Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models by Eric Vittinghoff (Author), David V. Glidden (Author), Stephen C. Shiboski (Author), Charles E. McCulloch (Author). This new e book gives a unified, in-depth, readable introduction to the multipredictor regression methods most generally used in Biostatistics: linear fashions for continuous outcomes, logistic fashions for binary outcomes, the Cox model for right-censored survival instances, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these subjects collectively takes benefit of all they've in common.
The authors level out the many-shared components in the methods they present for selecting, estimating, checking, and decoding every of those models. They also show that these regression strategies deal with confounding, mediation, and the interaction of causal results in essentially the identical way. The examples, analyzed utilizing state, are drawn from the biomedical context however generalize to different areas of application. Whereas a primary course in statistics is assumed, a chapter reviewing fundamental statistical strategies is included. Some superior subjects are covered but the presentation remains intuitive. A quick introduction to regression analysis of advanced surveys and notes for additional studying are provided.
You can really learn this e-book - which is stunning given the subject. I'm a grad student taking two Biostats courses for a grasp's degree. This book is nice and conceptl.
Regression Methods in Biostatistics: Linear, Logistic, Survival, and
Repeated Measures Models
Eric Vittinghoff (Author), David V. Glidden
(Author), Stephen C. Shiboski (Author), Charles E. McCulloch (Author)
529 pages
Springer; 2nd ed. 2012 edition (September 1, 2011)
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