  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.
