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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