

Types of treatment in trajectory classes 

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Greta posted on Thursday, March 20, 2008  3:35 am



Hi, I would like to test a Growth Mixture Model with longitudinal data. I assume that different interventions produce different withinclass trajectories. My question is: how can I reflect this assumption in the model statement? I thought of regressing the slope on intervention status for each class separately. But I do not really know how to handle the treatment variable (three different treatments). Should I specify it as a GROUPING variable, use the KNOWCLASS option or simply introduce it dummy coded? Do I have to make any specifications in the GRAPHS option, in order to get "splitted" trajectories for the different interventions (similar to Fig.2 in Muthén et al. 2002, Biostatistics)? Thanks a lot! 


You can create two dummy variables to represent the three treatments or use a multiple group approach. You don't need any special specifications for the PLOT command. 


Hi there, I am planning a GMM model like the one in Muthen et al. (2002)i have this figure from p.150 of the topic 6 short course notes however, instead of dummycoding my 3group treatment variable, i wanted to make it a knownclass variable. that way, i would regress the slope factor on latent class, treatment group and the effect of tx group would be free to vary across latent class. my problem is that I wanted to add a continuous, measured covariate "X" and look at the interaction between the 3group, knownclass treatment and the covariate_X, to determine whether the effect of covariate_X on the slope varies across the known classes (treatment group) AND/OR across latent class. I am not sure if this is possible? how? or should i just dummy code tx and use the 2 dummycoded variables and 2 treatmentxcovariate_x interactions? thanks for any advice you might have! susan 


For what you describe in the first paragraph it seems like you don't need a knownclass variable, but can do it via 2 dummies and the latent class variable. But to answer paragraph 2, you can do this via the construction: %cg#1.c#1% s on x; %cg#1.c#2% s on x; etc that combines the knownclass cg variable with the real class c variable. Then you can allow any equalities (or not) across these combinations that you like. 


Thanks so much for the info! that was really helpful. Unfortunately, even with that addition, my model is not converging. I think it might be because of the start values, but i am not sure. b/c the code is a little long, I sent it and our previous exchanges here to the support email address. Thanks so much if you get a chance to give me any tips on where my code is going wrong... Susan 


I would not give start values, but use the program defaults. 

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