"Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods reminds me of an old African proverb which says: "If you want to go fast, go alone. If you want to go far, go together." But I think this is the book that can help SAS practitioners go fast and far into key statistical issues in clinical trials. Like a travel book, collaborative authors brilliantly bring topics ranging from classical group sequential trials to innovative adaptive and Bayesian methods. This book provides a concise introduction to understand the topic, convenient SAS code to facilitate the productions, and a detailed reference list to dig further at your own pace. It also breaks the myth that only R can do this sort of programming. I simply love it."

Frank Shen, Ph.D.
Vice President, Data and Statistical Sciences
Global Pharmaceutical R&D
Abbvie

"Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods provides an excellent overview of novel statistical designs and methods used in different stages of drug development. The chapters are written by well-known experts from the pharmaceutical industry and academia. In addition to the review of statistical methods, the book contains numerous examples of practical implementations using SAS. I believe this book will be a valuable guide for practitioners who would like to learn and implement these designs and methods in SAS.

"Chapter 1 serves as a powerful introduction to the book by delivering a comprehensive overview of an evolution of clinical trials designs, specifically group sequential and adaptive designs. Both Frequentist and Bayesian methods that help improve decision making at an exploratory stage (e.g., CRM and its modifications, MCP-Mod) and designs that increase the probability of success at a confirmatory stage (e.g., GSD, SSR) are discussed in subsequent chapters of the book. I hope this book will fulfill the needs of many statisticians and encourage the broader use of these methods and designs."

Olga V. Marchenko
Vice President, Head of Quantitative Decision Strategies and Analytics
Quintiles

"Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods edited by Drs. Menon and Zink provides a comprehensive overview of key topics in modern clinical trial statistics. The book covers a broad set of topics, including well-established approaches such as group-sequential designs. However, the most attractive feature of the book is a detailed discussion of advanced methods in clinical trial statistics, for example, adaptive designs in early-stage and late-stage clinical trials, and Bayesian approaches to clinical trial design and analysis.

"The material in the book is presented in an easy-to-understand format with multiple examples and case studies with actual data sets. Virtually all of the chapters in the book are self-contained, which is very useful for the reader who would like to quickly come up to speed on a certain topic. The authors provide step-by-step instructions on how to implement the statistical methods in SAS, and often point out useful shortcuts that will be appreciated by the reader. Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods will be ideally suited for clinical trial statisticians who are interested in expanding their arsenal of biostatistical tools."

Alex Dmitrienko
Research Fellow
Mediana Inc.

"There have been a great deal of advances in clinical trials in the past few years and Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods captures a large portion of the exciting innovations in different stages of drug development, and makes them real with accompanying SAS procedures and macros. Different well-known experts from the drug industry and academia present the methodology and the SAS details for the various chapters. The chapters present the methodologies and then implement them with SAS code applied to particular data examples. This enables the reader to gain hands-on experience with the methodology, and to implement it in real-world applications. The references at the end of the chapters are very up-to-date.

"A reader with a general statistical background and some experience with clinical trials would be well prepared to fully take advantage of this book. The extensive SAS code and the data examples make this a particularly practical and useful book for both those who want to learn the latest innovative techniques in the literature with hands-on experience, and also those practitioners who want to be able to fully implement these novel methods that they have become familiar with in the recent literature using the supplied SAS code and the data examples.

"The main emphasis of the book is adaptive, with both frequentist and Bayesian approaches. The first chapter provides a sweeping introduction to the topics in the book, providing important history. All of the other chapters supply useful SAS code and examples to implement the presented methods. Chapter 2 provides a solid introduction to the classical group sequential testing framework, which some experts view as the classical introduction to adaptive designs.

"The chapter on the adaptive methods of sample size reassessment (SSR) is a great contribution to the book, covering both blinded and unblinded SSR. While all of the methods of unblinded SSR are well covered, of special note is the thorough treatment of the new innovative procedures of the promising zone, conditional power, and the information-based approach, with well-developed methodology followed by practical SAS code and examples.

"While most of the chapters are frequentist, two are entirely Bayesian: one on Bayesian survival meta experimental design and another on Bayesian dose response. In addition, there is a Bayesian section in the adaptive randomization chapter.

"The practical implementation of recent innovative statistical methods in early exploratory stages of drug development is treated in several chapters on dose selection (the classical continual reassessment method (CRM)), the efficient statistical methodology for model-based design and analysis of Phase II dose-finding studies using MCMod, and Bayesian continual reassessment. Other topics are treated in separate chapters on adaptive randomization and optimal response adaptive randomization with binary outcomes. The last chapter addresses recent advances in population enrichment designs that will prove so crucial in precision medicine development.

"While the main thrust of the book is for drug development, many of the chapters apply to non-pharmaceutical clinical trials as well, including the sample size reassessment chapter and the two chapters on adaptive randomization and the one enrichment.

"Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods is a great introduction to the recent, exciting innovations in adaptive and Bayesian methods for clinical trials; it is a state-of-the-art book for these twenty-first century techniques. It should help to promote the broader use of these newer innovative methodologies since the software to easily implemented and illustrated by examples."

Gregory Campbell, Ph.D.
President, GCStat Consulting
Former Director of Biostatistics, CDRH, FDA