Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized...

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Bibliographic Details
Published inCommunications for statistical applications and methods Vol. 22; no. 6; pp. 589 - 598
Main Authors Kyoung, Yujung, Lee, Keunbaik
Format Journal Article
LanguageKorean
Published 2015
Subjects
pattern mixture model
binary data
sensitivity analysis
nonignorable missing
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Summary:In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.
Bibliography:KISTI1.1003/JNL.JAKO201501255362862
ISSN:2287-7843