Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application. (21st January 2016)

• Record Type: Journal Article
• Authors: Yu, Jihnhee; Yang, Luge; Vexler, Albert; Hutson, Alan D.
• Is Part Of: Statistics in medicine. Volume 35:Number 13(2016)
• Page Start: 2251
• Page End: 2282
• Publication Date: 2016-01-21
• Subjects: classification -- empirical likelihood -- nonparametric methods -- pAUC -- two correlated biomarkers; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6863
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non‐disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U‐statistic with the plug‐in sample quantile, making the estimator a non‐traditional U‐statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U‐statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd.