Books  Data Analysis & Modelling  Data Analysis & Statistics 

Logistic Regression: A Self-Learning Text

Textbook

Series: Statistics for Biology and Health Series

By: David G Kleinbaum and Mitchel Klein

Springer-Verlag

Hardback | Dec 2010 | Edition: 3 | #126002 | ISBN-13: 9781441917416
Availability: Usually dispatched within 1-2 weeks Details
NHBS Price: £81.00 $103/€97 approx

About this book

This very popular textbook is now in its third edition. Whether students or working professionals, readers appreciate its unique "lecture book" format. They often say the book reads like they are listening to an outstanding lecturer. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams.

Like previous editions, this textbook provides a highly readable description of fundamental and more advanced concepts and methods of logistic regression. It is suitable for researchers and statisticians in medical and other life sciences as well as academicians teaching second-level regression methods courses.

The new chapters are:

- Additional Modeling Strategy Issues, including strategy with several exposures, screening variables, collinearity, influential observations and multiple-testing
- Assessing Goodness to Fit for Logistic Regression
- Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves

The Computer Appendix provides step-by-step instructions for using STATA (version 10.0), SAS (version 9.2), and SPSS (version 16) for procedures described in the main text.


Contents

Contents
Preface xiii
Acknowledgements xvii

Chapter 1 Introduction to Logistic Regression 1
Chapter 2 Important Special Cases of the Logistic Model 41
Chapter 3 Computing the Odds Ratio in Logistic Regression 73
Chapter 4 Maximum Likelihood Techniques: An Overview 103
Chapter 5 Statistical Inferences Using Maximum Likelihood Techniques 129
Chapter 6 Modeling Strategy Guidelines 165
Chapter 7 Modeling Strategy for Assessing Interaction and Confounding 203
Chapter 8 Additional Modeling Strategy Issues 241
Chapter 9 Assessing Goodness of Fit for Logistic Regression 301
Chapter 10 Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves 345
Chapter 11 Analysis of Matched Data Using Logistic Regression 389
Chapter 12 Polytomous Logistic Regression 429
Chapter 13 Ordinal Logistic Regression 463
Chapter 14 Logistic Regression for Correlated Data: GEE 489
Chapter 15 GEE Examples 539
Chapter 16 Other Approaches for Analysis of Correlated Data 567

Appendix Computer Programs for Logistic Regression 599
Test Answers 667
Bibliography 691
Index 695

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