Model-Assisted Bayesian Designs for Dose Finding and Optimization 1st Edition

Capture the cutting edge of clinical trial methodology with Model-Assisted Bayesian Designs for Dose Finding and Optimization, 1st Edition by Ying Yuan, Ruitao Lin, and J. Jack Lee. This authoritative volume brings Bayesian thinking into practical dose-finding and optimization, offering a modern toolkit that bridges theory and application for drug development teams worldwide.

Discover clear, applied guidance on model-assisted Bayesian approaches that streamline early-phase trials, improve decision-making, and enhance patient safety. The authors—leading experts in biostatistics and clinical trial design—walk readers through intuitive concepts, step-by-step methodology, and interpretable results tailored to oncology, immunotherapy, and other therapeutic areas. Rigorous yet accessible, the book explains adaptive dose-escalation, safety-efficacy trade-offs, and optimization strategies that align with contemporary regulatory expectations.

Designed for biostatisticians, clinical researchers, pharmacologists, and industry decision-makers, this book equips readers with practical examples, simulation-based insights, and implementation-ready frameworks that translate directly into trial protocols. Whether you are conducting trials in North America, Europe, or Asia, the principles and methods are applicable across global drug development settings.

If you seek to modernize your dose-finding practice, reduce trial risk, and make statistically sound choices with confidence, this book is an essential resource. Add Model-Assisted Bayesian Designs for Dose Finding and Optimization to your professional library today and elevate your ability to design efficient, ethical, and effective early-phase clinical studies. Perfect for teams, coursework, or individual practitioners aiming for excellence in modern trial design.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.