In early phase drug development of combination therapy, the primary objective is to preliminarily assess whether there is additive activity when a novel agent combined with an established monotherapy. Due to potential feasibility issues with a large randomized study, uncontrolled single-arm trials have been the mainstream approach in cancer clinical trials. However, such trials often present significant challenges in deciding whether to proceed to the next phase of development. A hybrid design, leveraging data from a completed historical clinical study of the monotherapy, offers a valuable option to enhance study efficiency and improve informed decision-making. Compared to traditional single-arm designs, the hybrid design may significantly enhance power by borrowing external information, enabling a more robust assessment of activity. The primary challenge of hybrid design lies in handling information borrowing. We introduce a Bayesian dynamic power prior (DPP) framework with three components of controlling amount of dynamic borrowing. The framework offers flexible study design options with explicit interpretation of borrowing, allowing customization according to specific needs. Furthermore, the posterior distribution in the proposed framework has a closed form, offering significant advantages in computational efficiency. The proposed framework's utility is demonstrated through simulations and a case study.

翻译：在联合疗法的早期药物开发阶段，主要目标是初步评估新型药物与已确立的单一疗法联合使用时是否具有叠加活性。由于大规模随机研究可能存在可行性问题，非对照单臂试验已成为癌症临床试验的主流方法。然而，此类试验在决定是否进入下一个开发阶段时往往面临重大挑战。借鉴已完成的历史单一疗法临床研究数据的混合设计，为提高研究效率和改善明智决策提供了宝贵选择。与传统单臂设计相比，混合设计能够通过借用外部信息显著增强统计效能，从而更稳健地评估活性。混合设计的主要挑战在于处理信息借用的方式。我们提出了一种包含三个控制动态借用量组件的贝叶斯动态先验（DPP）框架。该框架提供了灵活的研究设计选项，并赋予借用过程明确的解释意涵，可根据具体需求进行定制。此外，所提框架中的后验分布具有闭合形式，在计算效率方面具有显著优势。通过模拟研究和案例研究，我们验证了该框架的实用性。