Bayesian designs and the control of frequentist characteristics: a practical solution

Area of Science:

• Biostatistics
• Clinical Trial Design
• Regulatory Science

Background:

• Frequentist concepts like Type I error control are standard in medical research and regulatory submissions.
• Traditional Bayesian designs often lack explicit consideration of frequentist operating characteristics.
• Adjusting Bayesian designs to meet frequentist standards can be complex, impacting prior information and utility functions.

Purpose of the Study:

• To develop a Bayesian decision-theoretic framework for experimental designs that explicitly incorporates frequentist requisites.
• To define optimal Bayesian designs that satisfy regulatory constraints and required operating characteristics.
• To demonstrate the utility of this approach in group-sequential multi-arm Phase II and bridging trials.

Main Methods:

• Employed a Bayesian decision-theoretic approach for experimental design.
• Integrated interpretable utility functions with frequentist criteria (e.g., Type I error, false discovery rate).
• Utilized simulations to adjust tuning parameters and ensure compliance with targeted operating characteristics.

Main Results:

• Successfully defined optimal Bayesian designs that meet explicit frequentist criteria.
• Demonstrated the approach's applicability in complex trial designs like group-sequential multi-arm Phase II and bridging trials.
• Showcased the ability to balance Bayesian flexibility with frequentist rigor.

Conclusions:

• The proposed Bayesian decision-theoretic approach effectively combines Bayesian and frequentist principles for experimental design.
• This method allows for the creation of optimal designs that satisfy both statistical efficiency and regulatory demands.
• The framework offers a structured way to develop Bayesian clinical trial designs that meet established frequentist standards.