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

Vittinghoff, Eric

ISBN 10: 0387202757 / ISBN 13: 9780387202754

Verlag: Springer-Verlag New York Inc., 2005

Beschreibung

This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9780387202754.

Bestandsnummer des Verkäufers 5965086

Bibliografische Details

• Titel: Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models
• Verlag: Springer-Verlag New York Inc.
• Erscheinungsjahr: 2005
• Sprache: Englisch
• ISBN-10: 0387202757
• ISBN-13: 9780387202754
• Einband: Hardcover
• Zustand: Good
• Verkäuferkataloge:
  • Biology

Über diesen Titel

Inhaltsangabe

Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes. The authors describe shared elements in methods for selecting, estimating, checking, and interpreting each model, and show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.

Von der hinteren Coverseite

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. 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. 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).