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Statistical Modeling for Biomedical Researchers
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  • 70 b/w illus. 31 tables 74 exercises
  • Page extent: 0 pages

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 (ISBN-13: 9780511055416 | ISBN-10: 0511055412)

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. Numerous real examples from the medical literature are used to illustrate these techniques. Graphical methods are used extensively. Topics covered include linear regression, logistic regression, Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measures analysis of variance. Each method is introduced in its simplest form and is then extended to cover situations in which multiple explanatory variables are collected on each study subject.

• It covers advanced topics like generalized linear models, survival analysis, or repeated-measures analysis of variance without requiring students to have advanced mathematical skills • These techniques have become invaluable in analyzing complex data from population-based medical research • Numerous real examples from the medical literature are used to illustrate these techniques


Preface; 1. Introduction; 2. Simple linear regression; 3. Multiple linear regression; 4. Simple logistic regression; 5. Multiple logistic regression; 6. Introduction to survival analysis; 7. Hazard regression analysis; 8. Introduction to Poisson regression; 9. Multiple Poisson regression; 10. Fixed effects analysis of variation; 11. Repeated-measures analysis of variation; Index.


'I highly recommend Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data. I believe that it will be much appreciated by biomedical researchers who are eager to learn more about statistical modeling. I also believe that biostatisticians will enjoy using this book for teaching and consulting purposes.' Teaching of Statistics in the Health Services

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