Win-Ratio Regression for Prioritized Composite Outcomes in Observational Studies: Doubly Robust and Efficient Estimation with Future-Score Correction

2026-08-11

AI Summary

This study addresses the challenge of analyzing clinical outcomes with natural prioritization when censoring renders pairwise comparisons unobservable. The authors propose a win ratio regression framework that innovatively incorporates Future Score Correction (FC), substituting missing scores at censoring times with their conditional expectations. By integrating inverse probability weighting and baseline outcome augmentation, the method yields an estimator doubly robust to both treatment assignment and censoring mechanisms. Theoretical justification is grounded in U-statistics theory. Simulations demonstrate a relative efficiency of 1.50 under 65% censoring, with confidence interval coverage closely matching nominal levels. Application to OneFlorida electronic health records successfully analyzes a composite outcome where death is prioritized over hospitalization, confirming the approach’s practical utility and statistical efficiency.

Abstract

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.

Problem

• prioritized composite outcomes
• censoring
• win-ratio regression
• observational studies
• pairwise comparisons

Innovation

• win-ratio regression
• future-score correction
• doubly robust estimation
• composite outcomes
• censoring