Regression Models as a Tool in Medical Research

Content: Front Cover
Dedication
Contents
Preface
Acknowledgments
About the Author
Part I The Basics
Chapter 1 Why Use Regression Models?
Chapter 2 An Introductory Example
Chapter 3 The Classical Multiple Regression Model
Chapter 4 Adjusted Effects
Chapter 5 Inference for the Classical Multiple Regression Model
Chapter 6 Logistic Regression
Chapter 7 Inference for the Logistic Regression Model
Chapter 8 Categorical Covariates
Chapter 9 Handling Ordered Categories: A First Lesson in Regression Modelling Strategies
Chapter 10 The Cox Proportional Hazards Model Chapter 11 Common Pitfalls in Using Regression ModelsPart II Advanced Topics and Techniques
Chapter 12 Some Useful Technicalities
Chapter 13 Comparing Regression Coefficients
Chapter 14 Power and Sample Size
Chapter 15 Selection of the Sample
Chapter 16 Selection of Covariates
Chapter 17 Modelling Nonlinear Effects
Chapter 18 Transformation of Covariates
Chapter 19 Effect Modification and Interactions
Chapter 20 Applying Regression Models to Clustered Data
Chapter 21 Applying Regression Models to Longitudinal Data
Chapter 22 The Impact of Measurement Error Chapter 23 The Impact of Incomplete Covariate DataPart III Risk Scores and Predictors
Chapter 24 Risk Scores
Chapter 25 Construction of Predictors
Chapter 26 Evaluating the Predictive Performance
Chapter 27 Outlook: Construction of Parsimonious Predictors
Part IV Miscellaneous
Chapter 28 Alternatives to Regression Modelling
Chapter 29 Specific Regression Models
Chapter 30 Specific Usages of Regression Models
Chapter 31 What Is a Good Model?
Chapter 32 Final Remarks on the Role of Prespecified Models and Model Development
Part V Mathematical Details Appendix A Mathematics Behind the Classical Linear Regression ModelAppendix B Mathematics Behind the Logistic Regression Model
Appendix C The Modern Way of Inference
Appendix D Mathematics for Risk Scores and Predictors
Bibliography
Back Cover