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Applied Logistic Regression, 3rd Edition

Book Description

A new edition of the definitive guide to logistic regression modeling for health science and other applications

This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.

Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:

  • A chapter on the analysis of correlated outcome data

  • A wealth of additional material for topics ranging from Bayesian methods to assessing model fit

  • Rich data sets from real-world studies that demonstrate each method under discussion

  • Detailed examples and interpretation of the presented results as well as exercises throughout

Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

Table of Contents

  1. Cover
  2. Series
  3. Title Page
  4. Copyright
  5. Dedication
  6. Preface to the Third Edition
  7. Chapter 1: Introduction to the Logistic Regression Model
    1. 1.1 Introduction
    2. 1.2 Fitting the Logistic Regression Model
    3. 1.3 Testing for the Significance of the Coefficients
    4. 1.4 Confidence Interval Estimation
    5. 1.5 Other Estimation Methods
    6. 1.6 Data Sets Used in Examples and Exercises
    7. Exercises
  8. Chapter 2: The Multiple Logistic Regression Model
    1. 2.1 Introduction
    2. 2.2 The Multiple Logistic Regression Model
    3. 2.3 Fitting the Multiple Logistic Regression Model
    4. 2.4 Testing for the Significance of the Model
    5. 2.5 Confidence Interval Estimation
    6. 2.6 Other Estimation Methods
    7. Exercises
  9. Chapter 3: Interpretation of the Fitted Logistic Regression Model
    1. 3.1 Introduction
    2. 3.2 Dichotomous Independent Variable
    3. 3.3 Polychotomous Independent Variable
    4. 3.4 Continuous Independent Variable
    5. 3.5 Multivariable Models
    6. 3.6 Presentation and Interpretation of the Fitted Values
    7. 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables
    8. Exercises
  10. Chapter 4: Model-Building Strategies and Methods for Logistic Regression
    1. 4.1 Introduction
    2. 4.2 Purposeful Selection of Covariates
    3. 4.3 Other Methods for Selecting Covariates
    4. 4.4 Numerical Problems
    5. Exercises
  11. Chapter 5: Assessing the Fit of the Model
    1. 5.1 Introduction
    2. 5.2 Summary Measures of Goodness of Fit
    3. 5.3 Logistic Regression Diagnostics
    4. 5.4 Assessment of Fit Via External Validation
    5. 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
    6. Exercises
  12. Chapter 6: Application of Logistic Regression with Different Sampling Models
    1. 6.1 Introduction
    2. 6.2 Cohort Studies
    3. 6.3 Case-Control Studies
    4. 6.4 Fitting Logistic Regression Models to Data From Complex Sample Surveys
    5. Exercises
  13. Chapter 7: Logistic Regression for Matched Case-Control Studies
    1. 7.1 Introduction
    2. 7.2 Methods For Assessment of Fit in a 1− M Matched Study
    3. 7.3 An Example Using the Logistic Regression Model in a Matched Study
    4. 7.4 An Example Using the Logistic Regression Model in a Matched Study
    5. Exercises
  14. Chapter 8: Logistic Regression Models for Multinomial and Ordinal Outcomes
    1. 8.1 The Multinomial Logistic Regression Model
    2. 8.2 Ordinal Logistic Regression Models
    3. Exercises
  15. Chapter 9: Logistic Regression Models for the Analysis of Correlated Data
    1. 9.1 Introduction
    2. 9.2 Logistic Regression Models for the Analysis of Correlated Data
    3. 9.3 Estimation Methods for Correlated Data Logistic Regression Models
    4. 9.4 Interpretation of Coefficients From Logistic Regression Models for the Analysis of Correlated Data
    5. 9.5 An Example of Logistic Regression Modeling with Correlated Data
    6. 9.6 Assessment of Model Fit
    7. Exercises
  16. Chapter 10: Special Topics
    1. 10.1 Introduction
    2. 10.2 Application of Propensity Score Methods in Logistic Regression Modeling
    3. 10.3 Exact Methods for Logistic Regression Models
    4. 10.4 Missing Data
    5. 10.5 Sample Size Issues When Fitting Logistic Regression Models
    6. 10.6 Bayesian Methods for Logistic Regression
    7. 10.7 Other Link Functions for Binary Regression Models
    8. 10.8 Mediation ‡
    9. 10.9 More About Statistical Interaction
    10. Exercises
  17. References
  18. Index