  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.
