Combining biomarkers for classification with covariate adjustment. Stat Med 2017 Jul 10;36(15):2347-2362
Date
03/10/2017Pubmed ID
28276080Pubmed Central ID
PMC5459674DOI
10.1002/sim.7274Scopus ID
2-s2.0-85014963469 (requires institutional sign-in at Scopus site) 8 CitationsAbstract
Combining multiple markers can improve classification accuracy compared with using a single marker. 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 characteristic (ROC) curve has been proposed as a tool to tease out the covariate effect in the evaluation of a single marker; this curve characterizes the classification accuracy solely because of the marker of interest. 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 article, 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. The proposed estimator is shown to be consistent and asymptotically normally distributed. We conduct simulations to evaluate the performance of our estimator in cohort and case/control designs and compare several different weighting strategies during estimation with respect to efficiency. Our estimator is also compared with alternative regression-model-based estimators or estimators that maximize the empirical area under the ROC curve, with respect to bias and efficiency. We apply the proposed method to a biomarker study from an human immunodeficiency virus vaccine trial. Copyright © 2017 John Wiley & Sons, Ltd.
Author List
Kim S, Huang YAuthor
Soyoung Kim PhD, BS, MS Associate Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AIDS VaccinesArea Under Curve
Bias
Biomarkers
Biostatistics
Case-Control Studies
Cohort Studies
Computer Simulation
HIV Infections
Humans
Linear Models
Logistic Models
Models, Statistical
Multivariate Analysis
ROC Curve
Randomized Controlled Trials as Topic
Regression Analysis
