Bayesian inference of odds ratios in misclassified binary data with a validation substudy

Dewi Rahardja, Yan D. Zhao, Hao Helen Zhang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

We propose a fully Bayesian model with a non-informative prior for analyzing misclassified binary data with a validation substudy. In addition, we derive a closed-form algorithm for drawing all parameters from the posterior distribution and making statistical inference on odds ratios. Our algorithm draws each parameter from a beta distribution, avoids the specification of initial values, and does not have convergence issues. We apply the algorithm to a data set and compare the results with those obtained by other methods. Finally, the performance of our algorithm is assessed using simulation studies.

Original languageEnglish (US)
Pages (from-to)1845-1854
Number of pages10
JournalCommunications in Statistics: Simulation and Computation
Volume39
Issue number10
DOIs
StatePublished - Nov 1 2010
Externally publishedYes

Keywords

  • Bayesian inference
  • Binary data
  • Credible interval
  • Misclassification
  • Odds ratio

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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