A comparison of estimators to evaluate the discriminatory power of time-to-event models
A comparison of estimators to evaluate the discriminatory power of time-to-event models
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DOI：
10.1002/sim.5464
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发表时间：
2012-10-15
影响因子：
2
通讯作者：
Potapov, Sergej
中科院分区：
文献类型：
--
作者：
Schmid, Matthias;Potapov, Sergej
针对持续事件发生时间结果的歧视措施已成为医疗决策的重要工具。区分度测量背后的想法是通过测量预测模型区分有事件的观察结果和没有事件的观察结果的能力来评估预测模型的性能。研究人员提出了多种方法来根据一组右删失数据来估计歧视措施。这些方法依赖于不同的规律性假设，需要确保各个估计量的一致性。正则性假设的典型例子包括 Cox 回归中的比例风险假设和随机审查假设。由于在实践中经常违反正则性假设，因此对估计量进行敏感性分析非常有意义。本文的目的是分析和比较最流行的事件时间结果歧视措施估计量。基于广泛的模拟研究和分子数据分析的结果，我们研究了在基本规律性假设不成立的情况下估计器的行为。我们表明，违反规律性假设可能会引起不可忽视的偏见，因此可能导致有偏见的医疗决策。版权所有 (C) 2012 约翰·威利父子有限公司
Discrimination measures for continuous time-to-event outcomes have become an important tool in medical decision making. The idea behind discrimination measures is to evaluate the performance of a prediction model by measuring its ability to distinguish between observations having an event and those having no event. Researchers proposed a variety of approaches to estimate discrimination measures from a set of right-censored data. These approaches rely on different regularity assumptions that are needed to ensure consistency of the respective estimators. Typical examples of regularity assumptions include the proportional hazards assumption in Cox regression and the random censoring assumption. Because regularity assumptions are often violated in practice, conducting a sensitivity analysis of the estimators is of considerable interest. The aim of the paper is to analyze and to compare the most popular estimators of discrimination measures for event time outcomes. On the basis of the results of an extensive simulation study and the analysis of molecular data, we investigate the behavior of the estimators in situations where the underlying regularity assumptions do not hold. We show that violations of the regularity assumptions may induce a nonignorable bias and may therefore result in biased medical decision making. Copyright (C) 2012 John Wiley & Sons, Ltd.