Arbitrated Indirect Treatment Comparisons

2025-10-20

AI Summary

In health technology assessment (HTA), matching-adjusted indirect comparison (MAIC) can yield contradictory efficacy conclusions for the same evidence—termed the “MAIC paradox”—due to sponsor-specific, implicit definitions of the target population. Method: We propose Arbitrated Indirect Comparison (AIC), a novel framework that explicitly defines and focuses on the *inter-trial covariate overlap population* as a unified, objective target population, thereby eliminating subjective population specification bias. AIC integrates individual participant data (IPD) and aggregate data using covariate-balancing reweighting to produce unbiased, cross-trial comparable treatment effect estimates. Contribution/Results: Simulation and empirical analyses demonstrate that AIC substantially improves consistency, robustness, and fairness across multiple stakeholder analyses. It provides a reproducible, transparent, and methodologically grounded benchmark for indirect comparisons in HTA.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Addresses conflicting conclusions in indirect treatment comparisons
• Resolves the MAIC paradox by targeting a common population
• Estimates treatment effects in the overlap population consistently

Innovation

• Arbitrated methods target common overlap population
• Resolve MAIC paradox via consistent population definition
• Estimate treatment effects using shared target population