期刊
JOURNAL OF BIOPHARMACEUTICAL STATISTICS
卷 27, 期 4, 页码 620-638出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10543406.2016.1167075
关键词
Controlled imputations; delta-adjusted imputations; Markov chain Monte Carlo; mixed-effects model for repeated measures; monotone data augmentation
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.
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