Parametric G-computation for Compatible Indirect Treatment Comparisons with Limited Individual Patient Data

Abstract

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.

31 pages, 4 figures, 1 Table (19 additional pages in the Supplementary Material). This is the journal version of some of the research in the working paper arXiv:2008.05951. Accepted for publication by Research Synthesis Methods

References in corpus (4)

• Matching Methods for Causal Inference: A Review and a Look Forward
• Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
• Parametric G-computation for Compatible Indirect Treatment Comparisons with Limited Individual Patient Data
• Target estimands for population-adjusted indirect comparisons

Cited by in corpus (9)

• Parametric G-computation for Compatible Indirect Treatment Comparisons with Limited Individual Patient Data
• Target estimands for population-adjusted indirect comparisons
• Transportability of model-based estimands in evidence synthesis
• Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis
• Model-based standardization using multiple imputation
• Two-stage matching-adjusted indirect comparison
• Effect modification and non-collapsibility leads to conflicting treatment decisions: a review of marginal and conditional estimands and recommendations for decision-making
• Methodological considerations for novel approaches to covariate-adjusted indirect treatment comparisons
• Integrate Meta-analysis into Specific Study (InMASS) for Estimating Conditional Average Treatment Effect