<div align="center"><a target="_blank" href="http://www.dbebooks.biz/engine/redirect.php?url=http://www.gfxhost.ru/show.php/3234_41YYXYA17AL.jpg.html"><img title="Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models" alt="Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models" src="http://www.gfxhost.ru/out.php/t3234_41YYXYA17AL.jpg" style="border: medium none ;"/></a></div><br/><br/><b>Description:</b>
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. <br/><br/>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.<br/><br/>The examples, analyzed using Stata,
are drawn from the biomedical context but generalize to other areas of
application. While a first course is 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 many students and researchers
learning to use these methods, this one book may be all they need to
conduct and interpret multipredictor regression analyses. <br/><br/>The
authors are on the faculty in the Division of Biostatistics, Department
of Epidemiology and Biostatistics, University of California, San
Francisco, and are authors or co-authors of more than 200
methodological as well as applied papers in the biological and
biomedical sciences. <span style="color: rgb(255, 0, 0);">The senior author, Charles E. McCulloch, is head
of the Division and author of Generalized Linear Mixed Models (2003),
Generalized, Linear, and Mixed Models (2000), and Variance Components
(1992).</span>