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Logistic Regression

A Self-Learning Text

  • Textbook
  • © 2002

Overview

Authors:
  1. David G. Kleinbaum

    1. Department of Epidemiology, Emory University, Atlanta, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Mitchel Klein

    1. Department of Epidemiology, Emory University, Atlanta, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Statistics for Biology and Health (SBH)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Introduction to Logistic Regression

    Pages 1-38

  3. Important Special Cases of the Logistic Model

    Pages 39-72

  4. Computing the Odds Ratio in Logistic Regression

    Pages 73-99

  5. Maximum Likelihood Techniques: An Overview

    Pages 101-124

  6. Statistical Inferences Using Maximum Likelihood Techniques

    Pages 125-160

  7. Modeling Strategy Guidelines

    Pages 161-189

  8. Modeling Strategy for Assessing Interaction and Confounding

    Pages 191-226

  9. Analysis of Matched Data Using Logistic Regression

    Pages 227-265

  10. Polytomous Logistic Regression

    Pages 267-299

  11. Ordinal Logistic Regression

    Pages 301-325

  12. Logistic Regresion for Correlated Data: GEE

    Pages 327-375

  13. GEE Examples

    Pages 377-403

  14. Other Approaches for Analysis of Correlated Data

    Pages 405-436

  15. Back Matter

    Pages 437-513
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Keywords

About this book

This is the second edition of this text on logistic regression methods, ori- nally published in 1994. As in the first edition, each chapter contains a presentation of its topic in “lecture-book” format together with objectives, an outline, key formulae, practice exercises, and a test. The “lecture-book” has a sequence of illust- tions and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition has expanded the first edition by adding five new ch- ters and a new appendix. The five new chapters are Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data: GEE Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11–13 extend logistic regression to gen- alized estimating equations (GEE) and other methods for analyzing cor- lated response data. The appendix is titled “Computer Programs for Logistic Regression” and p- vides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The so- ware packages considered are SAS Version 8.0, SPSS Version 10.0, and STATA Version 7.0.

Authors and Affiliations

  • Department of Epidemiology, Emory University, Atlanta, USA

    David G. Kleinbaum, Mitchel Klein

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