Book Description
Praise for the First Edition of Design and Analysis of Clinical Trials
"An excellent book, providing a discussion of the clinical trial process from designing the study through analyzing the data, and to regulatory requirement . . . could easily be used as a classroom text to understand the process in the new drug development area."
-Statistical Methods in Medicine
A complete and balanced presentation now revised, updated, and expanded
As the field of research possibilities expands, the need for a working understanding of how to carry out clinical trials only increases. New developments in the theory and practice of clinical research include a growing body of literature on the subject, new technologies and methodologies, and new guidelines from the International Conference on Harmonization (ICH).
Design and Analysis of Clinical Trials, Second Edition provides both a comprehensive, unified presentation of principles and methodologies for various clinical trials, and a well-balanced summary of current regulatory requirements. This unique resource bridges the gap between clinical and statistical disciplines, covering both fields in a lucid and accessible manner. Thoroughly updated from its first edition, the Second Edition of Design and Analysis of Clinical Trials features new topics such as:
* Clinical trials and regulations, especially those of the ICH
* Clinical significance, reproducibility, and generalizability
* Goals of clinical trials and target population
* New study designs and trial types
* Sample size determination on equivalence and noninferiority trials, as well as comparing variabilities
Also, three entirely new chapters cover:
* Designs for cancer clinical trials
* Preparation and implementation of a clinical protocol
* Data management of a clinical trial
Written with the practitioner in mind, the presentation assumes only a minimal mathematical and statistical background for its reader. Instead, the writing emphasizes real-life examples and illustrations from clinical case studies, as well as numerous references-280 of them new to the Second Edition-to the literature. Design and Analysis of Clinical Trials, Second Edition will benefit academic, pharmaceutical, medical, and regulatory scientists/researchers, statisticians, and graduate-level students in these areas by serving as a useful, thorough reference source for clinical research.
Customer Reviews:
Most complete reference on the topic.......2006-09-07
I own several books on clinical trials and this one is my favorite. It is biblical in its treatment of the topic and always seems to contain what my other books don't.
There are a few strengths that are particularly worth pointing out:
1) Makes many references to regulatory guidelines.
2) Excellent coverage of the various trial designs.
3) Good sample size chapter.
4) Several chapters on how to practically implement a trial.
Other options include:
-Piantodosi (Clinical Trials: methodologic perspective): my second favorite, not as comprehensive as Chow and Liu
-Freidman and DeMets (Fundamentals of Clinical Trials): a bit too superficial but very well written
-Pocock (Clinical trials: practical approach): a bit dated and superficial
Book Description
Studies that are unimpeachably thorough, non-political, unbiased, and properly designed… These are the standards to which everyone in clinical research aspires. Yet, the difficulties in designing trials and interpreting data are subtle and ever present. The new edition of Clinical Trials in Oncology provides a concise, nontechnical, and now thoroughly up-to-date review of methods and issues related to clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the major pitfalls that are seemingly inherent in these processes. This edition includes a new section that describes recent innovations in Phase I designs. Another new section on microarray data examines the challenges presented by massive data sets and describes approaches used to meet those challenges. As always, the authors use clear, lucid prose and a multitude of real-world trials as examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Although the book focuses on cancer trials, the issues and concepts are important in any clinical setting. Clinical Trials in Oncology, Second Edition works to improve the mutual understanding by clinicians and statisticians of the principles of clinical trials and helps them avoid the many hazards that can jeopardize the success of a trial.
Customer Reviews:
Knowing What Works in Health Care.......1999-12-15
I should begin by admitting that I had the opportunity to review this little masterpiece in manuscript. Good then, it's even better now.
It's good because it informs the reader, in sober prose, how to determine what works and what doesn't in medical practice, and what's safe and what isn't. It's good because it reveals what can go wrong when anecdotes ("it worked for me!") substitute for sound research as the basis for clinical practice. And it's good because it shows how serious are the consequences of even subtle failures to observe protocols in designing and carrying out clinical trials.
It is reassuring to read of the care and precautions advocated for government-sponsored research; it is accordingly unsettling to contemplate the pressure that commercial interests (drug companies, for-profit hospitals, equipment manufacturers) might bring on researchers to cut a few corners.
After reading "Clinical Trials" I came to appreciate that case studies, longitudinal studies, and retrospective questionnaires, so frequently hyped in the press and on television, are no substitute for actual well-designed and well-executed experiments. Because you and I are different, certainly genetically and probably in other essential ways, what helps you may well harm me. Only the proper application of statistics in designing clinical trials and in analyzing data from them can distinguish what's generally valuable from what's useless (however plausible and authoritatively touted it may be). Although the authors had the good taste to reject the aphorism, usually attributed to a nameless statistician, that "if experimentation be the queen of science, then statistics stands as the guardian of the royal virtue", its pithiness may give the reader the crucial insight into why alternative modes of research are untrustworthy.
