Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Med Care. 2010 Jun;48(6 Suppl):S96-105. doi: 10.1097/MLR.0b013e3181d99107.
An evaluation of the effect of a healthcare intervention (or an exposure) must consider multiple possible outcomes, including the primary outcome of interest and other outcomes such as adverse events or mortality. The determination of the likelihood of benefit from an intervention, in the presence of other competing outcomes, is a competing risks problem. Although statistical methods exist for quantifying the probability of benefit from an intervention while accounting for competing events, these methods have not been widely adopted by clinical researchers.
(1) To demonstrate the importance of considering competing risks in the evaluation of treatment effectiveness, and (2) to review appropriate statistical methods, and recommend how they might be applied.
We reviewed 3 statistical approaches for analyzing the competing risks problem: (a) cause-specific hazard (CSH), (b) cumulative incidence function (CIF), and (c) event-free survival (EFS). We compare these methods using a simulation study and a reanalysis of a randomized clinical trial.
Simulation studies evaluating the statistical power to detect the effect of intervention under different scenarios showed that: (1) CSH approach is best for detecting the effect of an intervention if the intervention only affects either the primary outcome or the competing event; (2) EFS approach is best only when the intervention affects both primary and competing events in the same manner; and (3) CIF approach is best when the intervention affects both primary and competing events, but in opposite directions. Using data from a randomized controlled trial, we demonstrated that a comprehensive approach using all 3 approaches provided useful insights on the effect of an intervention on the relative and absolute risks of multiple competing outcomes.
CSH is the fundamental measure of outcome in competing risks problems. It is appropriate for evaluating treatment effects in the presence of competing events. Results of CSH analysis for primary and competing outcomes should always be reported even when EFS or CIF approaches are called for. EFS is appropriate for evaluating the composite effect of an intervention, only when combining different endpoints is clinically and biologically meaningful, and the treatment has similar effects on all event types. CIF is useful for evaluating the likelihood of benefit from an intervention over a meaningful period. CIF should be used for absolute risk calculations instead of the widely used complement of the Kaplan-Meier (1 - KM) estimator.
评估医疗干预（或暴露）的效果必须考虑多种可能的结果，包括感兴趣的主要结果和其他结果，如不良事件或死亡率。在存在其他竞争结果的情况下，确定干预的获益可能性是一个竞争风险问题。尽管存在用于量化干预获益概率的统计方法，但这些方法尚未被临床研究人员广泛采用。
（1）展示在评估治疗效果时考虑竞争风险的重要性，（2）回顾适当的统计方法，并推荐如何应用这些方法。
我们回顾了 3 种用于分析竞争风险问题的统计方法：（a）原因特异性风险（CSH），（b）累积发生率函数（CIF）和（c）无事件生存（EFS）。我们使用模拟研究和对一项随机临床试验的重新分析来比较这些方法。
评估在不同情况下干预效果的统计功效的模拟研究表明：（1）如果干预仅影响主要结果或竞争事件，则 CSH 方法最适合检测干预效果；（2）仅当干预以相同方式影响主要结果和竞争事件时，EFS 方法才是最佳的；（3）当干预以相反的方式影响主要结果和竞争事件时，CIF 方法是最佳的。使用来自一项随机对照试验的数据，我们证明了使用所有 3 种方法的综合方法提供了关于干预对多个竞争结果的相对和绝对风险的影响的有用见解。
CSH 是竞争风险问题中结果的基本衡量标准。它适用于存在竞争事件时评估治疗效果。即使需要使用 EFS 或 CIF 方法，也应始终报告 CSH 对主要和竞争结果的分析结果。EFS 适用于评估干预对复合效应的影响，仅当组合不同的终点在临床和生物学上有意义，并且治疗对所有事件类型具有相似的影响时。CIF 适用于评估在有意义的时间段内从干预中获益的可能性。应使用 CIF 进行绝对风险计算，而不是广泛使用 Kaplan-Meier（1 - KM）估计量的补数。
