ArticleTherapeutic innovation & regulatory science2026
Missing Data Handling in the Application of Matching-Adjusted Indirect Comparison.
Article in Therapeutic innovation & regulatory science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In support of Health Technology Assessment submission, we often need to conduct indirect treatment comparisons (ITC). One common type of ITC is population-adjusted indirect comparisons (Phillippo et al. in NICE DSU technical support document 18: methods for population-adjusted indirect comparisons in submissions to NICE. NICE Decision Support Unit, 2016), in which individual patient data in one trial and aggregate data in the other trial are used to adjust for the difference in the distributions of covariates (prognostic factors or effect modifiers) that influence the outcome. The most popular PAIC method is the Matching-Adjusted Indirect Comparison (MAIC) (Signorovitch et al. in Pharmacoeconomics 28:935-945, 2010). However, the literature lacks guidance on how to handle missing data in the application of MAIC. In this paper, we propose some weighting-based methods to handle missing data in the outcome variable and/or covariates when applying MAIC. These weights can be expressed as products of the inverse probability of not missing the outcomes and weights that account for the difference in baseline characteristics. The proposed methods fit seamlessly into the original MAIC framework and obtain treatment effect estimates based on weighted difference between IPD and AgD. Extensive simulation studies are conducted to evaluate the performance of these proposed methods.
Indexed as
Identifiers
41991751What OpenQuestion 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 OpenQuestion graph.