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

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

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Bibliographic Details
Author / Creator:	Dupont, William D. (William Dudley), 1946-
Imprint:	Cambridge : Cambridge University Press, 2002.
Description:	1 online resource (xvii, 386 pages) : illustrations
Language:	English
Subject:	Medicine -- Research -- Methodology. Medicine -- Research -- Mathematical models. Medicine -- Research -- Statistical methods. Biometry -- methods. Models, Statistical. Data Interpretation, Statistical. Mathematical Computing. Medicine -- Research -- Mathematical models. Medicine -- Research -- Methodology. Medicine -- Research -- Statistical methods. Electronic book. Problems and Exercises.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11144827

Hidden Bibliographic Details
ISBN:	0521820618 9780521820615 0521655781 9780521655781 0511061749 9780511061745 0511055412 9780511055416 0511070209 9780511070204 0511121164 9780511121166 9781139146098 1139146092 0511323859 9780511323850 1280162554 9781280162558 9786610162550 6610162557
Digital file characteristics:	data file
Notes:	Includes bibliographical references and index. English. Print version record.
Summary:	This text will enable biomedical researchers to use several advanced statistical methods that have proven valuable in medical research. 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.
Other form:	Print version: Dupont, William D. (William Dudley), 1946- Statistical modeling for biomedical researchers. Cambridge : Cambridge University Press, 2002

Table of Contents:

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