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On Uncertainty in Medical TestingFuqua School of Business, Duke University, Durham, North Carolina
Fuqua School of Business, Duke University, Durham, North Carolina 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.
Key Words: predictive value of tests • sensitivity and specificity • Bayesian analysis • Bayes’ theorem • uncertainty
Medical Decision Making, Vol. 24, No. 6,
654-658 (2004) This article has been cited by other articles:
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