Bayesian sample size determination in basket trials borrowing information between subsets

Haiyan Zheng, Michael Grayling, Pavel Mozgunov, Thomas Jaki, James Wason

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

Abstract

Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomized basket trial design where patients are randomly assigned to the new treatment or control within each trial subset (“subtrial” for short). Closed-form sample size formulae are derived to ensure that each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given prespecified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.
Original languageEnglish
Pages (from-to)1000–1016
Number of pages17
JournalBiostatistics
Volume24
Issue number4
Early online date22 Aug 2022
DOIs
Publication statusPublished - 18 Oct 2023

Bibliographical note

Funding
This work was supported by Cancer Research UK through Dr Zheng’s Population Research Postdoctoral Fellowship (RCCPDF\100008). T.J. and P.M. received funding from the UK Medical Research Council (MC_UU_00002/14). This report is independent research supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014) and the NIHR Advanced Fellowship (NIHR300576 to P.M.). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care (DHSC).

Funding

This work was supported by Cancer Research UK through Dr Zheng’s Population Research Postdoctoral Fellowship (RCCPDF\100008). T.J. and P.M. received funding from the UK Medical Research Council (MC_UU_00002/14). This report is independent research supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014) and the NIHR Advanced Fellowship (NIHR300576 to P.M.). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care (DHSC).

FundersFunder number
Medical Research CouncilMC_UU_00002/14
National Institute for Health and Care Research
Cancer Research UKRCCPDF\100008
UCLH Biomedical Research CentreBRC-1215-20014, NIHR300576

Keywords

  • Bayesian sample size determination
  • Borrowing strength
  • Master protocol
  • Mixture prior
  • Phase II

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