Another option is to use a linear mixed model approach.
Scott Millis
--- On Fri, 8/14/09, Polis, Chelsea B. <[email protected]> wrote:
> From: Polis, Chelsea B. <[email protected]>
> Subject: st: Thinking through best way to do a longitudinal analysis
> To: "[email protected]" <[email protected]>
> Date: Friday, August 14, 2009, 2:38 PM
> 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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