Modelling risk when binary outcomes are subject to error

Pat McInturff, Wesley O. Johnson, David Cowling, Ian Gardner

Research output: Contribution to journalArticlepeer-review

88 Scopus citations


We present methods for binomial regression when the outcome is determined using the results of a single diagnostic test with imperfect sensitivity and specificity. We present our model, illustrate it with the analysis of real data, and provide an example of WinBUGS program code for performing such an analysis. Conditional means priors are used in order to allow for inclusion of prior data and expert opinion in the estimation of odds ratios, probabilities, risk ratios, risk differences, and diagnostic test sensitivity and specificity. A simple method of obtaining Bayes factors for link selection is presented. Methods are illustrated and compared with Bayesian ordinary binary regression using data from a study of the effectiveness of a smoking cessation program among pregnant women. Regression coefficient estimates are shown to change noticeably when expert prior knowledge and imperfect sensitivity and specificity are incorporated into the model.

Original languageEnglish (US)
Pages (from-to)1095-1109
Number of pages15
JournalStatistics in Medicine
Issue number7
StatePublished - Apr 15 2004


  • Bayes factor
  • Bayesian
  • Logistic regression
  • Markov chain Monte Carlo
  • Sensitivity
  • Specificity

ASJC Scopus subject areas

  • Epidemiology


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