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
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:
a great resource.......2007-10-12
I wouldn't exactly call this book easy reading, but not many books that cover statistics at this level are. It is, however, very worthwhile reading for those who want a better understanding of biostatistics. I've taken a couple of graduate level statistics courses, and often when I couldn't quite understand what the textbook or teacher was talking about, reading this book clarified things for me. As a reasearcher, I continue to use this book occasionally to refresh my memory on various aspects of statistics, and it has served me very well. People may criticize the book for being "non-mathematical," but I don't think that's fair because it was not intended to be a rigorous math-stats book. There are plenty of those available, and none that I know of that can explain things as well to consumers of statistics like me.
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!
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.
Average customer rating:
- Outstanding
- The best second book of statistics for biologists.
- The best advanced statistics book for biologists
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Primer of Applied Regression & Analysis of Variance
Stanton A. Glantz , and
Bryan K. Slinker
Manufacturer: McGraw-Hill Medical
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Primer of Biostatistics 6/e Valuepack (Book and CDROM)
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Intuitive Biostatistics
ASIN: 0071360867 |
Book Description
Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA).
Customer Reviews:
Outstanding.......2006-01-31
I looked at several options for a regression textbook that would be both understandable and relatively complete for my introduction to the topic. This book won hands down. The authors keep it simple and use a wide variety of examples to get the point across. I felt that the sections on logistic and Cox regression could have been a bit better, but these subjects are best learned by dedicated textbooks such as Hosmer and Lemeshow and Collett.
I think that this may be the best introductory regression book out there.
The best second book of statistics for biologists........2000-11-13
Once you've learned the basic principles of statistics, how can a biologist learn more advanced techniques? Many books focus on math rather than on understanding concepts. Other books are too narrow -- discussing only a single method. And books that focus on multiple regression and ANOVA tend to have examples from psychology and social sciences. Glantz and Slinker do a great job of explaining the principles of multiple regression, analysis of variance, and analysis of covariance. The focus is not on mathematical proofs, but rather on making sense of the results in the context of biological and medical research.
This book also has excellent chapters on linear regression, nonlinear regression (curve fitting) and logistic and proportional hazards regression (regression when the outcome is an either-or binary variable).
New to the second edition are a chapter on analysis of covariance, more extensive discussions of multiple comparisons methods, and a discussion of Cox proportional hazards regression for analyses of survival data.
The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book for biologists to learn about various kinds of ANOVAS and regressions.
The best advanced statistics book for biologists.......1998-05-30
Like all advanced stats books, this one has mathematical rigor and plenty of examples. But unlike the others, this one is written from the point of view of a biologist. You won't just learn the math, you'll learn how to make sense of the results. The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book to learn about various kinds of ANOVAS and regressions.
Book Description
This text fulfills a need for an advanced-level work covering both the theory and application of geostatistics. It covers the most important areas of geostatistical methodology, introducing tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation. It also details the theoretical background underlying most GSLIB programs. The tools are applied to an environmental data set, but the book includes a general presentation of algorithms intended for students and practitioners in such diverse fields as soil science, mining, petroleum, remote sensing, hydrogeology, and the environmental sciences.
Customer Reviews:
A comprehensive treatment.......2007-03-12
Please be aware that Dr Merk's comment reflects his views of *all* geostatistics that use random fields, variogram models etc. You can read his page to see his view (which I have never been able to figure out) and look at the AI-GEOSTATS archives for plenty of discussion. In Merks' view, the rest of us are either idiots, lemmings, or have been brainwashed; in most practioner's view, the sort of geostatistics taught in this book has proven to be very useful and seems theoretically-sound.
If you decide that this approach can give useful insight into the natural world (i.e. if you agree with 99% of current practicioners), and especially map and predict, than this book is a thorough, detailed treatment. The dataset is available from the author's home page and also comes with the gstat package of R, so the reader can practice all the techniques.
The sections on co-regionalization are especially strong.
Be aware the notation is somewhat non-standard but it works well, especially for complex co-regionalization equations.
An invalid variant of mathematical statistics.......2002-12-05
Geostatistics for Natural Resources Evaluation is another textbook that violates the requirement of functional independence and ignores the concept of degrees of freedom. This author is one of many who believes that the true variance of the distance-weighted average can be replaced with the pseudo kriging variance of a SET of degrees-of-freedom and variance-deprived functionally dependent kriged estimates formely known as distance-weighted averages. Degrees of freedom, too, are missing in this textbook, which seems to suggest that this concept is as redundant as the variance of the single distance-weighted average.
THE book in geostatisctics.......2001-03-01
This book is the one book one should consult in order to study and apply geostatistics. It is very comprehensive, clear and practical. It presents classical and innovative methods in geostatistics, some of them only now appearing in textbooks.
Great for no statisticians.......2000-06-23
The book is at first out of reach because of the common notation. After three pages it became self explaining and incredibly usefull. I strongly recommend it to subjets new in geostatistics with a no very strong mathematical background.
