On the Double-Robustness and Semiparametric Efficiency of Matching-Adjusted Indirect Comparisons.

Matching-adjusted indirect comparison (MAIC) enables indirect comparisons of interventions across separate studies when individual patient-level data (IPD) are available for only one study. Due to its similarity with propensity score weighting, it has been speculated that MAIC can be combined with outcome regression models in the spirit of augmented inverse probability weighting (AIPW) estimators to improve robustness and efficiency. We show that MAIC enjoys intrinsic double-robustness and semiparametric efficiency properties for estimating the average treatment effect on the treated (ATT) in the limited IPD setting. A connection with MAIC and the method of simulated treatment comparisons (STC) is highlighted. These results clarify conditions under which MAIC is consistent and efficient, informing appropriate application and interpretation of MAIC analyses. This article is protected by copyright. All rights reserved.