A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data.
A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data.
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DOI：
10.1002/sim.5647
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发表时间：
2013-06-30
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;Tian, Lu;Cai, Tianxi;Kohane, Isaac S.;Wei, L. J.
关键词：
在循证医学中，风险预测程序对患者的治疗选择、预防策略或疾病管理非常有用。通常，除了常规标记物之外，还可获得潜在重要的新预测因子。问题是如何量化新标记物对患者风险预测的改善，以帮助做出成本效益决策。使用受试者工作特征曲线下面积来衡量附加值的标准方法可能不够灵敏，无法捕捉新标志物的增量改善。最近，一些新的替代方案，如集成的歧视性改进和净重新分类的改进，提出了接收器工作特征曲线下的面积。在本文中，我们考虑了一类措施，用于评估新的标记，其中包括作为特殊情况的前两个增量值。我们提出了一个统一的过程中进行推断的措施，在类与删失事件时间数据。我们的程序的大样本特性在理论上是合理的。我们用一项癌症研究的数据来说明新的建议，以评估一种新的基因评分来预测患者的生存率。
Risk prediction procedures can be quite useful for the patient’s treatment selection, prevention strategy, or disease management in evidence-based medicine. Often, potentially important new predictors are available in addition to the conventional markers. The question is how to quantify the improvement from the new markers for prediction of the patient’s risk in order to aid cost–benefit decisions. The standard method, using the area under the receiver operating characteristic curve, to measure the added value may not be sensitive enough to capture incremental improvements from the new markers. Recently, some novel alternatives to area under the receiver operating characteristic curve, such as integrated discrimination improvement and net reclassification improvement, were proposed. In this paper, we consider a class of measures for evaluating the incremental values of new markers, which includes the preceding two as special cases. We present a unified procedure for making inferences about measures in the class with censored event time data. The large sample properties of our procedures are theoretically justified. We illustrate the new proposal with data from a cancer study to evaluate a new gene score for prediction of the patient’s survival.
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