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


From   David Airey <david.airey@vanderbilt.edu>
To   statalist@hsphsun2.harvard.edu
Subject   re: st: Re: xtlogit and logistic-cluster (REVISED)
Date   Tue, 3 Aug 2004 22:40:28 -0500

Joseph Coveney wrote:

-logit, cluster()- produces the same results as -xtgee, family(binomial)
link(logit) corr(independent) robust- (this came up on the list last month in
the context of -mlogit, cluster()-, so I would recommend avoiding that approach
in circumstances in which population-averaged GEE would not be ideal. There
are those who would say that GEE is never ideal, but even among its adherents,
most would caution that, with only 50 physicians, GEE would be a little dicey.

-xtlogit, fe- would help see the influence of patient characteristics upon a
physician's inclination to refer, while, in a sense, controlling for physician
characteristics. (Where the predictor variables for patient characteristics do
not vary within a physician, the entire physician's caseload would be dropped.)
As you mention, because physician's characteristics do not vary within a
physician, -xtlogit, fe- doesn't seem to be the way to go to explore both
patient and physician characteristics together.

-xtlogit, re- would seem to be the remaining alternative available in Stata,
unless I'm overlooking something. Cautions would be similar to the case with
GEE. The number of physicians is limited. 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.
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? When does ignoring the clustering become more favorable to acknowledging the presence of fewer than an optimal number clusters? Why is it not the case that a good model on a small data set is not always better than a bad model on the same small data set? I hope I'm clear.

-Dave

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