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Re: st: Problems with ml max Survival analysis


From   "Amparo Nagore Garcia" <[email protected]>
To   [email protected]
Subject   Re: st: Problems with ml max Survival analysis
Date   Fri, 08 Nov 2013 14:51:10 +0100

Dear Stephen, 

Thank you very much for your answer. My objective is to estimate a MPH model with two competing risks (recurrent) and correlated unobserved heterogeneity. Maybe the best option will be mass points for frailty. I have the code of a multistate model with 4 exits as a reference.
My duration variable is continuous, data are multi-spell and there are time-varying covariates.
However, as I am a beginner programming my own functions with Stata I have moved to the easiest model. So, now I am assuming each spell independent (as if multi-spells are not there), no time-varying variables, and less covariates. The distribution of the baseline would be an exponential with one constant.

As it may be modeled with standard commands I have compared the results from them with the results form my code, and it should be any mistake in my code or I am confused with what standard command is returning.Just below you can find the results and code for that with both options.

Again, any help is really appreciated. Thank you very much. 

***Using standard command:
*******Exponential model with constant, no time-varying covariates, not cluster. 
streg  u_rate   male  h_skill m_skill non_manual  municipio spanish_speakers no_spanish_speaker Aged05_16_19  Aged05_20_24 Aged05_25_29 Aged05_30_34 Aged05_35_39 Aged05_40_44 Aged05_45_51 Aged05_older61 responsabilities if minusvalido==0, d(e) nohr difficult tech(nr) 

**************************************************************************
*Results
         failure _d:  f_s
   analysis time _t:  (fecha_baja_def-origin)
             origin:  time fecha_alta_def
  exit on or before:  time .

Iteration 0:   log likelihood = -99556.633  
Iteration 1:   log likelihood = -97671.107  
Iteration 2:   log likelihood = -97554.267  
Iteration 3:   log likelihood = -97553.745  
Iteration 4:   log likelihood = -97553.745  

Exponential regression -- log relative-hazard form 

No. of subjects =        92241                     Number of obs   =     92241
No. of failures =        35279
Time at risk    =     13882868
                                                   LR chi2(17)     =   4005.78
Log likelihood  =   -97553.745                     Prob > chi2     =    0.0000

------------------------------------------------------------------------------
          _t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      u_rate |  -3.470225   .1540974   -22.52   0.000     -3.77225   -3.168199
        male |   .1223463   .0125432     9.75   0.000     .0977621    .1469305
     h_skill |   .1232418   .0148745     8.29   0.000     .0940882    .1523953
     m_skill |  -.0622976    .013535    -4.60   0.000    -.0888258   -.0357694
  non_manual |  -.0295926   .0123417    -2.40   0.016    -.0537818   -.0054034
   municipio |  -.0982955   .0108838    -9.03   0.000    -.1196274   -.0769636
spanish_sp~s |   .0313498   .0340245     0.92   0.357    -.0353369    .0980365
no_spanish~r |   -.111788   .0268639    -4.16   0.000    -.1644403   -.0591357
Aged05_16_19 |   .4164596   .0764584     5.45   0.000     .2666039    .5663152
Aged05_20_24 |   .5386681   .0262935    20.49   0.000     .4871338    .5902024
Aged05_25_29 |   .5426311   .0227125    23.89   0.000     .4981154    .5871468
Aged05_30_34 |   .4748284   .0226967    20.92   0.000     .4303436    .5193132
Aged05_35_39 |   .4650016   .0234231    19.85   0.000     .4190931      .51091
Aged05_40_44 |    .564306   .0240364    23.48   0.000     .5171956    .6114165
Aged05_45_51 |   .5439411   .0238558    22.80   0.000     .4971845    .5906977
Aged0  .       .            .           .
>         tp11 |     -.8875          .        .       .            .           .
>         tp12 |       -.88          .        .       .            .           .
>         tp13 |     -.8825          .        .       .            .           .
>         tp14 |    -.89625          .        .       .            .           .
>         tp15 |     -.9025          .        .       .            .           .
>         tp16 |    -.89875          .        .       .            .           .
>         tp17 |      -.915          .        .       .            .           .
>         tp18 |       -.93          .        .       .            .           .
>         tp19 |    -.92625          .        .       .            .           .
>         tp20 |    -.93625          .        .       .            .           .
>         tp21 |    -.96375          .        .       .            .           .
>         tp22 |    -.92625          .        .       .            .           .
>         tp23 |      -.925          .        .       .            .           .
>         tp24 |     -.9475          .        .       .            .           .
>         tp25 |    -.82375          .        .       .            .           .
>         tp26 |      -1.12          .        .       .            .           .
>         tp27 |   -1.00875          .        .       .            .           .
>         tp28 |   -1.15375          .        .       .            .           .
>       u_rate |   -.469875          .        .       .            .           .
>         male |    .014625          .        .       .            .           .
>      h_skill |     .01775          .        .       .            .           .
>      m_skill |    .000375          .        .       .            .           .
>   non_manual |      .0035          .        .       .            .           .
>    municipio |     .00075          .        .       .            .           .
> spanish_sp~s |    -.01375          .        .       .            .           .
> no_spanish~r |    -.03125          .        .       .            .           .
>   Aged_16_19 |    -.00875          .        .       .            .           .
>   Aged_20_24 |       .026          .        .       .            .           .
>   Aged_25_29 |        .04          .        .       .            .           .
>   Aged_30_34 |       .041          .        .       .            .           .
>   Aged_35_39 |     .04325          .        .       .            .           .
>   Aged_40_44 |       .045          .        .       .            .           .
>   Aged_45_51 |    .042875          .        .       .            .           .
>      older61 |     -.0725          .        .       .            .           .
> responsabi~s |    .015625          .        .       .            .           .
> construction |     .02325          .        .       .            .           .
>     industry |       .006          .        .       .            .           .
>       size_0 |    -.00375          .        .       .            .           .
>   size_10_19 |    .012125          .        .       .            .           .
>   size_20_49 |      .0215          .        .       .            .           .
>  size_50_249 |       .029          .        .       .            .           .
>     size_250 |     .02475          .        .       .            .           .
> discontinu~s |       .223          .        .       .            .           .
>     fix_term |    .062625          .        .       .            .           .
>      on_call |    .115625          .        .       .            .           .
>   duration_1 |  -3.88e-06          .        .       .            .           .
> coefte_num_1 |    .002375          .        .       .            .           .
>    prest_emp |    -.08625          .        .       .            .           .
> - ------------------------------------------------------------------------------
> 
> . 
> end of do-file
> 
> 
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-- 
==========================
Amparo Nagore García
Profesor Ayudante
Departamento de Economia Aplicada
Facultad de Economia
Universidad de Valencia
====================== 


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