心血管结局试验中的竞争风险分析：Cox模型和Fine-Gray模型的仿真比较
Competing Risk Analysis in Cardiovascular Outcome Trials: A Simulation Comparison of Cox and Fine-Gray Models
AI总结：
本研究比较了Cox模型和Fine-Gray模型在心血管结局试验中的表现，发现低竞争风险下Cox模型更适用，而高竞争风险时需考虑替代方法。
摘要：
心血管结局试验常面临竞争风险，当非心血管死亡阻止观察主要不良心血管事件（MACE）时。虽然Cox比例风险模型将竞争事件视为独立删失，而Fine-Gray子分布风险模型明确处理竞争风险，针对不同的估计量。本仿真研究使用双变量Copula模型系统地变化竞争事件率（0.5%-5%每年）、治疗对竞争事件的影响（减少50%到增加50%）以及相关结构，以比较这些方法。在竞争事件率典型于心血管结局试验（~1%每年）时，Cox和Fine-Gray模型无论相关强度或治疗效果方向如何，产生的风险比估计值几乎相同。只有在高竞争事件率和方向不一致的治疗效果下，才会出现显著差异，尽管这两种估计器在这些条件下都不能提供无偏的真实边际风险比估计。在典型的CV试验设置中，当竞争事件率较低时，Cox模型仍适用于主要分析，因其具有更好的可解释性。预设的Cox模型不应因竞争风险方法而被放弃。重要的是，Fine-Gray模型并不构成对Cox模型的适当敏感性分析，因为它们针对不同的估计量，而不是检验假设。作为补充分析，使用Aalen-Johansen估计器的累积发病率可以提供关于竞争风险影响的透明度。在高竞争风险场景下，替代方法如逆概率删失加权、多重插补或在主要终点中包含所有原因死亡值得考虑。
英文摘要：
Cardiovascular outcome trials commonly face competing risks when non-CV death prevents observation of major adverse cardiovascular events (MACE). While Cox proportional hazards models treat competing events as independent censoring, Fine-Gray subdistribution hazard models explicitly handle competing risks, targeting different estimands. This simulation study using bivariate copula models systematically varies competing event rates (0.5%-5% annually), treatment effects on competing events (50% reduction to 50% increase), and correlation structures to compare these approaches. At competing event rates typical of CV outcome trials (~1% annually), Cox and Fine-Gray produce nearly identical hazard ratio estimates regardless of correlation strength or treatment effect direction. Substantial divergence occurs only with high competing rates and directionally discordant treatment effects, though neither estimator provides unbiased estimates of true marginal hazard ratios under these conditions. In typical CV trial settings with low competing event rates, Cox models remain appropriate for primary analysis due to superior interpretability. Pre-specified Cox models should not be abandoned for competing risk methods. Importantly, Fine-Gray models do not constitute proper sensitivity analyses to Cox models per ICH E9(R1), as they target different estimands rather than testing assumptions. As supplementary analysis, cumulative incidence using Aalen-Johansen estimator can provide transparency about competing risk impact. Under high competing-risk scenarios, alternative approaches such as inverse probability of censoring weighting, multiple imputation, or inclusion of all-cause mortality in primary endpoints warrant consideration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
