Clinical-Trials Books

I would recommend this book to anyone who is thinking about entering a clinical trial. Research Studies are valuable tools for learning about diseases, and sometimes the only way to find a cure for a disease. But you have to understand to risk and side affects associated with them. This is a must read book for the researcher, doctor, and patient alike.

I purchased this book because I am supervising clinical trials in both Europe and the US. I found it to be a concise but athouritative guide on how to run my studies in Europe. It lays things out in a step by step manner with appropriate references where needed. This is particularly helpful because there are some significant differences in the EU verses FDA requirements. It has been of particular help to me when I've needed to educate recalcitrant investigators who have balked at GCP requirements and the volumes of paperwork required. I would recommend it for the novice as well as the expert clinical research manager. I would also recommend purchasing a copy for each of the research sites used to facillitate communications between sponsor and investigator.

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.

Vance Berger spent part of his career at the FDA. I got to know him when he was at the FDA. He currently continuous to work on clinical research and clinical trials at the NIH. I have again crossed paths with Vance as we are both course instructors with statistics.com. Vance has taught a course at statistics.com base on this book and I had the privilege to be able to audit the course. As a clinical trials statistician with over ten years of experience there are not many topics in statistics related to clinical trials that I don't know about. But when I took the class and read the book I realized that there were many aspects of selection bias that I didn't know about or appreciate. Vance was very concerned about issues of bia while at the FDA and I think it is his FDA perspective that makes the book so valuable. Selection bias is incidious because it can unknowing occur even in well-planned placebo controlled trials. It is not easy to detect or to adjust for after the fact. It is a situation where the actual selection of subjects in the trial violates the intended randomization scheme and thus allows for the sample population to improperly represent the intended population of subjects. Such a violation of the randomization principle can lead to bias estimates of the efficacy parameters.

However, Vance shows based partly on his own original research how to detect and adjust for selection bias. He also provides a number of real cases where selection bias is suspected. In some instances the bias can be so severe that it invalidates the results of the trial. For this reason it is very important to eliminate or adjust for any potential bias in a controlled clinical trials with selection bias being as important as any other.

I highly recommend this book for statisticians and other practitioners involved in the design and conduct of clinical trials. I also would say that the best way to learn the material in the book would be to take the authors online course. There you have the opportunity to directly question the author on the issues covered in the book, discuss the homework exercises and their solutions and to get to know the author and your fellow students through the discussion boards.

This is a basic book that describes the main study designs used in medical research. It is an easy-to-read book about epidemiologic studies.

This is a basic book that describes the main study designs used in medical research. It is an easy-to-read book about epidemiologic studies.

The authors present a highly theoretical book designed for a graduate course in biostatistics. The idea is not new. In 1971 Efron wrote a very popular article on a method of using a biased coin to force balance in a sequential experiment. That was one of the early ideas of this type and with the new popularity of adaptive designs in clinical trials the research in this area has advanced greatly. Although Efron's approach was practical and possible to impliment it was not used very often. My sense is that the new developments in this topic will lead to more use, justs as group sequential methods have allowed the mathematics of sequential analysis to more readily be used in applications.

The most common and most frequently taught approach to statistical inference is the frequentist approach. In the frequentist approach the observed sample is viewed as though it were independently drawn from a population with a specific but unknown probability distribution associated with it. In most courses this construct for the data is taken for granted, However it does have practical limitations. A rare attribute of this book is that it points out the practical limitations and sometimes artificial nature of the frequentist approach. These authors take a completely different approach to statistical inference which may be called the randomization approach. This approach may fall under the category of Fisherian inference that was the method of inference developed by R. A. Fisher at the same time as Neyman and Pearson were developing hypothesis testing and confidence intervals based on a frequentist approach.

The advantage of the authors' approach is that they do not need the concepts of sample space and repeated sampling to construct an probabilistic model for inference. Instead their model is based on the data being a reference set that is compared to other outcomes that could have arisen had the observations been permuted to mix up the allocation of the observed values among the treatment groups. So the inference is based on a permutation distribution that is valid under the null hypothesis that there is no difference between the observations in the two groups. If there were a difference the observed statistic would fall in one of the extreme tails of the permutation distribution.

In practice what is necessary to make this work is to have a well-defined randomization procedure for the allocation of the subjects to the treatment groups. This is typically done in phase III pharmaceutical trials where the FDA usually requires a randomized controlled clinical trial to establish the safety and efficacy of a new treatment.

Although this approach seems less restrictive then the frequentist approach and can also be used in observational studies where the frquentist approach breaks down, it requires the reader to know or learn a new statistical paradigm which technically draws a different kind of conclusion than the standard frequentist approach has to hypothesis testing and interval estimation of parameters.

The book, going by the table of contents, provides a fairly comprehensive overview of the field of adaptive designs in drug development.
After having read it, I am somewhat disappointed. The topics are in fact all there, and the different approaches are presented. There is no real overview on how the different approaches link together though.
I think that other texts like Ting (Dose Finding in Drug Development (Statistics for Biology and Health)) do a much better job at providing the background.

