Baseline by Tx Interactions in LGM PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 Antonio A. Morgan-Lopez posted on Monday, May 22, 2006 - 7:28 am
I wanted to see if either of you thought the following was a defensible alternative for modeling baseline-treatment interaction effects:
I am working with a dataset where there are three intervention conditions that are effect-coded into two variables. But I did not want to re-create group indicators externally (out of laziness) in order to set up a multiple-group, additive treatment model in order to model baseline tx interaction effects (as in Muthen/Curran 97 or Khoo 01). With the availability of type=random for interaction effects between latent variables, I was thinking that the following code would give you equivalent results to the tried-and-true BTI approaches:

cva cvb on int2 int3;
cva with cvb;
s | cvb on int2;
s on cva;
r | cvb on int3;
r on cva;

where cva and cvb are growth parameters, int2 and int3 are the effect-coded variables for the 3 groups, and s & r are the random effects capturing the extent to which the intervention effect(s) vary across cva (estimated initial status on the outcome). Does this look reasonable?
 Bengt O. Muthen posted on Monday, May 22, 2006 - 6:56 pm
I'm afraid I don't understand this approach. First you define a random slope s for cvb on int2, and then you regress s on cva - what does that mean?

I would take the more transparent approach.
 Antonio A. Morgan-Lopez posted on Tuesday, May 23, 2006 - 4:38 am
I was thinking that with this setup, the main tx effect (cvb on int2) could vary as a function of initial status (cva) - analogous to the regression of Bt on Ac you illustrate in Figure 5 of MC 97 under the additional growth factor framework. But I'll stick with the more transparent approach as you suggest......
 Bengt O. Muthen posted on Tuesday, May 23, 2006 - 6:06 pm
I see what you mean now. I am not sure how well that would work.
 Antonio A. Morgan-Lopez posted on Wednesday, May 24, 2006 - 5:58 am
I compared it to MC97 and the estimates are not close enough for me to be confident that it works (though I would have made the same inferences on all parameters across either framework). BTI using type=random might be intuitive - but probably not mathematically equivalent to MC97....
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