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

TL;DR

This paper explores how to properly calibrate Bayesian success criteria in clinical trials to align with regulatory standards, ensuring reliable decision-making and public health safety.

Contribution

It provides a systematic analysis of Bayesian decision-error metrics, their theoretical relationships, and practical calibration guidance aligned with FDA regulatory perspectives.

Findings

Theoretical insights into Bayesian and Frequentist error metrics

Practical calibration strategies for Bayesian success criteria

Application example using cardiogenic shock trial data

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Recently, the U.S. Food and Drug Administration (FDA) released draft guidance \citep{FDA2026} signaling a paradigm shift that facilitates the use of Bayesian methodology as the primary analysis and decision framework for drug approval.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