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

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 s...

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Vydané v:Statistical methods in medical research Ročník 29; číslo 6; s. 962280219865579
Hlavný autor: Tang, Yongqiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 01.06.2020
Predmet:
monotone data augmentation
tipping point analysis
controlled imputations
penalized complexity prior
mixed effects model for repeated measures
Block sampling
ISSN:1477-0334, 1477-0334
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 23
ISSN:1477-0334
1477-0334
DOI:10.1177/0962280219865579