A basket trial design based on power priors

TL;DR

This paper explores a basket trial design based on power priors, enhancing Bayesian analysis flexibility in oncology trials by allowing adaptive information sharing across subgroups with computational efficiency.

Contribution

It introduces modifications to existing empirical Bayes methods using power priors, enabling more comprehensive information sharing in basket trials.

Findings

Power prior design performs comparably to fully Bayesian methods.

The approach is computationally efficient and allows analytical calculations.

Extensions consider outcomes of all baskets for weight computation.

Abstract

In basket trials a treatment is investigated in several subgroups. They are primarily used in oncology in early clinical phases as single-arm trials with a binary endpoint. For their analysis primarily Bayesian methods have been suggested, as they allow partial sharing of information based on the observed similarity between subgroups. Fujikawa et al. (2020) suggested an approach using empirical Bayes methods that allows flexible sharing based on easily interpretable weights derived from the Jensen-Shannon divergence between the subgroup-wise posterior distributions. We show that this design is closely related to the method of power priors and investigate several modifications of Fujikawa's design using methods from the power prior literature. While in Fujikawa's design, the amount of information that is shared between two baskets is only determined by their pairwise similarity, we also discuss extensions where the outcomes of all baskets are considered in the computation of the sharing weights. The results of our comparison study show that the power prior design has comparable performance to fully Bayesian designs in a range of different scenarios. At the same time, the power prior design is computationally cheap and even allows analytical computation of operating characteristics in some settings.