Department of Biometrics, Tesaro, Waltham, MA, USA.
Stat Methods Med Res. 2020 Jun;29(6):1542-1562. doi: 10.1177/0962280219865579. Epub 2019 Aug 7.
The mixed effects model for repeated measures 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 mixed effects model for repeated measures 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.
混合效应模型在重复测量中被广泛应用于分析在多个固定时间点收集的纵向临床数据。我们提出了一种基于多元 skew-t 分布的稳健扩展的重复测量混合效应模型，它包括多元正态分布、t 分布和 skew-正态分布作为特例。我们使用单调数据增强和参数扩展技术开发了一种有效的马尔可夫链蒙特卡罗算法。我们使用该算法对具有不可忽略缺失的纵向临床试验进行受控模式插补，以进行敏感性分析。通过实际数据分析说明了所提出的方法。在在线补充材料中提供了用于分析的示例 SAS 程序。
