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Re: st: repeated measures analysis: random mixed models, GEE and power analysis


From   David Airey <david.airey@Vanderbilt.Edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: repeated measures analysis: random mixed models, GEE and power analysis
Date   Sat, 15 Dec 2007 10:03:23 -0600

On Dec 14, 2007, at 6:50 PM, Diego Bellavia wrote:

Dear Statalisters,

I am writing a grant proposal (time is an issue here)
and by study design I will have to analyze serial measurements
in three predefined groups of patients (the outcome variable is continous).
The time between each measure should be uniform in all the patients.
I should have no problems to enroll more than 40 patients per group.
I'm curious as to when GEE would be chosen instead of a mixed model? In Stata there are some features missing in xtmixed versus xtgee (vce: autoregressive, stationary, nonstationary, for example), and this may cause the decision to be made in favor of xtgee for Dr. Sebastian's design. But assuming xtmixed had the features of sas proc mixed or R lme, when would the choice for GEE win out? I thought I had read somewhere that GEE could be robust to vce misspecification versus mixed models, so this could be a reason even when comparable vce features are available. Note that with just one level of clustering, xtreg can be used, and xtreg does have the vce types above that are not available in xtmixed. One point not mentioned, and not relevant to this design, is xtmixed can do multilevel but xtgee and xtreg can handle only one cluster level. Dr. Sebastian did not say why he had serial measures, but the choice for population average effects or subject specific effects may tip the balance away from xtgee, given Barth's comments.

-Dave

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