ArticleResearch synthesis methods2025
Regression augmented weighting adjustment for indirect comparisons in health decision modelling.
Article in Research synthesis methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Comparative Effectiveness of Cytisinicline and Varenicline for Smoking Cessation: A Matching-Adjusted Indirect Comparison (MAIC).Journal of health economics and outcomes research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding 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.
methodsWe 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.
resultsUnder 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.
conclusionG-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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.