We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). We construct a flexible statistical design using the proposed strategy. Our approach partitions potentially heterogeneous baskets into non-exchangeable blocks. Information borrowing is only allowed to occur locally, i.e., among similar baskets within the same block. The amount of borrowing is determined by between-basket similarities. The number of blocks and block memberships are inferred from data based on the posterior probability of each partition. The proposed method is compared to the multisource exchangeability model and Simon's two-stage design, respectively. In a variety of simulation scenarios, we demonstrate the proposed method is able to maintain the type I error rate and have desirable basket-wise power. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms.

翻译：我们提议了一个信息借款战略,用于设计和监测第二阶段篮子试验,其依据是篮子(疾病类型)之间的当地多来源互换假设;我们用拟议战略构建一个灵活的统计设计;我们的方法将可能各不相同的篮子分割成不易交换的区块;信息借款只允许在当地进行,即在同一区块内的类似篮子中进行;借款的数额由篮子之间的相似性决定;根据基于每个分区的后方概率的数据推断区块和区块成员的数目;拟议的方法分别与多来源互换模型和西蒙的两阶段设计进行比较;在各种模拟假设中,我们证明拟议的方法能够维持I型误差率,并具有可取的篮子能力;此外,我们的方法与巴耶斯现有方法相比,计算效率很高,即无需抽样算法就可以明确得出利益后方图。