发表机构
University of Florida; University of California San Diego; Stanford University School of Medicine; University of Rochester Medical Center（佛罗里达大学; 加州大学圣地亚哥分校; 斯坦福大学医学院; 罗切斯特大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对观察性研究中优先复合结局的删失估计问题，提出带未来评分校正的胜比回归框架，结合处理加权与基线增强实现双重稳健高效估计，模拟与OneFlorida数据应用验证了其有效性。
AI 中文摘要
当临床事件遵循自然层级时，优先成对结局具有实用价值，但在成对结局判定前发生删失会使估计变得复杂。针对该场景，我们开发了胜比回归框架，通过定义随访期间的完整数据目标并推导观测数据的估计方程。核心思想是未来评分校正（FC）：当删失导致无法观测后续成对比较时，该方法用剩余评分在观测历史下的条件期望替代，此校正可恢复仅用逆删失权重无法获取的成对信息。此外，我们结合处理加权与基线结局增强来解决基线混杂问题，这些组件共同实现了处理分配与删失的双重稳健性。基于U统计量理论进行推断，在标准正则条件下，当所有干扰函数均被正确设定时，AIPW-FC估计量渐近正态且高效。对删失率为30%、50%、65%的模拟显示，未来评分校正带来的效率提升随删失率增加而增大，65%删失率下相对效率达1.50，且AIPW-FC的覆盖率接近名义水平。将该方法应用于OneFlorida电子健康记录数据，验证了其用于优先死亡而非住院的复合结局的有效性。
英文摘要
Prioritized pairwise outcomes are useful when clinical events follow a natural hierarchy, but censoring before pair resolution complicates estimation. We develop a win-ratio regression framework for this setting by defining a complete-data target over follow-up and deriving an estimating equation for the observed data. The central idea is future-score correction (FC): when censoring prevents later pairwise comparisons from being observed, the method replaces the remaining score with its conditional expectation given the observed history. This correction recovers pairwise information beyond that provided by inverse censoring weights alone. Additionally, we incorporate treatment weighting and baseline outcome augmentation to address baseline confounding. Together, these components yield double robustness for treatment assignment and censoring. Inference is obtained from U-statistic theory. Under standard regularity conditions, the AIPW-FC estimator is asymptotically normal and efficient when all nuisance functions are correctly specified. Simulations with 30%, 50%, and 65% censoring show that efficiency gains from future-score correction increase with the censoring rate, with relative efficiency reaching 1.50 under 65% censoring and near-nominal coverage for AIPW-FC. An application to OneFlorida electronic health record data illustrates the method for a composite outcome that prioritizes death over hospitalization.
