解决药物比较中的数据矛盾，让不同研究结果指向同一人群。
Arbitrated Indirect Treatment Comparisons
- 通过重加权使不同研究人群重叠，统一比较基准
- 在重叠人群中估计治疗效果，避免结论冲突
- 适合用于医保评估中需跨研究对比的场景
匹配调整间接比较（MAIC）在卫生技术评估中应用日益广泛。通过将具有个体参与者数据（IPD）试验的受试者重新加权，使其协变量统计量与仅有汇总数据（AgD）的另一试验匹配，MAIC 可估算相对于 AgD 试验人群的治疗效应。本文提出一类新方法——仲裁间接比较，旨在解决由 Jiang 等（2025）揭示的“MAIC悖论”：不同申办方使用相同数据却得出相反疗效结论。其根本原因在于各申办方隐含针对不同人群。所提方法聚焦于一个共同目标人群——重叠人群，以估计治疗效应，从而消除不一致性。
原文摘要 · Abstract (English)
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.
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