Applied Logistic Regression

Chapter 1: Introduction to the Logistic Regression Model

• • 1.1 Introduction
• • 1.2 Fitting the Logistic Regression Model
• • 1.3 Testing for the Significance of the Coefficients
• • 1.4 Confidence Interval Estimation
• • 1.5 Other Estimation Methods
• • 1.6 Data Sets Used in Examples and Exercises
• • Exercises

Chapter 2: The Multiple Logistic Regression Model

• • 2.1 Introduction
• • 2.2 The Multiple Logistic Regression Model
• • 2.3 Fitting the Multiple Logistic Regression Model
• • 2.4 Testing for the Significance of the Model
• • 2.5 Confidence Interval Estimation
• • 2.6 Other Estimation Methods
• • Exercises

Chapter 3: Interpretation of the Fitted Logistic Regression Model

• • 3.1 Introduction
• • 3.2 Dichotomous Independent Variable
• • 3.3 Polychotomous Independent Variable
• • 3.4 Continuous Independent Variable
• • 3.5 Multivariable Models
• • 3.6 Presentation and Interpretation of the Fitted Values
• • 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables
• • Exercises

Chapter 4: Model-Building Strategies and Methods for Logistic Regression

• • 4.1 Introduction
• • 4.2 Purposeful Selection of Covariates
• • 4.3 Other Methods for Selecting Covariates
• • 4.4 Numerical Problems
• • Exercises

Chapter 5: Assessing the Fit of the Model

• • 5.1 Introduction
• • 5.2 Summary Measures of Goodness of Fit
• • 5.3 Logistic Regression Diagnostics
• • 5.4 Assessment of Fit via External Validation
• • 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model
• • Exercises

Chapter 6: Application of Logistic Regression with Different Sampling Models

• • 6.1 Introduction
• • 6.2 Cohort Studies
• • 6.3 Case-Control Studies
• • 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys
• • Exercises

Chapter 7: Logistic Regression for Matched Case-Control Studies

• • 7.1 Introduction
• • 7.2 Methods For Assessment of Fit in a 1–M Matched Study
• • 7.3 An Example Using the Logistic Regression Model in a 1–1 Matched Study
• • 7.4 An Example Using the Logistic Regression Model in a 1–M Matched Study
• • Exercises

Chapter 8: Logistic Regression Models for Multinomial and Ordinal Outcomes

• • 8.1 The Multinomial Logistic Regression Model
• • 8.2 Ordinal Logistic Regression Models
• • Exercises

Chapter 9: Logistic Regression Models for the Analysis of Correlated Data

• • 9.1 Introduction
• • 9.2 Logistic Regression Models for the Analysis of Correlated Data
• • 9.3 Estimation Methods for Correlated Data Logistic Regression Models
• • 9.4 Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data
• • 9.5 An Example of Logistic Regression Modeling with Correlated Data
• • 9.6 Assessment of Model Fit
• • Exercises

Chapter 10: Special Topics

• • 10.1 Introduction
• • 10.2 Application of Propensity Score Methods in Logistic Regression Modeling
• • 10.3 Exact Methods for Logistic Regression Models
• • 10.4 Missing Data
• • 10.5 Sample Size Issues when Fitting Logistic Regression Models
• • 10.6 Bayesian Methods for Logistic Regression
• • 10.7 Other Link Functions for Binary Regression Models
• • 10.8 Mediation
• • 10.9 More About Statistical Interaction
• • Exercises