Introduction to Statistical Methods for Clinical Trials (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition

This book is very well written. As a statistician in pharma industry, in my opinion, this is a very good introduction book for clinical trial stat.

Ordered from Sold by: Amazon.com Services LLC and stated to be in "New" condition but the cover wasn't even attached!This textbook was in worse condition than any book I've ever bought before whether new or used.

It may have been an introduction, but I did not find it to be elementary. I was hired at a new job and first introduced to clinical trials. I got this book hoping it would help me out. I am sure it has a good information, but it was a lot of information and most of it went over my head. I wish they would have given more formulas for sample size calculations rather than theoretical discussions. The book is too wordy--meaning there is a lot of text with very few formulas or workable examples.Overall, it is a pretty good book, but I would not recommend it if you have little or no background in clinical trials (even though it says "introduction" in the title). It does have good topics to get you thinking, but you might have to find other sources to really understand the material.

Very explicit

A wonderful resource for anyone working in clinical trials.

The author are very accomplished statisticians with many years of clinical trial experience and research. DeMets along with Gordon Lan is famous for the alpha spending function approach that allowed added flexibility to group sequential trials. In addition to authoring several chapters of the book, Cook and Demets edited the book and invited other prominent researchers to contribute to the chapters. The other contributors are Robin Bechhofer, T. Charles Casper, Richard Chappell, Jens Eickhoff, Jan Feyzi, Marian Fisher, Kyungmann Kim, Rebecca Koscik, Mary Lindstrom, and Ellen Roecker.The book covers a wide variety of topics and starts from the basics. But although some people equate introductory in a title to mean elementary that would be a wrong conclusion in this case. Many of the topics are advanced and involve state-of-the-art methodology. The area of adaptive designs is, for example, a very hot topic these days and is the subject of a great deal of research.The chapters are very well written and include most of the crucial topics that come up in trial design and development. For example, in the first chapter randomization is discussed in detail as are issues of trial organization, ethical issues, the reasons why randomized clinical trials are important and some regulatory issues.Chapter 2 covers problem definition, composite outcomes and the use of surrogate endpoints. Chapter 3 covers trial design for all phases of clinical trials and includes sections on early phase trials, phase III trials and the phase IV postmarketing trials. Methodology includes non-inferiority, screening , prevention, therapeutic and adaptive designs.Chapter 4 deals with the important issue of sample size determination primarily using frequentist approaches. This chapter includes the sticky issues of how to deal with clustered data, survival data and censoring due to loss to follow-up and non-adherence to the protocol.This is followed by complete chapters on randomization including response-adaptive randomization, data collection and data quality control, survival analysis. longitudinal data, quality of life data and instrument development, data monitoring and interim analysis, a chapter dealing with missing data, subgroup analysis, multiple testing and ways to avoid bias. The final chapter deals with the very important practical issues on how to close out a trial and prepare and report results.I like this book both as a possible introductory text and as a reference for clinical trial statisticians. The appendix provide sophisticated methods of inference including Brownian motion, information theory, asymptotic theory and the delta method.My only criticism of the book is lack of discussion of software. Statistical software pakages are crucial to the analysis of clinical trials with SAS being the most frequently used. Also there are now a number of fine packages for sample size determiniation and the design of group sequential trials. In this regard Demets and Lan have their own software product and Cytel has East which is now entering the area of adaptive trial design as is AddPlan by Wassmer and the software package produced by Mark Chang. So for a practical text on clinical trials the absence of coverage of the available software along with recommendations of what to use and how to use it is the one glaring omission of the book.I especially recommend this book because from the methodologic viewpoint there is no other book with more depth or broader coverage. Longitudinal analysis and repeated measure designs are very important in clinical trials but are not often covered in introductory biostatistics courses. Chapter 8 covers random effects models, population-average, and subject-specific models and various sophisticated estimation techniques including restricted maximum likelihood estimation, two-stage estimation and generalized estimating equations.

Though this might make for a fairly biased review as I had an extremely slow and boring professor, I found this book to be very enjoyable and clear in its presentation.