Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine) [Paperback]

William D. Dupont (Author)

Book Description

ISBN-10: 0521614805 | ISBN-13: 978-0521614801 | Publication Date: February 23, 2009 | Edition: 2

For biomedical researchers, the new edition of this standard text guides readers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is used to perform the analyses, in this edition employing the intuitive version 10.
Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University.

Editorial Reviews

Review

"This is a welcome edition of Dupont's book. The topics are comprehensive and well developed."
Ray Hoffmann, The Medical College of Wisconsin for the Teaching Statistics in Health Sciences Newsletter

"Although readers without prior training in biostatistics may benefit from the book, those with knowledge of introductory statistics would definitely benefit more. Traditional biomedical researchers may have a bit of difficulty in understanding some complex concepts without further communication with biostatisticians. Thus, this is an ideal textbook for non-biostatistics graduate students for learning advanced biostatistics. It also serves as a good reference for biostatisticians in collaborative work."
Doody's Review Service

"an excellent training and reference tool for young (as well as experienced) epidemiologic and biomedical researchers. It provides a comprehensive overview of traditional and novel approaches to model and interpret complex clinical, epidemiologic, and biomedical databases. In addition, an excellent and popular statistical package (Stata), widely used in the epidemiologic research population, is presented and demonstrated by using real data sets."
Stanley P. Azen, American Journal of Epidemiology

Book Description

The new edition of this standard text guides biomedical researchers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics.

See all Editorial Reviews

Product Details

• Paperback: 542 pages
• Publisher: Cambridge University Press; 2 edition (February 23, 2009)
• Language: English
• ISBN-10: 0521614805
• ISBN-13: 978-0521614801
• Product Dimensions: 9.6 x 6.7 x 1 inches
• Shipping Weight: 2.3 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (9 customer reviews)
• Amazon Best Sellers Rank: #236,290 in Books (See Top 100 in Books)

More About the Author

William D. Dupont, PhD, is a Professor of Biostatistics and Preventive Medicine at Vanderbilt University School of Medicine. He was born in Montreal, Canada and was educated at Trinity College School, McGill University, and the Johns Hopkins University. He has been a member the faculty of Vanderbilt University School of Medicine since 1977. He is best known for his work on the epidemiology of breast cancer, but he has also published papers on power calculations, the estimation of animal abundance, the foundations of statistical inference, and other topics. He is an author of over 150 refereed papers or book chapters or book reviews; as of October, 2009, more than 8,000 academic publications have cited his work (Google Web of Knowledge). He is a Fellow of the American Statistical Association and has published papers in over 70 journals, including the New England Journal of Medicine, Science, the American Journal of Epidemiology, Biometrics, Cancer Research, Controlled Clinical Trials and Statistics in Medicine. Dr. Dupont has been the principal investigator on seven grants on the epidemiology of breast cancer that were awarded by the National Cancer Institute. He has lectured on his research in North and South America and in Europe.

His textbook, Statistical Modeling for Biomedical Researchers, has evolved out of his experience in teaching a second semester course in Biostatistics to research physicians in the Masters of Public Health degree program at Vanderbilt University. Additional information on this text can be found at http://biostat.mc.vanderbilt.edu/dupontwd/wddtext/ . Reviews of his book have been published at http://aje.oxfordjournals.org/cgi/content/extract/kwp312 and at http://www.bio.ri.ccf.org/ASA_TSHS/pdf/TSHSnews09spring.pdf .

Customer Reviews

Most Helpful Customer Reviews

3 of 3 people found the following review helpful:

4.0 out of 5 stars Very useful during statistics class, June 14, 2003

By 

KC "Book Lover" (Nashville) - See all my reviews

This review is from: Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Paperback)

I used this book as the text for a biostatistics class that used STATA as the statistitical package. I found the organization, problems, and the STATA output the book provides, all very helpful. In addition, as I moved systematically through the book, the tips regarding using the STATA features were key to my learning many of the practical aspects of the STATA program.

2 of 2 people found the following review helpful:

4.0 out of 5 stars Accessible Intermediate Text, December 28, 2004

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kthdimension (Vienna, Austria) - See all my reviews

This review is from: Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Paperback)

Dupont's "Statistical Modeling for Biomedical Researchers" is an accessible, straightforward, easy-to-read text for students and/or researchers w/ some elementary background in biostatistics. As previous reviewers have indicated, this is largely a problem-based text, so for those of you who seek a detailed theoretical explanation of the tools presented therein, you may want to look elsewhere. A major advantage, however, is Dupont's presentation of how to run the respective analyses using the statistical software package, Stata, although it should be noted that the syntax presented is for version 7 of Stata -- not version 8. Parenthetically, all of the code -- w/ the exception of the graphing commands -- are essentially the same between versions. In short, this text is a good introduction to some of the techniques typically not discussed in an elementary biostatistics course, although the book is best characterized as an invaluable adjunct to more theoretical, comprehensive biostatistics textbooks.

5.0 out of 5 stars An excellent book for new and seasoned researchers alike!, June 8, 2010

By 

Ariel Linden "Ariel" (Hillsboro, OR USA) - See all my reviews
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Amazon Verified Purchase

This review is from: Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine) (Paperback)

In general, there are 3 types of books on statistics: (1) Those that describe general statistical methods (2) those that describe specific (esoteric) models, and (3) those that teach "how to" implement statistical models in specific software packages.

In this book, William D. Dupont does an excellent job of providing sufficient descriptions of each of the major statistical modeling approaches along with the specific Stata software commands to make this a rather complete book. Topics include simple and multiple regression models of the various types (linear, logistic, Poisson) as well as survival and longitudinal modeling approaches.

While as an experienced researcher these concepts are not new to me, what I found the most helpful was Dr. Dupont's thoughtful approach to choosing, testing, and displaying the results of each method. On countless occasions I found myself thinking "huh, that was a clever idea."

This book can serve as an excellent text for an intermediate biostatistics course (preferably a class that uses Stata), as well as serve as a resource to experienced researchers who may want to find streamlined approaches to implementing these models in Stata.