What does one do with binary, clustered data that must be weighted?
I am working on a project using Stata 9. The goal is to develop models
of various binary outcome measures pertaining to improved counseling by
health providers. I am, however, running into several challenges. The
first is that the sample taken was a cluster sample. Individuals were
interviewed at various health centers, so the health center was the
primary sampling unit-health centers were selected for participation and
then the individual study participants. Originally, I thought that I
could use regression with GEE to account for the clustering in the data;
however, I discovered a second problem that may limit my ability to do
so. Within each cluster, the individuals sampled were not sampled in
equal proportion on an important variable, which was provider training.
In other words, clients who received counseling from trained providers
were over-represented in the sample.
I thought the solution would be to apply a sample weight (pweight
command in Stata), but Stata does not allow the pweight to vary by unit
within a panel. That is, the individuals within a cluster are not
allowed to have their own weight, only the panel or cluster may be
weighted. Below are the commands I keyed in, and the error message that
I received.
Command:
iis cpihfano
xtgee cpi41 cpi2 ce39 [pweight = weight], family(binomial 1) link(logit)
corr(exchangeable)
Error Message:
weight must be constant within cpihfano
r(199);
I have done a bit of research on the topic, but am getting no where
other to discover that this problem may be; as yet, unsolved (please
refer to this link for further explanation
http://www.stata.com/support/faqs/stat/xtweight.html ).
So, what can one do with binary, clustered data that should be weighted?
Does anyone know if progress has been made on this front? What
solutions have others devised in similar situations? I can provide more
detail if necessary.
Gwyneth
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