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Approximate Bayesian Model Averaging for Latent Class Pattern Mixture Models with an Unknown Number of Classes
Mike Daniels, University of Florida *Jason Roy, Geisinger Center for Health Research Keywords: missing data, dropout, latent variable, longitudinal data We consider the problem of fitting pattern mixture models to longitudinal data when there are many unique dropout times. We propose a marginally specified latent class pattern mixture model. The marginal mean is assumed to follow a generalized linear model, while the mean conditional on the latent class and random effects is specified separately. Because the dimension of the parameter vector of interest (the marginal regression coefficients) does not depend on the assumed number of latent classes, we propose to treat the number of latent classes as a random variable. We specify a prior distribution for the number of classes, and calculate (approximate) posterior model probabilities. In order to avoid the complications with implementing a fully Bayesian model, we propose a simple approximation to these posterior probabilities.
