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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 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? |
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| I am, yet again, sorry about the title - it appears my password manager is entering my username into the title box, and even though I previewed the thread and ensured that the title WAS correctly entered, when I hit "post" following the preview, it changed the title...which should have been "Missing Data in Discrete Time Survival Analysis" |
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See UG ex6.19 for a current setup for discrete-time survival, not needing mixtures.
Algo=Int is fine and needed because cov1, cov2 are partially latent variables due to missingness which calls for numerical integration due to categorical outcomes. |
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| Thank you very much! |
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