

Missing Data in Discrete Time Surviva... 

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Hello all, 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@1 SH15@1 SH16@1 SH17@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 classspecific 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 nonproportionality; 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. 


Int=MonteCarlo is appropriate for the analysis. 

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