Three-group ROC analysis: a nonparametric predictive approach

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

doi:10.1016/j.csda.2014.04.005

Three-group ROC analysis: a nonparametric predictive approach

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

Authors

Dr Tahani Coolen-Maturi tahani.maturi@durham.ac.uk
Associate Professor

F.F. Elkhafifi

Professor Frank Coolen frank.coolen@durham.ac.uk
Professor

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.

Citation

Coolen-Maturi, T., Elkhafifi, F., & Coolen, F. (2014). Three-group ROC analysis: a nonparametric predictive approach. Computational Statistics & Data Analysis, 78, 69-81. https://doi.org/10.1016/j.csda.2014.04.005

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.

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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.