基于频率学派校准的贝叶斯组序贯设计与动态借用
Frequentist-calibrated Bayesian group sequential design with dynamic borrowing
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中文总结 AI 辅助
该研究针对临床试验设计的监管要求，提出带动态借用的贝叶斯组序贯设计，利用贝叶斯与频率学派检验的对应关系，通过数值研究及结核病预防试验验证了其性能。
中文摘要 AI 辅助
贝叶斯分析在临床试验中应用日益广泛，但在许多情况下，针对频率学派操作特征（如I类错误和效力）的设计评估仍是监管要求。已有研究证实，当从试验外部来源借用信息时，严格控制频率学派I类错误率相当于抵消借用效果，导致无效力提升。我们提出一种带动态借用的贝叶斯组序贯设计，该设计利用基于后验概率比的贝叶斯决策准则与频率学派一致最有效（UMP）检验之间的明确对应关系。在每次期中分析时，提供两个证据阈值：一个可精确复现频率学派UMP决策；另一个则允许研究者在适当时纳入历史信息。我们通过数值研究评估该方法的性能，并将该框架应用于一项III期结核病预防试验的设计，纳入成人与儿科的历史试验数据。
英文摘要
Bayesian analysis is increasingly used in clinical trials. However, assessment of the design with respect to the frequentist operating characteristics, such as type I error and power, remains a regulatory requirement in many cases. It is well established that, when information is borrowed from external sources to the trial, imposing strict frequentist type I error rate control is equivalent to offsetting the borrowing, which results in no power gains. We propose a Bayesian group sequential design with dynamic borrowing that exploits an explicit correspondence between Bayesian decision criteria based on posterior odds and frequentist uniformly most powerful (UMP) tests. At each interim analysis, two evidential thresholds are made available: the one that exactly retrieves the frequentist UMP decision; the other, that allows the investigator to incorporate historical information when appropriate. We assess the performance of the proposed approach in numerical studies, and apply the framework to the design of a phase III tuberculosis prevention trial, incorporating historical adult and pediatric trial data.
