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Bayesian borrowing for basket trials with longitudinal outcomes

Lookup NU author(s): Lou Whitehead, Professor Miles WithamORCiD, Professor James WasonORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.Basket trials are a novel clinical trial design in which a single intervention is investigated in multiple patient subgroups, or “baskets.” They offer the opportunity to share information between subgroups, potentially increasing power to detect treatment effects. Basket trials offer several advantages over running a series of separate trials, including reduced sample sizes, increased efficiency, and reduced costs. Primarily, basket trials have been undertaken in Phase II oncology settings, but could be a promising design in other areas where a shared underlying biological mechanism drives different diseases. One such area is chronic aging-related diseases. However, trials in this area frequently have longitudinal outcomes, and therefore suitable methods are needed to share information in this setting. In this paper, we extend three Bayesian borrowing methods for a basket design with continuous longitudinal endpoints. We demonstrate our methods on a real-world dataset and in a simulation study where the aim is to detect positive basketwise treatment effects. Methods are compared with standalone analysis of each basket without borrowing. Our results confirm that methods that share information can improve power to detect positive treatment effects and increase precision over independent analysis in many scenarios. In highly heterogeneous scenarios, there is a trade-off between increased power and increased risk of type I errors. Our proposed methods for basket trials with continuous longitudinal outcomes aim to facilitate their applicability in the area of aging related diseases. Choice of method should be made based on trial priorities and the expected basketwise distribution of treatment effects.

Publication metadata

Author(s): Whitehead LE, Sailer O, Witham MD, Wason JMS

Publication type: Article

Publication status: Published

Journal: Statistics in Medicine

Year: 2023

Pages: epub ahead of print

Online publication date: 30/04/2023

Acceptance date: 16/04/2023

Date deposited: 22/05/2023

ISSN (print): 0277-6715

ISSN (electronic): 1097-0258

Publisher: John Wiley and Sons Ltd


DOI: 10.1002/sim.9751

PubMed id: 37120858


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