A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal or t distributions

被引:1
|
作者
Tang, Yongqiang [1 ]
机构
[1] Tesaro, Dept Biometr, 1000 Winter St, Waltham, MA 02451 USA
关键词
Block sampling; controlled imputations; mixed effects model for repeated measures; monotone data augmentation; penalized complexity prior; tipping point analysis; LINEAR MIXED MODELS; OBJECTIVE BAYESIAN-ANALYSIS; PATTERN-MIXTURE-MODELS; MULTIPLE IMPUTATION; MISSING DATA; COVARIANCE STRUCTURE; SENSITIVITY-ANALYSIS; MONTE-CARLO; INFERENCE; VARIANCE;
D O I
10.1177/0962280219865579
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
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页码:1542 / 1562
页数:21
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