53652.49	20	107345	107505.2
m2_1_rp1	22287	.	-53652.5	20	107345	107505.2
m2_1_cox6	22287	.	-53666.24	26	107384.5	107592.8
m2_1_cox5	22287	.	-53672.78	25	107395.6	107595.9
m2_1_cox3	22287	.	-53682.84	23	107411.7	107596
m2_1_cox4	22287	.	-53678.35	24	107404.7	107597
m2_1_cox7	22287	.	-53665.67	27	107385.3	107601.6
m2_1_cox2	22287	.	-53714.09	22	107472.2	107648.4
m2_1_cox1	22287	.	-53742.41	21	107526.8	107695.1
m2_1_logl	22287	.	-54430.06	21	108902.1	109070.4
m2_1_logn	22287	.	-54497.62	12	109019.2	109115.4
m2_1_ggam	22287	.	-54658.18	7	109330.4	109386.4

In 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, and this is why we also considered the BIC. If a model with less parameters had greater or equal AIC (or differences lower than 2) but also had better BIC (<=2), we favoured the model with less parameters.

IPTW Royston-Parmar

. *reset time, only compatible with stteffects (same entry times)
. stset diff, failure(event ==1) 

     failure event:  event == 1
obs. time interval:  (0, diff]
 exit on or before:  failure

------------------------------------------------------------------------------
     70,863  total observations
          1  observation ends on or before enter()
------------------------------------------------------------------------------
     70,862  observations remaining, representing
     22,287  failures in single-record/single-failure data
 229,620.92  total analysis time at risk and under observation
                                                at risk from t =         0
                                     earliest observed entry t =         0
                                          last observed exit t =  10.75828

. *stset age_offending_imp, fail(event ==1) enter(edad_al_egres_imp)
. 
. cap rm bsreg1.dta bsreg2.dta

. 
. *count if missing(motivodeegreso_mod_imp_rec3, edad_al_ing_1, edad_ini_cons, dias_treat_imp_s
> in_na_1, esc_rec, sus_prin_mod, fr_sus_prin, comp_biosoc, ten_viv, dg_cie_10_rec, sud_severit
> y_icd10, macrozone, policonsumo, n_off_vio, n_off_acq, n_off_sud, n_off_oth)

First we calculated the difference between those patients who did and did not complete baseline treatment, given that the analysis is restricted to 2 values.

. *Micki Hill & Paul C Lambert & Michael J Crowther, 2021. "Introducing stipw: inverse probabil
> ity 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%2Fusug20
> 21%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 strpos(motivodeegreso_mod_imp_rec,"Early")>0
(15,797 real changes made)

. replace motivodeegreso_mod_imp_rec2 = 1 if strpos(motivodeegreso_mod_imp_rec,"Late")>0
(35,781 real changes made)

. 
. recode motivodeegreso_mod_imp_rec3 (1=0 "Tr Completion") (3=1 "Tr Non-completion (Late)") (2=
> 2 "Tr Non-completion (Early)"), gen(caus_disch_mod_imp_rec) 
(55066 differences between motivodeegreso_mod_imp_rec3 and caus_disch_mod_imp_rec)

. lab var caus_disch_mod_imp_rec "Baseline treatment outcome" 

. 
. global covs_3 "i.caus_disch_mod_imp_rec edad_al_ing_1 edad_ini_cons i.sex_enc i.esc_rec i.sus
> _prin_mod i.fr_sus_prin i.comp_biosoc i.ten_viv i.dg_cie_10_rec i.sud_severity_icd10 i.macroz
> one i.policonsumo i.n_off_vio i.n_off_acq i.n_off_sud i.clas"

. 
. global covs_3b "i.caus_disch_mod_imp_rec edad_al_ing_1 edad_ini_cons i.sex_enc i.esc_rec i.su
> s_prin_mod i.fr_sus_prin i.comp_biosoc i.origen_ingreso_mod numero_de_hijos_mod i.dg_cie_10_r
> ec i.sud_severity_icd10 i.macrozone i.policonsumo i.n_off_vio i.n_off_acq i.n_off_sud i.clas"

. 
. *______________________________________________
. *______________________________________________
. * ADJUSTED ROYSTON PARMAR - NO STAGGERED ENTRY, BINARY TREATMENT (1-DROPOUT VS. 0-COMPLETION)
. 
. stpm2 $covs_3 , scale(hazard) df(10) eform

Iteration 0:   log likelihood = -54900.691  
Iteration 1:   log likelihood = -54900.605  
Iteration 2:   log likelihood = -54900.605  

