Time‐Dependent Predictive Accuracy in the Presence of Competing Risks
Time‐Dependent Predictive Accuracy in the Presence of Competing Risks
复制标题
存在竞争风险时的时间依赖性预测准确性
作者：
P. Saha;P. Saha;P. Heagerty
总结在至事件发生时间研究中自然出现竞争风险。在本文中，当我们审查了生存时间和竞争风险时，我们提出了标记物的时间依赖性准确性度量。敏感性或真阳性（TP）分数的时间依赖性版本自然对应于在固定时间段内累积的累积（或流行）病例，或在任何选定时间在无事件受试者中观察到的事件病例。 时间依赖性（动态）特异性（1-假阳性（FP））可基于无事件受试者中的标志物分布。我们扩展了这些定义，以纳入竞争风险结局的失效原因。提出的原因特异性累积TP/动态FP的估计是基于标记物和事件时间的二元分布函数的最近邻估计。 另一方面，事件TP/动态FP可以使用针对特定原因危害的可能非比例危害考克斯模型和标志物分布的风险集重新加权来估计。 所提出的方法扩展了Heageland，Lumley和Pepe（2000，Biometrics 56，337-344）以及Heageland和Zheng（2005，Biometrics 61，92-105）的时间依赖性预测准确性测量。
Summary Competing risks arise naturally in time‐to‐event studies. In this article, we propose time‐dependent accuracy measures for a marker when we have censored survival times and competing risks. Time‐dependent versions of sensitivity or true positive (TP) fraction naturally correspond to consideration of either cumulative (or prevalent) cases that accrue over a fixed time period, or alternatively to incident cases that are observed among event‐free subjects at any select time. Time‐dependent (dynamic) specificity (1–false positive (FP)) can be based on the marker distribution among event‐free subjects. We extend these definitions to incorporate cause of failure for competing risks outcomes. The proposed estimation for cause‐specific cumulative TP/dynamic FP is based on the nearest neighbor estimation of bivariate distribution function of the marker and the event time. On the other hand, incident TP/dynamic FP can be estimated using a possibly nonproportional hazards Cox model for the cause‐specific hazards and riskset reweighting of the marker distribution. The proposed methods extend the time‐dependent predictive accuracy measures of Heagerty, Lumley, and Pepe (2000, Biometrics 56, 337–344) and Heagerty and Zheng (2005, Biometrics 61, 92–105).