Statistical Modeling for Biomedical Researchers: A Simple Introduction to the An Researchers, Biomedical, Introduction, Simple, Modeling

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by:William D. Dupont   


  
Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data
By William D. Dupont  
  
Publisher: Cambridge University Press
Number Of Pages: 542
ISBN-10 / ASIN: 0521849527
ISBN-13 / EAN: 9780521849524   

Product Description:   
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 readerselectthe most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University.  
  

Summary: Good guide
Rating: 4  
If you are working with Stata this book will be a good help to understand the basic concepts of the multivarite analysis.  
  
Summary: Accessible Intermediate Text
Rating: 4  
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.   
  
Summary: Very useful during statistics class
Rating: 4  
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.  
  
Summary: Useful in conjuction with the manuals
Rating: 5  
As a non-statistician with some stat background, I find Dupont book a delightful book. It is packed with interesting and useful information. It starts at t-test and ends with GEE models, covering Cox model with time covariates along the way. But as the author noted, the book assumes some statistical knowledge and access to STATA maual. One minor note: While the book introduction asserts that it only assumes "high school mathematics" knowldege, the high school the author attended must be very different than the one I went to.  
  
Summary: Buy this book if you like problem based learning
Rating: 4  
I have had the pleasure of using this book during a biostatistics level two course this year. The book is structured to assist in the course work in statistics using STATA. It is user friendly and gives mathematical explanations when appropriate but without losing the reader with too many equations. The book's approach uses problem based learning along with explanatory text which I found essential in learning to navigate STATA along with learning and understanding logistic regression, poisson regression etc. The best aspect of the book is the STATA output to assist with the problem solving. The book is a very good choice as an interactive tool for understanding advanced statistics using STATA.         
               
      
     
      
      
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