I am working on a discrete time survival analysis using mixture modeling as per the Muthen & Masyn 2001 paper. My current model appears to be kicking out participants with missing data on any covariate, which I do not want; I would like to bring those variables into the model by estimating their variance.
Here is the syntax: USEVARIABLES SH14 SH15 SH16 SH17 cov1 cov2; CATEGORICAL ARE SH14 SH15 SH16 SH17 cov1; CLASSES = c(1); ANALYSIS: TYPE = MIXTURE; MODEL: %OVERALL% f BY SH14@1SH15@1SH16@1SH17@1; f ON cov1 cov2; [f@0]; !cov1 cov2; %c#1% f ON cov1 cov2; [f@0]; !cov1 cov2;
When I try to add the lines commented out above, I get the following error:
One or more MODEL statements were ignored. Note that ON statements must appear in the OVERALL class before they can be modified in class-specific models. Some statements are only supported by ALGORITHM=INTEGRATION.
When I add ALGORITHM=INTEGRATION to the ANALYSIS code, it runs fine. Is this appropriate for use in mixture modeling with discrete time survival analysis?
I should perhaps also specify that adding ALGORITHM=INTEGRATION allows the syntax to run when I assume proportionality (using my SH variables as indicators on a latent factor), but will not run when I assume non-proportionality; in that case, I get an error saying that I need to use Monte Carlo estimation. In that case, adding the line INTEGRATION=MONTECARLO allows it to run, but again, I am not sure if this is appropriate for this type of analysis.