Preface to the Third Edition xiii 1 Introduction to the Logistic Regression Model 1 1.1Introduction, 1 1.2 Fitting the Logistic Regression Model, 8 1.3 Testing for the Significance of the Coefficients, 10 1.4 Confidence Interval Estimation, 15 1.5 Other Estimation Methods, 20 1.6 Data Sets Used in Examples and Exercises, 22 1.6.1 The ICU Study, 22 1.6.2 The Low Birth Weight Study, 24 1.6.3 The Global Longitudinal Study of Osteoporosis in Women, 24 1.6.4 The Adolescent Placement Study, 26 1.6.5 The Burn Injury Study, 27 1.6.6 The Myopia Study, 29 1.6.7 The NHANES Study, 31 1.6.8 The Polypharmacy Study, 31 Exercises, 32 2 The Multiple Logistic Regression Model 35 2.1 Introduction, 35 2.2 The Multiple Logistic Regression Model, 35 2.3 Fitting the Multiple Logistic Regression Model, 37 2.4 Testing for the Significance of the Model, 39 2.5 Confidence Interval Estimation, 42 2.6 Other Estimation Methods, 45 Exercises, 46 3 Interpretation of the Fitted Logistic Regression Model 49 3.1 Introduction, 49 3.2 Dichotomous Independent Variable, 50 3.3 Polychotomous Independent Variable, 56 3.4 Continuous Independent Variable, 62 3.5 Multivariable Models, 64 3.6 Presentation and Interpretation of the Fitted Values, 77 3.7 A Comparison of Logistic Regression and Stratified Analysis for 2 × 2 Tables, 82 Exercises, 87 4 Model-Building Strategies and Methods for Logistic Regression 89 4.1 Introduction, 89 4.2 Purposeful Selection of Covariates, 89 4.2.1 Methods to Examine the Scale of a Continuous Covariate in the Logit, 94 4.2.2 Examples of Purposeful Selection, 107 4.3 Other Methods for Selecting Covariates, 124 4.3.1 Stepwise Selection of Covariates, 125 4.3.2 Best Subsets Logistic Regression, 133 4.3.3 Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials, 139 4.4 Numerical Problems, 145 Exercises, 150 5 Assessing the Fit of the Model 153 5.1 Introduction, 153 5.2 Summary Measures of Goodness of Fit, 154 5.2.1 Pearson Chi-Square Statistic, Deviance, and Sum-of-Squares, 155 5.2.2 The Hosmer--Lemeshow Tests, 157 5.2.3 Classification Tables, 169 5.2.4 Area Under the Receiver Operating Characteristic Curve, 173 5.2.5 Other Summary Measures, 182 5.3 Logistic Regression Diagnostics, 186 5.4 Assessment of Fit via External Validation, 202 5.5 Interpretation and Presentation of the Results from a Fitted Logistic Regression Model, 212 Exercises, 223 6 Application of Logistic Regression with Different Sampling Models 227 6.1 Introduction, 227 6.2 Cohort Studies, 227 6.3 Case-Control Studies, 229 6.4 Fitting Logistic Regression Models to Data from Complex Sample Surveys, 233 Exercises, 242 7 Logistic Regression for Matched Case-Control Studies 243 7.1 Introduction, 243 7.2 Methods For Assessment of Fit in a 1--M Matched Study, 248 7.3 An Example Using the Logistic Regression Model in a 1--1 Matched Study, 251 7.4 An Example Using the Logistic Regression Model in a 1--M Matched Study, 260 Exercises, 267 8 Logistic Regression Models for Multinomial and Ordinal Outcomes 269 8.1 The Multinomial Logistic Regression Model, 269 8.1.1 Introduction to the Model and Estimation of Model Parameters, 269 8.1.2 Interpreting and Assessing the Significance of the Estimated Coefficients, 272 8.1.3 Model-Building Strategies for Multinomial Logistic Regression, 278 8.1.4 Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model, 283 8.2 Ordinal Logistic Regression Models, 289 8.2.1 Introduction to the Models, Methods for Fitting, and Interpretation of Model Parameters, 289 8.2.2 Model Building Strategies for Ordinal Logistic Regression Models, 305 Exercises, 310 9 Logistic Regression Models for the Analysis of Correlated Data 313 9.1 Introduction, 313 9.2 Logistic Regression Models for the Analysis of Correlated Data, 315 9.3 Estimation Methods for Correlated Data Logistic Regression Models, 318 9.4 Inter .