Regression Methods in Biostatistics : Linear, Logistic, Survival, and Repeated Measures Models
Language: English
Published by Humana, 2014
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 93.12
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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 # 9781489998545
- Title
- Regression Methods in Biostatistics : Linear, Logistic, Survival, and Repeated Measures Models
- Author
- David V. Glidden
- Publisher
- Humana
- Publication year
- 2014
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1489998543
- ISBN 13
- 9781489998545
- Edition
- 2nd Edition
- Item weight
- 797 grams
- Dimensions
- 235x155x29 mm
- 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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