Non-parametric estimation for time-dependent AUC

被引:27
|
作者
Chiang, Chin-Tsang [1 ]
Hung, Hung [1 ]
机构
[1] Natl Taiwan Univ, Dept Math, Taipei 10617, Taiwan
关键词
AUC; Bivariate estimation; Bootstrap; Gaussian process; Kaplan-Meier estimator; Non-parametric estimator; ROC; Smoothing parameter; Survival data; BIVARIATE DISTRIBUTION; REGRESSION;
D O I
10.1016/j.jspi.2009.10.012
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The area under the receiver operating characteristic (ROC) curve (AUC) is one of the commonly used measure to evaluate or compare the predictive ability of markers to the disease status. Motivated by an angiographic coronary artery disease (CAD) study, our objective is mainly to evaluate and compare the performance of several baseline plasma levels in the prediction of CAD-related vital status over time. Based on censored survival data, the non-parametric estimators are proposed for the time-dependent AUC. The limiting Gaussian processes of the estimators and the estimated asymptotic variance-covariance functions enable us to further construct confidence bands and develop testing procedures. Applications and finite sample properties of the proposed estimation methods and inference procedures are demonstrated through the CAD-related death data from the British Columbia Vital Statistics Agency and Monte Carlo simulations. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:1162 / 1174
页数:13
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