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Model-Assisted Bayesian Designs for Dose Finding and Optimization : Methods and Applications - J. Jack  Lee
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Model-Assisted Bayesian Designs for Dose Finding and Optimization

Methods and Applications

By: J. Jack Lee, Ying Yuan, Ruitao Lin

Hardcover | 11 November 2022

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Bayesian adaptive designs provide a critical approach to improve the efficiency and success rate of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they forms the basis for the development and success of subsequent phase II and III trials.

The objective of this book is to describes the state-of-the-art model-assisted designs to faciliate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs, yet their decision rules often can be pre-tabulated and included in the protocol-making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development.

Features

  • Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials
  • Describes the up-to-date theory and practice for model-assisted designs
  • Presents many practical challenges and issues arising from early-phase clinical trials
  • Illustrates with many real trial applications
  • Offers numerous tips and guidance on designing dose finding and optimization trials
  • Provides step-by-step illustration of using software to design trials
  • Develops a companion website (www.trialdesign.org) to provide easy-to-use software to assist learning and implementing model-assisted designs

Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the conduct of early-phase dose finding and optimization trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart!

Industry Reviews

"This book is a must for someone that wants to work with the aforementioned models using SAS and wants a step-by-step guide on how and when to implement those models. Each chapter is organized in a very similar manner... It is one of the best books on applied statistics I have read up to this point. I am sure you will find it great as well if you are part of the intended target audience, as I have described above. Particularly for non-statisticians that have an upcoming analysis where linear regression or ANOVA models are planned, the book is a must in order to make sure the proper method is used, what to check, what alternatives there are and how to properly read and interpret the results when using SAS."

David Manteigas, Portugal, ISCB News, May 2024.

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