Bayesian Optimal Phase II design with optimised stopping boundaries and response-adaptive randomisation

TL;DR

This paper enhances Bayesian Phase II trial design by optimizing stopping boundaries and response-adaptive randomisation, improving treatment allocation and sample efficiency with minimal power loss, supported by simulation evidence.

Contribution

It introduces optimized stopping boundaries and adaptive randomisation into the Bayesian Phase II framework, demonstrating improved operating characteristics over traditional methods.

Findings

Improved treatment allocation to the best arm.

Reduced expected sample size.

Maintained statistical power.

Abstract

The Bayesian Optimal Phase II (BOP2) framework is a flexible trial design that can naturally facilitate complex adaptations due to its Bayesian setting. BOP2 uses equal randomisation and equally placed interim analyses in its design, but it is unclear whether these give the best operating characteristics. By incorporating Bayesian Response-Adaptive Randomisation (BRAR) and optimal interim analysis placement, we show that allocation to the best treatment and expected sample size can be improved with minimal impact on power. We discuss recommendations on implementing these adaptations, using simulation-based evidence, to give practical advice to practitioners. Reproducible code for the simulations is freely provided.