Worst-Case Win Ratios Under Partially Specified Outcome Hierarchies

2026-08-30

AI Summary

论文解决了临床试验中部分指定结果层次结构下的最坏情况胜率问题，通过定义估计量为协议允许的比较规则中的最小净收益，并使用U-统计量和大样本理论方法。

Abstract

Win statistics require a prespecified outcome hierarchy. Clinical teams sometimes agree only on the highest priority outcome, leaving the order of lower priority outcomes unresolved, and clinically meaningful thresholds may be specified as ranges. Separate sensitivity analyses describe how the results change. A single inference for the full set of planned analyses is generally absent. We define the estimand as the smallest net benefit among the comparison rules allowed by the protocol or statistical analysis plan. Each rule uses the usual two-sample U-statistic. Large-sample results are developed for a finite list of rules and for a continuous threshold range. Inversion of an intersection-union test gives a one-sided lower confidence bound, with no multiplicity adjustment for the single claim that every individual net benefit is positive. Simulations show valid one-sided coverage and illustrate the gap between a favorable result for one selected hierarchy and a favorable result across all prespecified hierarchies. An application to ACTG 175 shows how the ordering of laboratory outcomes can affect the strength of the conclusion. A further example identifies an unfavorable threshold that is missed by a sparse grid.

Problem

• outcome hierarchy
• clinical trials
• sensitivity analysis
• net benefit
• U-statistic

Innovation

• Worst-Case Win Ratios
• Partially Specified Outcome Hierarchies
• U-statistic
• Intersection-Union Test
• Lower Confidence Bound