Bayesian sample size determination in basket trials borrowing information between subsets

TL;DR

This paper introduces a Bayesian method for determining sample sizes in basket trials that allows for information sharing between similar patient subgroups, reducing the required sample size while maintaining statistical accuracy.

Contribution

It provides closed-form sample size formulas for Bayesian basket trials with borrowing, enabling smaller, more efficient trials compared to traditional no-borrowing approaches.

Findings

Borrowing reduces required sample size significantly.

Method maintains desired true and false positive rates.

Applicable to real basket trial examples.

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 randomised basket trial design where patients are randomly assigned to the new treatment or a control within each trial subset (`subtrial' for short). Closed-form sample size formulae are derived to ensure 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 pre-specified 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.