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Three-group ROC analysis: a nonparametric predictive approach

Coolen-Maturi, T.; Elkhafifi, F.F.; Coolen, F.P.A.

Three-group ROC analysis: a nonparametric predictive approach Thumbnail


Authors

F.F. Elkhafifi



Abstract

Measuring the accuracy of diagnostic tests is crucial in many application areas, in particular medicine and health care. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three ordered classes or groups. Nonparametric predictive inference (NPI) is a frequentist statistical method that is explicitly aimed at using few modelling assumptions in addition to data, enabled through the use of lower and upper probabilities to quantify uncertainty. It focuses exclusively on a future observation, which may be particularly relevant if one considers decisions about a diagnostic test to be applied to a future patient. The NPI approach to three-group ROC analysis is presented, including results on the volumes under the ROC surfaces and choice of decision threshold for the diagnosis.

Journal Article Type Article
Acceptance Date Apr 8, 2014
Online Publication Date Apr 19, 2014
Publication Date Oct 1, 2014
Deposit Date May 6, 2014
Publicly Available Date Jun 4, 2014
Journal Computational Statistics & Data Analysis
Print ISSN 0167-9473
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 78
Pages 69-81
DOI https://doi.org/10.1016/j.csda.2014.04.005
Keywords Diagnostic accuracy, Lower and upper probability, Nonparametric predictive inference, Receiver operating characteristic (ROC) surface, Youden’s index.
Public URL https://durham-repository.worktribe.com/output/1433359

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Accepted Journal Article (657 Kb)
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Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, 78, 2014, 10.1016/j.csda.2014.04.005.






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