A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal or t distributions. (June 2020)

• Record Type: Journal Article
• Authors: Tang, Yongqiang
• Is Part Of: Statistical methods in medical research. Volume 29:Number 6(2020)
• Page Start: 1542
• Page End: 1562
• Publication Date: 2020-06
• Subjects: Block sampling -- controlled imputations -- mixed effects model for repeated measures -- monotone data augmentation -- penalized complexity prior -- tipping point analysis; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280219865579
• Languages: English
• ISSNs: 0962-2802

Abstract

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.