Incorporating external real-world data (RWD) in confirmatory adaptive design

Area of Science:

• Clinical Trial Design
• Biostatistics
• Real-World Data Integration

Background:

• Adaptive designs are crucial for efficient clinical trials, especially for unmet medical needs.
• Existing adaptive designs allow for interim decisions and adjustments while maintaining integrity.
• Innovation is needed to further optimize drug development efficiency.

Purpose of the Study:

• To propose a novel framework for incorporating external real-world data (RWD) into adaptive trial designs.
• To enhance interim and/or final decision-making processes within adaptive trials.
• To maintain objectivity and control Type I error while leveraging RWD.

Main Methods:

• Development of a new framework for integrating external RWD into adaptive designs.
• Prespecification of decision processes, timing, and data borrowing amounts.
• Simulation studies to evaluate power, Type I error, and performance metrics.
• Illustration using a case study in non-small cell lung cancer.

Main Results:

• The proposed framework allows for objective incorporation of RWD in adaptive designs.
• Simulation studies demonstrate the performance of the framework across various scenarios.
• The framework maintains statistical integrity, including Type I error control.
• A case study illustrates the practical application in non-small cell lung cancer.

Conclusions:

• Integrating external real-world data into adaptive designs offers a promising approach to improve clinical trial efficiency.
• The proposed framework provides a structured and controlled method for leveraging RWD.
• This methodology can aid in faster and more informed decision-making in drug development.