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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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