Modelling binary outcomes

Williams, Gail M. and Ware, Robert S. (2013). Modelling binary outcomes. In Suhail A. R. Doi and Gail M. Williams (Ed.), Methods of Clinical Epidemiology (pp. 141-164) Heidelberg, Germany: Springer. doi:10.1007/978-3-642-37131-8

Author Williams, Gail M.
Ware, Robert S.
Title of chapter Modelling binary outcomes
Title of book Methods of Clinical Epidemiology
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2013
Sub-type Chapter in textbook
DOI 10.1007/978-3-642-37131-8
Open Access Status
Series Springer Series on Epidemiology and Public Health
ISBN 9783642371301
ISSN 1869-7933
Editor Suhail A. R. Doi
Gail M. Williams
Start page 141
End page 164
Total pages 24
Language eng
Formatted Abstract/Summary
This chapter introduces regression, a powerful statistical technique applied to the problem of predicting health outcomes from data collected on a set of observed variables. We usually want to identify those variables that contribute to the outcome, either by increasing or decreasing risk, and to quantify these effects. A major task within this framework is to separate out those variables that are independently the most important, after controlling for other associated variables. We do this using a statistical model. We demonstrate the use of logistic regression, a particular form of regression when the health outcome of interest is binary; for example, dead/alive, recovered/not recovered.
Q-Index Code BX
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Book Chapter
Collection: School of Public Health Publications
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Created: Fri, 21 Mar 2014, 12:27:07 EST by Dr Robert Ware on behalf of School of Public Health