A bit of Monday afternoon pedantry:
Is there some strange new bit of language showing up on Statalist? Both
Jesper Sorensen and Per Ivar Kaaresen have used "f.ex.". It has taken me
most of the day to figure out that it is meant to signify "for example. I
was thrown by Per's phrase "observed f.ex. mortality". I thought it was some
disease I'd never heard of! "f.ex." is not in my dictionary of acronyms. Is
there something wrong with the time-honoured, well-known, and trans-lingual,
e.g. ?
Lee
> -----Original Message-----
> From: [email protected] [SMTP:[email protected]]
> Sent: Monday, March 31, 2003 2:44 PM
> To: [email protected]
> Subject: Re: st: desgin effects, weights, and cox proportional hazard
> models
>
> 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 <[email protected]>:
>
> > >>> [email protected] 03/31/03 12:22 PM >>>
> > On Mon, 31 Mar 2003 10:41:32 -0500 Robert Bozick <[email protected]>
> > 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?
> >
> >
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
>
>
>
> *
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