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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models

Handbook / Manual

Series: Statistics for Biology and Health Series

By: Eric Vittinghoff (Author), David V Glidden (Author), Stephen C Shiboski (Author), Charles E McCulloch (Author)

509 pages, b/w illustrations


Hardback | Dec 2011 | Edition: 2 | #207291 | ISBN-13: 9781461413523
Availability: Usually dispatched within 1-2 weeks Details
NHBS Price: £78.50 $95/€87 approx

About this book

This new book 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.

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


- Introduction
- Exploratory and Descriptive Methods
- Basic Statistical Methods
- Linear Regression
- Logistic Regression
- Survival Analysis
- Repeated Measures Analysis
- Generalized Linear Models
- Strengthening Casual Inference
- Predictor Selection
- Complex Surveys
- Summary

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The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).

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