Composite endpoints consisting of both terminal and non-terminal events, such as death and hospitalization, are frequently used as primary endpoints in cardiovascular clinical trials. The Win Ratio method (WR) proposed by Pocock et al. (2012) [1] employs a hierarchical structure to combine fatal and non-fatal events by giving death information an absolute priority, which adversely affects power if the treatment effect is mainly on the non-fatal outcomes. We hereby propose the Shrinking Coarsened Win Ratio method (SCWR) that releases the strict hierarchical structure of the standard WR by adding stages with coarsened thresholds shrinking to zero. A weighted adaptive approach is developed to determine the thresholds in SCWR. This method preserves the good statistical properties of the standard WR and has a greater capacity to detect treatment effects on non-fatal events. We show that SCWR has an overall more favorable performance than WR in our simulation that addresses the influence of follow-up time, the association between events, and the treatment effect levels, as well as a case study based on the Digitalis Investigation Group clinical trial data.

翻译：由死亡和住院等终点与非终点事件组成的复合终点，常被用作心血管临床试验的主要终点。Pocock等人（2012）提出的胜率比方法采用分层结构，通过赋予死亡信息绝对优先权来合并致命与非致命事件，但当治疗效果主要体现在非致命结局时，这种方法会降低检验效能。本文提出收缩粗化胜率比方法，通过添加阈值逐渐收缩至零的粗化阶段，释放了标准胜率比的严格分层结构。我们发展了一种加权自适应方法来确定收缩粗化胜率比中的阈值。该方法保留了标准胜率比的良好统计性质，并显著提升了检测非致命事件治疗效果的能力。通过模拟研究（考察随访时间、事件间关联及治疗效果水平的影响）以及基于洋地黄研究组临床试验数据的案例分析，我们证明收缩粗化胜率比在整体性能上优于标准胜率比。