Communications for Statistical Applications and Methods

  • 제22권6호
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  • Pages.589-598
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  • 2015
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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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)
  • 투고 : 2015.07.03
  • 심사 : 2015.10.23
  • 발행 : 2015.11.30
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초록

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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참고문헌

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