Book Description
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered."
Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book"
Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally."
Journal of the American Statistical Association, March 2006
Customer Reviews:
very good book, compact but comprehensive.......2007-05-12
This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.
Excellent book ..........2007-01-09
A very specific book, with a lot of details for a statistitian
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
Average customer rating:
- Deceptive
- An original approach. An excellent book on the subject.
- Hey, I got an A in Biostats I
- YES! I could speak and ask questions at journal club without looking like a fool.
- Excellent Statistics Book
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Intuitive Biostatistics
Harvey Motulsky
Manufacturer: Oxford University Press, USA
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Similar Items:
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Basic & Clinical Biostatistics (LANGE Basic Science)
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Biostatistics: The Bare Essentials, Second Edition
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Designing Clinical Research: An Epidemiologic Approach
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Primer of Biostatistics 6/e Valuepack (Book and CDROM)
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Pdq Statistics (PDQ Series) Third Edition (PDQ)
ASIN: 0195086074 |
Book Description
Designed to provide a nonmathematical introduction to biostatistics for medical and health science students, graduate students in the biological sciences, physicians, and researchers, this text explains statistical principles in non-technical language and focuses on explaining the proper scientific interpretation of statistical tests rather than on the mathematical logic of the tests themselves. Intuitive Biostatistics covers all the topics typically found in an introductory statistics text, but with the emphasis on confidence intervals rather than P values, making it easier for students to understand both. Additionally, it introduces a broad range of topics left out of most other introductory texts but used frequently in biomedical publications, including survival curves. multiple comparisons, sensitivity and specificity of lab tests, Bayesian thinking, lod scores, and logistic, proportional hazards and nonlinear regression. By emphasizing interpretation rather than calculation, this text provides a clear and virtually painless introduction to statistical principles for those students who will need to use statistics constantly in their work. In addition, its practical approach enables readers to understand the statistical results published in biological and medical journals.
Customer Reviews:
Deceptive.......2007-08-10
If you think you can learn Statistics intuitively and without mathematics or in otherwords the easy way, I have an intuitive Brain Surgery book for sale.
An original approach. An excellent book on the subject........2007-06-13
The majority of reviewers really liked this book. I can see why, I did too. The author uses a unique approach to teaching statistics that is focused on calculating and explaining Confidence Intervals (the minimum and maximum value you expect an outcome to be given a confidence level typically 95%) rather than P values (probability outcome is due to chance). He also uses common sense and clearly distinguishes between what is statistically significant and what is "significant." Thus, he translates well statistical mumbo jumbo into plain English. He tells you what you should care about and look for.
He shares with you all the statistical flaws that clinical studies may have including testing multiple hypothesis to come up with just a single statistically meaningful one, using large samples to prove something trivial, using small samples that raises uncertainty level, etc...
His section on Bayesian Logic is excellent. His table on what test or methodology to use given the shape of the data and objective you have is worth the price of the book alone. That's one of the clearest taxonomy of statistical methods I have seen anywhere.
Some knowledgeable reviewers have picked up a few errors the author made. I stumbled upon a couple while attempting to replicate the calculation of a few examples. I emailed the author and each time within an hour he either clarified the calculation or corrected the typo that was present in the book. Given his prompt answers, I can't ding him for the couple of typos I caught.
Although the author presents this book as an introductory one, I recommend the reader acquires a good foundation in basic statistics before studying this book. Forgotten Statistics would fit that bill. Indeed, `Intuitive Biostatistics' covers a huge amount of ground. It is far more than an introductory text. It covers material that is pretty advanced including nonparametric hypothesis tests, non linear regression, logistic regression, Bayesian analysis, etc... If it is the first time you come across that stuff you'd be well served having a solid stats foundation. Given that, this book has a lot to offer. I'll keep it as a great reference for years.
