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Regression Methods in Biostatistics

Linear, Logistic, Survival, and Repeated Measures Models

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

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

Table of contents

  1. Front Matter
    Pages i-xx
  2. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 1-6
  3. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 7-26
  4. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 27-67
  5. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 69-138
  6. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 139-202
  7. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 203-259
  8. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 261-308
  9. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 309-330
  10. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 331-394
  11. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 395-429
  12. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 431-467
  13. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 469-480
  14. Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
    Pages 481-488
  15. Back Matter
    Pages 489-509

About this book

Introduction

This new edition provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.

Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.

The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.

In the second edition, the authors have substantially expanded the core chapters, including new coverage of exact, ordinal, and multinomial logistic models, discrete time and competing risks survival models, within and between effects in longitudinal models, zero-inflated Poisson and negative binomial models, cross-validation for prediction model selection, directed acyclic graphs, and sample size, power and minimum detectable effect calculations; Stata code is also updated. In addition, there are new chapters on methods for strengthening causal inference, including propensity scores, marginal structural models, and instrumental variables, and on methods for handling missing data, using maximum likelihood, multiple imputation, inverse weighting, and pattern mixture models.

From the reviews of the first edition:

"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

Keywords

adopted-textbook applied regression methods for biomedical research confounding, mediation, and interaction linear, logistic, generalized linear, survival (Cox), GEE, a model selection and checking statistical computing with STATA

Authors and affiliations

  • Eric Vittinghoff
    • 1
  • David V. Glidden
    • 2
  • Stephen C. Shiboski
    • 3
  • Charles E. McCulloch
    • 4
  1. 1.Dept. Epidemiology & BiostatisticsUniversity of California, San FranciscoSan FranciscoUSA
  2. 2.Dept. Epidemiology & BiostatisticsUniversity of California, San FranciscoSan FranciscoUSA
  3. 3.Dept. Epidemiology & BiostatisticsUniversity of California, San FranciscoSan FranciscoUSA
  4. 4.Dept. Epidemiology & BiostatisticsUniversity of California, San FranciscoSan FranciscoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-1353-0
  • Copyright Information Springer Science+Business Media, LLC 2012
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-1352-3
  • Online ISBN 978-1-4614-1353-0
  • Series Print ISSN 1431-8776
  • Buy this book on publisher's site