TY - GEN
T1 - ROC analysis for multiple markers with tree-based classification
AU - Wang, Mei Cheng
AU - Li, Shanshan
N1 - Funding Information:
(quoted from http://adni.loni.ucla.edu/ ). The study is supported by the NIH, private pharmaceutical companies, and nonprofit organizations. Enrollment target was 800 participants – 200 normal controls, 400 patients with amnestic MCI, and 200 patients with mild AD – at 58 sites in the United States and Canada. Participants were enrolled on a rolling basis, and evaluated every six months. One of the major goals of the ADNI study is to identify biomarkers that are associated with progression from MCI to AD, and determine which biomarker measures (alone or in combination) are the best predictors of disease progression. Sensitivity and specificity for both cross-sectional and longitudinal diagnostic classification were considered important statistical techniques for assessing biomarkers in disease progression [].
Publisher Copyright:
© Springer Science+Business Media New York 2013.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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U2 - 10.1007/978-1-4614-8981-8_9
DO - 10.1007/978-1-4614-8981-8_9
M3 - Conference contribution
AN - SCOPUS:84945970182
SN - 9781461489801
T3 - Lecture Notes in Statistics
SP - 179
EP - 198
BT - Risk Assessment and Evaluation of Predictions
A2 - Gandy, Axel
A2 - Satten, Glen
A2 - Gail, Mitchell
A2 - Pfeiffer, Ruth
A2 - Cai, Tianxi
A2 - Lee, Mei-Ling Ting
PB - Springer Science and Business Media, LLC
T2 - International conference on Risk Assessment and Evaluation of Predictions, 2011
Y2 - 12 October 2011 through 14 October 2011
ER -