Graphic representation of sequential Bayesian analysis

I. Heller; M. Topilsky; I. Shapira; Aharon Isakov

doi:10.1055/s-0038-1634188

Graphic representation of sequential Bayesian analysis

I. Heller, M. Topilsky, I. Shapira, Aharon Isakov^*

^*Corresponding author for this work

Clinical Departments

Research output: Contribution to journal › Article › peer-review

Abstract

Previously undescribed algebraic transforms of Bayes' theorem define families of operating points in the receiver operating characteristic (ROC) space which, at given pre-test probability, produce constant post-test probabilities. These isopredictive operating points form straight lines in the ROC space. The lines can be used to emulate Bayesian sequential analysis in a strictly graphic procedure, which can be applied in clinical work and medical education.

Original language	English
Pages (from-to)	182-186
Number of pages	5
Journal	Methods of Information in Medicine
Volume	38
Issue number	3
DOIs	https://doi.org/10.1055/s-0038-1634188
State	Published - 1999

Keywords

Bayes' Theorem
Decision Support
Diagnosis
Predictive Value of Tests
ROC Space

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10.1055/s-0038-1634188

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KW - Decision Support

KW - Diagnosis

KW - Predictive Value of Tests

KW - ROC Space

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Graphic representation of sequential Bayesian analysis

Abstract

Keywords

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