Correlation-adjusted estimation of sensitivity and specificity of two diagnostic tests

Marios P. Georgiadis, Wesley O. Johnson, Ian Gardner, Ramanpreet Singh

Research output: Contribution to journalArticlepeer-review

143 Scopus citations


Models for multiple-test screening data generally require the assumption that the tests are independent conditional on disease state. This assumption may be unreasonable, especially when the biological basis of the tests is the same. We propose a model that allows for correlation between two diagnostic test results. Since models that incorporate test correlation involve more parameters than can be estimated with the available data, posterior inferences will depend more heavily on prior distributions, even with large sample sizes. If we have reasonably accurate information about one of the two screening tests (perhaps the standard currently used test) or the prevalences of the populations tested, accurate inferences about all the parameters, including the test correlation, are possible. We present a model for evaluating dependent diagnostic tests and analyse real and simulated data sets. Our analysis shows that, when the tests are correlated, a model that assumes conditional independence can perform very poorly. We recommend that, if the tests are only moderately accurate and measure the same biological responses, researchers use the dependence model for their analyses.

Original languageEnglish (US)
Pages (from-to)63-76
Number of pages14
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number1
StatePublished - Oct 1 2003


  • Bayesian approach
  • Data augmentation approach
  • Dependent screening tests
  • Gibbs sampling
  • Prevalence

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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