Understanding diagnostic tests 1: sensitivity, specificity and predictive values

TLDR

Diagnostic test usefulness is measured by sensitivity, specificity, positive predictive value, and negative predictive value. The article provides a practical explanation of these concepts and discusses their proper use and misuse. Sensitivity and specificity assess test accuracy but cannot estimate individual disease probability, whereas predictive values can estimate probability yet vary with prevalence, making them unsuitable for transfer between populations.

Abstract

The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value. In this article, the first of the series, a simple, practical explanation of these concepts is provided and their use and misuse discussed. It is explained that while sensitivity and specificity are important measures of the diagnostic accuracy of a test, they are of no practical use when it comes to helping the clinician estimate the probability of disease in individual patients. Predictive values may be used to estimate probability of disease but both positive predictive value and negative predictive value vary according to disease prevalence. It would therefore be wrong for predictive values determined for one population to be applied to another population with a different prevalence of disease.Sensitivity and specificity are important measures of the diagnostic accuracy of a test but cannot be used to estimate the probability of disease in an individual patient. Positive and negative predictive values provide estimates of probability of disease but both parameters vary according to disease prevalence.