Pepe Margaret S, Zheng Yingye, Jin Yuying, Huang Ying, Parikh Chirag R, Levy Wayne C
Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., M2-B500, Seattle, WA 98109, USA.
Lifetime Data Anal. 2008 Mar;14(1):86-113. doi: 10.1007/s10985-007-9073-x. Epub 2007 Dec 7.
Receiver operating characteristic (ROC) curves play a central role in the evaluation of biomarkers and tests for disease diagnosis. Predictors for event time outcomes can also be evaluated with ROC curves, but the time lag between marker measurement and event time must be acknowledged. We discuss different definitions of time-dependent ROC curves in the context of real applications. Several approaches have been proposed for estimation. We contrast retrospective versus prospective methods in regards to assumptions and flexibility, including their capacities to incorporate censored data, competing risks and different sampling schemes. Applications to two datasets are presented.
受试者工作特征（ROC）曲线在生物标志物评估和疾病诊断测试中起着核心作用。事件时间结局的预测指标也可以用ROC曲线进行评估，但必须考虑标志物测量与事件时间之间的时间间隔。我们在实际应用的背景下讨论了时间依赖性ROC曲线的不同定义。已经提出了几种估计方法。我们在假设和灵活性方面对比了回顾性方法与前瞻性方法，包括它们纳入删失数据、竞争风险和不同抽样方案的能力。还给出了在两个数据集上的应用。
