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Activity Number: 278 - Combining Markers for Classification in Practical Tasks
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #326598
Title: Robust Combination of Biomarkers for Classification with Covariate Adjustment
Author(s): Ying Huang* and Soyoung Kim
Companies: Fred Hutchinson Cancer Research Center and Medical College of Wisconsin
Keywords: Area under the ROC curve; Biomarker Combination; Classification; Covariate adjustment; Receiver operating characteristics curve

In practice, covariates associated with markers or disease outcome can affect the performance of a biomarker or biomarker combination in the population. The covariate-adjusted receiver operating characteristics (ROC) curve has been proposed as a tool to tease out the covariate effect in the evaluation of a single marker. However, research on the effect of covariates on the performance of marker combinations and on how to adjust for the covariate effect when combining markers is still lacking. In this project, we examine the effect of covariates on classification performance of linear marker combinations and propose to adjust for covariates in combining markers by maximizing the nonparametric estimate of the area under the covariate-adjusted ROC curve. The proposed method provides a way to estimate the best linear biomarker combination that is robust to risk model assumptions underlying alternative regression-model-based methods. We developed asymptotic properties of the estimator and conducted extensive simulations to compare its performance with alternative regression-model-based estimators or estimators that maximize the empirical area under the ROC curve.

Authors who are presenting talks have a * after their name.

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