
Regression Methods in Biostatistics
Linear, Logistic, Survival, and Repeated Measures Models
By: Charles E. McCulloch, Stephen C. Shiboski, Eric Vittinghoff, David V. Glidden
Hardcover | 14 March 2012 | Edition Number 2
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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.
Industry Reviews
ISBN: 9781461413523
ISBN-10: 1461413524
Series: Statistics for Biology and Health
Published: 14th March 2012
Format: Hardcover
Language: English
Number of Pages: 532
Audience: Professional and Scholarly
Publisher: Springer Nature B.V.
Country of Publication: GB
Edition Number: 2
Edition Type: Revised
Dimensions (cm): 23.8 x 16.6 x 3.6
Weight (kg): 0.93
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