Practical guide to logistic regression

Statistical Models What Is a Statistical Model? Basics of Logistic Regression Modeling The Bernoulli Distribution Methods of Estimation SAS Code Stata Code Logistic Models: Single Predictor Models with a Binary Predictor Predictions, Probabilities, and Odds Ratios Basic Model Statistics Models with a Categorical Predictor Models with a Continuous Predictor Prediction SAS Code Stata Code Logistic Models: Multiple Predictors Selection and Interpretation of Predictors Statistics in a Logistic Model Information Criterion Tests The Model Fitting Process: Adjusting Standard Errors Risk Factors, Confounders, Effect Modifiers, and Interactions SAS Code Stata Code Testing and Fitting a Logistic Model Checking Logistic Model Fit Classification Statistics Hosmer-Lemeshow Statistic Models with Unbalanced Data and Perfect Prediction Exact Logistic Regression Modeling Table Data SAS Code Stata Code Reference Grouped Logistic Regression The Binomial Probability Distribution Function From Observation to Grouped Data Identifying and Adjusting for Extra Dispersion Modeling and Interpretation of Grouped Logistic Regression Beta-Binomial Regression SAS Code Stata Code References Bayesian Logistic Regression A Brief Overview of Bayesian Methodology Examples: Bayesian Logistic Regression SAS Code Stata Code Concluding Comments References