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

William D. Dupont (Author)
4.0 out of 5 stars  See all reviews (9 customer reviews)


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Paperback, January 13, 2003 --  
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Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine) Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine) 4.0 out of 5 stars (9)
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Book Description

0521655781 978-0521655781 January 13, 2003 1
This text enables biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research, and uses a statistical software package (Stata® ) to avoid mathematics beyond the high school level. Intended for people who have had an introductory course in biostatistics, the volume emphasizes the assumptions underlying each method, using exploratory techniques to determine the most appropriate method. It presents results in a way that will be readily understood by clinical colleagues. Numerous real examples from medical literature and graphical methods are used to illustrate these techniques.


Editorial Reviews

Review

" I believe the book is user friendly...I also like the book's approach of using problem-based learning, accompanied by explanatory text...One of the most helpful features of this text is the collection of excellent examples included in each chapter...In conclusion I highly recommend [this book]." TSHS

Book Description

This text will enable biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research. It is intended for people who have had an introductory course in biostatistics. A statistical software package (Stata) is used to avoid mathematics beyond the high school level. The emphasis is on understanding the assumptions underlying each method, using exploratory techniques to determine the most appropriate method, and presenting results in a way that will be readily understood by clinical colleagues.

Product Details

  • Paperback: 408 pages
  • Publisher: Cambridge University Press; 1 edition (January 13, 2003)
  • Language: English
  • ISBN-10: 0521655781
  • ISBN-13: 978-0521655781
  • Product Dimensions: 9.4 x 6.7 x 1 inches
  • Shipping Weight: 1.5 pounds
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #1,934,702 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 .

 

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3 of 3 people found the following review helpful:
4.0 out of 5 stars Very useful during statistics class, June 14, 2003
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.
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2 of 2 people found the following review helpful:
4.0 out of 5 stars Accessible Intermediate Text, December 28, 2004
By 
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.
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5.0 out of 5 stars An excellent book for new and seasoned researchers alike!, June 8, 2010
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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.
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Inside This Book (learn more)
First Sentence:
This text is primarily concerned with the interrelationships between multiple variables that are collected on study subjects. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
depvar varlist, lincom command, glycolate levels, baseline oxygen delivery, graph apache, response feature analysis, sts graph, jth covariate pattern, isoproterenol data, log systolic blood pressure, following log file, entry biopsy, lowess regression, didactic data, overwhelming statistical significance, set textsize, covariate patterns, ith patient, baseline diastolic blood pressure, stata data, predict command, using stata, estimation command, standardized deviance residual, isoproterenol dose
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Framingham Heart Study, Odds Ratio Std, Obs Mean Std, Adj R-squared, Months of Follow-up, Two-sample Wilcoxon, Gender Women Men Baseline, Min Max, Nashville Breast Cohort, Probability of Hemorrhage-Free Survival, Stata Stata, Baseline Figure
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