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st: Thinking through best way to do a longitudinal analysis


From   "Polis, Chelsea B." <cpolis@jhsph.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: Thinking through best way to do a longitudinal analysis
Date   Fri, 14 Aug 2009 14:38:28 -0400

I am trying to do an analysis on the CD4 decline trajectory of 190 HIV+ women, comparing
those who were on hormonal contraception around the time of HIV seroconversion against those
who weren't.  Each subject in my sample has at least two (and as many as six) CD4 measurements,
the first and the last of which include a time span of at least one year.  I created a
variable to anchor the CD4 measurements in time by generating a variable that indicates how
many days since HIV seroconversion the CD4 measurement was taken.  The data are not balanced
(since women have anywhere between 2 to 6 measurements) and CD4 counts were not measured for
each individual at the same point in time after seroconversion (for example, the first measurement
available for each woman ranges in days since seroconversion from 69 to 1919).

I think one way to go about this would be to calculate the individual slope for each subject and
compare the slopes between contraceptive users and non-users using a t-test.  Is there a command
to obtain those kind of individual regression slopes for each woman, and would my data have to be
in long or wide format?

Or am I thinking about this improperly?  Would it be better to construct a longitudinal marginal
model with generalized estimating equations, and if so, can someone point me in the direction of
a text that might help me figure out how to do that with what I think is probably an unusual data
structure (many of the examples I have seen in coursework use data measured at regular intervals)?

Many thanks,
Chelsea

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