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Lookup NU author(s): Dr Svetlana CherlinORCiD, Professor James WasonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).In several clinical areas, traditional clinical trials often use a responder outcome, a composite endpoint that involves dichotomising a continuous measure. An augmented binary method that improves power while retaining the original responder endpoint has previously been proposed. The method leverages information from the undichotomised component to improve power. We extend this method for basket trials, which are gaining popularity in many clinical areas. For clinical areas where response outcomes are used, we propose the new augmented binary method for basket trials that enhances efficiency by borrowing information on the treatment effect between subtrials. The method is developed within a latent variable framework using a Bayesian hierarchical modelling approach. We investigate the properties of the proposed methodology by analysing point estimates and high-density intervals in various simulation scenarios, comparing them to the standard analysis for basket trials that assumes binary outcomes. Our method results in a reduction of 95% high-density interval of the posterior distribution of the log odds ratio and an increase in power when the treatment effect is consistent across subtrials. We illustrate our approach using real data from two clinical trials in rheumatology.
Author(s): Cherlin S, Wason JMS
Publication type: Article
Publication status: Published
Journal: Statistical Methods in Medical Research
Year: 2025
Pages: epub ahead of print
Online publication date: 05/12/2025
Acceptance date: 17/11/2025
Date deposited: 15/12/2025
ISSN (print): 0962-2802
ISSN (electronic): 1477-0334
Publisher: Sage Publications Ltd
URL: https://doi.org/10.1177/09622802251403365
DOI: 10.1177/09622802251403365
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