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<p>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 <i>arbitrated indirect treatment comparisons</i>, designed to address the “MAIC paradox”—a phenomenon highlighted by Jiang et al.&#xa0;(Res Synth Methods 16(3):569–574, 2025). The MAIC paradox arises when different sponsors, analyzing the same data, arrive at 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 <i>overlap population</i>.</p>

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Arbitrated Indirect Treatment Comparisons

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Yixin Fang,
  • Weili He

摘要

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. (Res Synth Methods 16(3):569–574, 2025). The MAIC paradox arises when different sponsors, analyzing the same data, arrive at 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.