Hey, I got an A in Biostats I.......2007-01-04
I am not a high faltuin' math person, the calculus I went through in undergrad was a struggle and I remember very little. I am a chemist by training, currently seeking my PhD in Public Health while working full time. What that came down to was little to no time to doof around with a muddled textbook or an equally muddled professor or a non-English speaking Teacher's Assistant.
I have no intention of becoming a biostatistician or an epidemiologist, I am interested in policy.
So coming from that perspective, as a student, this book was an absolute God-send.
Not only did I get an A in the class, but I feel like I have a sturdy foundation for my future coursework and career. I will not be intimidated by numbers or jargon because Dr. Motulsky made it all as straightforward and clear as possible, and I recall even laughing a few times.
Overall, if you are in school, facing a biostatics class with extreme trepidation, buy this book as a supplement. Look up the topics in the index as you go and you will have more than the $40 worth of "eureka" moments.
YES! I could speak and ask questions at journal club without looking like a fool........2006-09-25
Helped me from looking a fool during residency. Thank you Harvey!
Excellent Statistics Book.......2006-03-13
Intuitive Biostatistics takes a confidence interval approach that should be required reading for all persons interested in statistics. His discussion of basic statistical concepts in the context of interpreting lab test results is clear and informative and introduces the reader to Bayesian concepts that are presented very simply.
Book Description
A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data.
The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature.
Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include:
* Variable selection.
* Identification of the scale of continuous covariates.
* The role of interactions in the model.
* Interpretation of a fitted model.
* Assessment of fit and model assumptions.
* Regression diagnostics.
* Recurrent event models, frailty models, and additive models.
* Commercially available statistical software and getting the most out of it.
Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.
Customer Reviews:
A Good Read, but Read it Carefully!.......2005-05-05
The authors provide a really nice, non-technical survey of the landscape for Cox Proportional Hazards models. A nice aspect of their treatment is the care they take to reference all highly technical texts and journal articles. For example, if you'd like to find out more about goodness-of-fit tests for survival models, the authors provide ample references to the Counting Process Theory of Martingale Residuals.
The first chapter discusses the basic characteristics of survival data, including the notion of censoring (in all of its various forms). Examples of the principle types of censoring are included. The chapter also includes introductory material on the general survival model, including a nice description of the log likelihood function. Curiously, the rigorous definition of the hazard function has been omitted, probably to avoid intimidating readers who are not familiar with formal limits.
Chapter 2 continues to build up the general survival model and introduces the relationship between the survivor function and the cumulative hazard. Pointwise estimators for the survivor function are discussed, including the Kaplan-Meier estimator along with the various variance estimators. Test statistics for comparing two survival populations are introduced, including the Log-Rank and General Wilcoxon statistics. The reader is encouraged to read the counting process treatments of these statistics to see why they produced defensible hypothesis tests.
Chapter 3 is devoted to the Cox Model and Cox's partial likelihood function. Tests for significance of the coefficients are introduced, included the Wald test, log likelihood ratio test and the score test. These are used heavily in the later chapters as the basis of a model-building methodology.
Chapter 4 is a very short, but nicely written chapter explaining how to interpret the values of each regression coefficent. It also describes covariate-adjustment techniques for model diagnostics.
Chapter 5 is just a wonderful chapter which outlines classical model building techniques. This is a great chapter for anyone who has ever been thrown a ton of data (with a bushel of possible covariates) and asked to "fit a model to this stuff".
Readers who have done a lot of purposeful fitting of linear regression models won't find the basic techniques new, but use of survival specific residuals and selection criterion will probably be an eye-opener. The section on assessing the functional form for continuous covariates is also nicely written.
However, the section on Best Subsets Selection was a little too "cook-booky" for my taste.
Chapter 6 is another very nice chapter on goodness-of-fit. It discusses analysis of the various residuals and their use for analysis outliers, testing proportional hazards assumptions and overall Goodness-of-Fit.
Chapter 7 discusses the standard extensions of the Cox model, including stratification and time-varying covariates. Chapter 8 discusses parametric survival models, and is a good introduction to the SAS procedure LIFEREG. The generalization of the Cox model to recurring event data (also know as Aalen's multiplicative intensity model) can be found in Chapter 9.
