Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA.
Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA.
Stat Med. 2023 Dec 30;42(30):5708-5722. doi: 10.1002/sim.9936. Epub 2023 Oct 19.
As the roles of historical trials and real-world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages. Moreover, BEATS extends the Bayesian semiparametric meta-analytic predictive prior (BaSe-MAP) to incorporate time-to-event endpoints, enabling optimal borrowing performance. Initially, BEATS calibrates the expected sample size and initial randomization ratio based on heterogeneity among the external data. During the interim analysis, flexible sample size adaptation is performed to address conflicts between the concurrent and historical control, while also conducting futility analysis. At the final analysis, estimation is provided by incorporating the calibrated amount of external data. Therefore, our proposed design allows for an approximation of an ideal randomized controlled trial with an equal randomization ratio while controlling the size of the concurrent control to benefit patients and accelerate drug development. BEATS also offers optimal power and robust estimation through flexible sample size adaptation when conflicts arise between the concurrent control and external data.
随着历史试验和真实世界证据在药物开发中的作用大大增加，已经提出了几种方法来利用外部数据并改进临床试验的设计。虽然这些方法大多数都侧重于在分析阶段借用信息时的方法学开发，但由于外部数据中异质性的规范不足或不存在，可能会导致同期对照的入组不足，从而导致治疗效果的估计不可靠。在这项研究中，我们引入了一种具有灵活样本量自适应的贝叶斯混合设计（BEATS），该设计允许根据异质性水平在设计和中期分析阶段自适应地借用外部数据，以增强对照臂。此外，BEATS 将贝叶斯半参数荟萃分析预测先验（BaSe-MAP）扩展到包含生存时间终点，从而实现最佳的借用性能。最初，BEATS 根据外部数据之间的异质性来校准预期的样本量和初始随机化比例。在中期分析中，通过灵活的样本量调整来解决同期对照和历史对照之间的冲突，同时进行无效性分析。在最终分析中，通过纳入校准的外部数据量进行估计。因此，我们提出的设计允许在控制同期对照的大小以使患者受益和加速药物开发的同时，近似具有均等随机化比例的理想随机对照试验。BEATS 还通过灵活的样本量调整提供了最优的功效和稳健的估计，当同期对照和外部数据之间出现冲突时。
