On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.
On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.
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DOI：
10.1002/sim.4154
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发表时间：
2011-05-10
影响因子：
2
通讯作者：
Wei, L. J.
中科院分区：
文献类型：
--
作者：
Uno, Hajime;Cai, Tianxi;Pencina, Michael J.;D'Agostino, Ralph B.;Wei, L. J.
对于现代循证医学而言，一个经过深思熟虑的预测临床事件发生的风险评分系统对于选择预防和治疗策略具有重要作用。这样的指标体系通常是通过工作参数或半参数模型，基于受试者的“基线”遗传或临床标记建立的。为了评估这种系统的充分性，医学文献中通常使用c统计来量化估计的风险评分在不同事件时间的受试者之间的区分能力。c统计量提供了对连续事件时间的拟合生存模型的全局评估，而不是专注于预测固定时间的t年生存。然而，当事件时间可能被审查时，与常用的c统计量相对应的总体参数可能取决于研究特定的审查分布。在本文中，我们将介绍一个没有这个缺点的简单c统计量。新程序一致地估计一个传统的一致性措施，这是自由的审查。我们为这个估计量的分布提供了一个大样本近似值，以便对一致性度量进行推断。数值计算结果表明，该方法在有限样本条件下具有良好的性能。
For modern evidence-based medicine, a well thought-out risk scoring system for predicting the occurrence of a clinical event plays an important role in selecting prevention and treatment strategies. Such an index system is often established based on the subject’s “baseline” genetic or clinical markers via a working parametric or semi-parametric model. To evaluate the adequacy of such a system, C-statistics are routinely used in the medical literature to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. The C-statistic provides a global assessment of a fitted survival model for the continuous event time rather than focuses on the prediction of t-year survival for a fixed time. When the event time is possibly censored, however, the population parameters corresponding to the commonly used C-statistics may depend on the study-specific censoring distribution. In this article, we present a simple C-statistic without this shortcoming. The new procedure consistently estimates a conventional concordance measure which is free of censoring. We provide a large sample approximation to the distribution of this estimator for making inferences about the concordance measure. Results from numerical studies suggest that the new procedure performs well in finite sample.
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