Three-group ROC analysis: a nonparametric predictive approach

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

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
• Electronic ISSN: 1872-7352
• 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