A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times

TLDR: This article proposes a marginally specified latent class pattern mixture model, where the marginal mean is assumed to follow a generalized linear model, whereas the mean conditional on the latent class and random effects is specified separately.

Abstract: In this article 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, whereas 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. The ideas are illustrated using data from a longitudinal study of depression in HIV-infected women.