Please use this identifier to cite or link to this item: https://doi.org/10.1080/00949655.2021.1954184
Title: Hypervolume Under ROC Manifold for Discrete Biomarkers with Ties
Authors: Qunqiang Feng
Jialiang Li 
Xingrun Ping
Ben Van Calster
Keywords: Diagnostic medicine
discrete distribution
biomarkers
multi-category classification
hypervolume under ROC manifold
Issue Date: 20-Jul-2021
Publisher: Taylor & Francis
Citation: Qunqiang Feng, Jialiang Li, Xingrun Ping, Ben Van Calster (2021-07-20). Hypervolume Under ROC Manifold for Discrete Biomarkers with Ties. Journal of Statistical Computation and Simulation 91 (18) : 3864-3879. ScholarBank@NUS Repository. https://doi.org/10.1080/00949655.2021.1954184
Abstract: Medical multi-category diagnostic problems may involve discrete biomarkers. Many traditional accuracy measures are based on the assumption that all biomarkers follow continuous distributions and consequently may underestimate the true discrimination ability of the discrete markers. In particular, we focus on Hypervolume Under ROC Manifold (HUM) in this paper and propose an extension of the familiar continuous version of HUM to incorporate discrete biomarkers with ties. Statistical estimation and inference procedures are proposed along with asymptotic properties. We carry out simulation studies to examine the performance of our proposed estimators for the new HUM measure. A real medical example is analysed to illustrate our methodology.
Source Title: Journal of Statistical Computation and Simulation
URI: https://scholarbank.nus.edu.sg/handle/10635/210937
ISSN: 0094-9655
DOI: 10.1080/00949655.2021.1954184
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