title: An efficient monotone data augmentation algorithm for multiple imputation in a class of pattern mixture models.
abstract: We develop an efficient Markov chain Monte Carlo algorithm for the mixed-effects model for repeated measures (MMRM) and a class of pattern mixture models (PMMs) via monotone data augmentation (MDA). The proposed algorithm is particularly useful for multiple imputation in PMMs and is illustrated by the analysis of an antidepressant trial. We also describe the full data augmentation (FDA) algorithm for MMRM and PMMs and show that the marginal posterior distributions of the model parameters are the same in the MDA and FDA algorithms.
authors.affiliation: a Department of Biostatistics and Statistical Programming, Shire , Lexington , Massachusetts , USA.
authors.forename: Yongqiang
authors.initials: Y
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D000928:Antidepressive Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method
chemical_list: D000928:Antidepressive Agents
keywords: Controlled imputations; Markov chain Monte Carlo; delta-adjusted imputations; mixed-effects model for repeated measures; monotone data augmentation