Ordered multiple‐class ROC analysis with continuous measurements

Receiver operating characteristic (ROC) curves have been useful in two‐group classification problems. In three‐ and multiple‐class diagnostic problems, an ROC surface or hyper‐surface can be constructed. The volume under these surfaces can be used for inference using bootstrap techniques or U‐statistics theory. In this article, ROC surfaces and hyper‐surfaces are defined and their behaviour and utility in multi‐group classification problems is investigated. The formulation of the problem is equivalent to what has previously been proposed in the general multi‐category classification problem but the definition of ROC surfaces here is less complex and addresses directly the narrower problem of ordered categories in the three‐class and, by extension, the multi‐class problem applied to continuous and ordinal data. Non‐parametric manipulation of both continuous and discrete test data and comparison between two diagnostic tests applied to the same subjects are considered. A three‐group classification example in the context of HIV neurological disease is presented and the results are discussed. Copyright © 2004 John Wiley & Sons, Ltd.

[1]  E. Lehmann,et al.  Nonparametrics: Statistical Methods Based on Ranks , 1976 .

[2]  D. Bamber The area above the ordinal dominance graph and the area below the receiver operating characteristic graph , 1975 .

[3]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[4]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[5]  J. Sidtis,et al.  Evaluation of the AIDS dementia complex in clinical trials. , 1990, Journal of acquired immune deficiency syndromes.

[6]  Anthony C. Davison,et al.  Bootstrap Methods and Their Application , 1998 .

[7]  J A Hanley,et al.  Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: an update. , 1997, Academic radiology.

[8]  M A Fischl,et al.  A randomized, controlled, double-blind study comparing the survival benefit of four different reverse transcriptase inhibitor therapies (three-drug, two-drug, and alternating drug) for the treatment of advanced AIDS. AIDS Clinical Trial Group 193A Study Team. , 1998, Journal of acquired immune deficiency syndromes and human retrovirology : official publication of the International Retrovirology Association.

[9]  D. Mossman Three-way ROCs , 1999, Medical decision making : an international journal of the Society for Medical Decision Making.

[10]  M. Lederman,et al.  Neurological outcomes in late HIV infection: adverse impact of neurological impairment on survival and protective effect of antiviral therapy. AIDS Clinical Trial Group and Neurological AIDS Research Consortium study team. , 1999, AIDS.

[11]  M. Binder,et al.  Comparing Three-class Diagnostic Tests by Three-way ROC Analysis , 2000, Medical decision making : an international journal of the Society for Medical Decision Making.

[12]  P. Heckerling Parametric Three-Way Receiver Operating Characteristic Surface Analysis Using Mathematica , 2001, Medical decision making : an international journal of the Society for Medical Decision Making.

[13]  N A Obuchowski,et al.  Assessing physicians' accuracy in diagnosing paediatric patients with acute abdominal pain: measuring accuracy for multiple diseases , 2001, Statistics in medicine.

[14]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.