Clinical-Trials Books

Clinical research in the US is heavily regulated by the FDA to insure that safe and effective products including drugs vaccines and medical devices come to market and that the clinical trials expose products that either are unsafe, ineffective or both. In recent years with the advent of Bayesian hierarchical models that have posterior distributions of the model parameters determined by Markov Chain Monte Carlo methods, the FDA has accepted the use of Bayesian approaches in medical devices and there seems to be some progress in the area of pharmaceuticals as well.

This book is a welcome addition to the literature on applied Bayesian methods. The authors provide a clear description of the methods and how they can be applied to clinical trials and health economic studies. It is very timely to have such a great reference since industrial statisticians are in need of learning and applying the techniques.

This is an excellent introduction to Bayesian methods for anyone conducting health technology assessment, meta-analysis, and health economic analysis. Even if you're not a Bayesian, it adds depth and perspective to universal concepts such as statistical power, generalizability, and hypothesis testing. The chapters on cost-effectiveness and evidence-synthesis will be invaluable to anyone conducting pharmacoeconomics models or health technology assessments of pharmaceuticals or medical devices.

This is a great tool for health care professionals conducing clinical trials. It provides useful information, is practical and the resource information is great!

Being a small research site in a locality without many clinical trials we often have to train RNs or other allied health professionals to be research coordinators. I have used each version of this book. New coordinators are given a copy of this book to use as a resource. Potential coordinators are shown a copy to look at to find out if coordinating is actually something they would be interested in doing. It is great to use with investigators too. I find it invaluable.

As an optometrist, I've found that after reading this book, I have a better sense of when to refer my patients for various diseases. A must for all optometry students! It is a good synopsis of some of the most important studies in ophthalmology. It will be good for reference in the future.

These is an extremely useful and accessible summary of several of the landmark studies in ophthalmology. As a resident physician, I found it extremely useful. A "must have" for practicing ophthalmologists!

I am an epidemiologist responsible for designing and overseeing implementation, analysis and reporting of a number of cluster randomized health intervention trials in developing countries. I found this book absolutely terrific for several reasons. First, the scope of the book is broad. This is not just a compilation of statistical methods. The authors discuss the rationale for cluster randomization, its historic development, specific issues that arise in planning the studies, securing informed consent and other ethical issues affected by cluster design. The final chapter reviews the central themes of the book in discussing how to fully report a cluster randomized trial. Throughout these chapters the authors describe both the theoretical issues raised, as well as practical solutions. Second, the book represents a succinct and collected discussion of the relevant issues. As a non-statistician, I don't read the primary statistical literature. I do read health intervention studies that employ a cluster randomized method. I may attend an occasional short seminar on cluster design, or discuss particular issues with collaborating statisticians, but until I read this book, I felt I was freelancing each design and analysis issue. After reading the book I have exposure to the whole paradigm. I can understand the design and analysis choices, make better decisions, and make consultations with my collaborating statisticians more focused and productive. Third, options for analysis, including their assumptions, advantages and disadvantages, as well as methods for calculating sample size are systematically presented. This directly empowers the reader to make balanced and sound decisions about appropriate study size and analytic approach. Fourth, the book is readable. The ideas, at times, are quite dense, but because of the abundant use of practical examples, the authors consistently communicate their message clearly. I hesitated to buy this book for months, because of the price tag for a 178 page book of unknown quality. After reading it, I am convinced that a short course would not have taught me as much. Moreover I now have a reference book that I can use in thinking through each of the issues for my studies, for teaching my epidemiological trainees, and for citing when writing manuscripts. In short, a GREAT book for the right audience.

i love this book it is so good.my father wrote this book.my name is even in it.i am ten years old.but if you like epidiniology and biostatistcks you'll love this book. (...)

This book is a great resource and reference. It's a great collection of short articles useful for those interested in the ethical issues surrounding research. It has full copies of the Nuremberg and other ethical codes.

This book is an excellent textbook for bioethics with wonderful framework, comprehensive approach, and many relevant articles.
You can get overall outline and detailed information & discussion on bioethics by this book, therefore this book is good for beginner as well as for expert on this field.

Advances in the theory of repeated significance testing in the 1980s and 1990s has made sequential methods practical by identifying stopping rules for data collected sequentially but in groups. This material is now used to plan interim analyses and both safety and efficacy group sequential trials for clinical trials. This text provides for the first time thorough coverage of these advances with suitable references to the literature.

It should be on the bookshelf of any biostatistician who conducts clinical trials for pharmaceutical or medical device companies.

Advances in the theory of repeated significance testing in the 1980s and 1990s has made sequential methods practical by identifying stopping rules for data collected sequentially but in groups. This material is now used to plan interim analyses and both safety and efficacy group sequential trials for clinical trials. This text provides for the first time thorough coverage of these advances with suitable references to the literature.
It should be on the bookshelf of any biostatistician who conducts clinical trials for pharmaceutical or medical device companies.

My son who is now in his late 20s and in legal trouble has been diagnosed with multiple personality disorder and bipolar disorder. Reading this book has helped me tremendously understand the disease and assist in his legal defense as well as his healing. I highly recommend it due to it's ease of reading and factual information. Had I read this book when he was in his teens, perhaps the problems he now faces would not have occured.

