使用外部数据进行单臂和混合对照试验的贝叶斯临床试验设计的修改与扩展
Modification and extension of the Bayesian clinical trial design using external data for single-arm and hybrid-controlled trials
AI总结：
针对儿科临床试验样本量确定难题，提出修改抽样和分析先验规范并扩展到混合对照试验的方法，经模拟研究和案例验证，该方法能减少样本量，保持目标特征并控制偏差，为含外部信息的临床试验设计提供可靠途径。
AI中文摘要：
儿科临床试验中患者可用性有限使样本量确定复杂化。虽纳入外部数据的贝叶斯方法提供了解决方案，但严格控制I型错误率仍困难。Psioda和Ibrahim（2019）提出基于模拟的框架，但存在问题。本文提出对抽样和分析先验规范的修改并扩展到混合对照试验。重新定义零抽样先验，采用弱信息先验减轻偏差，扩展到混合对照试验。模拟研究和儿科皮肤红斑狼疮案例表明，该方法与传统方法相比大幅减少所需样本量，保持目标操作特征并控制偏差，为纳入外部信息的临床试验设计提供可靠有效方法。
英文摘要：
Limited patient availability complicates sample size determination in pediatric clinical trials. Although Bayesian methods incorporating external data offer a solution, rigorously controlling the type I error rate remains difficult. Psioda and Ibrahim (2019) proposed a simulation-based framework as a practical solution. However, although their framework was designed to relax the type I error control, this relaxation fails when the external data exhibit a large treatment effect, making it difficult to design clinical trials that incorporate external data. Furthermore, restricting the support of sampling priors can cause trial outcomes to fall outside of this support, leading to lower power. Additionally, their analytic prior formulation may induce bias, and their method is not applicable to hybrid-controlled trials involving two-group comparisons. Thus, we propose modifications to both the sampling and analytic prior specifications and extend the framework to hybrid-controlled trials. We redefine the null sampling prior as a normal distribution centered at the null boundary, ensuring a Bayesian type I error evaluation. For the analytic prior, we employ a weakly informative prior for the second component of a robust mixture prior to mitigate bias under prior-data conflict. Furthermore, we extend this methodology to hybrid-controlled trials. Simulation studies and a pediatric case study of cutaneous lupus erythematosus demonstrate that our method substantially reduces the required sample size compared with both frequentist and original Bayesian methods, while maintaining the target operating characteristics and controlling estimation bias under prior-data conflict. This framework provides a reliable and efficient approach for designing clinical trials that incorporate external information.
