Main description:

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. Regression Models as a Tool in Medical Research presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The text emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.

After reviewing popular models and basic methods, the book focuses on advanced topics and techniques. It considers the comparison of regression coefficients, the selection of covariates, the modeling of nonlinear and nonadditive effects, and the analysis of clustered and longitudinal data, highlighting the impact of selection mechanisms, measurement error, and incomplete covariate data. The text then covers the use of regression models to construct risk scores and predictors. It also gives an overview of more specific regression models and their applications as well as alternatives to regression modeling. The mathematical details underlying the estimation and inference techniques are provided in the appendices.

Contents:

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.