Literature DB >> 3276261

Posttest probability calculation by weights. A simple form of Bayes' theorem.

C M Rembold1, D Watson.   

Abstract

This article reintroduces a different form of Bayes' theorem that allows calculation of posttest probabilities by adding quantities known as "weights." A weight combines information found in both a test's sensitivity and specificity. A single value can describe how a given test result changes the posttest probability of disease. The use of weights and this form of Bayes' theorem should allow more widespread understanding and use of probability theory in clinical practice.

Mesh:

Year:  1988        PMID: 3276261     DOI: 10.7326/0003-4819-108-1-115

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  4 in total

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Authors:  R Cummins
Journal:  J Gen Intern Med       Date:  1990 Jul-Aug       Impact factor: 5.128

2.  A formula for estimating pretest probability: evaluation and clinical application.

Authors:  N M Gayed; D E Kern
Journal:  J Gen Intern Med       Date:  1990 Jul-Aug       Impact factor: 5.128

3.  Turtles all the way down: do biological mechanisms for epidemiological observations always matter?

Authors:  David T P Buis; Jos van Roosmalen
Journal:  Eur J Epidemiol       Date:  2021-10-07       Impact factor: 8.082

4.  How Much Overtesting Is Needed to Safely Exclude a Diagnosis? A Different Perspective on Triage Testing Using Bayes' Theorem.

Authors:  Jonne J Sikkens; Djoeke G Beekman; Abel Thijs; Patrick M Bossuyt; Yvo M Smulders
Journal:  PLoS One       Date:  2016-03-03       Impact factor: 3.240

  4 in total

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