

GMM/LCGA  nonlinear with few time ... 

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Jen posted on Tuesday, November 09, 2010  1:50 pm



I'm using GMM w/ nonlinear data: 4 pts, 1 pre & 3 postintervention. Change after the intervent. wears off. I'd like to use GMM to ID subgroups responding differently. I've noted some pubs using LCGA don't address intercepts & slopes at all; fit stats are used to decide on # of classes & comparisons made based on membership. Do you consider this an acceptable approach w/ LCGA &/or GMM? I tried 2 things: 1) A discontinuous model– i s1  a@1 b@0 c@0 d@0; i@0; s1@0; i s2  a@0 b@0 c@1 d@3; s2@0; (LCGA.) W/ only 2 pts for the 1st slope, the var for s1 will always be set to 0, but freeing the var of i & s2 would make this GMM. Is anything wrong w/ this approach? Would adding a quad to the 2nd part of the model (w/ var=0) hurt? 2) A cubic model is not IDed w/ 4 pts, but is anything wrong w/ fitting a cubic model & fixing var=0? I'm not interested in using the vars within classes as predictors, just class membership. If I'm only interested in IDing classes & not description, does it matter if the predicted trajectories are good fits? Thanks for your help! 


If you are looking for intervention effects I think it is awkward to use LCGA  you can't pinpoint the intervention effect. With GMM the intervention effect is on the slope of the withinclass development which is a clear concept. Looks like you find a big change from the 1st to the 2nd point. Piecewise can be good, but with only 4 time points, each piece has only 2 and cannot support both an intercept variance and a slope variance. Perhaps one quadratic model is ok. 

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