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

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.

journal_name

Stat Methods Med Res

authors

Tang Y

doi

10.1177/0962280219865579

subject

Has Abstract

pub_date

2020-06-01 00:00:00

pages

1542-1562

issue

6

eissn

0962-2802

issn

1477-0334

journal_volume

29

pub_type

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