Log likelihood = -54900.605                     Number of obs     =     59,755

----------------------------------------------------------------------------------------------
                             |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
xb                           |
      caus_disch_mod_imp_rec |
   Tr Non-completion (Late)  |   1.494949    .031923    18.83   0.000     1.433673    1.558845
  Tr Non-completion (Early)  |   1.525656   .0416374    15.48   0.000     1.446193    1.609486
                             |
               edad_al_ing_1 |   .9700205    .000926   -31.89   0.000     .9682074     .971837
               edad_ini_cons |   .9913011   .0018747    -4.62   0.000     .9876336    .9949823
                             |
                     sex_enc |
                      Women  |   .7622222   .0148482   -13.94   0.000     .7336689    .7918868
                             |
                     esc_rec |
2-Completed high school o..  |   1.142087   .0271726     5.58   0.000     1.090052    1.196605
3-Completed primary schoo..  |   1.206151   .0315316     7.17   0.000     1.145907    1.269563
                             |
                sus_prin_mod |
      Cocaine hydrochloride  |   1.057138   .0287189     2.05   0.041     1.002323    1.114952
              Cocaine paste  |   1.560431   .0346175    20.06   0.000     1.494036    1.629777
                  Marijuana  |   1.153466   .0400781     4.11   0.000     1.077529    1.234754
                      Other  |   1.231923   .0864084     2.97   0.003     1.073691    1.413474
                             |
                 fr_sus_prin |
         2 to 3 days a week  |   1.091211   .0390702     2.44   0.015      1.01726    1.170538
         4 to 6 days a week  |   1.105099   .0419621     2.63   0.008     1.025841    1.190481
                      Daily  |   1.133358   .0405086     3.50   0.000      1.05668    1.215601
     Less than 1 day a week  |   1.110128    .054731     2.12   0.034     1.007877    1.222753
                             |
                 comp_biosoc |
                 2-Moderate  |   1.128725   .0359002     3.81   0.000      1.06051    1.201328
                   3-Severe  |   1.341869   .0463825     8.51   0.000     1.253972    1.435927
                             |
                     ten_viv |
                     Others  |    1.01887   .0775933     0.25   0.806     .8775956    1.182887
Owner/Transferred dwellin..  |   .8610578   .0562491    -2.29   0.022     .7575776    .9786728
                    Renting  |    .895257   .0594824    -1.67   0.096     .7859455    1.019772
Stays temporarily with a ..  |   .8517026   .0553589    -2.47   0.014      .749828    .9674183
                             |
               dg_cie_10_rec |
Diagnosis unknown (under..)  |   1.070816   .0256028     2.86   0.004     1.021793    1.122191
With psychiatric comorbid..  |   1.045335   .0185729     2.50   0.013     1.009559    1.082378
                             |
          sud_severity_icd10 |
      Hazardous consumption  |   .9711785   .0189172    -1.50   0.133     .9348003    1.008972
                             |
                   macrozone |
                      North  |   1.314205   .0267705    13.41   0.000     1.262769    1.367735
                      South  |   1.439171   .0409016    12.81   0.000     1.361197    1.521611
                             |
               1.policonsumo |     1.1198   .0244414     5.18   0.000     1.072906    1.168744
                 1.n_off_vio |   1.322245   .0246035    15.01   0.000     1.274892    1.371357
                 1.n_off_acq |   1.747823   .0305752    31.92   0.000     1.688912    1.808789
                 1.n_off_sud |   1.188656    .021782     9.43   0.000     1.146721    1.232123
                             |
                        clas |
                      Rural  |   1.043824   .0396038     1.13   0.258     .9690182    1.124405
                     Urbana  |   1.023872    .027856     0.87   0.386     .9707054    1.079951
                             |
                       _rcs1 |   2.555244   .0168111   142.60   0.000     2.522506    2.588406
                       _rcs2 |   1.075417   .0065328    11.97   0.000     1.062689    1.088297
                       _rcs3 |    1.03671   .0046774     7.99   0.000     1.027582    1.045918
                       _rcs4 |   1.015191   .0029406     5.21   0.000     1.009444    1.020971
                       _rcs5 |   1.010607    .001969     5.42   0.000     1.006756    1.014474
                       _rcs6 |   1.007135   .0015152     4.73   0.000     1.004169    1.010109
                       _rcs7 |   1.005521   .0013161     4.21   0.000     1.002945    1.008104
                       _rcs8 |   1.004839   .0011874     4.09   0.000     1.002515    1.007169
                       _rcs9 |   1.003342   .0011288     2.97   0.003     1.001133    1.005557
                      _rcs10 |   1.002512   .0009811     2.56   0.010     1.000591    1.004436
                       _cons |   .2251128   .0221782   -15.14   0.000     .1855835    .2730618
----------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. stpm2 $covs_3b , scale(hazard) df(10) eform

Iteration 0:   log likelihood =   -58351.2  
Iteration 1:   log likelihood = -58351.127  
Iteration 2:   log likelihood = -58351.127  

Log likelihood = -58351.127                     Number of obs     =     62,500

----------------------------------------------------------------------------------------------
                             |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
xb                           |
      caus_disch_mod_imp_rec |
   Tr Non-completion (Late)  |    1.49207   .0310626    19.22   0.000     1.432414    1.554211
  Tr Non-completion (Early)  |   1.543017   .0406373    16.47   0.000      1.46539    1.624756
                             |
               edad_al_ing_1 |    .967424    .000975   -32.86   0.000     .9655148    .9693369
               edad_ini_cons |   .9906644    .001809    -5.14   0.000     .9871252    .9942163
                             |
                     sex_enc |
                      Women  |   .7234581   .0142783   -16.40   0.000     .6960076    .7519913
                             |
                     esc_rec |
2-Completed high school o..  |   1.129197   .0262295     5.23   0.000     1.078941    1.181794
3-Completed primary schoo..  |   1.170373    .029932     6.15   0.000     1.113154    1.230534
                             |
                sus_prin_mod |
      Cocaine hydrochloride  |    1.05051   .0278824     1.86   0.063     .9972587    1.106605
              Cocaine paste  |   1.545983   .0334069    20.16   0.000     1.481874    1.612866
                  Marijuana  |   1.135316   .0384636     3.75   0.000     1.062377    1.213262
                      Other  |   1.229555   .0833102     3.05   0.002     1.076647    1.404178
                             |
                 fr_sus_prin |
         2 to 3 days a week  |   1.087831   .0384052     2.38   0.017     1.015103    1.165769
         4 to 6 days a week  |   1.114967   .0417623     2.91   0.004     1.036046    1.199899
                      Daily  |   1.152445   .0405862     4.03   0.000     1.075581    1.234802
     Less than 1 day a week  |   1.110372   .0534013     2.18   0.029     1.010489    1.220129
                             |
                 comp_biosoc |
                 2-Moderate  |   1.126487   .0354037     3.79   0.000     1.059191    1.198058
                   3-Severe  |   1.349291   .0459323     8.80   0.000     1.262203    1.442388
                             |
          origen_ingreso_mod |
          Assisted Referral  |   1.073586   .0267551     2.85   0.004     1.022407    1.127326
                      Other  |   1.097436   .0340562     3.00   0.003     1.032677    1.166257
             Justice Sector  |   1.065516   .0287519     2.35   0.019     1.010627    1.123385
              Health Sector  |   .9715772   .0176659    -1.59   0.113     .9375623    1.006826
                             |
         numero_de_hijos_mod |   1.052273   .0064381     8.33   0.000      1.03973    1.064967
                             |
               dg_cie_10_rec |
Diagnosis unknown (under..)  |   1.065218   .0245268     2.74   0.006     1.018215    1.114391
With psychiatric comorbid..  |   1.048901   .0180992     2.77   0.006      1.01402    1.084981
                             |
          sud_severity_icd10 |
      Hazardous consumption  |   .9744063   .0184857    -1.37   0.172     .9388403     1.01132
                             |
                   macrozone |
                      North  |   1.323304    .026291    14.10   0.000     1.272765     1.37585
                      South  |   1.434639   .0399345    12.97   0.000     1.358465    1.515083
                             |
               1.policonsumo |   1.113604   .0233516     5.13   0.000     1.068764    1.160326
                 1.n_off_vio |   1.294079    .023461    14.22   0.000     1.248904    1.340888
                 1.n_off_acq |   1.752407   .0294914    33.33   0.000     1.695548    1.811173
                 1.n_off_sud |   1.170484   .0207484     8.88   0.000     1.130516    1.211865
                             |
                        clas |
                      Rural  |   1.027858   .0383553     0.74   0.462     .9553666    1.105851
                     Urbana  |   1.026253   .0273268     0.97   0.330     .9740668    1.081234
                             |
                       _rcs1 |   2.563261   .0163755   147.34   0.000     2.531365    2.595558
                       _rcs2 |   1.074514   .0063343    12.19   0.000      1.06217    1.087001
                       _rcs3 |   1.037835   .0045122     8.54   0.000     1.029029    1.046717
                       _rcs4 |   1.015276   .0028438     5.41   0.000     1.009718    1.020865
                       _rcs5 |   1.009604   .0019031     5.07   0.000     1.005881    1.013341
                       _rcs6 |   1.006482   .0014636     4.44   0.000     1.003617    1.009355
                       _rcs7 |   1.005489   .0012703     4.33   0.000     1.003002    1.007981
                       _rcs8 |   1.004587   .0011473     4.01   0.000     1.002341    1.006838
                       _rcs9 |   1.003303   .0010928     3.03   0.002     1.001164    1.005447
                      _rcs10 |   1.002468   .0009493     2.60   0.009     1.000609     1.00433
                       _cons |   .2061561   .0150603   -21.62   0.000     .1786544    .2378915
----------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. *______________________________________________
. *______________________________________________
. * INVERSE PROBABILITY WEIGHTED ADJUSTED ROYSTON PARMAR - NO STAGGERED ENTRY, BINARY TREATMENT
>  (1-DROPOUT VS. 0-COMPLETION)
. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc ten_viv dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n
> _off_vio n_off_acq n_off_sud clas), distribution(rp) df(10) genw(rpdf10_m_nostag_ten_viv) ipw
> type(stabilised) vce(mestimation) eform
11108 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -33474.525  
Iteration 2:   log likelihood = -33449.458  
Iteration 3:   log likelihood =  -33449.44  
Iteration 4:   log likelihood =  -33449.44  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -35121.157  

