Sample size calculations for studies designed to evaluate diagnostic test accuracy

Adam J. Branscum, Wesley O. Johnson, Ian Gardner

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


We developed a Bayesian approach to sample size calculations for cross-sectional studies designed to estimate sensitivity and specificity of one or more diagnostic tests. Sample size calculations can be made for common study designs such as one test in one population, two conditionally independent or dependent tests in ≤ 2 populations, and three tests in ≤ 2 populations.We determine a sample size combination that yields high predictive probability, with respect to the future study data, of accurate and precise estimates of sensitivity and specificity. We also consider hypothesis testing for demonstrating the superiority or equivalence of one diagnostic test relative to another. The predictive probability can also be computed when the sample size combination is fixed in advance, thereby providing a "power-like" measure for the future study. The method is straightforward to implement using the S-Plus/R library emBedBUGS together with WinBUGS.

Original languageEnglish (US)
Pages (from-to)112-127
Number of pages16
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number1
StatePublished - Mar 1 2007


  • Bayesian modeling
  • Prediction
  • Screening tests
  • Sensitivity
  • Specificity
  • WinBUGS

ASJC Scopus subject areas

  • Statistics and Probability
  • Environmental Science(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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