Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data

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Title:

Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data

Author:

Dupont, William D. (William Dudley), 1946-

ISBN:

9780521820615

9780521655781

Personal Author:

Dupont, William D. (William Dudley), 1946-

Publication Information:

Cambridge : Cambridge University Press, 2002.

Physical Description:

xvii, 386 p. : ill. ; 26 cm.

Contents:

Machine derived contents note: 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.

Subject Term:

Models, Statistical.

Medicine -- Research -- Methodology.

Medicine -- Research -- Mathematical models.

Medicine -- Research -- Statistical methods.

Electronic Access:

Publisher description http://www.loc.gov/catdir/description/cam0210/2002073487.html
Table of contents http://www.loc.gov/catdir/toc/cam031/2002073487.html
Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0731/2002073487-b.html

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Summary

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.