title: Posttest probability calculation by weights. A simple form of Bayes' theorem.
abstract: This article reintroduces a different form of Bayes' theorem that allows calculation of posttest probabilities by adding quantities known as "weights." A weight combines information found in both a test's sensitivity and specificity. A single value can describe how a given test result changes the posttest probability of disease. The use of weights and this form of Bayes' theorem should allow more widespread understanding and use of probability theory in clinical practice.
authors.affiliation: Department of Internal Medicine, University of Virginia Medical Center, Charlottesville.
authors.forename: C M
authors.initials: CM
authors.lastname: Rembold
authors.forename: D
authors.initials: D
authors.lastname: Watson
mesh_terms: D001499:Bayes Theorem; D001707:Biopsy, Needle; D003327:Coronary Disease / Q000175:diagnosis; D006801:Humans; D011336:Probability; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging; D011877:Radionuclide Imaging; D012680:Sensitivity and Specificity; D013964:Thyroid Neoplasms / Q000473:pathology; D014692:Ventilation-Perfusion Ratio