In this on-demand webinar we perform an in-depth review of cutting-edge statistical methods in tumor-agnostic clinical trials! Discover the latest advancements in basket trials, with a special focus on innovative Bayesian approaches. Learn how Bayesian hierarchical models can effectively borrow information to enhance trial designs.

We will provide a review of statistical methods for tumor-agnostic clinical trials. In particular, the review focuses on basket trials and provides methodological insights into various Bayesian approaches. The key concept of borrowing information through Bayesian hierarchical models is emphasized, and some novel trial designs are introduced. The review is expected to provide oncology and biostatistics researchers with more exposure to powerful Bayesian methods for the design and analysis of tumor-agnostic clinical trials.

Key topics

• Insightful discussions on the concept of exchangeability in treatment effects.
• A deep dive into the various degrees of information borrowing and their implications.
• Engaging simulation studies demonstrating the real-world impact of these methods.
• Practical examples from phase I and phase II basket trials.

We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.

Speaker

Yuan Ji, Professor of Biostatistics at The University of Chicago