  The mixed effects model for repeated measures (MMRM) has been widely used for
the analysis of longitudinal clinical data collected at a number of fixed time
points. We propose a robust extension of the MMRM for skewed and heavy-tailed
data on basis of the multivariate skew-t distribution, and it includes the
multivariate normal, t, and skew-normal distributions as special cases. An
efficient Markov chain Monte Carlo algorithm is developed using the monotone
data augmentation and parameter expansion techniques. We employ the algorithm
to perform controlled pattern imputations for sensitivity analyses of
longitudinal clinical trials with nonignorable dropouts. The proposed methods
are illustrated by real data analyses. Sample SAS programs for the analyses are
provided in the online supplementary material.
