A Basket Trial Design Based on Power Priors

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. <i>Fujikawa et al.</i> 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.

篮子试验（basket trial）是指在多个亚组中对某一治疗手段开展研究的试验设计。该类试验多作为单臂试验应用于肿瘤学领域的早期临床阶段，且常采用二分类终点。针对这类试验的分析，学界多建议采用贝叶斯方法（Bayesian methods），因其可基于亚组间观测到的相似性实现信息的部分共享。Fujikawa等（Fujikawa et al.）提出了一种基于经验贝叶斯方法（empirical Bayes methods）的分析策略，该策略可通过亚组间后验分布的詹森-香农散度（Jensen-Shannon divergence）推导得到易于解释的权重，以此实现灵活的信息共享。本文证明该试验设计与幂先验法（power priors）高度相关，并借助幂先验领域的相关方法，对Fujikawa提出的试验设计开展了多项改进研究。在Fujikawa的试验设计中，两个篮子亚组间共享的信息量仅由二者的两两相似性决定，本文还探讨了将所有篮子亚组的结局纳入共享权重计算的扩展方案。本对比研究的结果表明，在多种不同场景下，幂先验法的试验设计性能与全贝叶斯设计相当。与此同时，幂先验法的试验设计计算成本更低，在部分场景下甚至可通过解析法计算其操作特征（operating characteristics）。