Beyond sensitivity, specificity and statistical independence

Abstract

Sensitivity and specificity have clear definitions when there is a single test for one disease, and the test is either positive or negative. This paper presents a unified appraoch for obtaining posterior probabilities (predictive values) when there are more than two test outcomes and/or more than one disease state. In these cases, sensitivity and specificity do not have clear definitions. Three examples from the literature demonstrate how this approach simplifies the presentation of Bayesian revision of prior probabilities. Use of proper care in data collection for the purpose of estimating conditional probabilities can avoid assumptions of statistical independence.