# Regression Models as a Tool in Medical Research

Werner Vach

November 27, 2012 by Chapman and Hall/CRC
Textbook - 496 Pages - 158 B/W Illustrations
ISBN 9781466517486 - CAT# K15111

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### Features

• Helps readers improve their understanding of the role of regression models in the medical field
• Illustrates each technique with a concrete example, enabling readers to better appreciate the properties and theory of the methods
• Uses Stata to demonstrate the practical use of the models
• Discusses how and when regression models can fail
• Describes the basic principles behind statistical computations, with more mathematical details given in the appendices
• Offers the data sets, solutions to all exercises, and a short introduction to Stata on the author’s website

Figure slides available with qualifying course adoption

### 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.

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.

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