The code seems quite useful, but the typesetting is fairly disastrous. Most functions and macros have many parameters, and they are listed in floating text style instead of a tabular layout, making it very hard to read.
The code is typeset in proportional font (where monospace is standard) and does not contain any comments and documentation of particular blocks.

Finally, the text comprises 27 SAS programs and only 6 R programs. The SAS programs are in the corresponding chapters, the R programs are all put at the very end of the book in its own appendix chapter. The R code is of fairly low quality, suggesting that the author is a SAS user and transcribed the code into R.
Example of some typical and not so great R code:
for(i in 1:nStgs) { TSc[i] = 0}

So the benefit of the book might be in the SAS library. It is not in the introduction to adaptive design theory and certainly not in the small R library, making the title somewhat misleading.

This book just came out but I know a lot about it and about the author before I even got a copy. In November of last year Mark Chang coauthored a book in this Chapman and Hall series that I reviewed with praise because of the importance of the topic and the way it was demonstrated to work in a variety of real problems in pharmaceutical clinical trials. This book is even better as it goes more deeply into the methodology, the controversies and the results from simulation studies. Also it is much more practical because for every case where an application is given a SAS macro is also included to allow the reader to try the methodology for himself. In March of 2007 I actually designed a two-stage adaptive design with sample size reestimation for bioequivalence trials. I met mark at a conference where he presented much of his recent work and he was instrumental in helping me through his first book and his journal articles. This book had already gone to the publisher but he realized that this important design had overlooked. He added it when the copyedited version came to him. The design and the simulations related to it are very close to what I actually used. For those who like to program in R, he provides R code corresponding to each of the SAS macros that he gave. These programs make the new methodology readily available to interested users. The book is very comprehensive in that it covers a wide variety of applications for phase 2, phase 3 and combined phase trials. With the FDAs new initiative to speed up the drug discovery process this book will be an invaluable tool to statisticians in the pharmaceutical industry who would like to learn and apply these methods that along with the group sequential methodsare gaining favor within the FDA.

There are explosions of adaptive design papers in past several years. This book alone has included about 400 references. It is very confusing to most new researchers in this field. This book use a unified approach to treat the major hypothesis test based adaptive design methods, i.e., view different methods as some forms of stagewise p-values combinations for test statistics. Chapter 1 provides overview of adaptive designs. Chapter 2 provides background for various clinical trials including superior, non-inferiority, equivalence and dose-response trials. The unified approach is presented in chapter 3 for stopping boundary determination, adjusted p-value, early futility and efficacy stopping, expected sample-size and clinical trial duration, conditional power, and futility index. All the formulations for these operating characteristics are presented in multiple-integration forms. In the next several chapters, all the integrations for the operating characteristics are carried out for particular combinations of p-values - lead to particular statistical methods for adaptive designs. In the most cases, the book avoid to using approaches from the original papers when the ideas were first proposed to avoid confusions and reduce the amount of material to be included. Chapter 7 presented another way (conditional error approach) to look at the common and different characteristics among different methods. Almost all methods for adaptive design can be reviewed as the conditional error approach. The difference is that each method uses a different conditional error function. In the chapter, different conditional error function and conditional power formulations are summarized. Chapter 8 discusses the recursive conditional error method so that it can be used for a K-stage adaptive design. Chapters 9 to 14 discuss different types of adaptive trials using the statistical methods that have been discussed in the previous chapters. These trials include sample-size reestimation, drop-loser design, biomarker adaptive design, response-adaptive randomization, adaptive treatment switch, and multiple endpoint issues. Chapters 15 and 16 discuss Bayesian adaptive approach for clinical trials. Chapter 17 talks about implementation issues. Chapter 18 is for readers who are interested in philosophical debates.

If you have not read too much adaptive design research papers, you wouldn't be confused, and you may not appreciate the unified approach in this book.

For most chapters, computer programs (SAS Macro) are provided with illustrated examples from clinical trials. However, it is not the author's intention to teach to how to implement adaptive design using SAS. The main purpose to include computer programs is to provide tools that you can use to design your adaptive trials since the software for adaptive design is very expensive (some reach [..]annual license for single user). It is not a computer book. Hence the algorithm of the computer program is usually not provided your clinical trials. However, each program is written with clear logic flows and is only about a page long. It should not be a challenge to most readers who have coding knowledge. The corresponding R functions are presented in Appendix. Because they are so similar between a SAS Macro and the corresponding R function. It is wise to put one of them in the appendix. The R functions cover the typical adaptive designs. Others can be directly translated from SAS macros without any difficulties.

There are exercises at end of chapters. Some are good, some are OK. This should be enhanced for the revision.

For some reasons, Amozon.com does not include the sample pages from the book. I am the author of book; I think it is helpful to use this feature to provide some insight for the readers. More information can be found online, where you can obtain the table of contents and the electronic computer programs. Rank it 5 starts that could be author's bias.

