Binary Regression Models with Log-Link in the Cohort Studies
Katri Jalava*, 1, Sirpa Räsänen2, Kaija Ala-Kojola2, Saara Nironen3, Jyrki Möttönen4, Jukka Ollgren1
Identifiers and Pagination:Year: 2013
First Page: 18
Last Page: 20
Publisher Id: TOEPIJ-6-18
Article History:Received Date: 14/06/2013
Revision Received Date: 04/09/2013
Acceptance Date: 04/09/2013
Electronic publication date: 4/10/2013
Collection year: 2013
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Regression models have been used to control confounding in food borne cohort studies, logistic regression has been commonly used due to easy converge. However, logistic regression provide estimates for OR only when RR estimate is lower than 10%, an unlikely situation in food borne outbreaks. Recent developments have resolved the binary model convergence problems applying log link. Food items significant in the univariable analysis were included for the multivariable analysis of two recent Finnish norovirus outbreaks. We used both log and logistic regression models in R and Bayesian model in Winbugs by SPSS and R. The log-link model could be used to identify the vehicle in the two norovirus outbreak datasets. Convergence problems were solved using Bayesian modelling. Binary model applying log link provided accurate and useful estimates of RR estimating the true risk, a suitable method of choice for multivariable analysis of outbreak cohort studies.