Growth mixture modeling in treatment ...
Message/Author
 Nour Azhari posted on Wednesday, February 28, 2018 - 6:49 pm
Hi,

I am trying to do mixture modeling on a dataset where the outcome variable is alcohol consumption per day during a treatment study. The treatment involves 5 week outpatient psychotherapy with a randomized medication infusion once on Week 2 for all participants. My problem is that each participant has a different length of treatment (because of missed appointments and rescheduling, some who dropped out earlier). How should I clean my data so that I have a meaningful interpretation once I run it in Mplus? And do all participants need to have the same number of time points?

Also, because the medication infusion is such a pivotal part of the treatment (from preliminary descriptives it seems that the day where participants received the medication, alcohol consumption decreased dramatically for many of them), I was wondering if there was a way I could arrange my data or model so that I could know that a specific time point in my model corresponds to the day of the infusion for all participants. In order to visualize the changes in a more meaningful way.

I would really appreciate your help.

Thank you
 Bengt O. Muthen posted on Thursday, March 01, 2018 - 6:27 pm
1st paragraph:

See the paper on our website:

Muthén, B., Asparouhov, T., Hunter, A. & Leuchter, A. (2011). Growth modeling with non-ignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17-33. Click here to view Mplus outputs used in this paper.

2nd paragraph:

This general analysis question is suitable for SEMNET.
 Nour Azhari posted on Sunday, March 04, 2018 - 4:24 pm

Someone suggested 2 options for my 2nd question:

(1) To center the data around the infusion (XXXXX number of days before and X after)
(2) To start from the beginning of treatment and put the date of the infusion as covariate.

For (1) How can you center the data around a range of time variables? I thought one could only center at one time point.

For (2) I am unsure how that would work. If my covariate is the date of each participant's infusion, how can I interpret my results in my latent growth mixture model?