A simple Bayesian analysis of misclassified binary data with a validation substudy

Gordon James Prescott, P. H. Garthwaite

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


A two-stage Bayesian method is presented for analyzing case-control studies in which a binary variable is sometimes measured with error but the correct values of the variable are known for a random subset of the study group. The first stage of the method is analytically tractable and MCMC methods are used for the second stage. The posterior distribution from the first stage becomes the prior distribution for the second stage, thus transferring all relevant information between the stages. The method makes few distributional assumptions and requires no asymptotic approximations. It is computationally fast and can be run using standard software. It is applied to two data sets that have been analyzed by other methods, and results are compared.

Original languageEnglish
Pages (from-to)454-458
Number of pages4
Issue number2
Publication statusPublished - Jun 2002


  • errors in variables
  • measurement error
  • misclassification
  • odds ratio

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