Recently, the U.S. Food and Drug Administration (FDA) released draft guidance \citep{FDA2026} signaling a paradigm shift that facilitates the use of Bayesian methodology as the primary analysis and decision framework for drug approval. The cornerstone and fundamental challenge of this framework is the specification and calibration of Bayesian success criteria to control decision errors, ensuring reliable clinical and regulatory outcomes. In this work, we systematically investigate various Bayesian decision-error metrics, their theoretical interrelationships, and their alignment with conventional Frequentist counterparts. This investigation provides critical theoretical insights and practical guidance on calibrating Bayesian success criteria and operating characteristics to ensure robust decision-making and the integrity of public health decisions. We illustrate this framework using a clinical trial evaluating revascularization strategies for cardiogenic shock. A Shiny application will be available at www.trialdesign.org to assist sponsors and regulators in evaluating calibration strategies consistent with recent regulatory perspectives.

翻译：近期，美国食品药品监督管理局（FDA）发布了指南草案（\citep{FDA2026}），标志着监管范式转变，支持将贝叶斯方法作为药物审批的主要分析与决策框架。该框架的核心与根本挑战在于如何规范定义并校准贝叶斯成功准则以控制决策错误，确保临床试验与监管结果的可靠性。本研究系统探讨了多种贝叶斯决策错误度量指标、其理论关联性，以及它们与传统频率学派方法的一致性。这些探讨为校准贝叶斯成功准则与操作特性提供了关键理论见解与实践指导，以保障稳健决策制定及公共卫生决策的完整性。我们通过一项评估心源性休克血运重建策略的临床试验阐述了该框架。配套Shiny应用程序将在www.trialdesign.org上线，用于协助申办方与监管机构依据最新监管视角评估校准策略。