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The New England Journal of Statistics in Data Science


Bayesian Interim Analysis in Basket Trials
Volume 2, Issue 1 (2024), pp. 54–71
Pub. online: 4 September 2023      Type: Cancer Research      Open accessOpen Access

1 Department of Statistics, University of Connecticut.
3 Department of Biostatistics, Vir Biotechnology.
2 Department of Biostatistics, Vertex Pharmaceuticals.
Accepted
5 August 2023
Published
4 September 2023

Abstract

Basket trials have captured much attention in oncology research in recent years, as advances in health technology have opened up the possibility of classification of patients at the genomic level. Bayesian methods are particularly prevalent in basket trials as the hierarchical structure is adapted to basket trials to allow for information borrowing. In this article, we extend the Bayesian methods to basket trials with treatment and control arms for continuous endpoints, which are often the cases in clinical trials for rare diseases. To account for the imbalance in the covariates which are potentially strong predictors but not stratified in a randomized trial, our models make adjustments for these covariates, and allow different coefficients across baskets. In addition, comparisons are drawn between two-stage design and one-stage design for the four Bayesian methods. Extensive simulation studies are conducted to examine the empirical performance of all models under consideration. A real data analysis is carried out to further demonstrate the usefulness of the Bayesian methods.

Supplementary material

 Supplementary Material
The Supplementary Material includes the models without covariates supplementary results (Section S.1), sensitivity analysis of MFM (Section S.2), derivation of MFM sampling algorithm formula (Section S.3), and testing treatment difference among baskets for CBHM (Section S.4).

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Keywords
Bayesian hierarchical model Calibrated Bayesian hierarchical model Covariates adjustment Mixture of finite mixtures One-minimum power

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