Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/53141
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dc.titleROC analysis with multiple classes and multiple tests: Methodology and its application in microarray studies
dc.contributor.authorLi, J.
dc.contributor.authorFine, J.P.
dc.date.accessioned2014-05-19T02:54:45Z
dc.date.available2014-05-19T02:54:45Z
dc.date.issued2008-07
dc.identifier.citationLi, J., Fine, J.P. (2008-07). ROC analysis with multiple classes and multiple tests: Methodology and its application in microarray studies. Biostatistics 9 (3) : 566-576. ScholarBank@NUS Repository.
dc.identifier.issn14654644
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/53141
dc.description.abstractThe accuracy of a single diagnostic test for binary outcome can be summarized by the area under the receiver operating characteristic (ROC) curve. Volume under the surface and hypervolume under the manifold have been proposed as extensions for multiple class diagnosis (Scurfield, 1996, 1998). However, the lack of simple inferential procedures for such measures has limited their practical utility. Part of the difficulty is that calculating such quantities may not be straightforward, even with a single test. The decision rule used to generate the ROC surface requires class probability assessments, which are not provided by the tests. We develop a method based on estimating the probabilities via some procedure, for example, multinomial logistic regression. Bootstrap inferences are proposed to account for variability in estimating the probabilities and perform well in simulations. The ROC measures are compared to the correct classification rate, which depends heavily on class prevalences. An example of tumor classification with microarray data demonstrates that this property may lead to substantially different analyses. The ROC-based analysis yields notable decreases in model complexity over previous analyses. © The Author 2008. Published by Oxford University Press. All rights reserved.
dc.sourceScopus
dc.subjectClass prevalence
dc.subjectDiagnostic accuracy
dc.subjectMaximum likelihood estimation
dc.subjectMulticategory classification
dc.subjectMultinomial logistic regression
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleBiostatistics
dc.description.volume9
dc.description.issue3
dc.description.page566-576
dc.identifier.isiut000256977000015
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