Competing Risk Analysis in Cardiovascular Outcome Trials: A Simulation Comparison of Cox and Fine-Gray Models

2026-02-17

AI Summary

In cardiovascular outcome trials, non-cardiovascular death acts as a competing risk that may bias estimates of major adverse cardiovascular events (MACE). This study systematically compares the performance of the Cox proportional hazards model and the Fine–Gray subdistribution hazard model under typical trial parameters using a bivariate copula framework. It provides the first quantitative assessment of estimation discrepancies between the two models across varying rates of competing risks, directions of treatment effects, and dependence structures between event types, revealing that their target parameters are fundamentally distinct. The findings demonstrate that the Fine–Gray model should not be used as a sensitivity analysis substitute for the Cox model. While both models yield consistent results under typical low competing-risk rates (~1% per year), substantial bias emerges only when competing risks are high and treatment effects act in opposing directions—yet neither model recovers the true marginal risk ratio without bias.

Technology Category

• Reasoning under Uncertainty: Graphical Models
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• competing risks
• cardiovascular outcome trials
• MACE
• non-CV death
• statistical analysis

Innovation

• competing risks
• Cox model
• Fine-Gray model
• simulation study
• cardiovascular outcome trials