In the era of precision medicine, it is of increasing interest to consider multiple strata (e.g. indications, regions, or subgroups) within a single oncology dose-finding study when identifying the maximum tolerated dose (MTD). We propose two Bayesian semi-parametric designs (BSD) for dose-finding with multiple strata to allow for both adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under different prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing it with existing methods, including the fully stratified, exchangeability, and exchangeability–non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD. The BSD models are robust to various heterogeneity assumptions and can be easily extended to other binary and time to event endpoints.

在精准医学时代，在确定最大耐受剂量 (MTD) 时，在单个肿瘤学剂量发现研究中考虑多个层面（例如适应症、区域或亚组）越来越受到关注。我们提出了两种贝叶斯半参数设计 (BSD) 用于多层次的剂量发现，以允许基于各种毒性特征的适应性给药患者和有效识别每个层次的 MTD。我们基于 Dirichlet 过程开发了非参数先验，以允许灵活的先验分布并消除对预先指定的可交换性参数的需求。这两个 BSD 模型建立在不同的地层异质性先验信念下，并允许跨相似地层适当借用信息。通过将 BSD 模型与现有方法（包括完全分层、可交换和可交换-不可交换模型）进行比较，进行模拟研究以评估 BSD 模型的性能。一般来说，我们的 BSD 模型在正确识别不同层的 MTD 方面优于竞争方法，并且需要较小的样本量来确定 MTD。BSD 模型对各种异质性假设具有鲁棒性，并且可以轻松扩展到其他二进制和时间到事件端点。