In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

中文翻译：

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用于查找和优化亚组特定剂量的自适应贝叶斯信息借用方法

在精准肿瘤学中，将多个癌症患者亚组整合到一个主方案中，可以同时评估这些亚组的治疗效果，并促进它们之间的信息共享，最终减少样本量和成本并提高科学有效性。然而，这些疗法的安全性和有效性可能因不同亚组而异，从而导致不同的结果。因此，在早期临床试验中确定亚组特异性最佳剂量对于未来试验的开展至关重要。在本文中，我们回顾了各种创新的贝叶斯信息借用策略，旨在确定和优化亚组特定剂量。具体来说，我们讨论贝叶斯分层建模、贝叶斯聚类、贝叶斯模型平均或选择、成对借用和其他相关方法。通过采用这些贝叶斯信息借用方法，研究人员可以更好地了解每个亚组的剂量、毒性和疗效之间的复杂关系。这种加深的了解显着提高了确定适合每个特定亚组的最佳剂量的机会。此外，我们提出了一些实用建议，以指导使用贝叶斯信息借用方法时涉及多个亚组的未来早期肿瘤学试验的设计。