The win ratio is increasingly used to analyze prioritized composite endpoints in clinical trials, but standard implementations rely on deterministic pairwise comparisons and can perform poorly in the presence of censoring and endpoint-specific missingness. In such settings, unresolved comparisons are often treated as ties, leading to loss of efficiency and potentially biased inference, particularly when lower-priority outcomes are incompletely observed. We propose the probabilistic win ratio (PWR), a framework for estimating the classical win ratio under coarsened observation. The PWR replaces deterministic pairwise decisions with conditional probabilities of win, loss, or tie given the observed data, allowing partially observed comparisons to contribute fractionally while being explicitly penalized according to their uncertainty. Comparisons with greater coarsening receive smaller effective weight, whereas fully observed comparisons contribute as in the classical analysis, preserving the clinical priority structure. When outcomes are fully observed, the PWR reduces exactly to the standard win ratio estimator. Simulation studies show that the PWR maintains low bias and mean squared error across a range of censoring and missingness scenarios. Two clinical trial case studies illustrate complementary data regimes, demonstrating calibration in near-complete data and stability under substantial right censoring.

翻译：胜率法在临床试验中越来越多地用于分析优先排序的复合终点，但标准实现依赖于确定性成对比较，在存在删失和终点特异性缺失时表现欠佳。在此类场景中，未解决的比较常被视为平局，导致效率损失和潜在偏倚推断，尤其是当低优先级结局不完整观测时。我们提出概率胜率法（PWR），一种在粗化观测下估计经典胜率的框架。PWR将确定性成对决策替换为基于观测数据的胜、负或平局条件概率，使部分观测比较以分数形式纳入分析，同时根据其不确定性明确施加惩罚。粗化程度越高的比较获得的有效权重越小，而完全观测比较的贡献与经典分析一致，从而保留临床优先结构。当结局被完全观测时，PWR精确退化为标准胜率估计量。模拟研究显示，PWR在多种删失和缺失场景中保持低偏差和均方误差。两项临床试验案例研究展示了互补的数据机制，在近乎完整数据中验证校准效果，并在显著右删失条件下保持稳定性。