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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in 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.
- GenresTextbooks
529 pages, Hardcover
First published January 1, 2005
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Displaying 1 - 3 of 3 reviews
December 27, 2017
I read it for my biostatistics course. Not wildly impressed. Not a fan of Stata on which most of the coding information is based. Did present concepts in a fairly simple fashion, but not the friendliest text for non-stats majors. Maybe I'm asking for too much here. I mean, it's a textbook.
August 11, 2016
Since studying this work I reference it quite frequently in my work.
Displaying 1 - 3 of 3 reviews