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Bayesian sensitivity analysis of incomplete data: bridging pattern-mixture and selection models.

Niko A Kaciroti1, Trivellore Raghunathan

  • 1Center of Human Growth and Development, University of Michigan, Ann Arbor, MI, U.S.A.; Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.

Statistics in Medicine
|September 27, 2014
PubMed
Summary
This summary is machine-generated.

Pattern-mixture models (PMM) and selection models (SM) offer ways to analyze incomplete data. This study introduces new parameterizations for unified sensitivity analysis in both PMM and SM, enhancing transparency and ease of use.

Keywords:
identifiabilityignorability indexmissing not at randommixture analysistime-varying covariates

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Incomplete data analysis requires specialized statistical models like pattern-mixture models (PMM) and selection models (SM).
  • Both PMM and SM rely on unverifiable assumptions and necessitate additional constraints for parameter identification.
  • Addressing nonignorable missing-data mechanisms is crucial for robust statistical inference.

Purpose of the Study:

  • To introduce novel, intuitive parameterizations for identifying PMM across various exponential family distributions.
  • To translate these PMM parameterizations into equivalent Selection Model (SM) approaches.
  • To establish a unified framework for conducting sensitivity analysis under both PMM and SM settings.

Main Methods:

  • Development of transparent and user-friendly parameterizations for PMM.
  • Equivalence translation of PMM parameterizations to SM.
  • Application of a Bayesian approach for sensitivity analysis using informative prior distributions on sensitivity parameters.
  • Utilizing Gibbs sampling for model fitting.

Main Results:

  • New parameterizations facilitate intuitive identification of PMM for diverse outcome distributions.
  • The developed parameterizations offer dual interpretations from both PMM and SM perspectives.
  • A unified framework for sensitivity analysis is established, applicable to both modeling approaches.
  • Bayesian inference with informative priors allows for robust sensitivity analysis.

Conclusions:

  • The proposed parameterizations enhance the transparency and ease of use for PMM and SM.
  • The unified framework simplifies sensitivity analysis in the presence of nonignorable missing data.
  • The Bayesian approach with informative priors provides a powerful tool for assessing the impact of missing data assumptions.