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Re: st: selection correction in survival models


From   "Austin Nichols" <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: selection correction in survival models
Date   Thu, 11 Sep 2008 12:22:07 -0400

Murali Kuchibhotla <muralik@iastate.edu>:
Google is your friend here. There is a paper on "Heckman style
corrections" for survival models called "A Generalized Parametric
Selection Model for Non-Normal Data" by James E. Prieger.  On IV for
duration models, there is some work as well, e.g. "Instrumental
Variable Estimation for Duration Data" by Govert E. Bijwaard.  These
models will not make it into official Stata until they have been used
a bunch, which might not happen until some user makes that easy by
programming a version of them...

On Thu, Sep 11, 2008 at 11:23 AM, Murali Kuchibhotla
<muralik@iastate.edu> wrote:
> Austin,
>       Thank you for your suggestions. A follow up question-are you aware of
> the existence of Heckman style corrections in the context of duration
> models?

>> A better question might be: Is there a way to correct for this in the
>> real world?  Probably not, in most senses.  Think of it as a
>> logit/probit regressing "survival" on the k variables in X and a
>> training dummy T and the k interactions T*X, so you have at least k+1
>> endogenous variables measuring the effects of training.  You could
>> switch to an -ivprobit- model if you had a large number of
>> instruments, but that seems unlikely (plus, I think you would want an
>> -ivcloglog- command which does not exist).  One way forward is to
>> reweight the samples so they look identical on observables, using
>> propensity scores, as described in
>> http://www.stata-journal.com/article.html?article=st0136 and elsewhere
>> (the sample will look identical in the sense that a -hotelling- test
>> would fail to reject the null that the mean of X is the same in the
>> training group and the non-training group--the distributions may
>> differ in other ways).  But this corrects only for selection on
>> observables, not unobservable differences--for the latter, you need
>> training to be randomly assigned, for a start.  Or you need a very
>> convincing natural experiment.

>> On Wed, Sep 10, 2008 at 11:22 PM, Murali Kuchibhotla
>> <muralik@iastate.edu> wrote:
>> >
>> > Hello,
>> >      I am interested in estimating separate Cox regression models for 2
>> > groups of individuals- trainees and non-trainees. The problem is that the
>> > training decision is endogenously determined, so that the differences
>> > between the 2 sets of parameter estimates may well be driven by the
>> > differences in the unobservable characteristics between the 2 groups of
>> > respondents. Is there a way to correct for this in Stata?
>> >
>> > Murali Kuchibhotla
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