One of very few well-written math related books.......1998-08-07
I picked up this book so that I could understand a geostatisical model I was using for petroleum exploration, and it did a beautiful job of laying out the conceptual framework of the math. As a geologist with not so much mathematical background, I was able to pick through the equations and derivations (which I imagine would be useful for those so inclined) to get at the actual substance. This is a surprisingly readable text for a math book, highly recommended.
Book Description
An important role of diagnostic medicine research is to estimate and compare the accuracies of diagnostic tests. This book provides a comprehensive account of statistical methods for design and analysis of diagnostic studies, including sample size calculations, estimation of the accuracy of a diagnostic test, comparison of accuracies of competing diagnostic tests, and regression analysis of diagnostic accuracy data. Discussing recently developed methods for correction of verification bias and imperfect reference bias, methods for analysis of clustered diagnostic accuracy data, and meta-analysis methods, Statistical Methods in Diagnostic Medicine explains:
* Common measures of diagnostic accuracy and designs for diagnostic accuracy studies
* Methods of estimation and hypothesis testing of the accuracy of diagnostic tests
* Meta-analysis
* Advanced analytic techniques-including methods for comparing correlated ROC curves in multi-reader studies, correcting verification bias, and correcting when an imperfect gold standard is used
Thoroughly detailed with numerous applications and end-of-chapter problems as well as a related FTP site providing FORTRAN program listings, data sets, and instructional hints, Statistical Methods in Diagnostic Medicine is a valuable addition to the literature of the field, serving as a much-needed guide for both clinicians and advanced students.
Customer Reviews:
And the winner is..........2004-11-04
There are two modern books in the field. This one by Profs Zhou, Obuchowski, McClish and the book by Prof. Pepe. All four are experts in this field. Both books present the same aspects of statistical diagnostic testing and both can be of invaluable help for researchers (both applied and more academic) and graduate students. However, Professor Pepe has done an excellent job (if I may) using a clear, concise notation and language throughout. On the other hand this book (ZOM) is not that well written, giving more weight in the presentation of the personal research of the authors. As a result there is some notation inconsistency (not too puzzling though) and the flow of the text is not that smooth. Both books have full reference lists, they present interesting applications and give a number of exercises at the end of each chapter.
Book Description
Today, mathematics, biology, medicine, and statistics are closing the interdisciplinary gap in an unprecedented way and many of the important unanswered questions now emerge at the interface of these disciplines. Now in its Second Edition, this user-friendly guide on biostatistics focuses on the proper use and interpretation of statistical methods. This textbook does not require extensive background in mathematics, making it user-friendly for all students in the public health sciences field. Instead of highlighting derivations of formulas, the authors provide rationales for the formulas, allowing students to grasp a better understanding of the link between biology and statistics. The material on life tables and survival analysis allows students to better understand the recent literature in the health field, particularly in the study of chronic disease treatment.
Biostatistics now includes a companion website to demonstrate the different applications of computer packages for performing the various analyses presented in this text.
* Includes access to a companion website with further examples and a full explanation of computer packages
* Over 40% new material with modern real-life examples, exercises and references
* New chapters on Logistic Regression; Analysis of Survey Data; and Study Designs
* Introduces strategies for analyzing complex sample survey data
* Written in a conversational style more accessible to students with real data
Book Description
Wildlife researchers and ecologists make widespread use of multivariate statistics in their studies. With its focus on the practical application of the techniques of multivariate statistics, this book shapes the powerful tools of statistics for the specific needs of ecologists and makes statistics more applicable to their course of study. Multivariate Statistics for Wildlife and Ecology Research gives the reader a solid conceptual understanding of the role of multivariate statistics in ecological applications and the relationships among various techniques, while avoiding detailed mathematics and underlying theory. More important, the reader will gain insight into the type of research questions best handled by each technique and the important considerations in applying each one. Whether used as a textbook for specialized courses or as a supplement to general statistics texts, the book emphasizes those techniques that students of ecology and natural resources most need to understand and employ in their research. Detailed examples use real wildlife data sets analyzed using the SAS statistical software program. The book is specifically targeted for upper-division and graduate students in wildlife biology, forestry, and ecology, and for professional wildlife scientists and natural resource managers, but it will be valuable to researchers in any of the biological sciences. Kevin McGarigal is Assistant Professor and Sam Cushman is a doctoral candidate in the Department of Forestry and Wildlife Management at the University of Massachusetts. Susan Stafford is Head of the Forest Science Department at Colorado State University.
Customer Reviews:
A good introduction to multivariate statistics.......2007-01-26
This book is fairly easy to understand, even with little knowledge of multivariate statistics. The author uses specific examples relevant to ecological fields and does not focus on theory (which is a rarity in statistical manuals). It is, however, starting to get a bit outdated with some of the techniques gaining favor in the literature recently.
grad students.......2002-04-01
I am an ecology grad student and I have returned to this text again and again.
