The U.S. Food and Drug Administration (FDA) released a landmark draft guidance in January 2026 on the use of Bayesian methodology to support primary inference in clinical trials of drugs and biological products. For sponsors, the central message is not merely that ``Bayes is allowed,'' but that Bayesian designs should be justified through explicit success criteria, thoughtful priors (especially when borrowing external information), prospective operating-characteristic evaluation (often via simulation when simulation is used), and computational transparency suitable for regulatory review. This paper provides a practical, regulatory-oriented synthesis of the draft guidance, highlighting where Bayesian designs can be calibrated to traditional frequentist error-rate targets and where, with sponsor--FDA agreement, alternative Bayesian operating metrics may be appropriate. We illustrate expectations through examples discussed in the guidance (e.g., platform trials, external/nonconcurrent controls, pediatric extrapolation) and conclude with an actionable checklist for planning documents and submission packages.

翻译：美国食品药品监督管理局（FDA）于2026年1月发布了一份具有里程碑意义的指南草案，针对贝叶斯方法在药物和生物制品临床试验中支持主要推断的应用。对申办方而言，其核心信息不仅是“允许使用贝叶斯方法”，更强调贝叶斯设计必须通过以下要素进行论证：明确的成功标准、审慎的先验设置（尤其在借用外部信息时）、前瞻性的操作特征评估（当采用模拟时通常通过模拟实现），以及适用于监管审查的计算透明度。本文从实践与监管视角对该指南草案进行综合性解读，重点阐明贝叶斯设计在哪些场景下可校准至传统频率学错误率目标，以及在申办方与FDA协商一致时，哪些场景可采用替代的贝叶斯操作指标。我们通过指南中讨论的实例（如平台试验、外部/非同期对照、儿科外推）具体说明监管期望，最后为方案设计与申报资料提供可操作的核查清单。