| |
| |
| Preface to the Third Edition | |
| |
| |
| |
| Introduction to the Logistic Regression Model | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Fitting the Logistic Regression Model | |
| |
| |
| |
| Testing for the Significance of the Coefficients | |
| |
| |
| |
| Confidence Interval Estimation | |
| |
| |
| |
| Other Estimation Methods | |
| |
| |
| |
| Data Sets Used in Examples and Exercises | |
| |
| |
| |
| The ICU Study | |
| |
| |
| |
| The Low Birth Weight Study | |
| |
| |
| |
| The Global Longitudinal Study of Osteoporosis in Women | |
| |
| |
| |
| The Adolescent Placement Study | |
| |
| |
| |
| The Burn Injury Study | |
| |
| |
| |
| The Myopia Study | |
| |
| |
| |
| The NHANES Study | |
| |
| |
| |
| The Polypharmacy Study | |
| |
| |
| Exercises | |
| |
| |
| |
| The Multiple Logistic Regression Model | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| The Multiple Logistic Regression Model | |
| |
| |
| |
| Fitting the Multiple Logistic Regression Model | |
| |
| |
| |
| Testing for the Significance of the Model | |
| |
| |
| |
| Confidence Interval Estimation | |
| |
| |
| |
| Other Estimation Methods | |
| |
| |
| Exercises | |
| |
| |
| |
| Interpretation of the Fitted Logistic Regression Model | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Dichotomous Independent Variable | |
| |
| |
| |
| Polychotomous Independent Variable | |
| |
| |
| |
| Continuous Independent Variable | |
| |
| |
| |
| Multivariable Models | |
| |
| |
| |
| Presentation and Interpretation of the Fitted Values | |
| |
| |
| |
| A Comparison of Logistic Regression and Stratified Analysis for 2 � 2 Tables | |
| |
| |
| Exercises | |
| |
| |
| |
| Model-Building Strategies and Methods for Logistic Regression | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Purposeful Selection of Covariates | |
| |
| |
| |
| Methods to Examine the Scale of a Continuous Covariate in the Logit | |
| |
| |
| |
| Examples of Purposeful Selection | |
| |
| |
| |
| Other Methods for Selecting Covariates | |
| |
| |
| |
| Stepwise Selection of Covariates | |
| |
| |
| |
| Best Subsets Logistic Regression | |
| |
| |
| |
| Selecting Covariates and Checking their Scale Using Multivariable Fractional Polynomials | |
| |
| |
| |
| Numerical Problems | |
| |
| |
| Exercises | |
| |
| |
| |
| Assessing the Fit of the Model | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Summary Measures of Goodness of Fit | |
| |
| |
| |
| Pearson Chi-Square Statistic, Deviance, and Sum-of-Squares | |
| |
| |
| |
| The Hosmer-Lemeshow Tests | |
| |
| |
| |
| Classification Tables | |
| |
| |
| |
| Area Under the Receiver Operating Characteristic Curve | |
| |
| |
| |
| Other Summary Measures | |
| |
| |
| |
| Logistic Regression Diagnostics | |
| |
| |
| |
| Assessment of Fit via External Validation | |
| |
| |
| |
| Interpretation and Presentation of the Results from a Fitted Logistic Regression Model | |
| |
| |
| Exercises | |
| |
| |
| |
| Application of Logistic Regression with Different Sampling Models | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Cohort Studies | |
| |
| |
| |
| Case-Control Studies | |
| |
| |
| |
| Fitting Logistic Regression Models to Data from Complex Sample Surveys | |
| |
| |
| Exercises | |
| |
| |
| |
| Logistic Regression for Matched Case-Control Studies | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Methods For Assessment of Fit in a 1-M Matched Study | |
| |
| |
| |
| An Example Using the Logistic Regression Model in a 1-1 Matched Study | |
| |
| |
| |
| An Example Using the Logistic Regression Model in a l-M Matched Study | |
| |
| |
| Exercises | |
| |
| |
| |
| Logistic Regression Models for Multinomial and Ordinal Outcomes | |
| |
| |
| |
| The Multinomial Logistic Regression Model | |
| |
| |
| |
| Introduction to the Model and Estimation of Model Parameters | |
| |
| |
| |
| Interpreting and Assessing the Significance of the Estimated Coefficients | |
| |
| |
| |
| Model-Building Strategies for Multinomial Logistic Regression | |
| |
| |
| |
| Assessment of Fit and Diagnostic Statistics for the Multinomial Logistic Regression Model | |
| |
| |
| |
| Ordinal Logistic Regression Models | |
| |
| |
| |
| Introduction to the Models, Methods for Fitting, and Interpretation of Model Parameters | |
| |
| |
| |
| Model Building Strategies for Ordinal Logistic Regression Models | |
| |
| |
| Exercises | |
| |
| |
| |
| Logistic Regression Models for the Analysis of Correlated Data | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Logistic Regression Models for the Analysis of Correlated Data | |
| |
| |
| |
| Estimation Methods for Correlated Data Logistic Regression Models | |
| |
| |
| |
| Interpretation of Coefficients from Logistic Regression Models for the Analysis of Correlated Data | |
| |
| |
| |
| Population Average Model | |
| |
| |
| |
| Cluster-Specific Model | |
| |
| |
| |
| Alternative Estimation Methods for the Cluster-Specific Model | |
| |
| |
| |
| Comparison of Population Average and Cluster-Specific Model | |
| |
| |
| |
| An Example of Logistic Regression Modeling with Correlated Data | |
| |
| |
| |
| Choice of Model for Correlated Data Analysis | |
| |
| |
| |
| Population Average Model | |
| |
| |
| |
| Cluster-Specific Model | |
| |
| |
| |
| Additional Points to Consider when Fitting Logistic Regression Models to Correlated Data | |
| |
| |
| |
| Assessment of Model Fit | |
| |
| |
| |
| Assessment of Population Average Model Fit | |
| |
| |
| |
| Assessment of Cluster-Specific Model Fit | |
| |
| |
| |
| Conclusions | |
| |
| |
| Exercises | |
| |
| |
| |
| Special Topics | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| Application of Propensity Score Methods in Logistic Regression Modeling | |
| |
| |
| |
| Exact Methods for Logistic Regression Models | |
| |
| |
| |
| Missing Data | |
| |
| |
| |
| Sample Size Issues when Fitting Logistic Regression Models | |
| |
| |
| |
| Bayesian Methods for Logistic Regression | |
| |
| |
| |
| The Bayesian Logistic Regression Model | |
| |
| |
| |
| MCMC Simulation | |
| |
| |
| |
| An Example of a Bayesian Analysis and Its Interpretation | |
| |
| |
| |
| Other Link Functions for Binary Regression Models | |
| |
| |
| |
| Mediation | |
| |
| |
| |
| Distinguishing Mediators from Confounders | |
| |
| |
| |
| Implications for the Interpretation of an Adjusted Logistic Regression Coefficient | |
| |
| |
| |
| Why Adjust for a Mediator? | |
| |
| |
| |
| Using Logistic Regression to Assess Mediation: Assumptions | |
| |
| |
| |
| More About Statistical Interaction | |
| |
| |
| |
| Additive versus Multiplicative Scale-Risk Difference versus Odds Ratios | |
| |
| |
| |
| Estimating and Testing Additive Interaction | |
| |
| |
| Exercises | |
| |
| |
| References | |
| |
| |
| Index | |