The win ratio method has received much attention in methodological research, ad hoc analyses, and designs of prospective studies. As the primary analysis, it supported the approval of tafamidis for the treatment of cardiomyopathy to reduce cardiovascular mortality and cardiovascular-related hospitalization. However, its dependence on censoring is a potential shortcoming. In this article, we propose the inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic (i.e., the IPCW-adjusted win ratio statistic) to overcome censoring issues. We consider independent censoring, common censoring across endpoints, and right censoring. We develop an asymptotic variance estimator for the logarithm of the IPCW-adjusted win ratio statistic and evaluate it via simulation. Our simulation studies show that, as the amount of censoring increases, the unadjusted win proportions may decrease greatly. Consequently, the bias of the unadjusted win ratio estimate may increase greatly, producing either an overestimate or an underestimate. We demonstrate theoretically and through simulation that the IPCW-adjusted win ratio statistic gives an unbiased estimate of treatment effect.

胜率方法在方法学研究、临时分析和前瞻性研究设计中受到了广泛关注。作为主要分析，它支持批准 tafamidis 用于治疗心肌病，以降低心血管死亡率和心血管相关住院治疗。然而，它对审查的依赖是一个潜在的缺点。在本文中，我们提出了反审查概率加权 (IPCW) 调整获胜率统计（即 IPCW 调整获胜率统计）来克服审查问题。我们考虑独立审查、跨端点的共同审查和右审查。我们为 IPCW 调整的获胜率统计量的对数开发了一个渐近方差估计器，并通过模拟对其进行评估。我们的模拟研究表明，随着审查数量的增加，未调整的获胜比例可能会大幅下降。因此，未经调整的赢率估计的偏差可能会大大增加，导致高估或低估。我们从理论上和通过模拟证明了 IPCW 调整后的获胜率统计数据给出了对治疗效果的无偏估计。