On Uncertainty in Medical Testing

Robert L. Winkler and James E. Smith
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Robert L. Winkler: Fuqua School of Business, Duke University, Durham, North Carolina
James E. Smith: Fuqua School of Business, Duke University, Durham, North Carolina

Medical Decision Making, 2004, vol. 24, issue 6, 654-658

Abstract: 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.

Keywords: predictive value of tests; sensitivity and specificity; Bayesian analysis; Bayes’ theorem; uncertainty (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:24:y:2004:i:6:p:654-658

DOI: 10.1177/0272989X04271045

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