Regression Methods in Biostatistics
David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch, Eric Vittinghoff
Language: English
Published by Springer-Verlag New York Inc., US, 2011
- Hardcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
£ 131.44
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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.…
Seller Inventory # LU-9781461413523
- Title
- Regression Methods in Biostatistics
- Author
- David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch, Eric Vittinghoff
- Publisher
- Springer-Verlag New York Inc., US
- Publication year
- 2011
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 1461413524
- ISBN 13
- 9781461413523
- Edition
- 2nd ed. 2012.
- Series
- Book 46 of 69: Statistics for Biology and Health
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.
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London, London, United Kingdom
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