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Bayesian modelling strategies for borrowing of information in randomised basket trials.

Published version
Peer-reviewed

Type

Article

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Abstract

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.

Description

Keywords

biomarker‐guided trial, master protocol, personalised medicine, precision medicine, randomised controlled basket trial

Journal Title

J R Stat Soc Ser C Appl Stat

Conference Name

Journal ISSN

0035-9254
1467-9876

Volume Title

Publisher

Oxford University Press (OUP)
Sponsorship
Medical Research Council (MR/S014357/1)
Cancer Research UK (RCCPDF\100008)