Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review

Area of Science:

• Medical Statistics
• Clinical Trial Design
• Regulatory Science

Background:

• Bayesian statistics offers advantages over classical methods in medical research, particularly for incorporating prior information from external data sources.
• The complexity of modern clinical trials often necessitates advanced statistical approaches where frequentist methods may be insufficient.
• There's a growing trend in regulatory submissions utilizing Bayesian statistics for its flexibility and decision-making insights.

Purpose of the Study:

• To provide a comprehensive overview of Bayesian statistics in clinical trial design and regulatory submissions.
• To detail the application of Bayesian methods in key areas such as sample size determination, type I error control, and external data borrowing.
• To serve as a resource for developing complex and innovative clinical trial designs.

Main Methods:

• Tutorial review of Bayesian statistical principles and applications in clinical trials.
• Discussion of frequentist operating characteristics for Bayesian analysis in regulatory contexts.
• Illustrative examples of Bayesian sample size determination and other methodologies.

Main Results:

• Bayesian statistics provides a flexible framework for assessing treatment safety and efficacy.
• The use of Bayesian methods is increasing in regulatory submissions due to their ability to handle complex trial designs and incorporate external data.
• Consideration of frequentist error rates is crucial for regulatory acceptance of Bayesian strategies.

Conclusions:

• Bayesian statistics is a vital tool for advancing medical science and improving clinical trial design.
• Its application in regulatory submissions offers significant advantages for decision-making in complex healthcare scenarios.
• This review equips researchers and statisticians with foundational knowledge for innovative Bayesian trial designs.