I meet the second author, Mark Chang, at a conference on adaptive designs. I work as a professional statistician in the pharmaceutical industry. For the past several years, at least ten, these ideas have been the topic of research and it is being investigated as a possible way to speed up drug development and its development is being encouraged by the FDA. There has not been a formal statistical text covering the existing theory and its application to clinical trials. Consequently, when we knew this was coming out we preordered it and have been studying it since it came out last November.

The book has lived up to expectations. Adaptive designs are very similar to group sequential designs in that they have planned times to make preliminary assessment of the trial data and then decide whether or not to continue the trial or modify the design. Adaptive designs can be more flexible than their group sequential counterparts. They even can allow changes to the protocol as long as the criteria for making such changes are mapped out in advance of the trial.

These methods have been controversial in the past and simulation studies are often required to determine their properties. But there has been enough development now that some designs are being applied in real trials. In fact we are considering a two stage adaptive design similar to the ones described in this text (except applied to bioequivalence).

Later this year Mark Chang is coming out with an applied text that include SAS macros to aid in the implementation of the methods. A preview of the manuscript was displayed at an adaptive trials conference that I attended recently. I can enthusiastically recommend that one even more than this one! However, any biostatistician working on clinical trials should have this book on his or her bookshelf.

This book's best features are its bibliography (about 240 entries) and its broad survey and taxonomy of adaptive methods. Its publication represents an important step in popularizing adaptive trials and, thus, streamlining drug/device/biologic development pipelines.

The book is, however, filled with inaccuracies on several levels: incorrect grammar and equation references, undefined symbols, a reference to a non-existent appendix, unclear language (e.g., what is the "statistical strength for rejecting Ho" on Page 150?), mathematical typos [e.g., P(x|y,theta) rather than P(y|theta) in the integrand for the posterior predictive probability distribution P(y|x)], and misapplications of statistical philosophy (e.g., using Neyman-Pearson hypothesis testing for statistical inference, identifying the p-value as a post-hoc type I error rate). In the sample I took of about 1/3 of the pages, about 120 errors occur. The book should be considered only a pre-publication "beta" version. Any second edition should receive much more attention to detail.

A statistician or clinical scientist planning a potentially adaptive trial could use this book to learn about some of the aspects of a trial that can be made adaptive. The book could also help him/her to assess the assumptions and mathematical complexity of methods under consideration. However, when it comes to actually performing an analysis, one would want to use the bibliography to obtain the relevant articles and books, perhaps together with Chang's "Adaptive Design Theory and Implementation Using SAS and R" (Chapman & Hall/CRC Biostatistics).

Overall, this book disappointed me. The authors should have had several more collaborators and copyeditors check their work.

It's true that managed care is on trial--literally and figuratively. But this rambling, disjointed, and wholly incoherent book offers little to justify its publication. Do not take my word for it. Go to the library and pick it up. Open it and read ... you will be shocked that it is for sale at any price.

This book unpacks many of the hidden agendas in managed care and provides useful and practical advice to the reader on how to better understand and navigate the turbulent waters of managed care. This book is written by a well-known health systems advisor and leader,keynote speaker and expert witness with over 20 years experience in the field coupled with a dynamic foreward and introduction by Mark Hiepler often called the "Rainmaker" or "89 million dollar man" because of his record breaking 89.3 million dollar jury verdict against a large managed care. organization and involved in over 150 cases representing patients and physicians in diverse managed care scenarios. Learn the intricacies of why you have such difficulty getting a referral, how to challenge a denial of care determination,and even how to find the right attorney, if needed. Managed Care on Trial also explains how financial incentives can induce your physician to delay or undertreat and what you can do in such circumstances, Discussion of the key elements of the 1200 legislative and regulatory initiativies to tame managed care last year are highlighted, including helpful guidance and cutting edge information to assist you. State of the art information has been collected from such organizations as the AMA, prominent national trial attorneys and key figures in American Health Care. The book has been reviewed by national figures who say that the "insightful and incisive analyses of emerging controversies and challenges in mananged care are unparalled and offered in a refreshing and captivating style which transforms complicated issues into useful information for the consumer" Jeanne Boling MSN, CCRN,CDMS, CCM Exec Director Case Management Society of America. Another reviewer indicates that "not only does the book deliciously entertain but also offers uselful tactics as well. The book is not only informative but is enjoyable as a courtroom thriller. Indeed in his final chapter Dr. Robbins puts ! managed care on trial brilliantly and provocatively"... Jacek Franaschek M.D. FACEP Past President American College of Emergency Physicians. Philip Corboy, former president of the American Trial Attorneys Assn indicates that "readers as diverse as a lawyer planning to depose the CEO of an HMO or a patient seeking assistance in comparing providers will finds this book a well reasoned resource". Mark Hiepler writes that " if HMOs read and put into action Robbins practical ideas, patients will bypass the courtroom and go directly to the operating room. The patients who value their lives, the doctors who value their practices, and the HMOs that value their bottom lines can all learn life saving as well as cost saving principles by reading Managed Care on Trial".