Some readers may feel disheartened to learn the truth that many, probably most, promising therapies prove, when adequately tested, worthless, and some may feel in some fuzzy way that to accept this reality is cruelly to deny hope to those who need it badly. On the contrary, this book makes it clear that to offer false hope is the ultimate cruelty, for without experimentation there can be no knowledge, and without knowledge there can be no real hope.
Notwithstanding the slightly technical nature of this book (yes, there IS a chapter with mathematics), I recommend it highly for the general reader who is interested in such topics as personal health care, alternative medicine, managed care cost containment, and the like. Buy a copy for yourself, and, if you feel philanthropic, you might consider donating a copy to your health care provider. The world would be better if doctors' waiting rooms (like hotel rooms with their Gideon Bibles) all had a copy of "Clinical Trials in Oncology" available for patients' perusal.
Book Description
A compendium of cutting-edge statistical approaches to solving problems in clinical oncology, Handbook of Statistics in Clinical Oncology, Second Edition focuses on clinical trials in phases I, II, and III, proteomic and genomic studies, complementary outcomes and exploratory methods. Cancer Forum called the first edition a "¼good reference book for statisticians who will be designing and analyzing cancer trials." The second edition includes over 1000 references, more than forty world-renowned contributors, and 300 equations, tables, and drawings. During the five years since publication of the first edition, there has been an explosion in the technological capabilities supporting genomic and proteomic research, which are is now firmly implanted in clinical oncology. Reflecting these developments, the second edition contains a new section devoted to analyses of high-throughput data and bioinformatics. Previous chapters of the first edition have been revised to reflect current state of the art in their respective domains. The intended audience is primarily statisticians working in cancer and more generally, in any discipline of medicine. But oncologists too will find the material accessible and will benefit from a rudimentary understanding of the fundamental concepts laid forth in each chapter. Completely revised while keeping the features that made the first edition a bestseller, this is the best single source for up-to-date statistical approaches to research in clinical medicine. More than just an update of the handbook that became the gold standard, this second edition brings you fully into the genomic era of medicine.
Book Description
Learn rigorous statistical methods to ensure valid clinical trials
This Second Edition of the critically hailed Clinical Trials builds on the text's reputation as a straightforward and authoritative presentation of statistical methods for clinical trials. Readers are introduced to the fundamentals of design for various types of clinical trials and then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides commonsense solutions.
The author bases the revisions and updates on his own classroom experience, as well as feedback from students, instructors, and medical and statistical professionals involved in clinical trials. The Second Edition greatly expands its coverage, ranging from statistical principles to controversial topics, including alternative medicine and ethics. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First Edition will discover completely new chapters, including:
- Contexts for clinical trials
- Statistical perspectives
- Translational clinical trials
- Dose-finding and dose-ranging designs
Each chapter is accompanied by a summary to reinforce the key points. Revised discussion questions stimulate critical thinking and help readers understand how they can apply their newfound knowledge, and updated references are provided to direct readers to the most recent literature.
This text distinguishes itself with its accessible and broad coverage of statistical design methods—the crucial building blocks of clinical trials and medical research. Readers learn to conduct clinical trials that produce valid qualitative results backed by rigorous statistical methods.
Customer Reviews:
Clinical Trials: A Methodologic Perspective Second Edition.......2007-05-29
This is an excellent book. It outlines the important issues of clinical trials well. It is understandable and thorough. A must for anyone who is interested in actually doing trials. Not a good book for a brief, superficial overview.
presents clinical trials issues and methodology clearly.......2000-09-07
This book is very unique. Basic statistical concepts are clearly presented but only those concepts that are important in clinical trials. The author presents all the issues with clinical trials including ethical issues with some historical perspective. Principles of randomization and statistical design are clearly presented. It offers discussion of Bayesian techniques and meta-analyses, cross-over designs and group sequential methods (interim analyses). For statisticians doing clinical research like myself, this is a valuable reference source.
The best start in clinical trial.......2000-05-23
The amount of knowledge and the scope of this book are the exact need for the first contact with clinical trials. Yet, it is not a simple or superficial text. Instead, it not only will guide the reader through the basics of trials (and there is so much that is not basic in it) but the author points the reader to hundreds of papers and books that are landmarks. I regard this book itself as one of these landmarks!
Most up-to-date and thorough cover of Clinical Trials.......1999-01-14
Covers many aspects of trials (particularly facets of design and analysis)not yet covered by other books, eg randomisation with minimisation, and meta-analysis of trial results. Readable, applicable, practical, good references, well structured.
