A case study in teaching and learning the design, conduction, and analysis of Bayesian adaptive trials through an...

Area of Science:

• Biostatistics Education
• Clinical Trial Design
• Adaptive Trial Methodologies

Background:

• Clinical trial design and management experience is crucial for students but often challenging to gain in academic settings.
• Limited resources exist for students to practice adaptive trial design and implementation.
• Hands-on experience is essential for students to succeed after graduation.

Purpose of the Study:

• To develop an open-source, instructor-ready case study framework for teaching Bayesian adaptive trial design.
• To provide students with practical, hands-on experience in designing and implementing adaptive clinical trials.
• To offer a flexible template adaptable to various course levels and scientific questions.

Main Methods:

• Developed the open-source DRIVE trial case study framework, comprising seven components mapped to clinical trial stages.
• Integrated a worked example demonstrating the selection and implementation of a Bayesian adaptive design with response-adaptive randomization.
• Designed components with adaptable materials for instructors to tailor to specific course needs.

Main Results:

• The framework offers a complete, ready-to-use template for incorporating adaptive trial design into coursework.
• The DRIVE trial highlighted the importance of comparing study designs and the necessity of collaboration in clinical trials.
• The case study provides valuable practical training and resources for future biostatisticians.

Conclusions:

• The open-source framework effectively facilitates hands-on learning of Bayesian adaptive trial design for students.
• This resource addresses the gap in practical training materials for adaptive clinical trial methodologies.
• The DRIVE trial framework serves as a valuable template for biostatistics education and clinical trial training across disciplines.