Vittinghoff / Glidden / Shiboski Regression Methods in Biostatistics

Linear, Logistic, Survival, and Repeated Measures Models
1. Auflage 2006
ISBN: 978-0-387-27255-9
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 340 Seiten, eBook

Reihe: Statistics for Biology and Health

Theprimarybiostatisticaltoolsinmodernmedicalresearcharesingle-outcome, multiple-predictor methods: multiple linear regression for continuous o- comes, logistic regression for binary outcomes, and the Cox proportional h- ardsmodelfortime-to-eventoutcomes. Morerecently,generalizedlinearm- els and regression methods for repeated outcomes have come into widespread use in the medical research literature. Applying these methods and interpr- ing the results requires some introduction. However, introductory statistics courses have no time to spend on such topics and hence they are often r- egated to a third or fourth course in a sequence. Books tend to have either very brief coverage or to be treatments of a single topic and more theoretical than the typical researcher wants or needs. Our goal in writing this book was to provide an accessible introduction to multipredictor methods, emphasizing their proper use and interpretation. We feel strongly that this can only be accomplished by illustrating the te- niques using a variety of real datasets. We have incorporated as little theory as feasible. Further, we have tried to keep the book relatively short and to the point. Our hope in doing so is that the important issues and similarities between the methods, rather than their di?erences, will come through. We hope this book will be attractive to medical researchers needing familiarity with these methods and to students studying statistics who would like to see them applied to real data.

Zielgruppe

Research

Autoren/Hrsg.

Vittinghoff, Eric

Glidden, David V.

Shiboski, Stephen C.

McCulloch, Charles E.

Inhaltsverzeichnis

Exploratory and Descriptive Methods.- Basic Statistical Methods.- Linear Regression.- Predictor Selection.- Logistic Regression.- Survival Analysis.- Repeated Measures and Longitudinal Data Analysis.- Generalized Linear Models.- Complex Surveys.- Summary.