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

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Cambridge University Press, Feb 12, 2009 - Medical
The second 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. The Stata statistical software package is again used to perform the analyses, this time employing the much improved version 10 with its intuitive point and click as well as character-based commands. 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 at http://biostat.mc.vanderbilt.edu/dupontwd/wddtext/.
 

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Contents

Probability density function
25
Simple linear regression
45
l
82
Multiple linear regression
97
Simple logistic regression
159
Multiple logistic regression
201
covariates on the response variable
211
Introduction to survival analysis
287
inferences
373
Multiple Poisson regression
401
IO Fixed effects analysis of variance
429
GEE
476
A Summary of statistical models discussed
485
B Summary of Stata commands used in this text
491
References
507
Index
513

Hazard regression analysis
315

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About the author (2009)

Wiliam D. Dupont is Professor of Biostatistics and Preventive Medicine at the Vanderbilt University School of Medicine, Nashville, Tennessee.

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