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) employs a hierarchical structure to combine fatal and non-fatal events by giving death information an absolute priority, which can adversely affect power if the treatment effect is mainly on the non-fatal outcomes. We hereby propose the Win Ratio with Multiple Thresholds (WR-MT) that releases the strict hierarchical structure of the standard WR by adding stages with non-zero thresholds. A weighted adaptive approach is also developed to determine the thresholds in WR-MT. This method preserves the statistical properties of the standard WR but can sometimes increase the chance to detect treatment effects on non-fatal events. We show that WR-MT has a particularly favorable performance than standard WR when the second layer has stronger signals and otherwise comparable performance in our simulations that vary the follow-up time, the correlation between events, and the treatment effect sizes. A case study based on the Digitalis Investigation Group clinical trial data is presented to further illustrate our proposed method. An R package "WRMT" that implements the proposed methodology has been developed.

翻译：由终点事件与非终点事件（如死亡与住院）组成的复合终点，常被用作心血管临床试验的主要终点。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死性与非致死性事件；若治疗效果主要体现在非致死性结局上，该方法可能降低检验效能。本文提出具有多重阈值的胜率比方法，通过引入具有非零阈值的阶段来释放标准胜率比的严格分层结构。同时开发了一种加权自适应方法以确定WR-MT中的阈值。该方法保留了标准胜率比的统计特性，但有时能提高检测非致死性事件治疗效应的机会。我们通过模拟研究发现：在改变随访时间、事件间相关性及治疗效应量的条件下，当第二层存在更强信号时，WR-MT相比标准胜率比具有更优的性能，而在其他情况下则表现相当。本文基于洋地黄调查组的临床试验数据进行了案例研究，以进一步阐明所提出的方法。已开发实现该方法的R软件包"WRMT"。