Reference-based pattern-mixture models for analysis of longitudinal binary data

Abstract

Pattern-mixture model (PMM)-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions or to estimate treatment effectiveness. The methodology is well established for continuous responses but less well established for binary responses. In this study, we formulate the copy-reference and jump-to-reference PMMs for longitudinal binary data using a multivariate probit model with latent variables. We discuss the maximum likelihood, Bayesian, and multiple imputation methods for estimating the treatment effect under the specified PMM. Simulation studies are conducted to evaluate the performance of these methods. These methods are also illustrated using data from a bipolar mania study.