My only complaint is that each chapter was designed to be read in one sitting. Individual ideas, topics and formulas can be buried in a seemingly unbroken chain of paragraphs. The lack of sub-sub section titles,etc, makes using the text as is somewhat cumbersome to use as a desk reference. I've gotten around this limitation by marking key concepts, etc., in the margin in order to give a "quick search" capability enhancement to the index.
nice introduction.......2003-04-03
This book provides a good, clear, concise explanation of Cox's proportional hazards models. For someone seeking a non-mathematical description this is a great guide. The original datasets from the text examples can even be downloaded and you can go through the same process yourself. Because of some mistakes in the text, I would recomend looking at other sources as well.
Great conceptual Introduction to Cox regression analysis.......2000-02-09
I enjoyed the authors' book on logistic regression analysis in 1989, and this book is just as good, or better, with many extremely practical suggestions on building regression models for survival data. Happily, the authors summarize, compare, and contrast several major texts on survival analysis which have appeared in the past 10 years. For example, they discuss different names used by different authors for score residuals. They present a helpful appendix on the counting process approach to survival analysis, which will make more advanced texts accessible to students; thus, anyone who wants to use survival analysis, at any level, should consult this book, even if he has already studied books by Miller, Lee, Collett, Fleming-Harington,Andersen, et al, etc. An unfortunate drawback to this book is that the first printing contains many careless errors, some of which may affect student learning: for example, the definition of a survival function is misstated. I recommend that you insist on the second or third printing when buying this book, and you will be quite satisfied.
A clear, simple introduction to survival models.......2000-01-07
Hosmer and Lemeshow have given us a clear, nontechnical introduction to using survival models. The book strikes a good balance between covering the basics and addressing the most recent, state-of-the-art techniques, including repeated events, frailty models, and others. They also do a good job of addressing practical issues, including estimation details and available software. While most of the examples are drawn from medicine and biostatistics, this book could also serve as a useful starting point for social and behavioral scientists interesting in learning the fundamentals of these models, as well as a useful reference for applied researchers.
Excellent Nontechnical Coverage of Survival Analysis.......1999-12-07
Applied Survival Analysis is an excellent book for someone seeking a non-mathematicial explanation of survival analysis. The book covers the motivation behind the development of survival analysis, estimation of survival curves, the Cox proportionial hazards, and some parametric models. The book also covers the major methods used in variable selection, model building, and diagnostics. Someone with an undergraduate background in statistics and econometrics will understand the book. The book relies on text to discuss the methods and uses mathematical formulas only when absolutely necessary. Numerous examples are used to highlight what the text covers. The math that is used is easily understandable. This book is ideal for someone who needs to learn the tools of survival analysis but not how they were derived.
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
The most important techniques available for longitudinal data analysis are discussed in this book. The discussion includes simple techniques such as the paired t-test and summary statistics, but also more sophisticated techniques such as generalized estimating equations and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous, and categorical outcome variables. This practical guide is especially suitable for non-statisticians and all those undertaking medical research or epidemiological studies.
Customer Reviews:
GREAT book! .......2004-12-15
This book is really useful and handy. It is very well written and easy to read. As the name stated, it provides very practical guides for those who don't have strong background in Statistics but are dealing with longitudinal data. It is written in an example guided format. The outputs from the analysis and guidelines on how to interpret them step by step are included. There is no heavy Statistical notation and you don't need to translate Statistics into English. At the end of the book, there are chapters of how to handle missing data and softwares used in longitudinal data analysis. This book is probably too boring if you are a hardcore Statistician.
