Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
Biostatistics. 2013 Jan;14(1):42-59. doi: 10.1093/biostatistics/kxs021. Epub 2012 Jun 25.
A major biomedical goal associated with evaluating a candidate biomarker or developing a predictive model score for event-time outcomes is to accurately distinguish between incident cases from the controls surviving beyond t throughout the entire study period. Extensions of standard binary classification measures like time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves have been developed in this context (Heagerty, P. J., and others, 2000. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337-344). We propose a direct, non-parametric method to estimate the time-dependent Area under the curve (AUC) which we refer to as the weighted mean rank (WMR) estimator. The proposed estimator performs well relative to the semi-parametric AUC curve estimator of Heagerty and Zheng (2005. Survival model predictive accuracy and ROC curves. Biometrics 61, 92-105). We establish the asymptotic properties of the proposed estimator and show that the accuracy of markers can be compared very simply using the difference in the WMR statistics. Estimators of pointwise standard errors are provided.
评估候选生物标志物或为事件时间结果开发预测模型评分的主要生物医学目标之一是在整个研究期间准确区分存活至 t 时间以上的对照与发病病例。在这种情况下，已经开发了标准二分类测量的扩展，如时间依赖性敏感性、特异性和接收者操作特征（ROC）曲线（Heagerty，PJ，等人，2000. 用于删失生存数据和诊断标记的时间依赖性 ROC 曲线。生物统计学 56，337-344）。我们提出了一种直接的、非参数方法来估计时间依赖性曲线下面积（AUC），我们称之为加权平均秩（WMR）估计量。与 Heagerty 和 Zheng（2005. 生存模型预测准确性和 ROC 曲线。生物统计学 61，92-105）提出的半参数 AUC 曲线估计量相比，该估计量表现良好。我们建立了所提出的估计量的渐近性质，并表明可以通过 WMR 统计量的差异非常简单地比较标记物的准确性。还提供了点估计标准误差的估计量。
