Logistic Regression

1. Front Matter

   Pages i-xvii

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2. Introduction to Logistic Regression

   • David G. Kleinbaum, Mitchel Klein

   Pages 1-39

3. Important Special Cases of the Logistic Model

   • David G. Kleinbaum, Mitchel Klein

   Pages 41-71

4. Computing the Odds Ratio in Logistic Regression

   • David G. Kleinbaum, Mitchel Klein

   Pages 73-101

5. Maximum Likelihood Techniques: An Overview

   • David G. Kleinbaum, Mitchel Klein

   Pages 103-127

6. Statistical Inferences Using Maximum Likelihood Techniques

   • David G. Kleinbaum, Mitchel Klein

   Pages 129-164

7. Modeling Strategy Guidelines

   • David G. Kleinbaum, Mitchel Klein

   Pages 165-202

8. Modeling Strategy for Assessing Interaction and Confounding

   • David G. Kleinbaum, Mitchel Klein

   Pages 203-239

9. Additional Modeling Strategy Issues

   • David G. Kleinbaum, Mitchel Klein

   Pages 241-299

10. Assessing Goodness of Fit for Logistic Regression

    • David G. Kleinbaum, Mitchel Klein

    Pages 301-343

11. Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves

    • David G. Kleinbaum, Mitchel Klein

    Pages 345-387

12. Analysis of Matched Data Using Logistic Regression

    • David G. Kleinbaum, Mitchel Klein

    Pages 389-428

13. Polytomous Logistic Regression

    • David G. Kleinbaum, Mitchel Klein

    Pages 429-462

14. Ordinal Logistic Regression

    • David G. Kleinbaum, Mitchel Klein

    Pages 463-488

15. Logistic Regression for Correlated Data: GEE

    • David G. Kleinbaum, Mitchel Klein

    Pages 489-538

16. GEE Examples

    • David G. Kleinbaum, Mitchel Klein

    Pages 539-565

17. Other Approaches for Analysis of Correlated Data

    • David G. Kleinbaum, Mitchel Klein

    Pages 567-598

18. Back Matter

    Pages 599-701

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This is the third edition of this text on logistic regression methods, originally published in 1994, with its second e- tion published in 2002. As in the first two editions, each chapter contains a pres- tation 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, formulae, or summary statements 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 third edition has expanded the second edition by adding three new chapters and a modified computer appendix. We have also expanded our overview of mod- ing strategy guidelines in Chap. 6 to consider causal d- grams. The three new chapters are as follows: Chapter 8: Additional Modeling Strategy Issues Chapter 9: Assessing Goodness of Fit for Logistic Regression Chapter 10: Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves In adding these three chapters, we have moved Chaps. 8 through 13 from the second edition to follow the new chapters, so that these previous chapters have been ren- bered as Chaps. 11–16 in this third edition.

• Computerassistierte Detektion
• Likelihood
• Logistic Regression
• SAS
• SPSS
• Statistical Inference
• best fit

From the reviews of the third edition:

“The third edition of this book continues the tradition of the authors of a two-column book that really does act as a self-learning text. The left-hand column is like a collection of PowerPoint slides, including generic-style computer output and diagrams to visualize the relationship between concepts. Each chapter contains about 10 exercises, some routine calculation and some asking for explanation of particular points. Answers are provided immediately. … The reference list includes about 40 items and has been updated to include publications up to 2008.” (Alice Richardson, International Statistical Review, Vol. 79 (2), 2011)

• Rollins School of Public Health, Emory University, Atlanta, USA

  David G. Kleinbaum

• Rollins School of Public Health, Dept. Epidemiology, Emory University, Atlanta, USA

  Mitchel Klein

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