In contrast to most of the other macros this fits a random coefficient regression model.
- The longitudinal measurement process model follows a standard random-coefficient mixed effect model
- The dropout mechanism model uses a complementary log-log link or logit link.
These 2 models are linked by latent(unobservable) subject random effect vector Ui which is selected from one of 3 options:
- only random intercept is shared;
- both random intercept and random linear time slope are shared;
- random intercept, random linear time slope and random quadratic time slope are shared.
The underlying model is a random coefficient regression model.
Alternatively a wide range of unstructured repeated measures regression model with shared parameters can be fitted using the Mymcmc macro (Gaussian Repeated Measures with conjugate priors fitted using proc MCMC in SAS) in the Direct likelihood / Bayesian approaches section. But these require user coding of the dropout mechanism model.
The macros can be downloaded from Shared Parameter Model_20120726
For explanation see the set of Powerpoint slides.