Toggle Main Menu Toggle Search

Open Access padlockePrints

Bayesian modelling strategies for borrowing of information in randomised basket trials

Lookup NU author(s): Luke Ouma, Dr Michael Grayling, Professor James WasonORCiD, Dr Haiyan ZhengORCiD



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


© 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects (‘treatment effect borrowing’, TEB) to borrowing over the subtrial groupwise responses (‘treatment response borrowing’, TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.

Publication metadata

Author(s): Ouma LO, Grayling MJ, Wason JMS, Zheng H

Publication type: Article

Publication status: Published

Journal: Journal of the Royal Statistical Society. Series C: Applied Statistics

Year: 2022

Volume: 71

Issue: 5

Pages: 2014-2037

Print publication date: 01/11/2022

Online publication date: 28/10/2022

Acceptance date: 01/09/2022

Date deposited: 21/11/2022

ISSN (print): 0035-9254

ISSN (electronic): 1467-9876

Publisher: John Wiley and Sons Inc


DOI: 10.1111/rssc.12602


Altmetrics provided by Altmetric


Funder referenceFunder name