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Arbitrated Indirect Treatment Comparisons.

Yixin Fang1, Weili He2

  • 1Data and Statistical Sciences, AbbVie Inc., 1 North Waukegan Rd, North Chicago, IL, 60064, USA. yixin.fang@abbvie.com.

Therapeutic Innovation & Regulatory Science
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Matching-adjusted indirect comparison (MAIC) methods in health technology assessments can yield conflicting results. New arbitrated indirect treatment comparison methods estimate treatment effects in a common target population to resolve this MAIC paradox.

Keywords:
ArbitrationEffect modifiersEstimandIndirect treatment comparisonOverlap weights

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Area of Science:

  • Health Economics
  • Biostatistics
  • Evidence Synthesis

Background:

  • Matching-adjusted indirect comparison (MAIC) is widely used in health technology assessments (HTA) for estimating treatment effects.
  • MAIC reweights individual participant data (IPD) trials to match aggregate data (AgD) trial covariates.
  • Existing MAIC methods can lead to the 'MAIC paradox,' where different analyses of the same data yield conflicting conclusions on treatment effectiveness.

Purpose of the Study:

  • To introduce a novel class of methods: arbitrated indirect treatment comparisons.
  • To address the MAIC paradox by resolving inconsistencies in treatment effect estimations.
  • To propose a framework for estimating treatment effects in a common, well-defined target population.

Main Methods:

  • Development of arbitrated indirect treatment comparison methodologies.
  • Focus on estimating treatment effects within the overlap population, defined by shared covariate distributions across trials.
  • Utilizing IPD from one trial and AgD from another to facilitate comparisons.

Main Results:

  • The proposed methods provide a consistent approach to indirect treatment comparisons.
  • Arbitrated indirect treatment comparisons resolve the MAIC paradox by targeting a common population.
  • This approach enhances the reliability of treatment effect estimates in HTA.

Conclusions:

  • Arbitrated indirect treatment comparisons offer a robust solution to the MAIC paradox.
  • Estimating treatment effects in the overlap population ensures consistency and reduces ambiguity in HTA.
  • These new methods improve the validity of evidence synthesis for healthcare decision-making.