Tang Yongqiang
a Department of Biostatistics and Statistical Programming, Shire , Lexington , Massachusetts , USA.
J Biopharm Stat. 2017;27(4):620-638. doi: 10.1080/10543406.2016.1167075. Epub 2016 Mar 24.
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.
我们通过单调数据增广（MDA）为重复测量的混合效应模型（MMRM）和一类模式混合模型（PMM）开发了一种高效的马尔可夫链蒙特卡罗算法。所提出的算法对于PMM中的多重填补特别有用，并通过一项抗抑郁试验的分析进行了说明。我们还描述了MMRM和PMM的全数据增广（FDA）算法，并表明在MDA和FDA算法中模型参数的边际后验分布是相同的。
