On the Calibration of Bayesian Success Criteria and Operating Characteristics for Clinical Trials

2026-03-20

AI Summary

This study addresses the need for more reliable regulatory decision-making in Bayesian clinical trials by systematically calibrating Bayesian success criteria to control decision errors. It establishes the first theoretical correspondence between Bayesian decision error metrics and frequentist operating characteristics—specifically Type I and Type II error rates—and proposes a practical calibration strategy grounded in this relationship. The approach is illustrated through a case study on a revascularization trial in cardiogenic shock. To facilitate adoption under the FDA’s emerging Bayesian framework, the authors also developed an interactive Shiny web application that enables sponsors and regulators to efficiently and reliably formulate decisions while maintaining rigorous error control.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Decision/Utility Theory
• Game Theory and Economic Paradigms: Applications

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Recently, the U.S. Food and Drug Administration (FDA) released draft guidance \citep{FDA2026} signaling a paradigm shift that facilitates the use of Bayesian methodology as the primary analysis and decision framework for drug approval.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.

Problem

• Bayesian success criteria
• decision errors
• calibration
• operating characteristics
• clinical trials

Innovation

• Bayesian calibration
• decision error control
• operating characteristics
• clinical trial design
• regulatory guidance