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Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
- Sales Rank: #2531549 in Books
- Brand: Brand: Springer
- Published on: 2010-08-10
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .82" w x 6.10" l, 1.11 pounds
- Binding: Paperback
- 360 pages
- Used Book in Good Condition
Review
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 "This book is … about regression methods, with examples and terminology from the biostatistics field. It should, however, also be useful for practitioners from other disciplines where regression methods can be applied. … Most chapters end with a Problems section, and a section of further notes and references, making the book suitable as a text for a course on regression methods for Ph. D. students in medicine … . Many of the analyses in the book are illustrated with output from the statistical package Stata." (Göran Broström, Zentralblatt MATH, Vol. 1069, 2005) "The authors have written have written the book with the intention to provide an accessible introduction to multipredictor methods, emphasizing their proper use and interpretation. … In summary it may be said that this book is excellently readable. Because of the … detailed aspects of modeling, the applied tips as well as many medical examples, it can be recommended ... . In addition it can be recommended as background literature for biometrics advisors because of the high didactic quality of the book." (Rainer Muche, ISBC Newsletter, Issue 42, 2006) "The authors have written a very readable book focusing on the most widely used regression models in biostatistics: Multiple linear regression, logistic regression and Cox regression. … The book is written for a non-statistical audience, focusing on ideas and how to interpret results … . The book will be … useful as a reference to give to a non-statistical colleague … ." (Soren Feodor Nielsen, Journal of Applied Statistics, Vol. 33 (6), 2006) "Readership: Biostatistics readers, post-graduate research physicians. … This text is nicely written and well arranged and provides excellent, reasonably brief, information on the selected-topics." (N. R. Draper, Short Book Reviews, Vol. 25 (2), 2005) "This book is designed for those who want to use statistical tools in the biosciences. … It provides an excellent exposition of the application of different tools of regression analysis in biostatistics. … This book can be a bridge between biostatistics and regression analysis … . Survival analysis, repeated measurement analysis and generalized linear models are covered comprehensively. It could be used as a text-book for an advanced course in biostatistics, and it will also be helpful to biostatisticians … ." (Shalabh, Journal of the Royal Statistical Society, Vol. 169 (1), 2006) "The focus is on understanding key statistical and analytical concepts--interpreting regression coefficients, understanding the impact of the failure of model assumptions, grasping how correlation in clustered sample designs affects analysis--rather than on mathematical derivations." (Michael Elliott, Biometrics, December 2006)
From the Back Cover
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).
Most helpful customer reviews
38 of 38 people found the following review helpful.
Nice coverage of important topics for biostatisticians
By Michael R. Chernick
The authors say that they created this book to fit with a course they taught at UC San Francisco to medical students. The book is very sophisticated and a great reference source for practicing biostatisticians in industry or research. It surprises me a little that they find it effective for there non-technical audience. Although the topics are technical and many are advanced they do cover it in a conceptual way without heavy mathematics but still requiring some statistics classes as prerequisite.
Regression does not cover all the techniques of biostatistics but as the authors point out the four topics in the subtitle are among the most important. I know this from my many years of experience as a bisostatistician in the medical device and pharmaceutical industries. They use many good practical examples useing many of the common variables studies in many clinical trials where physical exams are given to record blood pressure and other vital signs and chemistry labs are done to determine cholesterol levels and other things that can be factors in various diseases. Also glucose levels are very important to monitor for diabetes trials.
In addition to the standard topics general estimating equations and generalized linear models are covered and where appropriate bootstrap confidence intervals. There is even a chapter on complex surveys a topic important when quality of life is an endpoint and survey instruments are used to measure it.
In the survival analysis chapter the Kaplan-Meier curves, log rank tests and Cox proportional hazards models are covered as expected but the authors go further to include extensions of the Cox model when the proportional hazards assumption fails. My only disappointment is that there is no coverage of actuarial life tables. At the medical device companies that I worked for it was common to get interval data on events rather than continuous data and then the Cutler-Ederer life table method is the analog for interval data to the Kaplan-Meier estimator for continuous data.
The book covers many topics but is concise as the authors claim. The authors provide a lot of examples that they work out using the statistical package Stata. The authors claim that Stata is the package of choice for biostatistics. This may be the case in academic settings but is certainly not the case in the pharmaceutical industry where SAS is used almost exclusively. I think that it would have been better to show how to write the computer code for solving these problems both in SAS and Stata. To the authors credit Stat is a very good package for their purpose and they do at times mention SAS and SPSS which are the other two major statistical packages used in industry.
All in all this is a very good book that is worth its list price. I will use it as a reference. it also contains a very nice bibliography of 9 pages.
6 of 6 people found the following review helpful.
very good book, compact but comprehensive
By Student_PhD
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.
6 of 7 people found the following review helpful.
Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models
By J. Wong
Regression Methods in Biostatistics is clearly a very well-organized book, covering topics from simple linear regression theory and methods, to the more complex survival analyses. The material is especially recommended for students who have just completed introductory biostatistics and statistical programming, and are looking for practical applications of their skills (of course, for those looking for more thorough practice, it is recommended that those individuals take more advanced biostatistics courses). Relevant examples are abundant throughout the chapters, and the authors are also very thoughtful in providing a website ([...]) where one is able to download the data (in all types of files) used in all the examples in the book, as well as for the practice problems. One drawback to this book, however, is the authors' reliance on only STATA to present the modeling examples; this is incredibly useful for primarily STATA users (the authors provide tips on STATA codes) but not particularly helpful for SAS users, for example (though it is certainly not a very huge learning barrier).
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