Should I forgive myself after all these years? Or just kill myself. My father,My garndfather and my great grandfather on my fathers side of the family all commited suicide between the ages or 43 and 46. I am 44 as of feb 14.Is it genitic? If I could afford your book I would buy it. It may help me decide what to do with this useless life. Depression and guilt have haunted me for 22 years.

"This book is must-have reading for anyone in the business..." (Clinical Chemistry, Vol. 49, No. 7, 2003)

"The book is very prescriptive and full of lists and tables with which to guide managers in making effective decisions in using computer-assisted clinical trials in pharmaceutical studies." (Technometrics, Vol. 45, No. 1, February 2003)

Senn is a great writer. He has written an excellent text on cross-over trials that raises many issues about when such design can be used and what their limitations are. This book covers the gamut of issues in drug development concentrating on important and sometimes subtle issues in clinical trials including design and analysis, intention to treat principle, multiple testing, Bayesian and frequentist approaches and interpretations, meta analysis, regulatory issues and ethics. It also covers cross-over designs, pharmacokinetics, pharmacodynamics and pharmacoeconomics.

The introduction gives you a feeling for the approach in the book and how it splits into two parts. Part I, consisting of chapters 2-5, provides some history of the development of statistical methods and some introductory topics that are fundamental to the discussion in Part II.

Part II is the heart of the book where the practical statistical issues in clinical trials are raised. The text is intended for non-statisticians who work in the pharmaceutical industry but to quote part of Senn's preface he states "Although addressed to the life-scientist it is my hope that many statisticians, in particular those studying medical statistics or embarking on a career in drug development, will also find it useful. Above all I hope that it will help communication between the disciplines: a process by which the statistician stands to benefit as much as any other professional in drug development."

I can really appreciate what Senn has done. He explains the issues of intention-to-treat, washout, multiplicity and other problems that I have had to wrestle with and try to explain to MDs and clinical managers. But even more importantly to me than helping me communicate the many issues that I was aware of, he also raises many subtle issues that I was not aware of. This includes questions of bioequivalence, the use of baseline information and particularly percentage change from baseline versus covariate adjustment, problems of inference regarding measurements taken after titration and issuesw in N of 1 trials. I even learned a few new techniques (e.g. Taves minimiization and Atkinson's generalization of it for allocating patients to treatment groups).

The only complaint I can see is that there is not enough detail. However, remember the text was not designed for statisticians and so much of the mathematics and technicalities are deliberately left out.

But Senn does provides a detailed list of relevant references at the end of each chapter that allows the reader to find in texts and journal articles all the detail one might need. Also to aid with communication there is a large glossary of terms at teh back of the book.

This is a great reference for scientists and statisticians as well!

Senn is a great writer. He has written an excellent text on cross-over trials that raises many issues about when such design can be used and what their limitations are. This book covers the gamut of issues in drug development concentrating on important and sometimes subtle issues in clinical trials including design and analysis, intention to treat principle, multiple testing, Bayesian and frequentist approaches and interpretations, meta analysis, regulatory issues and ethics. It also covers cross-over designs, pharmacokinetics, pharmacodynamics and pharmacoeconomics.
The introduction gives you a feeling for the approach in the book and how it splits into two parts. Part I, consisting of chapters 2-5, provides some history of the development of statistical methods and some introductory topics that are fundamental to the discussion in Part II.

Part II is the heart of the book where the practical statistical issues in clinical trials are raised. The text is intended for non-statisticians who work in the pharmaceutical industry but to quote part of Senn's preface he states "Although addressed to the life-scientist it is my hope that many statisticians, in particular those studying medical statistics or embarking on a career in drug development, will also find it useful. Above all I hope that it will help communication between the disciplines: a process by which the statistician stands to benefit as much as any other professional in drug development."

I can really appreciate what Senn has done. He explains the issues of intention-to-treat, washout, multiplicity and other problems that I have had to wrestle with and try to explain to MDs and clinical managers. But even more importantly to me than helping me communicate the many issues that I was aware of, he also raises many subtle issues that I was not aware of. This includes questions of bioequivalence, the use of baseline information and particularly percentage change from baseline versus covariate adjustment, problems of inference regarding measurements taken after titration and issuesw in N of 1 trials. I even learned a few new techniques (e.g. Taves minimiization and Atkinson's generalization of it for allocating patients to treatment groups).

The only complaint I can see is that there is not enough detail. However, remember the text was not designed for statisticians and so much of the mathematics and technicalities are deliberately left out.

But Senn does provides a detailed list of relevant references at the end of each chapter that allows the reader to find in texts and journal articles all the detail one might need. Also to aid with communication there is a large glossary of terms at teh back of the book.

This is a great reference for scientists and statisticians as well!

My sister has breast cancer and a co-worker's brother is HIV-positive. We dove into this book and kept underlining passages that are of help. My summary: Very compelling reading; you, the layperson, really learn about medicine that seemed intimidating and confusing before - this can help the person you love make choices.

I was the Managing Editor of this book and it had me in tears as I worked on it. The stories are so compelling and the writing so superb that it just draws you in. The people and their families are YOUR family and friends and neighbors--I felt like I knew them. I usually try not to get too emotionally involved with the books I work on, but it was impossible not to with THREE ON THE EDGE.