My apologies for the erroneous capitalization of Stata.
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Martin Weiss
Sent: Sunday, August 16, 2009 4:33 PM
To: [email protected]
Subject: AW: st: Thinking through best way to do a longitudinal analysis
<>
" ...about doing this in STATA?"
http://www.stata.com/support/faqs/res/statalist.html#spell
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Polis, Chelsea
B.
Gesendet: Sonntag, 16. August 2009 22:23
An: [email protected]
Betreff: RE: st: Thinking through best way to do a longitudinal analysis
Thanks so much to both of you for the suggestions and all of these great
references! I will download all of these articles and read them. In the
meantime, do you think calculating individual slopes is a bad idea? If not,
do you know how I might go about doing this in STATA?
Thank you!
Chelsea
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Cameron McIntosh
Sent: Friday, August 14, 2009 10:36 PM
To: STATA LIST
Subject: RE: st: Thinking through best way to do a longitudinal analysis
Chelsea,
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
http://users.fmg.uva.nl/cdolan/semmix.pdf
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.
http://www.unc.edu/~curran/pdfs/Biesanz,Deeb-Sossa,Papadakis,Bollen&Curran(2
004).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,
Cam
----------------------------------------
> Date: Fri, 14 Aug 2009 13:27:15 -0700
> From: [email protected]
> Subject: Re: st: Thinking through best way to do a longitudinal analysis
> To: [email protected]
>
> 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: "[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
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
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