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Multivariate Methods in Epidemiology$
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Theodore R. Holford

Print publication date: 2002

Print ISBN-13: 9780195124408

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780195124408.001.0001

Regression Models for Proportions

Chapter:
(p.141) 6 Regression Models for Proportions
Source:
Multivariate Methods in Epidemiology
Author(s):

Theodore R. Holford

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195124408.003.0006

This chapter considers the problem of fitting binary response models to data in which there are multiple regressor variables that may be either discrete or continuous in nature. The linear logistic model, the most commonly used model for this type of response, provides estimates of parameters that are assumed to have linear effects on the log odds ratio, thus yielding values that can be interpreted as log odds ratios. The more flexible generalized linear models family that can readily be adapted for fitting many alternative forms for the relationship between exposure and disease outcome are considered: the log-linear hazard, the probit model, the linear odds model, and the linear power of the odds model. Exercises are provided at the end of the chapter.

Keywords:   regression methods, linear logistic model, log-linear hazard model, probit model, odds model, proportions

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