患者水平异质性下联合治疗的评估
Evaluation of Combination Therapy amid Patient-Level Heterogeneity
AI总结：
研究针对联合治疗中患者水平异质性问题，引入无模型统计推断框架，解决相关不可识别性和非线性问题，建立可识别条件，开发最优匹配方案及构建置信区间，通过ACTG 175试验重新分析证明其有效性。
摘要：
联合治疗涉及两种或更多单一疗法，普遍认为能提高疗效。过去常因成分单一疗法间的药理相互作用而认为联合治疗有益，但近期药理学家通过实验指出，部分联合治疗的益处可能很大程度上由患者对单一疗法的不同反应驱动。若不考虑患者水平异质性，传统统计推断框架可能不足且过于乐观。本文引入新颖的无模型统计推断框架，在调整患者对单一疗法反应的异质性后评估联合治疗。解决了调整患者水平异质性时固有的不可识别性和非线性问题，建立了交叉世界目标参数（部分）可识别的条件。开发了基于结果的最优匹配方案以实现渐近正态性，并为目标参数构建\(\sqrt{N}\)速率的置信区间，通过对ACTG 175试验的重新分析证明了该框架的益处。
英文摘要：
Combination therapy, a treatment approach that involves two or more monotherapies, is widely considered to enhance therapeutic efficacy across different medical conditions. It was often believed that combination therapy is beneficial because of pharmacological interactions between its component monotherapies. However, through laboratory experiments, pharmacologists have recently noted that the benefits of some combination therapies might be largely driven by varying patient-level responses to their component monotherapies. Without accounting for such patient-level heterogeneity, classical statistical inference frameworks for combination therapy might be inadequate and overly optimistic. In this paper, we introduce a novel and model-free statistical inference framework to complement the classical one and evaluate combination therapy after adjusting for patient-level heterogeneity in responses to monotherapies. We address the non-identifiability and nonlinearity issue inherent in adjustment of patient-level heterogeneity and establish conditions for the (partial) identifiability of the cross-world target parameter. We develop an outcome-based optimal matching scheme to achieve asymptotic normality and construct $\sqrt{N}$-rate confidence intervals for the target parameter, thereby enabling reliable, efficient and transparent evaluation of combination therapy amid patient-level heterogeneity. The benefits of the proposed framework are demonstrated through a reanalysis of the ACTG 175 trial.
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