Regression with Linear Predictors

  • Per Kragh Andersen
  • Lene Theil Skovgaard
Part of the Statistics for Biology and Health book series (SBH, volume 0)

Table of contents

  1. Front Matter
    Pages 1-12
  2. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 1-42
  3. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 43-94
  4. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 95-171
  5. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 173-230
  6. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 231-302
  7. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 303-366
  8. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 367-430
  9. Per Kragh Andersen, Lene Theil Skovgaard
    Pages 431-455
  10. Back Matter
    Pages 457-494

About this book

Introduction

This text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. This is done by focusing on the many common aspects of these models, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choices of parametrization of the effects of the covariates (categorical as well as quantitative) and interaction among these are elaborated upon. The merits and drawbacks of different link functions relating the linear predictor to the outcome are discussed with an emphasis on interpretational issues, and the fact that different research questions arise from adding or deleting covariates from the model is emphasized in both theory and practice. Regression models with a linear predictor are commonly used in fields such as clinical medicine, epidemiology, and public health, and the book, including its many worked examples, builds on the authors' more than thirty years of experience as teachers, researchers and consultants at a biostatistical department. The book is well-suited for readers without a solid mathematical background and is accompanied by Web pages documenting in R, SAS, and STATA, the analyses presented throughout the text. The authors are since 1978 affiliated with the Department of Biostatistics, University of Copenhagen. Per Kragh Andersen is professor; he is a co-author of the Springer book "Statistical Models Based on Counting Processes," and has served on editorial boards on several statistical journals. Lene Theil Skovgaard is associate professor; she has considerable experience as teacher and consultant, and has served on the editorial board of Biometrics.

Keywords

Cox Logistic Regression Radiologieinformationssystem SAS linear regression

Authors and affiliations

  • Per Kragh Andersen
    • 1
  • Lene Theil Skovgaard
    • 2
  1. 1.Dept. BiostatisticsUniversity of CopenhagenKobenhavn KDenmark
  2. 2.Dept. BiostatisticsUniversity of CopenhagenKobenhavn KDenmark

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-7170-8
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-7169-2
  • Online ISBN 978-1-4419-7170-8
  • Series Print ISSN 1431-8776
  • About this book