I would like to ask for some advice/guidance. I have been using generalised linear mixed models for a particular dataset without much success. After some reading I think that a growth mixture model might be appropriate and that Mplus is the software I should use (which I have just acquired a license for).
The data come from a study of a substance abuse cessation attempts. 100 participants report their use on 10 occasions including baseline, after which the cessation attempt begins.
Clinical interest lies in two areas. First, is there a divergence in subsequent use (that is, lapse/relapse) between participants who are diagnosed with a particular disorder at baseline and those who are not ? Second, are there any distinct usage patterns/trajectories (classes) evident within the study population, and is membership of these classes predicted by the diagnosis aforementioned ?
The outcome variable is zero inflated since many of the participants do not lapse/relapse.
Potential confounders, both time varying and time invariant are also measured.
There is some loss to follow-up and I would like to handle the missing data appropriately (in the glmm framework I was using multiple imputation).
Is Mplus suited to this kind of analysis ? If so, I would be grateful for some guidance and/or links to any online examples of similar models.
It would seem Growth Mixture Modeling could be used for these data. See the criminology example in the following paper which is available on the website:
Muthén, B. & Asparouhov, T. (2009). Growth mixture modeling: Analysis with non-Gaussian random effects. In Fitzmaurice, G., Davidian, M., Verbeke, G. & Molenberghs, G. (eds.), Longitudinal Data Analysis, pp. 143-165. Boca Raton: Chapman & Hall/CRC Press.
See also the GMM papers on the website under Papers and the examples in Chapter 8 of the user's guide.
jeon small posted on Friday, March 01, 2013 - 6:53 pm
The journal editor requested that I perform a post-hoc power analysis. The sample size is 438. The results from the measurement model are: X2=95.85, DF=40, probability level= .000, CFI=.954, RMSEA=.050
Thank you Linda. In the paper you referenced it says that "input scripts for the analyses are available at http://www.statmodel.com" but I have not been able to locate this on the website. Please advise.