"Preface Regression models are today a standard tool in medical research. However, their application and the interpretation of their results is often challenging. It is the purpose of this book to introduce medical researchers to basic concepts and other important aspects of regression models, which have to be taken into account in their application to ensure an optimal use and in the interpretation of their results to support the adequate addressing of subject matter questions. Three regression models are in particular popular in medical research: The classical regression model for a continuous outcome, the logistic regression model for a binary outcome and the Cox proportional hazards model for survival data. Additionally, you can find other regression models for more specific types of data or for specific situations, for example Poisson regression for count and incidence data or conditional logistic regression for the analysis of matched case control studies. We will meet all these models in this book. The emphasis will be, however, on topics of relevance across all types of regression models. Such topics are for example - the interpretation of effect estimates, confidence intervals, and p-values - the adequate presentation of results of regression analyses in a publication - typical pitfalls in the interpretation of the results - the impact of different types of research questions (establishing of an effect, quantification of an effect, prediction) and research designs (observational studies, experiments) on the use of regression models - the preparing steps prior to a regression analysis like the choice of variables, their coding and the use of transformations"-- Provided by publisher.
Includes bibliographical references (pages 465-469) and index.