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Growth mixture modeling in treatment ... |
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Nour Azhari posted on Wednesday, February 28, 2018 - 6:49 pm
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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 |
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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. download paper contact first author show abstract 2nd paragraph: This general analysis question is suitable for SEMNET. |
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Thanks for your response. 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? I would really appreciate if you could help me understand more those two options and give your opinion about this. Thank you |
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It's hard to say which strategy to take without knowing more about your study - and we don't have time to get into such detailed communication. It sounds to me that you have T=35 time points and at varying time points you randomize to different medications added to the psych therapy and that added treatment has a large and perhaps lasting effect and is of primary interest. If so, you could take the approach that we show in our Short Course topic 3, slides 157-159 where the 0/1 "status" variables correspond to your medication treatment. |
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