Hello
We have the following dataset:
Monthly survey of patients who report to an emergency department (across
15 months) and if they use medication Y (y/n). We also have other data
on the patients' characteristics (X1, X2, etc).
6 different hospital emergency departments were surveyed. Each month we
targeted 15 patients, but sometimes less (or more) were enrolled.
ID Emer Dept Date Smoker Age Medication Y
1 1 jan03 Y 10 Yes
2 1 jan03 N 45 No
3 2 feb03 Y 23 No
And so on
We are trying to assess whether:
1) Use of Y changes across time
2) If X1, X2 are predictors of use of Y
3) Use of Y is associated with a particular emergency department
4) A quality initiative started at month 7 makes a difference in the use
of Y
I initially thought that GEE would be an appropriate way to analyze this,
but we don't repeatedly measure one individual over 15 months. Instead,
we are sampling 6 emergency departments monthly over 15 months. Then I
thought STATA's time series would be appropriate. This doesn't appear to
be appropriate, as again, STATA's time series functions seem to operate
with one individual as the unit being measured repeatedly. Finally, I
thought STATA's survey functions would be helpful, but those don't seem to
be setup for repeated measurements.
I've extensively checked STATA's reference manuals, Fitzmaurice's Applied
Longitudinal Analysis, Twisk's Applied Longitudinal Data Analysis for
Epidemology, and Levy's Sampling of Populations. I understand the data
structures for the examples cited in their discussions, and did not find a
parallel to the above.
Now I'm in a quandry. The best I could currently think of is to consider
each ED as a "unit" and consider that it is measured repeatedly across
time. But then I can't seem to understand how to use STATA's GEE as each
"unit" has mutiple measurements at one time!
Thank you in advance,
Richard Lenhardt
Assistant Professor of Medicine
Rush University Medical Center
Chicago, IL
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