Regression Methods in Biostatistics

Regression Methods in Biostatistics

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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.For example, hormone therapy (HT) has a smaller beneficial effect on LDL levels among postmenopausal women who are also taking ... The problem of sorting out complex relationships is not restricted to continuous outcomes; the same issues arise with the binary outcomes ... The topic of this chapter is the multipredictor linear regression model, a flexible and widely used tool for assessing the jointanbsp;...


Title:Regression Methods in Biostatistics
Author: Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch
Publisher:Springer Science & Business Media - 2012-03-06
ISBN-13:

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