Book Description
Clinical researchers, with or without a statistical background, will find this book an invaluable aid in understanding the statistical methods cited most frequently in clinical protocols, statistical analysis plans, clinical and statistical reports, and medical journals. Written in a manner which leads the nonstatistician through each test by example, substantive details are presented which will benefit even the experienced data analysts. Introductory chapters provide elementary statistical concepts as applied to clinical trials and an overview of statistical inference, including discussions of power, sample size calculations, p-values and the logic behind hypothesis testing. Numerous examples from clinical research are worked through both manually and using SAS. Methods presented include t-tests, analysis of variance, repeated measures ANOVA, linear regression, analysis of covariance, non-parametric tests, binomial tests, chi-square test, Fisher's exact test, McNemar's test, Cochran-Mantel-Haenszel test, logistic regression, log-rank test, and Cox proportional hazards model.
Supports releases 6.08 and higher of SAS software.
Customer Reviews:
Statistical Methods with SAS Examples.......2007-07-30
Great book to show the two main pieces of biostatistical studies; choosing the best statistical design for the clinical study, and how to run data points through SAS software to produce statistical output. Calculations by hand for the statistics are also presented, so the reader can see that the software yields the same answer. Best book I have seen showing explicitly how conduct a clinical research statistical study.
decent book.......2004-01-08
This book have a lot of examples with sas codes, outputs, and explanations of outputs, which is useful for practitioners. Recommended for practitioners, but not for serious statisticians who seek in-depth and accurate, a little more mathematical treatment of the topics instead of just explanations of sas codes/outputs.
One of a kind.......2003-03-16
This book is very instructive for those interested in the pharmaceutical field or might already be in, especially for those not having a statistics degree. The different tests are explained and tell you for what situations they can be applied to. Good examples with SAS code and sample data are provided. For most problems, manual calculations are shown before doing them in SAS. This book was not that easy to digest though. Multiple reading will make the material more clear.
Average customer rating:
- Statistics
- fooled me!!
- Statistical Methods for Health Care Research
- Great book
- A very user friendly approach to understanding statistics.
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Statistical Methods for Health Care Research
Barbara Hazard Munro
Manufacturer: Lippincott Williams & Wilkins
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Similar Items:
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Nursing Research: Generating and Assessing Evidence for Nursing Practice (Nursing Research (Polit))
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Qualitative Research in Nursing: Advancing the Humanistic Imperative (Nursing Research)
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ASIN: 0781748402 |
Book Description
Focusing on the statistical methods most frequently used in the health care literature and featuring numerous charts, graphs, and up-to-date examples from the literature, this text provides a thorough foundation for the statistics portion of nursing and all health care research courses. All Fifth Edition chapters include new examples and new computer printouts using the latest software, SPSS for Windows, Version 12. New material on regression diagnostics has been added. Includes a complete set of powerpoint slides on the Connection website
Customer Reviews:
Statistics.......2007-03-26
This book is written for PhD level students. It is not an easy read, even for someone who has had statistics before. I would not recommend this text to anyone except experienced statistics students.
fooled me!!.......2007-01-18
I believed this was the text book by the exact same name instead it turned out to be the study guide for the text book. I do not have the text book so this book is useless for me. I think I am done doing business with Amazon!!
Statistical Methods for Health Care Research.......2003-06-01
A very well-written and helpful statistics book. It was required reading for a course in Quantitative Nursing Research last semester. Overall, the concepts such as t-tests, correlation and regression were explained in relatively simple terms. Examples with answers were found at the end of each chapter and the book came with a data disk. This was the most user-friendly statistics book that I have used so far!
Great book.......2000-03-14
I had a chance to review this book. I think the authors can make us much better understand, particularly one who is about to learn the concept of statistics. You can apply this book with the SPSS program in order to interpret the results. Anyway, I still like the calculation parts which are removed in this edition (3rd) compared with the last one. I think they can help someone, who is very interested in statistics, to understand better before go ahead to use the computer. It's a great book for the beginner.
A very user friendly approach to understanding statistics........1999-01-04
An excellent beginning reference book for anyone in need of understanding or interpreting univariate and multivariate statistics. Geared toward health care research, with many examples. A data set is provided with the book for students to use with different analyses. Reading audience is upper division undergraduate or graduate student.
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.
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