Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness

  • Kyoung, Yujung (Department of Statistics, Sungkyunkwan University) ;
  • Lee, Keunbaik (Department of Statistics, Sungkyunkwan University)
  • Received : 2015.07.03
  • Accepted : 2015.10.23
  • Published : 2015.11.30
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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.

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References

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