Physicians, Probabilities, and Populations—Estimating the Likelihood of Disease Invited Commentary Invited Commentary Physicians, Probabilities, and Populations—Estimating the Likelihood of Disease for Common Clinical Scenarios Arjun K. Manrai, PhD An enviably close and influential collaboration during the 1970s without specific risk factors or symptoms for breast cancer. The between the psychologists Amos Tversky and Daniel Kahneman median (IQR) pretest probability estimate of breast cancer reshaped our beliefs about intuitive probabilistic reasoning. One was 5% (1%-10%), while the authors’ literature-based esti- of their many contributions was a demonstration of the base- mate was 0.2% to 0.3%. After a positive finding on mammog- rate fallacy, the tendency for raphy, the median (IQR) posttest probability estimate was 50% (30%-80%) among respondents, whereas the authors people to neglect prior prob- Related article abilities, or “base rates,” when computed a literature-based range of 3% to 9%. Probability calculating the chances of an event given more specific data. estimates for 2 other scenarios involving pneumonia and For example, the chances that a patient has a disease being urinary tract infection similarly differed starkly from the lit- tested reflects not only the test result and the test’s sensitivity erature-based estimates. Positive and negative likelihood ra- and specificity,
JAMA Internal Medicine – American Medical Association
Published: Jun 5, 2021
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