Understanding the relative costs and effectiveness of all competing interventions is crucial to informing health resource allocations. However, to receive regulatory approval for efficacy, novel pharmaceuticals are typically only compared against placebo or standard of care. The relative efficacy against the best alternative intervention relies on indirect comparisons of different interventions. When treatment effect modifiers are distributed differently across trials, population adjustment is necessary to ensure a fair comparison. Matching-Adjusted Indirect Comparisons (MAIC) is the most widely adopted weighting-based method for this purpose. Nevertheless, MAIC can exhibit instability under poor population overlap. Regression-based approaches to overcome this issue are heavily dependent on parametric assumptions.

We introduce a novel method, ‘G-MAIC,’ which combines outcome regression and weighting-adjustment to address these limitations. Inspired by Bayesian survey inference, G-MAIC employs Bayesian bootstrap to propagate the uncertainty of population-adjusted estimates. We evaluate the performance of G-MAIC against standard non-adjusted methods, MAIC and Parametric G-computation, in a simulation study encompassing 18 scenarios with varying trial sample sizes, population overlaps, and covariate structures.

Under poor overlap and small sample sizes, MAIC can produce non-sensible variance estimations or increased bias compared to non-adjusted methods, depending on covariate structures in the two trials compared. G-MAIC mitigates this issue, achieving comparable performance to parametric G-computation with reduced reliance on parametric assumptions.

G-MAIC presents a robust alternative to the widely adopted MAIC for population-adjusted indirect comparisons. The underlying framework is flexible such that it can accommodate advanced nonparametric outcome models and alternative weighting schemes.