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 interpreteable weights derived from the Jensen-Shannon divergence between the subgroupwise 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 compareable 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.

翻译：在篮子试验中，一种疗法在多个亚组中进行研究。它们主要应用于肿瘤学早期临床阶段，作为具有二元终点的单臂试验。其分析主要采用贝叶斯方法，因为这些方法允许根据观察到的亚组间相似性进行部分信息共享。Fujikawa等人（2020）提出了一种基于经验贝叶斯方法的设计，该设计通过源于亚组后验分布间Jensen-Shannon散度的易于解释的权重，实现灵活的信息共享。我们表明，该设计与先验权方法密切相关，并利用先验权文献中的方法研究了Fujikawa设计的几种变体。在Fujikawa的设计中，两个篮子之间共享的信息量仅由它们的成对相似性决定，而我们同时讨论了在计算共享权重时考虑所有篮子结局的扩展方案。我们的比较研究结果表明，在多种不同场景下，先验权设计的性能可与全贝叶斯设计相媲美。与此同时，先验权设计计算成本低，在某些情况下甚至允许对操作特性进行解析计算。