基于仲裁的间接治疗比较 (Arbitrated Indirect Treatment Comparisons)

Matching-adjusted indirect comparison (MAIC) has been increasingly employed in health technology assessments (HTA). By reweighting subjects from a trial with individual participant data (IPD) to match the covariate summary statistics of another trial with only aggregate data (AgD), MAIC facilitates the estimation of a treatment effect defined with respect to the AgD trial population. This manuscript introduces a new class of methods, termed arbitrated indirect treatment comparisons, designed to address the ``MAIC paradox'' -- a phenomenon highlighted by Jiang et al.~(2025). The MAIC paradox arises when different sponsors, analyzing the same data, reach conflicting conclusions regarding which treatment is more effective. The underlying issue is that each sponsor implicitly targets a different population. To resolve this inconsistency, the proposed methods focus on estimating treatment effects in a common target population, specifically chosen to be the overlap population.

翻译：匹配调整间接比较（MAIC）在卫生技术评估（HTA）中的应用日益广泛。该方法通过对拥有个体参与者数据（IPD）的试验中的受试者进行重新加权，以匹配仅拥有聚合数据（AgD）的另一试验的协变量汇总统计量，从而促进针对AgD试验人群定义的治疗效果估计。本文介绍了一类新方法，称为基于仲裁的间接治疗比较，旨在解决Jiang等人（2025）所强调的“MAIC悖论”。该悖论指不同申办方分析相同数据时，就哪种治疗更有效得出相互矛盾的结论。其根本问题在于，每个申办方隐含地针对了不同的人群。为解决这种不一致性，所提出的方法聚焦于估计一个共同目标人群（特别选定为重叠人群）中的治疗效果。