Fitting weighted survival model to obtain point estimates

Iteration 0:   log pseudolikelihood = -58081.561  
Iteration 1:   log pseudolikelihood = -58081.511  
Iteration 2:   log pseudolikelihood = -58081.511  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood = -58081.511               Number of obs     =     59,755

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |   1.486292   .0298174    19.75   0.000     1.428985    1.545897
                      _rcs1 |   2.478247   .0164627   136.62   0.000      2.44619    2.510725
                      _rcs2 |   1.087221   .0065404    13.90   0.000     1.074477    1.100116
                      _rcs3 |   1.040208   .0046858     8.75   0.000     1.031064    1.049433
                      _rcs4 |   1.015783   .0029602     5.37   0.000     1.009998    1.021602
                      _rcs5 |   1.009912   .0019514     5.10   0.000     1.006095    1.013744
                      _rcs6 |   1.006696   .0014869     4.52   0.000     1.003786    1.009614
                      _rcs7 |   1.005096   .0012746     4.01   0.000     1.002601    1.007598
                      _rcs8 |   1.004342   .0011445     3.80   0.000     1.002101    1.006587
                      _rcs9 |   1.003227   .0010828     2.99   0.003     1.001107    1.005352
                     _rcs10 |   1.002447   .0009367     2.62   0.009     1.000613    1.004284
                      _cons |   .1639382   .0030736   -96.45   0.000     .1580233    .1700744
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc origen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec sud_severit
> y_icd10 macrozone policonsumo n_off_vio n_off_acq n_off_sud clas), distribution(rp) df(10) ge
> nw(rpdf10_m_nostag_or_ing_num_hij) ipwtype(stabilised) vce(mestimation) eform
8363 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -34725.723  
Iteration 2:   log likelihood =  -34696.15  
Iteration 3:   log likelihood = -34696.127  
Iteration 4:   log likelihood = -34696.127  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -36548.788  

Fitting weighted survival model to obtain point estimates

Iteration 0:   log pseudolikelihood = -61807.972  
Iteration 1:   log pseudolikelihood = -61807.792  
Iteration 2:   log pseudolikelihood = -61807.792  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood = -61807.792               Number of obs     =     62,500

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |    1.47849   .0288293    20.05   0.000     1.423052    1.536088
                      _rcs1 |   2.483026    .015976   141.35   0.000     2.451911    2.514537
                      _rcs2 |   1.087456   .0062516    14.58   0.000     1.075271    1.099778
                      _rcs3 |   1.040839   .0044886     9.28   0.000     1.032079    1.049674
                      _rcs4 |   1.016675   .0028588     5.88   0.000     1.011087    1.022294
                      _rcs5 |   1.008991   .0018857     4.79   0.000     1.005301    1.012693
                      _rcs6 |   1.006208   .0014361     4.34   0.000     1.003397    1.009027
                      _rcs7 |   1.005133   .0012299     4.18   0.000     1.002725    1.007547
                      _rcs8 |   1.004046   .0011027     3.68   0.000     1.001887     1.00621
                      _rcs9 |   1.003117   .0010446     2.99   0.003     1.001071    1.005166
                     _rcs10 |   1.002437   .0009038     2.70   0.007     1.000667     1.00421
                      _cons |   .1679124    .003065   -97.75   0.000     .1620114    .1740283
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. predict rmst03 in 1, at(motivodeegreso_mod_imp_rec2 0) rmst stdp tmax(3)

. predict rmst13 in 1, at(motivodeegreso_mod_imp_rec2 1) rmst stdp tmax(3)

. predictnl drmst= predict(rmst at(motivodeegreso_mod_imp_rec2 1) tmax(3))- predict(rmst at(mot
> ivodeegreso_mod_imp_rec2 1) tmax(3)) in 1, se(drmst_se)
Warning: prediction doesn't vary with respect to e(b).
(70,862 missing values generated)
Warning: prediction constant over observations; perhaps you meant to run nlcom.

. 
. cap list rmst03 rmst13  drmst in 1

We used a gompertz distribution, assuming that baseline treatment outcome showed proportional hazards

. *______________________________________________
. *______________________________________________
. * INVERSE PROBABILITY WEIGHTED ADJUSTED GOMPERTZ - NO STAGGERED ENTRY, BINARY TREATMENT (1-DR
> OPOUT VS. 0-COMPLETION)
. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc ten_viv dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n
> _off_vio n_off_acq n_off_sud clas), distribution(gompertz) genw(gomp_m_nostag_ten_viv) ipwtyp
> e(stabilised) vce(mestimation)
11108 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -33474.525  
Iteration 2:   log likelihood = -33449.458  
Iteration 3:   log likelihood =  -33449.44  
Iteration 4:   log likelihood =  -33449.44  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -35121.157  

Fitting weighted survival model to obtain point estimates

         failure _d:  event == 1
   analysis time _t:  diff
             weight:  [pweight=gomp_m_nostag_ten_viv]

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -59928.032  
Iteration 1:   log pseudolikelihood = -58508.762  
Iteration 2:   log pseudolikelihood = -58433.663  
Iteration 3:   log pseudolikelihood = -58433.438  
Iteration 4:   log pseudolikelihood = -58433.438  

Fitting full model:

Iteration 0:   log pseudolikelihood = -58433.438  
Iteration 1:   log pseudolikelihood = -58198.862  
Iteration 2:   log pseudolikelihood = -58196.768  
Iteration 3:   log pseudolikelihood = -58196.768  

Displaying weighted survival model with M-estimation standard errors

Gompertz PH regression                          Number of obs     =     59,755
                                                Wald chi2(1)      =     388.84
Log pseudolikelihood = -58196.768               Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------------
                            |            M-estimation
                         _t | Haz. Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec2 |   1.486505   .0298844    19.72   0.000     1.429071    1.546246
                      _cons |   .1183316   .0024402  -103.50   0.000     .1136444    .1232122
----------------------------+----------------------------------------------------------------
                     /gamma |  -.2579066    .005512   -46.79   0.000      -.26871   -.2471033
---------------------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.

. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc origen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec sud_severit
> y_icd10 macrozone policonsumo n_off_vio n_off_acq n_off_sud clas), distribution(gompertz) gen
> w(gomp_m_nostag_or_ing_num_hij) ipwtype(stabilised) vce(mestimation)
8363 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -34725.723  
Iteration 2:   log likelihood =  -34696.15  
Iteration 3:   log likelihood = -34696.127  
Iteration 4:   log likelihood = -34696.127  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -36548.788  

Fitting weighted survival model to obtain point estimates

         failure _d:  event == 1
   analysis time _t:  diff
             weight:  [pweight=gomp_m_nostag_or_ing_num_hij]

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -63841.182  
Iteration 1:   log pseudolikelihood = -62276.569  
Iteration 2:   log pseudolikelihood =  -62188.61  
Iteration 3:   log pseudolikelihood = -62188.365  
Iteration 4:   log pseudolikelihood = -62188.365  

Fitting full model:

Iteration 0:   log pseudolikelihood = -62188.365  
Iteration 1:   log pseudolikelihood = -61946.035  
Iteration 2:   log pseudolikelihood =  -61943.94  
Iteration 3:   log pseudolikelihood =  -61943.94  

Displaying weighted survival model with M-estimation standard errors

Gompertz PH regression                          Number of obs     =     62,500
                                                Wald chi2(1)      =     400.51
Log pseudolikelihood =  -61943.94               Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------------
                            |            M-estimation
                         _t | Haz. Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec2 |    1.47871   .0289028    20.01   0.000     1.423133    1.536457
                      _cons |    .122016   .0024504  -104.75   0.000     .1173065    .1269145
----------------------------+----------------------------------------------------------------
                     /gamma |  -.2620415   .0053539   -48.94   0.000    -.2725349   -.2515481
---------------------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.

. 
. predict rmst03_c in 1, at(motivodeegreso_mod_imp_rec2 0) rmst stdp tmax(3)
option at() not allowed
r(198);

. predict rmst13_c in 1, at(motivodeegreso_mod_imp_rec2 1) rmst stdp tmax(3)
option at() not allowed
r(198);

. predictnl drmst_c= predict(rmst at(motivodeegreso_mod_imp_rec2 1) tmax(3))- predict(rmst at(m
> otivodeegreso_mod_imp_rec2 1) tmax(3)) in 1, se(drmst_c_se)
option rmst not allowed
predict(rmst at(motivodeegreso_mod_imp_rec2 1) tmax(3)) invalid
r(198);

. 
. cap list rmst03_c rmst13_c  drmst_c in 1

We used another model with only 6 degrees of freedom according to the lowest BIC

. 
. *______________________________________________
. *______________________________________________
. * ADJUSTED ROYSTON PARMAR - DF6,  NO STAGGERED ENTRY, BINARY TREATMENT (1-DROPOUT VS. 0-COMPL
> ETION)
. 
. stpm2 $covs_3 , scale(hazard) df(6) eform

Iteration 0:   log likelihood = -54905.324  
Iteration 1:   log likelihood =  -54903.92  
Iteration 2:   log likelihood =  -54903.92  

Log likelihood =  -54903.92                     Number of obs     =     59,755

----------------------------------------------------------------------------------------------
                             |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
xb                           |
      caus_disch_mod_imp_rec |
   Tr Non-completion (Late)  |   1.495153   .0319269    18.84   0.000     1.433868    1.559056
  Tr Non-completion (Early)  |   1.525687   .0416376    15.48   0.000     1.446223    1.609517
                             |
               edad_al_ing_1 |   .9700225    .000926   -31.88   0.000     .9682094    .9718391
               edad_ini_cons |   .9912942   .0018747    -4.62   0.000     .9876267    .9949753
                             |
                     sex_enc |
                      Women  |     .76205   .0148447   -13.95   0.000     .7335033    .7917076
                             |
                     esc_rec |
2-Completed high school o..  |   1.142123   .0271734     5.59   0.000     1.090086    1.196643
3-Completed primary schoo..  |    1.20616   .0315317     7.17   0.000     1.145915    1.269571
                             |
                sus_prin_mod |
      Cocaine hydrochloride  |   1.056844   .0287102     2.04   0.042     1.002045     1.11464
              Cocaine paste  |   1.559907   .0346042    20.04   0.000     1.493537    1.629226
                  Marijuana  |   1.153005   .0400609     4.10   0.000     1.077101    1.234258
                      Other  |   1.231228    .086359     2.97   0.003     1.073086    1.412674
                             |
                 fr_sus_prin |
         2 to 3 days a week  |   1.091056   .0390644     2.43   0.015     1.017115    1.170371
         4 to 6 days a week  |    1.10503   .0419594     2.63   0.009     1.025776    1.190406
                      Daily  |    1.13344   .0405113     3.50   0.000     1.056757    1.215688
     Less than 1 day a week  |   1.110074   .0547285     2.12   0.034     1.007827    1.222693
                             |
                 comp_biosoc |
                 2-Moderate  |    1.12866   .0358978     3.81   0.000      1.06045    1.201258
                   3-Severe  |     1.3416   .0463727     8.50   0.000     1.253721    1.435638
                             |
                     ten_viv |
                     Others  |   1.018468   .0775626     0.24   0.810     .8772494     1.18242
Owner/Transferred dwellin..  |   .8611232   .0562533    -2.29   0.022     .7576351     .978747
                    Renting  |   .8952171   .0594797    -1.67   0.096     .7859106    1.019726
Stays temporarily with a ..  |   .8515812    .055351    -2.47   0.013     .7497211    .9672804
                             |
               dg_cie_10_rec |
Diagnosis unknown (under..)  |   1.070731   .0256005     2.86   0.004     1.021712    1.122101
With psychiatric comorbid..  |    1.04532   .0185727     2.49   0.013     1.009544    1.082362
                             |
          sud_severity_icd10 |
      Hazardous consumption  |   .9711936   .0189173    -1.50   0.133     .9348151    1.008988
                             |
                   macrozone |
                      North  |   1.314184   .0267699    13.41   0.000     1.262749    1.367713
                      South  |   1.439204   .0409016    12.81   0.000      1.36123    1.521645
                             |
               1.policonsumo |   1.119562   .0244362     5.17   0.000     1.072678    1.168496
                 1.n_off_vio |   1.322582   .0246105    15.03   0.000     1.275216    1.371709
                 1.n_off_acq |   1.748289   .0305845    31.93   0.000     1.689361    1.809273
                 1.n_off_sud |   1.188935   .0217876     9.44   0.000      1.14699    1.232414
                             |
                        clas |
                      Rural  |   1.043732   .0396002     1.13   0.259     .9689324    1.124305
                     Urbana  |   1.023872   .0278558     0.87   0.386     .9707054     1.07995
                             |
                       _rcs1 |   2.555417   .0168163   142.57   0.000     2.522669     2.58859
                       _rcs2 |   1.077987   .0064688    12.51   0.000     1.065383     1.09074
                       _rcs3 |   1.034651   .0043514     8.10   0.000     1.026157    1.043214
                       _rcs4 |   1.013478   .0026786     5.07   0.000     1.008241    1.018741
                       _rcs5 |   1.008231   .0018517     4.46   0.000     1.004608    1.011866
                       _rcs6 |   1.004597   .0014291     3.22   0.001       1.0018    1.007402
                       _cons |   .2252147   .0221882   -15.13   0.000     .1856676    .2731853
----------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. stpm2 $covs_3b , scale(hazard) df(6) eform

