Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects

• 成果类型: Article; Early Access
• 署名作者: Yadlowsky, Steve; Fleming, Scott; Shah, Nigam; Brunskill, Emma; Wager, Stefan
• 署名单位: Alphabet Inc.; DeepMind; Stanford University; Stanford University; Stanford University; Stanford University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2393466
• 发表日期: 2024
• 关键词: acute ischemic-stroke; early aspirin use; Heterogeneous treatment; TRIAL; care

摘要

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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.