title: 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 approach 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.
authors.affiliation: Department of Preventive Medicine and Biometrics, School of Medicine, University of Colorado Health Sciences, Denver 80262.
authors.forename: R H
authors.initials: RH
authors.lastname: Jones
authors.forename: M W
authors.initials: MW
authors.lastname: McClatchey
mesh_terms: D003661:Decision Support Techniques; D012680:Sensitivity and Specificity; D013223:Statistics as Topic