Arbitrated Indirect Treatment Comparisons

TL;DR

This paper introduces arbitrated indirect treatment comparison methods to resolve inconsistencies in treatment effect estimates caused by different target populations in MAIC analyses.

Contribution

It proposes new methods to estimate treatment effects in a common overlap population, addressing the MAIC paradox and conflicting conclusions.

Findings

Methods successfully address the MAIC paradox.

Estimates focus on the overlap population.

Enhances consistency in treatment comparisons.

Abstract

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.