  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 based on propensity score weighting, which 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 relevant population 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. The approach views the covariate
adjustment regression as a nuisance model and separates its estimation from the
evaluation of the marginal treatment effect of interest. The method can
accommodate a Bayesian statistical framework, which naturally integrates the
analysis into a probabilistic framework. A simulation study provides
proof-of-principle and benchmarks the method's performance against MAIC and the
conventional outcome regression. Parametric G-computation achieves more precise
and more accurate estimates than MAIC, particularly when covariate overlap is
poor, and yields 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.
