Regression models as a tool in medical research / by Werner Vach.

Format:

Book

Author/Creator:

Vach, Werner, author.

Series:

A Chapman & Hall Book

Language:

English

Subjects (All):

Medicine--Research--Statistical methods.

Medicine.

Regression analysis.

Physical Description:

1 online resource (494 p.)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : Chapman and Hall/CRC, an imprint of Taylor and Francis, [2012].

Language Note:

English

Summary:

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.

Contents:

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

Notes:

Description based upon print version of record.

Includes bibliographical references.

Description based on print version record.

ISBN:

9781040077207

104007720X

9780429185526

0429185529

9781466517493

1466517492

OCLC:

908078411