Iteration 0:   log likelihood = -58354.916  
Iteration 1:   log likelihood = -58354.255  
Iteration 2:   log likelihood = -58354.255  

Log likelihood = -58354.255                     Number of obs     =     62,500

----------------------------------------------------------------------------------------------
                             |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
xb                           |
      caus_disch_mod_imp_rec |
   Tr Non-completion (Late)  |   1.492252    .031066    19.23   0.000      1.43259      1.5544
  Tr Non-completion (Early)  |   1.543058   .0406374    16.47   0.000     1.465431    1.624797
                             |
               edad_al_ing_1 |   .9674285    .000975   -32.86   0.000     .9655193    .9693414
               edad_ini_cons |   .9906588    .001809    -5.14   0.000     .9871195    .9942107
                             |
                     sex_enc |
                      Women  |   .7233092   .0142752   -16.41   0.000     .6958647    .7518362
                             |
                     esc_rec |
2-Completed high school o..  |   1.129211   .0262298     5.23   0.000     1.078954    1.181809
3-Completed primary schoo..  |    1.17039   .0299324     6.15   0.000     1.113169    1.230552
                             |
                sus_prin_mod |
      Cocaine hydrochloride  |   1.050239   .0278745     1.85   0.065     .9970024    1.106318
              Cocaine paste  |    1.54551    .033395    20.15   0.000     1.481423    1.612369
                  Marijuana  |   1.134827   .0384458     3.73   0.000     1.061921    1.212737
                      Other  |   1.228991   .0832717     3.04   0.002     1.076155    1.403534
                             |
                 fr_sus_prin |
         2 to 3 days a week  |   1.087697   .0384004     2.38   0.017     1.014979    1.165626
         4 to 6 days a week  |   1.114909   .0417601     2.90   0.004     1.035993    1.199837
                      Daily  |   1.152539   .0405893     4.03   0.000     1.075669    1.234903
     Less than 1 day a week  |   1.110249   .0533954     2.17   0.030     1.010377    1.219993
                             |
                 comp_biosoc |
                 2-Moderate  |   1.126358   .0353993     3.79   0.000      1.05907     1.19792
                   3-Severe  |   1.348953   .0459201     8.79   0.000     1.261888    1.442025
                             |
          origen_ingreso_mod |
          Assisted Referral  |    1.07346   .0267519     2.84   0.004     1.022287    1.127194
                      Other  |   1.097395   .0340549     2.99   0.003     1.032638    1.166213
             Justice Sector  |   1.065616   .0287546     2.36   0.019     1.010723    1.123491
              Health Sector  |   .9714717   .0176641    -1.59   0.111     .9374604    1.006717
                             |
         numero_de_hijos_mod |    1.05226   .0064379     8.33   0.000     1.039718    1.064954
                             |
               dg_cie_10_rec |
Diagnosis unknown (under..)  |   1.065144   .0245246     2.74   0.006     1.018145    1.114313
With psychiatric comorbid..  |   1.048895   .0180991     2.77   0.006     1.014014    1.084975
                             |
          sud_severity_icd10 |
      Hazardous consumption  |    .974408   .0184856    -1.37   0.172     .9388421    1.011321
                             |
                   macrozone |
                      North  |   1.323271   .0262902    14.10   0.000     1.272734    1.375816
                      South  |   1.434717   .0399359    12.97   0.000     1.358541    1.515164
                             |
               1.policonsumo |     1.1134   .0233473     5.12   0.000     1.068568    1.160114
                 1.n_off_vio |   1.294353   .0234669    14.23   0.000     1.249166    1.341174
                 1.n_off_acq |   1.752917   .0295009    33.35   0.000      1.69604    1.811702
                 1.n_off_sud |   1.170754   .0207536     8.89   0.000     1.130776    1.212145
                             |
                        clas |
                      Rural  |   1.027752   .0383513     0.73   0.463     .9552676    1.105736
                     Urbana  |   1.026245   .0273265     0.97   0.331     .9740598    1.081226
                             |
                       _rcs1 |   2.563398   .0163796   147.32   0.000     2.531494    2.595703
                       _rcs2 |   1.076923   .0062662    12.74   0.000     1.064711    1.089275
                       _rcs3 |   1.036121   .0042049     8.74   0.000     1.027912    1.044395
                       _rcs4 |   1.012328   .0025889     4.79   0.000     1.007267    1.017415
                       _rcs5 |    1.00772   .0017851     4.34   0.000     1.004228    1.011225
                       _rcs6 |   1.004481   .0013795     3.26   0.001     1.001781    1.007189
                       _cons |   .2062306   .0150657   -21.61   0.000     .1787189    .2379775
----------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. *______________________________________________
. *______________________________________________
. * INVERSE PROBABILITY WEIGHTED ADJUSTED ROYSTON PARMAR - DF6, NO STAGGERED ENTRY, BINARY TREA
> TMENT (1-DROPOUT VS. 0-COMPLETION)
. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc ten_viv dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n
> _off_vio n_off_acq n_off_sud clas), distribution(rp) df(6) genw(rpdf6_m_nostag_ten_viv) ipwty
> pe(stabilised) vce(mestimation) eform
11108 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -33474.525  
Iteration 2:   log likelihood = -33449.458  
Iteration 3:   log likelihood =  -33449.44  
Iteration 4:   log likelihood =  -33449.44  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -35121.157  

