部分指定结果层级下的最坏情况胜率
Worst-Case Win Ratios Under Partially Specified Outcome Hierarchies
- Bristol Myers Squibb（百时美施贵宝）
- University of Texas Houston（休斯顿德州大学）
- Takeda Pharmaceuticals（武田制药）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文针对部分指定结果层级的胜率统计问题，定义了最小净获益作为估计目标，推导了大样本结果并通过模拟验证，其在ACTG 175等案例中展现了实际应用价值。
中文摘要 AI 辅助
胜率统计需要预先指定结果层级。临床团队有时仅就最高优先级结果达成一致，而未明确较低优先级结果的顺序，且具有临床意义的阈值可能被指定为范围。单独的敏感性分析描述了结果如何变化，但通常缺乏针对全部计划分析的单一推断。我们将估计目标定义为方案或统计分析计划允许的比较规则中最小的净获益，每个规则采用常规两样本U统计量。针对有限规则列表和连续阈值范围，我们推导了大样本结果。通过交并检验的逆变换得到单侧下界置信区间，且无需对“每个个体净获益均为正”这一单一主张进行多重性调整。模拟结果显示单侧覆盖有效，并阐明了某一选定层级的有利结果与所有预先指定层级的有利结果之间的差距。对ACTG 175的应用表明，实验室结果的排序会影响结论的强度；另一个示例则识别出稀疏网格遗漏的不利阈值。
英文摘要
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.
