The growing interest in using combination drugs to treat various complex diseases has spawned the development of many novel statistical methodologies. The theoretical development, coupled with advances in statistical computing, makes it possible to apply these emerging statistical methods in in vitro and in vivo drug combination assessments. However, despite these advances, no book has served as a single source of information for statistical methods in drug combination research, nor has there been any guidance for experimental strategies.
Statistical Methods in Drug Combination Studies fills that gap, covering all aspects of drug combination research, from designing in vitro drug combination studies to analyzing clinical trial data. Featuring contributions from researchers in industry, academia, and regulatory agencies, this comprehensive reference:
- Describes statistical models used to characterize dose–response patterns of monotherapies and evaluate the combination drug synergy
- Offers guidance for estimating interaction indices and constructing their associated confidence intervals to assess drug interaction
- Introduces a practical and innovative Bayesian approach to Phase I cancer trials, including actual trial examples to illustrate use
- Examines strategies in the fixed-dose combination therapy clinical development via case studies stemming from regulatory reviews
- Evaluates computational tools and software packages used to apply novel statistical methods in combination drug development
Statistical Methods in Drug Combination Studies provides researchers with a solid understanding of the available statistical methods and computational tools and how to apply them in drug combination studies. The book is equally useful for statisticians to become better equipped to deal with drug combination study design and analysis in their practice.
Table of Contents
Drug Combination: Much Promise and Many Hurdles; Harry Yang and Laura Richman
Drug Combination Synergy; Harry Yang, Steven J. Novick, and Wei Zhao
Drug Combination Design Strategies; Steven J. Novick and John J. Peterson
Confidence Interval for Interaction Index; Maiying Kong and J. Jack Lee
Two-Stage Response Surface Approaches to Modeling Drug Interaction; Wei Zhao and Harry Yang
A Bayesian Industry Approach to Phase I Combination Trials in Oncology; Beat Neuenschwander, Alessandro Matano, Zhongwen Tang, Satrajit Roychoudhury, Simon Wandel, and Stuart Bailey
Statistical Methodology for Evaluating Combination Therapy in Clinical Trials; H.M. James Hung and Sue-Jane Wang
Challenges and Opportunities in Analysis of Drug Combination Clinical Trial Data; Yaning Wang, Hao Zhu, Liang Zhao, and Ping Ji
Software and Tools to Analyze Drug Combination Data; Cheng Su
"This book is a welcome addition to the literature and fills a needed niche since last book written on drug synergism was over 15 years ago . . . each chapter presents a different technique for solving common challenges in the development of drug combinations. . . Overall, this book serves as a good reference for both researchers in the field of statistics and drug combination development."
~ Jessica L. Jaynes, California State University, Fullerton
"Investigating drug combinations is a steadily growing area in preclinical and clinical research.As usual, specific statistical methodology is needed to handle these kinds of trials. . . .the book is generallywell structured and the majority of sections actually builds up on and refers to each other. This book provides a good overview on and a good introduction to the topic of statistical methods for drug combination studies, especially regarding the preclinical part."
~ Tim Holland-Letz, German Cancer Research Center Heidelberg