Average customer rating:
- A must-have book for young ecologists
- Excellent reference text
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Ecological Methodology (2nd Edition)
Charles J. Krebs
Manufacturer: Benjamin Cummings
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A Primer of Ecological Statistics
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Analysis and Management of Animal Populations
ASIN: 0321021738 |
Customer Reviews:
A must-have book for young ecologists.......2004-03-28
This is a wonderful book. As a graduating senior in Ecology, I can say that there is no way I could have completed my research without this book. Krebs is cited in journal articles constantly, and there is a reason - this reference work is thorough, well written and gives many examples to follow. I also recommend the EcoMeth software mentioned in the text, it is well worth the cost. Just browsing through the book will give you ideas on how to analyze your data. It even provides wonderful advice for those just in the process of setting up their experiments. All in all, it's top notch!
Excellent reference text.......2000-11-10
Krebs' text is a thorough and outstanding reference for any ecologist. This is is the ideal balance of technical background and practical application of methods commonly used in ecology. Ive found other sources either too cursory or far too involved with derivations of formulae, etc. Krebs hits the major points for the methods he discusses, describes the strengths and weaknesses, and gives the original citations (for most) so the reader can seek more information if necessary.
Book Description
This book describes the recursive partitioning methodology and demonstrates its effectiveness as a response to the challenge of analyzing and interpreting multiple complex pathways to many illnesses, diseases, and ultimately death. For comparison purposes, standard regression methods are presented briefly and they are applied in the examples. We emphasize particularly the importance of scientific judgment and interpretation while guided by statistical output. This book is suitable for three broad groups of readers: 1) Biomedical researchers, clinicians, public health practitioners including epidemiologists, health service researchers, environmental policy advisers; 2) Consulting statisticians who can use the recursive partitioning technique as a guide in providing effective and insightful solutions to clients' problems; and 3) Statisticians interested in methodological and theoretical issues. The book provides an up-to-date summary of the methodological and theoretical underpinnings of recursive partitioning. It also presents a host of unsolved problems whose solutions whould advance the rigorous underpinnings of statistics in general. Heping Zhang is Associate Professor of Biostatistics and Child Study at Yale University. In addition to the methodology and application of recursive partitioning, he is interested in developing statistical methods for analyzing correlated data, especially family and genetic studies, and brain imaging problems. Burton Singer, a member of the National Academy of Sciences, is Professor of Demography and Public Affairs at Princeton University. His research interests include combinatorial formulation of randomness, infectious disease epidemiology, and bio-demography of aging.
Customer Reviews:
sequel to CART.......2001-04-28
Brieman, Olshen, Friedman and Stone introduced CART in their 1984 book. It is an effective methodology and software tool for constructin classification and regression trees. The procedure is also referred to as recursive partitioning. There has been a great deal of research over the past 16 on this topic and the authors cover the basics and the new material well. New ideas include survival trees and adaptive splines (including MARS). It provides interesting applications to health science problems. Th authors compare tree based methods to logistic regression. This is a notable successor to the CART text.
Recursive Partitioning in the Health Sciences.......2000-06-13
Zhang and Singer have done a splendid job of explaining recursive partitioning, a topic that should be of great interest to anyone who wants to make sense of data in which there are many potentially important variables contributing to some outcome or variable of interest. One should not be put off by the "... in the Health Sciences" part of the book's title; the potential audience of readers who can benefit from reading it is much greater than this implies (I'm an ecologist, for example). Why? First, because the topics covered have wide applicability in many fields; and second, because the writing is exceptionally clear and easy to follow. If you are able to use a typical introductory text on multiple regression, for example, you should have no difficulty getting a lot out of Zhang and Singer. If you are able to handle a mathematically rigorous approach to statistics but are new to the topics covered here, this book will provide an excellent starting place before you jump into the many references to the recent literature provided by the authors.
Recursive Partitioning.......2000-05-16
Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic.
Recursive Partitioning.......2000-05-16
Recursive Partitioning in the Health Sciences is one of the few statistical texts specifically written with the epidemiologist as a target end user, similar in genre to Schlesselman's Case Control Studies. The subject matter is relatively new in the field of epidemiology and as such needs to be related contextually to more traditional statistical approaches. The authors accomplish this by incorporating introductory chapters on methods corresponding to those being addressed by the nonparametric methods of recursive partitioning and multivariate adaptive regression splines (MARS). Additionally, they compare results between these tried and true statistical methods and recursive partitioning and MARS with many illustrative examples. This last is a strength of this book. Examples of each topic under discussion are carefully considered in a stepwise manner. The book is nicely balanced in terms of theoretic background and practical applications, with the writing generally intelligible to the non-statistician. The book has provided our group with background material to allow utilization of recursive partitioning in our research. As the technique of recursive partitioning becomes recognized and subsequently applied in the epidemiological field, this book may well become a classic.
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