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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/176317

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Title
Relative risk regression : reliable and flexible methods for log-binomial models
Related
Biostatistics, Vol. 13, No. 1, (2012), p.179-192
DOI
10.1093/biostatistics/kxr030
Publisher
Oxford University Press
Date
2012
Author/Creator
Marschner, Ian C
Author/Creator
Gillett, Alexandra C
Description
Relative risks (RRs) are generally considered preferable to odds ratios in prospective studies. However, unlike logistic regression for odds ratios, the standard log-binomial model for RR regression does not respect the natural parameter constraints and is therefore often subject to numerical instability. In this paper, we develop a reliable and flexible method for fitting log-binomial models. We use an Expectation-Maximization (EM) algorithm where the multiplicative event probability is viewed as the joint probability for a collection of latent binary outcomes. This gives a simple iterative scheme that provides stable convergence to the maximum likelihood estimate. In addition to reliability, the method offers some flexible generalizations, including models with unspecified isotonic regression functions. We examine the method's performance using simulations and data analyses of the age-specific RR of mortality following heart attack. These analyses demonstrate the potential for numerical instability in RR regression and show how this can be overcome using the proposed approach. Source code to implement the method in R is provided as supplementary material available at Biostatistics online.
Description
14 page(s)
Subject Keyword
Binomial regression
Subject Keyword
EM algorithm
Subject Keyword
Generalized linear models
Subject Keyword
Isotonic regression
Subject Keyword
Relative risk
Resource Type
journal article
Organisation
Macquarie University. Dept. of Statistics

Identifier
http://hdl.handle.net/1959.14/176317
Identifier
ISSN:1465-4644
Identifier
mq-rm-2011007272
Identifier
mq_res-ext-2-s2.0-83755163650
Language
eng
Reviewed
Reviewed
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Citation Format
E-mail Address
Subject
"Biostatistics"
 
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