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Logistic Regression: A Self-Learning Text, Third Edition

Authors:
David G. Kleinbaum and Mitchel Klein
Publisher: Springer
Copyright: 2010
ISBN-13: 978-1-4419-1741-6
Pages: 701; hardcover
Price: $69.75

Comment from the Stata technical group

This book, the third edition of the text originally published in 1994, succeeds in demonstrating that one need not be a mathematician to fully understand the underpinnings of logistic regression in all its forms. Ideally suited for a graduate course for students in the medical sciences, the text has the look and feel of a course textbook; formulas, diagrams, and important points are set off in the side margins for emphasis. Also, each chapter contains a summary, detailed outline, objectives, practice exercises (with answers), and a chapter test.

The mathematics are kept to the most basic level, but nevertheless, because of its completeness in coverage of logistic regression, this text would be a good reference for even the most theoretical statistician. In fact, those readers already well versed in logistic regression methods will benefit from seeing advanced topics such as generalized estimating equations (GEE) explained from first principles.

The third edition features new chapters on modeling strategies (for example, dealing with influential observations), goodness of fit, and receiver operating characteristic (ROC) analysis. An appendix on computer software describes how to perform the analyses described in the text using Stata version 10.0.


Table of contents

Preface
Acknowledgments
Chapter 1 Introduction to Logistic Regression
Chapter 2 Important Special Cases of the Logistic Model
Chapter 3 Computing the Odds Ratio in Logistic Regression
Chapter 4 Maximum Likelihood Techniques: An Overview
Chapter 5 Statistical Inferences Using Maximum Likelihood Techniques
Chapter 6 Modeling Strategy Guidelines
Chapter 7 Modeling Strategy for Assessing Interaction and Confounding
Chapter 8 Additional Model Strategy Issues
Chapter 9 Assessing Goodness of Fit for Logistic Regression
Chapter 10 Assessing Discriminatory Performance of a Binary Logistic Model: ROC curves
Chapter 11 Analysis of Matched Data Using Logistic Regression
Chapter 12 Polytomous Logistic Regression
Chapter 13 Ordinal Logistic Regression
Chapter 14 Logistic Regression for Correlated Data: GEE
Chapter 15 GEE Examples
Chapter 16 Other Approaches for Analysis of Correlated Data
Appendix: Computer Programs for Logistic Regression
Datasets
SAS
SPSS
Stata
Test Answers
Bibliography
Index
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