Fitting weighted survival model to obtain point estimates

Iteration 0:   log pseudolikelihood = -58085.896  
Iteration 1:   log pseudolikelihood = -58084.895  
Iteration 2:   log pseudolikelihood = -58084.895  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood = -58084.895               Number of obs     =     59,755

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |   1.486419   .0298203    19.76   0.000     1.429107     1.54603
                      _rcs1 |   2.478382   .0165127   136.22   0.000     2.446228    2.510958
                      _rcs2 |   1.089911   .0066192    14.18   0.000     1.077015    1.102962
                      _rcs3 |   1.037793   .0043798     8.79   0.000     1.029245    1.046413
                      _rcs4 |   1.012703    .002653     4.82   0.000     1.007517    1.017916
                      _rcs5 |   1.007612    .001803     4.24   0.000     1.004085    1.011152
                      _rcs6 |   1.004139   .0013815     3.00   0.003     1.001435     1.00685
                      _cons |   .1639291   .0030735   -96.45   0.000     .1580145    .1700651
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc origen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec sud_severit
> y_icd10 macrozone policonsumo n_off_vio n_off_acq n_off_sud clas), distribution(rp) df(6) gen
> w(rpdf6_m_nostag_or_ing_num_hij) ipwtype(stabilised) vce(mestimation) eform
8363 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -34725.723  
Iteration 2:   log likelihood =  -34696.15  
Iteration 3:   log likelihood = -34696.127  
Iteration 4:   log likelihood = -34696.127  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -36548.788  

Fitting weighted survival model to obtain point estimates

Iteration 0:   log pseudolikelihood = -61811.426  
Iteration 1:   log pseudolikelihood = -61811.093  
Iteration 2:   log pseudolikelihood = -61811.092  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood = -61811.092               Number of obs     =     62,500

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |   1.478613   .0288321    20.06   0.000     1.423169    1.536216
                      _rcs1 |   2.483098   .0160022   141.13   0.000     2.451931    2.514661
                      _rcs2 |   1.089759   .0062634    14.96   0.000     1.077552    1.102105
                      _rcs3 |   1.039321   .0041833     9.58   0.000     1.031154    1.047552
                      _rcs4 |   1.012079   .0025639     4.74   0.000     1.007066    1.017117
                      _rcs5 |   1.007205   .0017368     4.16   0.000     1.003807    1.010615
                      _rcs6 |   1.003959   .0013316     2.98   0.003     1.001353    1.006573
                      _cons |   .1679021   .0030648   -97.76   0.000     .1620014    .1740177
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. 
. predict rmst03_b in 1, at(motivodeegreso_mod_imp_rec2 0) rmst stdp tmax(3)

. predict rmst13_b in 1, at(motivodeegreso_mod_imp_rec2 1) rmst stdp tmax(3)

. predictnl drmst_b= predict(rmst at(motivodeegreso_mod_imp_rec2 1) tmax(3))- predict(rmst at(m
> otivodeegreso_mod_imp_rec2 1) tmax(3)) in 1, se(drmst_b_se)
Warning: prediction doesn't vary with respect to e(b).
(70,862 missing values generated)
Warning: prediction constant over observations; perhaps you meant to run nlcom.

. 
. cap list rmst03_b rmst13_b  drmst_b in 1

Staggered entry

. *______________________________________________
. *______________________________________________
. * ADJUSTED ROYSTON PARMAR - DF10,  STAGGERED ENTRY, BINARY TREATMENT (1-DROPOUT VS. 0-COMPLET
> ION)
. 
. stset age_offending_imp, fail(event ==1) enter(edad_al_egres_imp)

     failure event:  event == 1
obs. time interval:  (0, age_offending_imp]
 enter on or after:  time edad_al_egres_imp
 exit on or before:  failure

------------------------------------------------------------------------------
     70,863  total observations
          1  observation ends on or before enter()
------------------------------------------------------------------------------
     70,862  observations remaining, representing
     22,287  failures in single-record/single-failure data
 229,620.92  total analysis time at risk and under observation
                                                at risk from t =         0
                                     earliest observed entry t =  10.95068
                                          last observed exit t =  90.65027

. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc ten_viv dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n
> _off_vio n_off_acq n_off_sud clas), distribution(rp) df(10) genw(rpdf10_m_stag_ten_viv) ipwty
> pe(stabilised) vce(mestimation) eform
11108 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -33474.525  
Iteration 2:   log likelihood = -33449.458  
Iteration 3:   log likelihood =  -33449.44  
Iteration 4:   log likelihood =  -33449.44  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -35121.157  

Fitting weighted survival model to obtain point estimates
note: delayed entry models are being fitted

Iteration 0:   log pseudolikelihood =  5104.8033  (not concave)
Iteration 1:   log pseudolikelihood =  5176.2585  
Iteration 2:   log pseudolikelihood =  5193.8101  
Iteration 3:   log pseudolikelihood =  5205.6083  
Iteration 4:   log pseudolikelihood =  5206.1498  
Iteration 5:   log pseudolikelihood =  5206.5622  
Iteration 6:   log pseudolikelihood =  5206.6369  
Iteration 7:   log pseudolikelihood =  5206.6591  
Iteration 8:   log pseudolikelihood =  5206.6607  
Iteration 9:   log pseudolikelihood =  5206.6609  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood =  5206.6609               Number of obs     =     59,755

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |   1.465739   .0318036    17.62   0.000     1.404712    1.529417
                      _rcs1 |   1.205886   .0478464     4.72   0.000     1.115663    1.303406
                      _rcs2 |   1.049635   .0160457     3.17   0.002     1.018652     1.08156
                      _rcs3 |    .997187   .0031668    -0.89   0.375     .9909994    1.003413
                      _rcs4 |   1.002525   .0005953     4.25   0.000     1.001359    1.003693
                      _rcs5 |   1.002097   .0004379     4.79   0.000      1.00124    1.002956
                      _rcs6 |   1.002123   .0003722     5.71   0.000     1.001393    1.002852
                      _rcs7 |   1.001583   .0003331     4.76   0.000     1.000931    1.002236
                      _rcs8 |   1.001693   .0003438     4.93   0.000      1.00102    1.002367
                      _rcs9 |      1.002   .0003931     5.09   0.000      1.00123    1.002771
                     _rcs10 |   1.001065   .0002963     3.59   0.000     1.000484    1.001645
                      _cons |   3.952784   .7985168     6.80   0.000      2.66041    5.872967
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. estimates store df10_stipw

. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc origen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec dg_cie_10_r
> ec sud_severity_icd10 macrozone policonsumo n_off_vio n_off_acq n_off_sud clas), distribution
> (rp) df(10) genw(rpdf10_m_stag_or_ing_num_hij) ipwtype(stabilised) vce(mestimation) eform
8363 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

note: dg_cie_10_rec omitted because of collinearity
Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -34725.723  
Iteration 2:   log likelihood =  -34696.15  
Iteration 3:   log likelihood = -34696.127  
Iteration 4:   log likelihood = -34696.127  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -36548.788  

Fitting weighted survival model to obtain point estimates
note: delayed entry models are being fitted

Iteration 0:   log pseudolikelihood =  5823.4671  (not concave)
Iteration 1:   log pseudolikelihood =  5895.9876  
Iteration 2:   log pseudolikelihood =   5910.387  
Iteration 3:   log pseudolikelihood =  5912.2768  
Iteration 4:   log pseudolikelihood =  5924.7636  
Iteration 5:   log pseudolikelihood =  5924.8447  
Iteration 6:   log pseudolikelihood =  5924.8707  
Iteration 7:   log pseudolikelihood =  5924.8726  
Iteration 8:   log pseudolikelihood =  5924.8727  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood =  5924.8727               Number of obs     =     62,500

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |   1.452345   .0306439    17.69   0.000     1.393509    1.513665
                      _rcs1 |   1.217683   .0445067     5.39   0.000     1.133502    1.308115
                      _rcs2 |   1.053325   .0153528     3.56   0.000      1.02366     1.08385
                      _rcs3 |   .9961881   .0033639    -1.13   0.258     .9896167    1.002803
                      _rcs4 |   1.002599   .0006196     4.20   0.000     1.001386    1.003815
                      _rcs5 |   1.002386   .0004444     5.37   0.000     1.001515    1.003257
                      _rcs6 |   1.002316   .0003751     6.18   0.000     1.001581    1.003051
                      _rcs7 |   1.001784   .0003375     5.29   0.000     1.001123    1.002446
                      _rcs8 |   1.001819   .0003396     5.36   0.000     1.001153    1.002484
                      _rcs9 |   1.002075   .0003735     5.56   0.000     1.001343    1.002807
                     _rcs10 |   1.001084   .0002891     3.75   0.000     1.000518    1.001651
                      _cons |   3.874558   .6825268     7.69   0.000     2.743329    5.472257
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. estimates store df10_stipw2

. *______________________________________________
. *______________________________________________
. * INVERSE PROBABILITY WEIGHTED ADJUSTED GOMPERTZ - STAGGERED ENTRY, BINARY TREATMENT (1-DROPO
> UT VS. 0-COMPLETION)
. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc ten_viv dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n
> _off_vio n_off_acq n_off_sud clas), distribution(gompertz) genw(gomp_m_stag_ten_viv) ipwtype(
> stabilised) vce(mestimation)
11108 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -33474.525  
Iteration 2:   log likelihood = -33449.458  
Iteration 3:   log likelihood =  -33449.44  
Iteration 4:   log likelihood =  -33449.44  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -35121.157  

Fitting weighted survival model to obtain point estimates

         failure _d:  event == 1
   analysis time _t:  age_offending_imp
  enter on or after:  time edad_al_egres_imp
             weight:  [pweight=gomp_m_stag_ten_viv]

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =  2772.7327  
Iteration 1:   log pseudolikelihood =  4810.1985  
Iteration 2:   log pseudolikelihood =  4957.1582  
Iteration 3:   log pseudolikelihood =  4957.3094  
Iteration 4:   log pseudolikelihood =  4957.3094  

Fitting full model:

Iteration 0:   log pseudolikelihood =  4957.3094  
Iteration 1:   log pseudolikelihood =  5175.8881  
Iteration 2:   log pseudolikelihood =  5177.7427  
Iteration 3:   log pseudolikelihood =  5177.7429  

Displaying weighted survival model with M-estimation standard errors

Gompertz PH regression                          Number of obs     =     59,755
                                                Wald chi2(1)      =     311.00
Log pseudolikelihood =  5177.7429               Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------------
                            |            M-estimation
                         _t | Haz. Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec2 |   1.466724   .0318568    17.64   0.000     1.405597    1.530511
                      _cons |   .4679572   .0179511   -19.80   0.000     .4340639    .5044971
----------------------------+----------------------------------------------------------------
                     /gamma |  -.0509706   .0008901   -57.27   0.000    -.0527151   -.0492261
---------------------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.

. estimates store gomp_stipw

. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc origen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec sud_severit
> y_icd10 macrozone policonsumo n_off_vio n_off_acq n_off_sud clas), distribution(gompertz) gen
> w(gomp_m_stag_or_ing_num_hij) ipwtype(stabilised) vce(mestimation)
8363 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -34725.723  
Iteration 2:   log likelihood =  -34696.15  
Iteration 3:   log likelihood = -34696.127  
Iteration 4:   log likelihood = -34696.127  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -36548.788  

Fitting weighted survival model to obtain point estimates

         failure _d:  event == 1
   analysis time _t:  age_offending_imp
  enter on or after:  time edad_al_egres_imp
             weight:  [pweight=gomp_m_stag_or_ing_num_hij]

Fitting constant-only model:

Iteration 0:   log pseudolikelihood =  3340.2736  
Iteration 1:   log pseudolikelihood =  5520.8211  
Iteration 2:   log pseudolikelihood =  5677.3002  
Iteration 3:   log pseudolikelihood =  5677.4684  
Iteration 4:   log pseudolikelihood =  5677.4684  

Fitting full model:

Iteration 0:   log pseudolikelihood =  5677.4684  
Iteration 1:   log pseudolikelihood =  5897.8164  
Iteration 2:   log pseudolikelihood =  5899.5999  
Iteration 3:   log pseudolikelihood =     5899.6  

Displaying weighted survival model with M-estimation standard errors

Gompertz PH regression                          Number of obs     =     62,500
                                                Wald chi2(1)      =     312.63
Log pseudolikelihood =     5899.6               Prob > chi2       =     0.0000

---------------------------------------------------------------------------------------------
                            |            M-estimation
                         _t | Haz. Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
motivodeegreso_mod_imp_rec2 |   1.452801   .0306884    17.68   0.000     1.393881    1.514211
                      _cons |   .4786454   .0177921   -19.82   0.000     .4450136     .514819
----------------------------+----------------------------------------------------------------
                     /gamma |  -.0508382   .0008603   -59.09   0.000    -.0525243   -.0491521
---------------------------------------------------------------------------------------------
Note: _cons estimates baseline hazard.

