 Three-group ROC analysis: A nonparametric predictive approachTahani Coolen-Maturi, Faiza F. Elkhafifi and Frank P.A. Coolen Computational Statistics & Data Analysis, 2014, vol. 78, issue C, 69-81 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. Keywords: Diagnostic accuracy; Lower and upper probability; Nonparametric predictive inference; Receiver operating characteristic (ROC) surface; Youden’s index (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:78:y:2014:i:c:p:69-81 DOI: 10.1016/j.csda.2014.04.005 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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