A critical assessment of matching-adjusted indirect comparisons in relation to target populations.

ABSTRACT: Matching-adjusted indirect comparison (MAIC) has been increasingly applied in health technology assessments (HTA). By reweighting subjects from a trial with individual participant data (IPD) to match the summary statistics of covariates in another trial with aggregate data (AgD), MAIC enables a comparison of the interventions for the AgD trial population. However, when there are imbalances in effect modifiers with different magnitudes of modification across treatments, contradictory conclusions may arise if MAIC is performed with the IPD and AgD swapped between trials. This can lead to the "MAIC paradox," where different entities reach opposing conclusions about which treatment is more effective, despite analyzing the same data. In this paper, we use synthetic data to illustrate this parad

SUBMITTER: Jiang Z 

PROVIDER: S-EPMC12527493 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature