Arbitrated Indirect Treatment Comparisons
This addresses inconsistency in health technology assessments for decision-makers, though it is incremental as it builds on existing MAIC methods.
The paper tackles the 'MAIC paradox' in health technology assessments, where different sponsors analyzing the same data reach conflicting conclusions about treatment effectiveness, by introducing arbitrated indirect treatment comparisons to estimate effects in a common overlap population.
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.