9781461413523-1461413524-Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health)

3.5
ISBN-13: 9781461413523
ISBN-10: 1461413524
Edition: 2nd ed. 2012
Author: Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
Publication date: 2011
Publisher: Springer
Format: Hardcover 532 pages
FREE shipping on ALL orders
Marketplace
from $64.00

Book details

ISBN-13: 9781461413523
ISBN-10: 1461413524
Edition: 2nd ed. 2012
Author: Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
Publication date: 2011
Publisher: Springer
Format: Hardcover 532 pages

Summary

Acknowledged authors Eric Vittinghoff , David V. Glidden , Stephen C. Shiboski , Charles E. McCulloch wrote Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) comprising 532 pages back in 2011. Textbook and eTextbook are published under ISBN 1461413524 and 9781461413523. Since then Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) textbook received total rating of 3.5 stars and was available to sell back to BooksRun online for the top buyback price of $ 37.77 or rent at the marketplace.

Description

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.

Rate this book Rate this book

We would LOVE it if you could help us and other readers by reviewing the book