Marginalization of Regression-Adjusted Treatment Effects in Indirect Comparisons with Limited Patient-Level Data

Population adjustment methods such as matching-adjusted indirect comparison (MAIC), based on propensity score weighting, are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. Current outcome regression-based alternatives 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 the ideas of 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 the ideas underlying multiple imputation, which we term multiple imputation marginalization (MIM) and is applicable to a wide range of models, including parametric survival models. Both methods can accommodate a Bayesian statistical framework which naturally integrates the analysis into a probabilistic framework, typically required for health technology assessment. A simulation study provides proof-of-principle for the methods and benchmarks their performance against MAIC and the conventional outcome regression. The simulations are based on scenarios with binary outcomes and continuous covariates, with the log-odds ratio as the measure of effect. The marginalized outcome regression approaches achieve more precise and more accurate estimates than MAIC, particularly when covariate overlap is poor, and yield unbiased treatment effect estimates under no failures of assumptions. Furthermore, the marginalized covariate-adjusted estimates provide greater precision than the conditional estimates produced by the conventional outcome regression.