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Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness
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 Title & Authors
Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness
Kyoung, Yujung; Lee, Keunbaik;
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 Abstract
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.
 Keywords
pattern mixture model;nonignorable missing;sensitivity analysis;binary data;
 Language
English
 Cited by
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