Department of Medical Informatics, Biometry and Epidemiology, University of Erlangen-Nuremberg, Waldstr. 6, 91054, Erlangen, Germany.
Stat Med. 2012 Oct 15;31(23):2588-609. doi: 10.1002/sim.5464. Epub 2012 Jul 25.
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.
用于连续时间事件结局的判别措施已成为医学决策中的重要工具。判别措施的基本思想是通过测量预测模型区分有事件和无事件观察的能力来评估其性能。研究人员提出了多种方法，从一组右删失数据中估计判别措施。这些方法依赖于不同的正则性假设，这些假设是确保各自估计量一致性所必需的。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删失假设。由于正则性假设在实践中经常被违反，因此对估计量进行敏感性分析具有相当大的意义。本文的目的是分析和比较用于事件时间结局的判别措施的最流行估计量。基于广泛的模拟研究和分子数据的分析结果，我们研究了在基础正则性假设不成立的情况下，估计量的行为。我们表明，违反正则性假设可能会导致不可忽略的偏差，并因此导致有偏差的医学决策。
