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Re: st: cluster() or svy? (analysis of cluster-randomized trials)

From   Jeph Herrin <[email protected]>
To   [email protected]
Subject   Re: st: cluster() or svy? (analysis of cluster-randomized trials)
Date   Mon, 08 Sep 2008 16:23:42 -0400

I think there are good reasons to avoid both. You don't
say what kinds of analyses you have, but see

 ssc describe cltest

for some tools and a reference for analyzing cluster
randomized outcomes using adjustments to the standard
chi-2 and t-tests. (As author I'll note that the CIs
for the group means reported for the t-test are correct
as documented but nonetheless ad hoc; this doesn't effect
either the p-value or the CI for the difference in means.)

Another preferred option is to use panel methods such
as -xtmixed- with the clusters specified as panels. Even
if you don't have covariates (and in an RCT you will need
to make a case for including them), these are often

More detail on your design might produce more detailed

Hope this helps,

Michael I. Lichter wrote:
Hello, friends. I have a question about the analysis of data from cluster-randomized trials (CRTs). CRTs are experiments where subjects are randomly assigned to conditions (control, treatment) based on their group membership rather than being assigned individually as is usually the case in randomized controlled trials. In my study, the clusters are medical practices, so when a medical practice is assigned to a condition, all of the eligible patients therein are also assigned to the condition. CRTs should be analyzed using methods that take account of the clustering in the study design, of course.

My question is this: For CRTs, is there any statistical reason for preferring the cluster() option on estimation commands (e.g., regress, logit) over the survey commands, or vice-versa? I've used both and the results are similar, but the survey commands estimate larger standard errors. If the answer is that they're both equally appropriate but produce different results because they use somewhat different methods of estimation, that's fine.



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