Book Description
This comprehensive guide bridges the gap between modern statistical methodology and real-world clinical trial applications. Step-by-step instructions illustrated with examples from actual trials and case studies serve to define a statistical method and its relevance in a clinical trials setting and to illustrate how to implement the method rapidly and efficiently using the power of SAS software. Topics reflect the International Conference on Harmonization (ICH) guidelines for the pharmaceutical industry and address important statistical problems encountered in clinical trials, including analysis of stratified data, incomplete data, multiple inferences, issues arising in safety and efficacy monitoring, and reference intervals for extreme safety and diagnostic measurements. Clinical statisticians, research scientists, and graduate students in biostatistics will greatly benefit from the decades of clinical research experience compiled in this book. Numerous ready-to-use SAS macros and example code are included.
Customer Reviews:
ideal book for those analyzing clinical trials .......2007-08-14
I have previously reviewed a more recent book in the SAS series "Pharmaceutical Statistics using SAS: A Practical Guide" that Dmitrienko and Chuang-Stein also edited. Both books are very similar because they provide a variety of chapters by top experts in the field. Each chapter is well written and understandable to nonstatisticians who do clinical research as well as for statisticians who can use it as a reference and guide to the appropriate techniques to handle various common situations. I highly recommend this book to anyone who analyzes clinical trials data.
Overly complicated language.......2006-11-30
Good history of methods with lots of citations. Not so good for practical application. Programming language is not fully explained and results are difficult to understand and reproduce. Limited output shown. I want Ron Cody to write an "applied statistics for clinical trials" workbook.
Comprehensive and thorough.......2006-08-24
This is an excellent easy to follow book, from which both statisticians and interested medical professionals can greatly benefit, I believe, because I am working with both. It has helped me a lot in my graduate project on data analysis of laboratory animal trials.
Book Description
READ ALL ABOUT IT!
David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries.
Order a copy of this author’s comprehensive text TODAY!
The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis.
Covers a broad array of essential topics, building from the basics to more advanced techniques.
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Illustrated throughout by detailed case studies and worked examples
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Includes exercises in all chapters
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Accessible to anyone with a basic knowledge of statistics
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Authors are at the forefront of research into Bayesian methods in medical research
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Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS - the most widely used Bayesian modelling package
Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.
Download Description
The Bayesian approach involves collecting data from past experience in order to reach conclusions about future events. Bayesian methods have become increasingly popular in recent years, notably in medical research. There are a large number of books on Bayesian analysis, but very few that cover clinical trials and biostatistical applications in any capacity. There is no book available that is introductory in nature and covers such a broad array of essential topics. This book provides a valuable overview of this rapidly evolving field, not only for statisticians in the pharmaceutical industry, but also to anyone involved in conducting clinical trials and HTA work. Comprehensive coverage of Bayesian methods in medical research
- Illustrated throughout by case studies and worked examples
- Authors are at the forefront of research into Bayesian methods in medical research
- Suitable for those with a limited statistical background
- Accompanied by a Web site featuring data sets and worked examples in WinBUGS - the most widely accepted Bayesian modelling package
Customer Reviews:
Bayes for Health Technology Assessment.......2006-07-23
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.
Book Description
Group sequential methods answer the needs of clinical trial monitoring committees who must assess the data available at an interim analysis. These interim results may provide grounds for terminating the study-effectively reducing costs-or may benefit the general patient population by allowing early dissemination of its findings. Group sequential methods provide a means to balance the ethical and financial advantages of stopping a study early against the risk of an incorrect conclusion. Group Sequential Methods with Applications to Clinical Trials describes group sequential stopping rules designed to reduce average study length and control Type I and II error probabilities. The authors present one-sided and two-sided tests, introduce several families of group sequential tests, and explain how to choose the most appropriate test and interim analysis schedule. Their topics include placebo-controlled randomized trials, bio-equivalence testing, crossover and longitudinal studies, and linear and generalized linear models. Research in group sequential analysis has progressed rapidly over the past 20 years. Group Sequential Methods with Applications to Clinical Trials surveys and extends current methods for planning and conducting interim analyses. It provides straightforward descriptions of group sequential hypothesis tests in a form suited for direct application to a wide variety of clinical trials. Medical statisticians engaged in any investigations planned with interim analyses will find this book a useful and important tool.
Customer Reviews:
clear coverage for practicing biostatisticians.......2000-06-28
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.
Book Description
Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis. The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. All of the data sets used in the book are available for download from www.crcpress.com/e_products/downloads. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices.
Customer Reviews:
Good introduction.......2000-03-30
A well-written introductory book. Broad range of material make it a good reference for new comers in survival analysis.
