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Re: st: desgin effects, weights, and cox proportional hazard models


From   sorensen@mit.edu
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: desgin effects, weights, and cox proportional hazard models
Date   Mon, 31 Mar 2003 14:43:58 -0500

discrete-time eha models can handle right-censoring fine -- see, f.ex., Paul
Allison's Sage book on event-history for a discussion.  So a logit is fine with
right-censored data.  On whether or not it is appropriate to handle the sampling
issues by switching to svylogit, I don't know.

//Jesper

Quoting Robert Bozick <rbozick@smtp.aed.org>:

> >>> stephenj@essex.ac.uk 03/31/03 12:22 PM >>>
> On Mon, 31 Mar 2003 10:41:32 -0500 Robert Bozick <rbozick@smtp.aed.org> 
> wrote:
> 
> > Hello everyone --
> > I am currently working on a project that requires the use of 
> hazard/event history models.  I am relatively new to estimating these 
> models.  The basic model I am estimating is the rate of college degree 
> completion as a function of a set of covariates > COMPLETION = a + bX
> > > I have two questions:
> > 1) How do I adjust the estimates for the clustered-stratified nature 
> of the sample?  > The data set I am using is a two stage 
> stratified-cluster sample (i.e. the National Education Longitudinal 
> Study for those of you who use NCES data sets). In the first stage, 
> schools are randomly sampled with a probability proportional to a given 
> strata (defined by socioeconomic status, urban v. suburban, etc.) In 
> the second stage, students are sampled randomly within the schools. In 
> typical logit models using this data, I use 'survey' commands to adjust 
> for the strata and hierarchically clustered desigin of the sample using 
> the code : > 
> > svyset psu psu > svyset strata stratum
> > > (*where psu = the primary sampling unit (the school) and stratum is 
> the strata that the schools were proportionally sampled from)....then 
> when I estimate a logit model, I use the command: > 
> > svylogit y x1 x2 x3 > 
> > That command estimates the model correcting for the sample design.  I 
> noticed there is no 'survey' command for cox proportional hazard 
> models.  How do I correct for the sample design (cluster and strata) 
> when estimating a cox proportional hazard model?   > 
> > > 2)  How do you weight data when estimating a cox proportional 
> hazard model? > I tried the command:
> > stcox x1 x2 x3 [pweight = weight] > Stata gave me the response:  
> weights not allowed > Are you not allowed to weight data when 
> estimating a cox proportional hazard model or is there some other 
> procedure that I need to do to incorporate a probability weight when 
> estimating this type of model? > Thanks in advance for any help with 
> these issues! > 
> 
> As far as I know, there is no Stata -svy- command for the Cox 
> proportional hazard model (though there might be in specialist software 
> such as SUDAAN).
> 
> ... but how about the following idea?
>  
> The Cox PH model is a continuous time hazard model. Suppose instead 
> that you used a discrete time model instead (see Manual entry under 
> -discrete- in version 8 Manual ST). This may be what you should use 
> anyway if your data are interval-censored. (Do you have exact dates for 
> survival times? Or are they grouped?)
> If you went the discrete time route, and estimated a discrete time 
> logistic hazard model, then maybe you could then take advantage of 
> Stata's -svylogit- estimator. 
> 
> Perhaps the survey design effect experts out there could comment on 
> whether this 'trick' is OK?
> 
> Stephen
> 
> Thanks Stephen --
> 
> I do have exact dates of degree completion (month/year). I had wanted to use
> hazard models because of the right censoring issue in the data: a large
> proportion of the sample had not completed a degree before the time of the
> interview.  I guess that leaves me in a bind:  If I use the logit model, I
> can obtain the 'proper' standard errors, but not correct for the censoring. 
> If I use the hazard model, I can correct for the right censoring problem, but
> not have the proper standard errors. 
> 
> Am I looking at this correctly?  Any other thoughts?
> 
> 
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