There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules. RATE metrics are agnostic as to how the prioritization rules were derived, and only assess how well they identify individuals that benefit the most from treatment. We define a family of RATE estimators and prove a central limit theorem that enables asymptotically exact inference in a wide variety of randomized and observational study settings. RATE metrics subsume a number of existing metrics, including the Qini coefficient, and our analysis directly yields inference methods for these metrics. We showcase RATE in the context of a number of applications, including optimal targeting of aspirin to stroke patients.

翻译：现有多种方法用于选择优先治疗的对象，包括基于治疗效果估计、风险评分及人工规则的方法。本文提出排名加权平均处理效应（RATE）指标，作为一类简洁通用的指标族，用于比较和检验治疗优先级规则的质量。RATE指标与优先级规则的推导方式无关，仅评估其识别最受益个体的效能。我们定义了RATE估计量族，并证明了中心极限定理，使该指标在广泛的随机化研究和观察性研究场景中能实现渐近精确推断。RATE指标整合了包括Qini系数在内的现有多项指标，而本研究的分析直接提供了这些指标的推断方法。我们通过多项应用案例展示RATE指标的实施，包括阿司匹林对中风患者的最佳定向给药方案。