. estimates store gomp_stipw2

. *______________________________________________
. *______________________________________________
. * ADJUSTED ROYSTON PARMAR - DF6,  STAGGERED ENTRY, BINARY TREATMENT (1-DROPOUT VS. 0-COMPLETI
> ON)
. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc ten_viv dg_cie_10_rec sud_severity_icd10 macrozone policonsumo n
> _off_vio n_off_acq n_off_sud clas), distribution(rp) df(6) genw(rpdf6_m_stag_ten_viv) ipwtype
> (stabilised) vce(mestimation) eform
11108 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -33474.525  
Iteration 2:   log likelihood = -33449.458  
Iteration 3:   log likelihood =  -33449.44  
Iteration 4:   log likelihood =  -33449.44  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -35121.157  
Iteration 1:   log likelihood = -35121.157  

Fitting weighted survival model to obtain point estimates
note: delayed entry models are being fitted

Iteration 0:   log pseudolikelihood =  5064.2301  (not concave)
Iteration 1:   log pseudolikelihood =  5158.7869  
Iteration 2:   log pseudolikelihood =  5168.8776  
Iteration 3:   log pseudolikelihood =  5185.9337  
Iteration 4:   log pseudolikelihood =  5186.8572  
Iteration 5:   log pseudolikelihood =  5188.1591  
Iteration 6:   log pseudolikelihood =  5188.4588  
Iteration 7:   log pseudolikelihood =  5188.6259  
Iteration 8:   log pseudolikelihood =  5188.7511  
Iteration 9:   log pseudolikelihood =  5188.7535  
Iteration 10:  log pseudolikelihood =  5188.7536  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood =  5188.7536               Number of obs     =     59,755

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |    1.46624   .0318231    17.63   0.000     1.405176    1.529958
                      _rcs1 |   1.164809   .0355471     5.00   0.000     1.097181    1.236606
                      _rcs2 |   1.036778    .010557     3.55   0.000     1.016292    1.057677
                      _rcs3 |   1.000078   .0013519     0.06   0.954     .9974323    1.002732
                      _rcs4 |   1.003635   .0006128     5.94   0.000     1.002435    1.004837
                      _rcs5 |   1.002177   .0004288     5.08   0.000     1.001337    1.003018
                      _rcs6 |   1.001678   .0004083     4.11   0.000     1.000878    1.002478
                      _cons |   4.808785   .9293965     8.13   0.000      3.29248    7.023402
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. estimates store df6_stipw

. 
. stipw (logit motivodeegreso_mod_imp_rec2 edad_al_ing_1 edad_ini_cons sex_enc esc_rec sus_prin
> _mod fr_sus_prin comp_biosoc origen_ingreso_mod numero_de_hijos_mod dg_cie_10_rec dg_cie_10_r
> ec sud_severity_icd10 macrozone policonsumo n_off_vio n_off_acq n_off_sud clas), distribution
> (rp) df(6) genw(rpdf6_m_stag_or_ing_num_hij) ipwtype(stabilised) vce(mestimation) eform
8363 observations have missing treatment and/or missing confounder values and/or _st = 0.
These observations are excluded from the analysis, see variable _stipw_flag

Fitting logistic regression to obtain denominator for weights

note: dg_cie_10_rec omitted because of collinearity
Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -34725.723  
Iteration 2:   log likelihood =  -34696.15  
Iteration 3:   log likelihood = -34696.127  
Iteration 4:   log likelihood = -34696.127  
Fitting second logistic regression with no confounders to obtain numerator for stabilised weigh
> ts

Iteration 0:   log likelihood = -36548.788  
Iteration 1:   log likelihood = -36548.788  

Fitting weighted survival model to obtain point estimates
note: delayed entry models are being fitted

Iteration 0:   log pseudolikelihood =   5776.587  (not concave)
Iteration 1:   log pseudolikelihood =   5872.657  
Iteration 2:   log pseudolikelihood =  5884.0866  
Iteration 3:   log pseudolikelihood =  5904.1192  
Iteration 4:   log pseudolikelihood =  5905.2187  
Iteration 5:   log pseudolikelihood =  5906.3979  (not concave)
Iteration 6:   log pseudolikelihood =  5906.4483  
Iteration 7:   log pseudolikelihood =  5906.6934  
Iteration 8:   log pseudolikelihood =  5906.8886  
Iteration 9:   log pseudolikelihood =  5906.9095  
Iteration 10:  log pseudolikelihood =  5906.9104  
Iteration 11:  log pseudolikelihood =  5906.9104  

Displaying weighted survival model with M-estimation standard errors

Log pseudolikelihood =  5906.9104               Number of obs     =     62,500

---------------------------------------------------------------------------------------------
                            |            M-estimation
                            |     exp(b)   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
xb                          |
motivodeegreso_mod_imp_rec2 |   1.452639   .0306594    17.69   0.000     1.393774    1.513991
                      _rcs1 |   1.172943   .0339511     5.51   0.000     1.108252     1.24141
                      _rcs2 |   1.038952   .0102753     3.86   0.000     1.019006    1.059287
                      _rcs3 |   .9996766   .0014711    -0.22   0.826     .9967974    1.002564
                      _rcs4 |    1.00405   .0006293     6.45   0.000     1.002818    1.005284
                      _rcs5 |   1.002395   .0004283     5.60   0.000     1.001556    1.003235
                      _rcs6 |   1.001828   .0004044     4.52   0.000     1.001036    1.002621
                      _cons |   4.738374   .8286742     8.90   0.000     3.363306    6.675632
---------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.

. estimates store df6_stipw2

. 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 df6_stipw gomp_stipw df10_stipw, n(`r(N)')

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
   df6_stipw |     22,287          .   5188.754       8  -10361.51  -10297.41
  gomp_stipw |     22,287   4957.309   5177.743       3  -10349.49  -10325.45
  df10_stipw |     22,287          .   5206.661      12  -10389.32  -10293.18
-----------------------------------------------------------------------------

.         //we store in a matrix de survival
. matrix stats_stipw=r(S)

. 
. estwrite df6_stipw gomp_stipw df10_stipw df6_stipw2 gomp_stipw2 df10_stipw2 using "${pathdata
> 2}parmodels_m2_stipw_22.sters", replace
(saving df6_stipw)
(saving gomp_stipw)
(saving df10_stipw)
(saving df6_stipw2)
(saving gomp_stipw2)
(saving df10_stipw2)
(file parmodels_m2_stipw_22.sters saved)

Saved at= 23:56:55 16 Feb 2023