Bayes’ rule in diagnosis

M.J.L. Bours*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Establishing an accurate diagnosis is crucial in everyday clinical practice. It forms the starting point for clinical decision-making, for instance regarding treatment options or further testing. In this context, clinicians have to deal with probabilities (instead of certainties) that are often hard to quantify. During the diagnostic process, clinicians move from the probability of disease before testing (prior or pretest probability) to the probability of disease after testing (posterior or posttest probability) based on the results of one or more diagnostic tests. This reasoning in probabilities is reflected by a statistical theorem that has an important application in diagnosis: Bayes' rule. A basic understanding of the use of Bayes' rule in diagnosis is pivotal for clinicians. This rule shows how both the prior probability (also called prevalence) and the measurement properties of diagnostic tests (sensitivity and specificity) are crucial determinants of the posterior probability of disease (predictive value), on the basis of which clinical decisions are made. This article provides a simple explanation of the interpretation and use of Bayes' rule in diagnosis. (C) 2020 The Author(s). Published by Elsevier Inc.
Original languageEnglish
Pages (from-to)158-160
Number of pages3
JournalJournal of Clinical Epidemiology
Publication statusPublished - 1 Mar 2021


  • Diagnosis
  • Diagnostic testing
  • Bayes' rule
  • Prior probability
  • Prevalence
  • Predictive values
  • Sensitivity
  • Specificity

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