This book has a clear purpose. It provides a practical guide for applying appropriate statistical techniques to specific stages of clinical trials data analysis. This practicality is its strength. Because of this focus on relevant, practical statistical methods, it doesn’t directly address other aspects of clinical trial data analysis. It doesn’t overpromise and succeeds in keeping what it promises.

The book is very well-written and the authors’ style is clear and easy to understand. It’s praiseworthy to see high-level expertise explained to readers who don’t need to be statistical gurus to understand the subject matter. The authors’ choice of topics for this book was based on International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry entitled “Structure and Content of Clinical Study Reports,” also known as ICH E3. This illustrates the fundamentally positive virtue of the book, which is that it stays on track with common professional practices and in tune with regulatory agencies. In general, though, 80% of data analysis is data management. That applies to AI too. The authors could have acknowledged more of the importance of data management while not deviating from the book’s stated purpose.

Another positive characteristic of the book is that it’s receptive to the use of new SAS procedures and also has kept abreast of recently added features to existing procedures. The authors are progressive and show how to apply new technology creatively.

Summing up, the book’s virtue is its practicality. Through the use of well-chosen examples, it explains how to navigate statistically through complex realities encountered during a clinical trial. It’s an excellent educational resource for newcomers to clinical trials analysis.

Jim Sattler, President
Satmari Software Systems, Inc

This book is unique in that it presents the mathematical theory and SAS programming examples side by side. Since SAS is popular software for data analysis in pharmaceutical industry, this book is a useful reference for people using SAS to report clinical trials data.

There are significant changes that are implemented in this second edition. A new feature includes the topics based on the stages (early or late) of clinical trials, which is beneficial to statisticians working in clinical trials for regulatory submissions. The topics in this edition are expanded to cover new approaches addressing statistical problems that were introduced in the first edition. These new approaches are the recent developmental research in clinical trials.

There are 7 chapters in this second edition which are grouped into three parts:

The first part (Chapters 1 and 2) presents the general statistical methods used at all stages of drug development. It contains the same content as the first edition with additional revisions.

The second part (Chapters 3 and 4) contains information not found in the first edition. It focuses on statistical methods in dose-escalation in Phase I (Chapter 3) and dose-finding in Phase II clinical trials (Chapter 4). The most attentions in does-escalation designs are given to continual reassessment method with examples from oncology trials. For dose-finding methods, this book confers the pairwise contrast-based and multi-contrast tests for dose-finding algorithms based on the MCP-Mod procedure.

The third part (Chapters 5-7) describes the statistical methods in late phase clinical trials. It is an extension of the three chapters (multiplicity adjustment method, interim data analysis, and incomplete data) from the first edition. More specifically, the extension of these three chapters include new macro to support gatekeeping procedures (Chapter 5), SAS procedures (PROC SEQDESIGN and PROC SEQTEST) that support a broad class of group-sequential designs (Chapter 6), PROC GEE to support weighted generalized estimating equations analyses, and PROC MI to run MNAR assumption for missing data (Chapter 7).

One thing to note is that the complicated methods used in this second edition rely heavily on SAS macros, which cannot be run through standard SAS procedures directly. Instead, you will need to download the macros through the designated websites.

Overall, this book is well balanced since it contains not only the SAS applications but also the theories behind statistical methods. It is a notable addition to the growing collection of Design and Analysis of Clinical Trials books already published.

Annpey Pong, PhD.
Merck Research Laboratories,
Rahway, New Jersey, USA