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- W4318212877 abstract "Abstract This chapter introduces readers to the use of Bayes’ theorem as a potential way to address the drawbacks of sensitivity, specificity, and predictive values. It describes the process of updating the probability of a condition based on test results and that the principal statistics (Bayes’ factors) are the likelihood ratios which are calculated from all four squares of a 2×2 table. It explains how the application of Bayes’ theorem to medical testing begins with a calculation of the pre-test probability of a condition using intuition and experience or, where available, a data-driven approach. It continues with the method of combining pre-test probabilities with likelihood ratios to give post-test probabilities, which can be illustrated with a Fagan nomogram but is best performed with a smartphone app. It concludes with a discussion of screening tests which are typically performed at very low pre-test probability when tests with very high diagnostic accuracy are needed." @default.
- W4318212877 created "2023-01-27" @default.
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- W4318212877 date "2022-12-01" @default.
- W4318212877 modified "2023-09-27" @default.
- W4318212877 title "Test Accuracy As Likelihood Ratios" @default.
- W4318212877 doi "https://doi.org/10.1093/med/9780192866615.003.0005" @default.
- W4318212877 hasPublicationYear "2022" @default.
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