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Regression Methods in Biostatistics

Linear, Logistic, Survival, and Repeated Measures Models

  • Book
  • © 2005

Overview

Authors:
  1. Eric Vittinghoff

    1. Department of Epidemiology and Biostatistics, University of California, San Francisco, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Stephen C. Shiboski

    1. Department of Epidemiology and Biostatistics, University of California, San Francisco, USA

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  3. David V. Glidden

    1. Department of Epidemiology and Biostatistics, University of California, San Francisco, USA

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  4. Charles E. McCulloch

    1. Department of Epidemiology and Biostatistics, University of California, San Francisco, USA

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  • Short and to the point so that the important issues and similarities between the methods, rather than the differences, shine through
  • Includes supplementary material: sn.pub/extras
  • Request lecturer material: sn.pub/lecturer-material

Part of the book series: Statistics for Biology and Health (SBH)

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Table of contents (11 chapters)

  1. Front Matter

    Pages I-XV
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  2. Introduction

    Pages 1-5

  3. Exploratory and Descriptive Methods

    Pages 7-27

  4. Basic Statistical Methods

    Pages 29-67

  5. Linear Regression

    Pages 69-131

  6. Predictor Selection

    Pages 133-156

  7. Logistic Regression

    Pages 157-209

  8. Survival Analysis

    Pages 211-252

  9. Repeated Measures and Longitudinal Data Analysis

    Pages 253-289

  10. Generalized Linear Models

    Pages 291-303

  11. Complex Surveys

    Pages 305-316

  12. Summary

    Pages 317-322

  13. Back Matter

    Pages 323-340
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Keywords

About this book

Theprimarybiostatisticaltoolsinmodernmedicalresearcharesingle-outcome, multiple-predictor methods: multiple linear regression for continuous o- comes, logistic regression for binary outcomes, and the Cox proportional h- ardsmodelfortime-to-eventoutcomes. Morerecently,generalizedlinearm- els and regression methods for repeated outcomes have come into widespread use in the medical research literature. Applying these methods and interpr- ing the results requires some introduction. However, introductory statistics courses have no time to spend on such topics and hence they are often r- egated to a third or fourth course in a sequence. Books tend to have either very brief coverage or to be treatments of a single topic and more theoretical than the typical researcher wants or needs. Our goal in writing this book was to provide an accessible introduction to multipredictor methods, emphasizing their proper use and interpretation. We feel strongly that this can only be accomplished by illustrating the te- niques using a variety of real datasets. We have incorporated as little theory as feasible. Further, we have tried to keep the book relatively short and to the point. Our hope in doing so is that the important issues and similarities between the methods, rather than their di?erences, will come through. We hope this book will be attractive to medical researchers needing familiarity with these methods and to students studying statistics who would like to see them applied to real data.

Reviews

From the reviews:

"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005

"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book" Technometrics, February 2006

"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006

"This book is … about regression methods, with examples and terminology from the biostatistics field. It should, however, also be useful for practitioners from other disciplines where regression methods can be applied. … Most chapters end with a Problems section, and a section of further notes and references, making the book suitable as a text for a course on regression methods for Ph. D. students in medicine … . Many of the analyses in the book are illustrated with output from the statistical package Stata." (Göran Broström, Zentralblatt MATH, Vol. 1069, 2005)

"The authors have written have written the book with the intention to provide an accessible introduction to multipredictor methods, emphasizing their proper use and interpretation. … In summary it may be said that this book is excellently readable. Because of the … detailed aspects of modeling, the applied tips as well as many medical examples, it can berecommended ... . In addition it can be recommended as background literature for biometrics advisors because of the high didactic quality of the book." (Rainer Muche, ISBC Newsletter, Issue 42, 2006)

"The authors have written a very readable book focusing on the most widely used regression models in biostatistics: Multiple linear regression, logistic regression and Cox regression. … The book is written for a non-statistical audience, focusing on ideas and how to interpret results … . The book will be … useful as a reference to give to a non-statistical colleague … ." (Soren Feodor Nielsen, Journal of Applied Statistics, Vol. 33 (6), 2006)

"Readership: Biostatistics readers, post-graduate research physicians. … This text is nicely written and well arranged and provides excellent, reasonably brief, information on the selected-topics." (N. R. Draper, Short Book Reviews, Vol. 25 (2), 2005)

"This book is designed for those who want to use statistical tools in the biosciences. … It provides an excellent exposition of the application of different tools of regression analysis in biostatistics. … This book can be a bridge between biostatistics and regression analysis … . Survival analysis, repeated measurement analysis and generalized linear models are covered comprehensively. It could be used as a text-book for an advanced course in biostatistics, and it will also be helpful to biostatisticians … ." (Shalabh, Journal of the Royal Statistical Society, Vol. 169 (1), 2006)

"The focus is on understanding key statistical and analytical concepts--interpreting regression coefficients, understanding the impact of the failure of model assumptions, grasping how correlation in clustered sample designs affects analysis--rather than on mathematical derivations." (Michael Elliott, Biometrics, December 2006)

Authors and Affiliations

  • Department of Epidemiology and Biostatistics, University of California, San Francisco, USA

    Eric Vittinghoff, Stephen C. Shiboski, David V. Glidden, Charles E. McCulloch

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