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re: re: st: Re: xtlogit and logistic-cluster (REVISED)


From   David Airey <david.airey@vanderbilt.edu>
To   statalist@hsphsun2.harvard.edu
Subject   re: re: st: Re: xtlogit and logistic-cluster (REVISED)
Date   Sat, 7 Aug 2004 14:31:13 -0500

Thank you Joseph and David for the replies. I have a
couple of follow-up questions:

--- Joseph Coveney <jcoveney@bigplanet.com> wrote:

[snip]
> -xtlogit, re- would seem to be the remaining
> alternative available in Stata,
> unless I'm overlooking something.  If there
> is a substantial correlation between the fixed
effects (physician covariates) and the random
> effect, then the parameters are liable not to be
> consistently estimated.

How can I test this?
If doing an <xtlogit, re> command, you could get the intraclass correlation with loneway as they did at:

http://www.ats.ucla.edu/stat/stata/stat130/long01.htm

I was not sure if I understood the question if this was not an answer.


--- David Airey <david.airey@vanderbilt.edu> wrote:

> But when stuck with a small data set, why not run a
> model designed for
> that data structure, as opposed to running a model
> not designed for the data structure?

This is an interesting point, because in my data I
have 50 clusters and 410 observations, so the number
of patients referred by a physician ranged from 1 to
43 with a median of 5 referrals.

I guess the question that remains is whether or not I
can justifiably use this approach?
One answer I received off list for my query was sometimes the underlying math of the method doesn't like the small sample, even if the data fit the model by design. I'd still use the model that fit the design, since the conclusion has to be qualified by small sample size or by ignored clustering anyway. Ignore the clustering and see what differences happen. That's usually my approach, not being one with the underlying math!



Thank you again,
Ricardo.
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