While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners.

THE BASICS: Why Use Regression Models? An Introductory Example. The Classical Multiple Regression Model. Adjusted Effects. Inference for the Classical Multiple Regression Model. Logistic Regression. Inference for the Logistic Regression Model. Categorical Covariates. Handling Ordered Categories: A First Lesson in Regression Modeling Strategies. The Cox Proportional Hazard Model. Common Pitfalls in Using Regression Models. ADVANCED TOPICS AND TECHNIQUES: Some Useful Technicalities. Comparing Regression Coefficients. Power and Sample Size. The Selection of the Sample. The Selection of Covariates. Modeling Nonlinear Effects. Transformation of Covariates. Effect Modification and Interactions. Applying Regression Models to Clustered Data. Applying Regression Models to Longitudinal Data. The Impact of Measurement Error. The Impact of Incomplete Covariate Data. RISK SCORES AND PREDICTORS: Risk Scores. Construction of Predictors. Evaluating the Predictive Performance. Outlook: Construction of Parsimonious Predictors. MISCELLANEOUS: Alternatives to Regression Modeling. Specific Regression Models. Specific Usages of Regression Models. What Is a Good Model? Final Remarks on the Role of Prespecified Models and Model Development. MATHEMATICAL DETAILS: Mathematics behind the Classical Linear Regression Model. Mathematics behind the Logistic Regression Model. The Modern Way of Inference. Mathematics for Risk Scores and Predictors. Bibliography. Index.