Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application

Statistics in Medicine
Jihnhee YuAlan D Hutson

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 simp...Continue Reading

References

Jul 1, 1989·Medical Decision Making : an International Journal of the Society for Medical Decision Making·D K McClish
Nov 7, 2003·Biometrics·Lori E Dodd, Margaret S Pepe
May 25, 2013·Journal of Multivariate Analysis·Ao YuanGengsheng Qin
Jan 21, 2014·European Urology·Stacy LoebRuth Etzioni

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