Average customer rating:
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Statistical Methodology in the Pharmaceutical Sciences (Statistics: a Series of Textbooks and Monogrphs)
D. A. Berry
Manufacturer: CRC
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ASIN: 0824781171 |
Book Description
A state-of-the-art handbook of statistical analysis for use in the pharmaceutical industry. Areas covered in this reference/text include: bioavailability, repeated-measures designs, dose-response, population models, multicenter trials, handling dropouts, survival analysis, robust data analysis, cate
Book Description
First published in 1986, this unique reference to clinical experimentation remains just as relevant today. Focusing on the principles of design and analysis of studies on human subjects, this book utilizes and integrates both modern and classical designs. Coverage is limited to experimental comparisons of treatments, or in other words, clinical studies in which treatments are assigned to subjects at random.
Customer Reviews:
now a classic and still a great reference.......2000-12-23
This book was first published in 1986. As Fleiss states in his preface, the intention is to fill a gap in the standard texts on experimental designs by emphasizing and illustrating those that are useful in clinical studies. This book was clearly marketed for the rapidly growing and highly regulated pharmaceutical industry. In addition to the classic experimental designs, Fleiss covers cross-over designs and repeated measure designs that are important in clinical trials. He writes clearly and deals with the important issues in clinical trials including potential biases, blinding, randomized controls, multiple comparisons and repeated measures. The book starts off with a chapter that emphasizes the effect of measurement error and also provides some simple experiments on reliability of measurements.
There is a wealth of methods included, many different designs and various parametric and nonparametric analysis techniques. It is aimed at biostatisticians in the pharmaceutical industry and the medical research field. The book is very much suited for an advanced undergraduate or graduate level course for students majoring in statistics or biostatistics. The level of mathematics is high but not excessively used. Mathematical results on sample size determination are deferred to an appendix.
The Wiley editors choose only successful books to be included in their Classics Library series. The intent of the Classics series is to take popular books by distinguished authors and create a paperback edition that may be more affordable than the hardcovered edition still in print. It is not a revision of the book. This book entered the Classics series in 1999.
It is a great reference source and I plan to consult it a great deal in the future. The only drawback to it that I see is that it is not up to date. The last 15 years has seen many advances in group sequential methods, Bayesian designs and longitudinal data analysis that this text misses. So Fleiss' book is not one stop shopping for a clinical biostatistician but it does offer a lot and presents it eloquently.
With regard to reviewer Izadi's comments on Amazon, I think the appropriate way to ask this question is really to write the author. Since it is here in print for the readers, I will attempt a reply. I think there is a misinterpretation of the terminology. When Fleiss refers to mean logarithms he is not referring to the population means on the log scale but rather the logarithm of the population mean on the original scale. With the latter interpretation equation 3.25 makes perfect sense. It is the former interpretation of the parameters that the reviewers point addresses. The importance in the example is to demonstrate the lack of robustness of t or F tests to non-normal (e.g. lognormal) data and to show that tests and confidence intervals can still be accomplished using the normal theory after the transformation. The key point is that it is the ratio of the parameters that is transformed into differences of the log of the parameters.
A must for biostatisticians.......2000-12-22
This is the standard text on this subject. Recommend you have at least a Master's degree in statistics to take full advantage of this book. This book is too technical for non-statisticians, although they may get some useful infomation from the non-statistical discussions.
A question from the autour.......2000-12-11
Dear Sirs I do not know if I can use this part as a tool for contacting the autour or not; however please excuse me if I have not used it properly: I am a student of epidemiology, and presently we are studying the following book: The Design and Analysis of Clinical Experiments; Joseph L. Fleiss; John Wiley & Sons; 1986. But on page 67 it seems that there is some misunderstandings: When we do a log transformation, in fact we are changing (or shifting) the previous distribution to a normal distribution. Without any doubts in this transformation the means of our previous distributions do not transfer to the means of the new distributions! In fact these are the medians of the previous distributions which are transferred to the place of the means of new distributions (of course if we presume that the new distributions are almost perfectly normal) and by finding the confidence interval of the difference of the means of new distributions (lambda1 - lambda2) we are finding the CI of ratio Median1/Median2. In this way it seems reasonable that the formula 3.25 be changed to Median1/ Median2. It would be very kind of you if you help me in this problem!
With best regards Dr. Shahrokh Izadi
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Fundamentals of Clinical Trials
Clinical Trials: A Methodologic Perspective Second Edition(Wiley Series in Probability and Statistics)
A Manager's Guide to the Design and Conduct of Clinical Trials (Manager's Guide Series)
Sample Size Calculations in Clinical Research (Biostatistics, 11)
Analysis of Clinical Trials Using SAS: A Practical Guide
