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st: survival


From   "Sebastián Daza" <[email protected]>
To   [email protected]
Subject   st: survival
Date   Fri, 2 Jan 2009 09:52:37 -0300

Hi Steven,
In fact, I created a new observation for each person and period. I
have used svy: logit and plain logit, but there aren't differences
between results. Sampling desing is stratified and in person-level
data (not expanded) each person have a weight (inverse of the
probability that the observation is included in strata). I keep this
factor with the expanded person-period dataset.

svyset _n [pw=exp], strata(estrato)
svy: logit nevent  d4-d18 sex, nocons

(running logit on estimation sample)

Survey: Logistic regression

Number of strata   =        34                  Number of obs      =     16098
Number of PSUs     =     16098                  Population size    =  50866.46
                                                Design df          =     16064
                                                F(  16,  16049)    =    210.37
                                                Prob > F           =    0.0000

------------------------------------------------------------------------------
             |             Linearized
      nevent |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          d4 |  -8.328679   1.000458    -8.32   0.000    -10.28969   -6.367669
          d5 |  -7.196868   .5784475   -12.44   0.000     -8.33069   -6.063046
          d6 |  -6.457124   .8443208    -7.65   0.000    -8.112087    -4.80216
          d7 |  -5.112672   .3778669   -13.53   0.000    -5.853334   -4.372011
          d8 |  -4.454505   .2654388   -16.78   0.000    -4.974795   -3.934216
          d9 |   -4.36023   .2578381   -16.91   0.000    -4.865621   -3.854838
         d10 |  -2.906085   .1389361   -20.92   0.000    -3.178415   -2.633755
         d11 |  -3.376975   .1718178   -19.65   0.000    -3.713757   -3.040192
         d12 |  -2.344076   .1135556   -20.64   0.000    -2.566658   -2.121495
         d13 |  -2.003841   .1034748   -19.37   0.000    -2.206663   -1.801019
         d14 |  -1.533507   .1007537   -15.22   0.000    -1.730995   -1.336018
         d15 |   -1.20536   .1039184   -11.60   0.000    -1.409051   -1.001668
         d16 |   -1.09949   .1303548    -8.43   0.000       -1.355   -.8439799
         d17 |  -1.283726   .2046521    -6.27   0.000    -1.684867   -.8825846
         d18 |  -3.116538    1.02575    -3.04   0.002    -5.127122   -1.105953
         sex |  -.6876329   .1434859    -4.79   0.000    -.9688814   -.4063845
------------------------------------------------------------------------------


plain logit

. logit nevent d4-d18 sex [pw=exp], nolog nocons
(sum of wgt is   5.0866e+04)

Logistic regression                               Number of obs   =      16098
                                                  Wald chi2(16)   =    3351.56
Log pseudolikelihood = -2734.8293                 Prob > chi2     =     0.0000

------------------------------------------------------------------------------
             |               Robust
      nevent |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          d4 |  -8.328679   1.000511    -8.32   0.000    -10.28964   -6.367714
          d5 |  -7.196868   .5785008   -12.44   0.000    -8.330709   -6.063027
          d6 |  -6.457124   .8442876    -7.65   0.000    -8.111897    -4.80235
          d7 |  -5.112672   .3779941   -13.53   0.000    -5.853527   -4.371817
          d8 |  -4.454505   .2654323   -16.78   0.000    -4.974743   -3.934268
          d9 |   -4.36023   .2578938   -16.91   0.000    -4.865692   -3.854767
         d10 |  -2.906085   .1389697   -20.91   0.000    -3.178461    -2.63371
         d11 |  -3.376975   .1718226   -19.65   0.000    -3.713741   -3.040208
         d12 |  -2.344076   .1135966   -20.64   0.000    -2.566721   -2.121431
         d13 |  -2.003841   .1035396   -19.35   0.000    -2.206775   -1.800907
         d14 |  -1.533507   .1007861   -15.22   0.000    -1.731044   -1.335969
         d15 |   -1.20536   .1040183   -11.59   0.000    -1.409232   -1.001487
         d16 |   -1.09949   .1303473    -8.44   0.000    -1.354966    -.844014
         d17 |  -1.283726   .2046078    -6.27   0.000     -1.68475   -.8827017
         d18 |  -3.116538   1.025502    -3.04   0.002    -5.126485    -1.10659
         sex |  -.6876329   .1436219    -4.79   0.000    -.9691267   -.4061391
------------------------------------------------------------------------------


When I computed plain logit I can compute deviance, BIC and AIC
without problem, it don't happen when I use svy logit. In this case,
are this methods equivalent?
Thanks for all you responses in advance.

Regards, Sebastián



> Sebastián,
> Without more information, I cannot tell whether your survival setup is correct.   With logistic regression, one has to create a new observation for each person and period. I think that this is what you have done. However, with a complex sample design you should -svyset- your data and use -svy: logistic-, not plain -logistic-.  This will compute standard errors appropriate to the design.  You might also try -gllamm-, which will accept PSU's and -pweights- and   will fit a model with added heterogeneity.
>
>
> With the expanded person-period setup, -svy: cloglog- will fit a grouped proportional hazards model.  Download Stephen Jenkins's -hshaz- from SSC and see the references  in the -help-.
>
> -Steven
>
> On Dec 31, 2008, at 1:13 PM, Sebastián Daza wrote:
>
>>
>> I'm working with a discrete-time survival model, and I have doubts
>> about how to use weight (pweights) in a person-period data. I did the
>> following:
>>
>> logit  nevent  d4-d18 [pw=exp], nolog nocons
>>
>> Is it correct?
>> exp is sampling weight, inverse of the probability that the
>> observation is included because of the sampling design. When I use
>> pweights I have problem with lrtest too, because I have to "force" the
>> process.
>>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>


-- 

Sebastián Daza Aranzaes
(56 2) 8 921 04 60 / (56 2) 28 307 45
[email protected]




-- 
Sebastián Daza Aranzaes
Sociólogo UC
[email protected]

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



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