ROC analysis for multiple markers with tree-based classification

Mei Cheng Wang, Shanshan Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Multiple biomarkers are frequently observed or collected for detecting or understanding a disease. The research interest of this paper is to extend tools of ROC analysis from univariate marker setting to multivariate marker setting for evaluating predictive accuracy of biomarkers using a tree-based classification rule. Using an arbitrarily combined and-or classifier, an ROC function together with a weighted ROC function (WROC) and their conjugate counterparts are introduced for examining the performance of multivariatemarkers. Specific features of the ROC and WROC functions and other related statistics are discussed in comparison with those familiar properties for univariate marker. Nonparametric methods are developed for estimating the ROC and WROC functions, and area under curve (AUC) and concordance probability.With emphasis on population average performance of markers, the proposed procedures and inferential results are useful for evaluating marker predictability based on multivariate marker measurements with different choices of markers, and for evaluating different and-or combinations in classifiers.

Original languageEnglish (US)
Title of host publicationRisk Assessment and Evaluation of Predictions
EditorsAxel Gandy, Glen Satten, Mitchell Gail, Ruth Pfeiffer, Tianxi Cai, Mei-Ling Ting Lee
PublisherSpringer Science and Business Media, LLC
Number of pages20
ISBN (Print)9781461489801
StatePublished - 2015
EventInternational conference on Risk Assessment and Evaluation of Predictions, 2011 - Silver Spring, United States
Duration: Oct 12 2011Oct 14 2011

Publication series

NameLecture Notes in Statistics
ISSN (Print)0930-0325
ISSN (Electronic)2197-7186


OtherInternational conference on Risk Assessment and Evaluation of Predictions, 2011
Country/TerritoryUnited States
CitySilver Spring

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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