  Population adjustment methods such as matching-adjusted indirect comparison
(MAIC) are increasingly used to compare marginal treatment effects when there
are cross-trial differences in effect modifiers and limited patient-level data.
MAIC is sensitive to poor covariate overlap and cannot extrapolate beyond the
observed covariate space. Current outcome regression-based alternatives can
extrapolate but target a conditional treatment effect that is incompatible in
the indirect comparison. When adjusting for covariates, one must integrate or
average the conditional estimate over the population of interest to recover a
compatible marginal treatment effect. We propose a marginalization method based
on parametric G-computation that can be easily applied where the outcome
regression is a generalized linear model or a Cox model. In addition, we
introduce a novel general-purpose method based on multiple imputation, which we
term multiple imputation marginalization (MIM) and is applicable to a wide
range of models. Both methods can accommodate a Bayesian statistical framework,
which naturally integrates the analysis into a probabilistic framework. A
simulation study provides proof-of-principle for the methods and benchmarks
their performance against MAIC and the conventional outcome regression. The
marginalized outcome regression approaches achieve more precise and more
accurate estimates than MAIC, particularly when covariate overlap is poor, and
yield unbiased marginal treatment effect estimates under no failures of
assumptions. Furthermore, the marginalized regression-adjusted estimates
provide greater precision and accuracy than the conditional estimates produced
by the conventional outcome regression, which are systematically biased because
the measure of effect is non-collapsible.
