A basket trial design based on power priors
Lukas Baumann, Lukas Sauer, Meinhard Kieser

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedical Education and Admissions
