On uncertainty in medical testing.
There is confusion in the medical decision-making literature about how to handle uncertainty in medical tests. In this article, the authors consider the situation in which there is uncertainty about the pretest probability of a disease in a patient as well as uncertainty about the sensitivity and specificity of a diagnostic test for that disease. They discuss how to calculate posttest probabilities of a disease under such uncertainty and how to calculate a distribution for a posttest probability. They show that given certain independence assumptions, uncertainty about these parameters need not complicate the calculation of patient positive predictive values: One can simply use the expected values of the parameters in the standard Bayesian formula for posttest probabilities. The discussion on how to calculate distributions for positive predictive values corrects a common and potentially important error.
Winkler
Robert L
RL
Fuqua School of Business, Duke University, Durham, North Carolina 27708-0120, USA. rwinkler@duke.edu
Smith
James E
JE
D016001:Confidence Intervals; D003657:Decision Making; D019937:Diagnostic Techniques and Procedures / Q000706:statistics & numerical data*; D006801:Humans; D011237:Predictive Value of Tests; D035501:Uncertainty