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RE: st: Thinking through best way to do a longitudinal analysis

From   Cameron McIntosh <>
Subject   RE: st: Thinking through best way to do a longitudinal analysis
Date   Fri, 14 Aug 2009 22:36:01 -0400


I'll try and point you in what I think might be a good direction. You may want to read up on growth mixture modeling:

Wang, M., & Bodner, T.E. (2007). Growth Mixture Modeling: Identifying and Predicting Unobserved Subpopulations With Longitudinal Data. Organizational Research Methods, 10(4), 635-656.

Dolan, C.V., Schmittmann, V.D., Lubke, G.H. & Neale, M.C. (2005). Regime switching in the latent growth curve mixture model. Structural Equation Modeling, 12, 94-119

Muthén, B.O. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D.Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage.

In this case you "know" your "latent classes" (hormonal contraception or not), but that's no problem. The varying assessment times and missing data could be a bit tricky but there is some literature out there:

Blozis, S.A., & Cho, Y.I. (2008). Coding and centering of time in latent curve models in the presence of interindividual time heterogeneity. Structural Equation Modeling, 15(3), 413-433.

Biesanz. J.C., Deeb-Sossa, N., Papadakis, A.A., Bollen, K.A., Curran, P..J. (2004). The role of coding time in estimating and interpreting growth curve models. Psychological Methods, 9(1), 30–52.,Deeb-Sossa,Papadakis,Bollen&Curran(2004).pdf

Duncan, S.C., & Duncan, T.E. (1994). Modeling incomplete longitudinal substance use data using latent variable growth curve methodology. Multivariate Behavioral Research, 29(4), 313-338.

-gllamm- might be able to do what you want (if not, Mplus for sure). Hope this is helpful,


> Date: Fri, 14 Aug 2009 13:27:15 -0700
> From:
> Subject: Re: st: Thinking through best way to do a longitudinal analysis
> To:
> Another option is to use a linear mixed model approach.
> Scott Millis
> --- On Fri, 8/14/09, Polis, Chelsea B.  wrote:
>> From: Polis, Chelsea B. 
>> Subject: st: Thinking through best way to do a longitudinal analysis
>> To: "" 
>> 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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