Abstract
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.
Highlights
The diagnostic process in clinical practice [1e3] is based on probabilities and, consequentially, filled with uncertainty
During the process of establishing a diagnosis, the probability of the disease of interest is continuously shifting, either upward or downward, depending on the specific information gathered during the diagnostic process
Sensitivity refers to the probability of a truepositive test result in someone with the disease, whereas specificity refers to the probability of a true-negative test
Summary
The diagnostic process in clinical practice [1e3] is based on probabilities and, consequentially, filled with uncertainty. During the process of establishing a diagnosis, the probability of the disease of interest is continuously shifting, either upward or downward, depending on the specific information gathered during the diagnostic process. Clinicians often tend to overestimate the predictive value of a diagnostic test result when not appropriately considering the prior disease probability [4]. This is where a famous statistical theorem can help: Bayes’ rule [5]. One of the uses of Bayes’ rule is quantifying the diagnostic process of updating prior into posterior probabilities [6]
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