The Statistical Performance of Matching-Adjusted Indirect Comparisons

TL;DR

This paper assesses the statistical properties of Matching-Adjusted Indirect Comparisons (MAIC), a method used to compare treatments across studies with different baseline characteristics, focusing on its bias, variance, and confidence interval accuracy.

Contribution

It formulates causal assumptions for MAIC, analyzes its large-sample properties, and evaluates standard error estimation strategies without full individual patient data.

Findings

MAIC provides unbiased estimates under certain assumptions

Standard error estimators vary in accuracy, affecting confidence interval coverage

Simulation results highlight the finite-sample bias and variance of MAIC estimates

Abstract

Indirect comparisons of treatment-specific outcomes across separate studies often inform decision-making in the absence of head-to-head randomized comparisons. Differences in baseline characteristics between study populations may introduce confounding bias in such comparisons. Matching-adjusted indirect comparison (MAIC) (Signorovitch et al., 2010) has been used to adjust for differences in observed baseline covariates when the individual patient-level data (IPD) are available for only one study and aggregate data (AGD) are available for the other study. The approach weights outcomes from the IPD using estimates of trial selection odds that balance baseline covariates between the IPD and AGD. With the increasing use of MAIC, there is a need for formal assessments of its statistical properties. In this paper we formulate identification assumptions for causal estimands that justify MAIC estimators. We then examine large sample properties and evaluate strategies for estimating standard errors without the full IPD from both studies. The finite-sample bias of MAIC and the performance of confidence intervals based on different standard error estimators are evaluated through simulations. The method is illustrated through an example comparing placebo arm and natural history outcomes in Duchenne muscular dystrophy.