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.

翻译：心血管结局试验常面临竞争风险，即非心血管死亡会阻碍主要不良心血管事件（MACE）的观测。Cox比例风险模型将竞争事件视为独立删失，而Fine-Gray亚分布风险模型则显式处理竞争风险，二者针对不同的估计目标。本研究通过双变量copula模型进行仿真，系统调整竞争事件发生率（年发生率0.5%-5%）、治疗对竞争事件的影响（降低50%至增加50%）及相关性结构，以比较这两种方法。在心血管结局试验典型的竞争事件发生率（约年1%）条件下，无论相关性强度或治疗效果方向如何，Cox与Fine-Gray模型所得风险比估计值几乎完全一致。仅当竞争事件发生率较高且治疗效果方向不一致时，两者才出现显著差异，但在此条件下两种估计量均无法获得真实边际风险比的无偏估计。在竞争事件发生率较低的典型心血管试验场景中，由于Cox模型具有更优的可解释性，仍适用于主要分析。不应为竞争风险方法而放弃预先设定的Cox模型。需特别指出的是，根据ICH E9(R1)指南，Fine-Gray模型不能作为Cox模型的恰当敏感性分析，因其针对不同估计目标而非检验假设。作为补充分析，采用Aalen-Johansen估计量计算的累积发生率可清晰展现竞争风险的影响。在高竞争风险场景下，可考虑采用逆概率删失加权、多重插补或将全因死亡率纳入主要终点等替代方法。