Authors
Kim May Lee, Ziyan Wang, Richard Emsley, Nigel Stallard
Published in
Statistical methods in medical research. Pages 9622802261475687. Aug 21, 2026. Epub Aug 21, 2026.
Abstract
In most multi-arm randomized controlled trials, all participants need to be eligible to be randomized to all the arms. This means some participants are excluded from participating in multi-arm studies. We propose to relax this by considering a 'differential randomization' approach that allows the inclusion of participants who are eligible for some but not all arms, as in some innovative trial approaches. We refer to a multi-arm design that employs differential randomization as an 'inclusive design' because it maximizes participant inclusion. Considering superiority comparisons for a normal endpoint, we evaluate the performance of some analysis methods for a three-arm inclusive design by simulation studies. Methods include pairwise or overall regression analysis, an averaging approach that pools summary statistics using prevalence rate, and a meta-analysis type framework. We compare the statistical power when the inclusive design employs different treatment allocation schemes. We find that using equal allocation ratio within each subpopulation leads to a higher disjunctive power than using equal allocation across arms, when the many-to-one comparisons are analysed using the corresponding pairwise data separately as opposed to using all data in a single regression analysis. We find that differential covariate-outcome relations among patients can affect the property of the inference.
PMID:
42627367
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.
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