Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models

Salmerón, D.; Cano, J.A.; Chirlaque López, María Dolores

Publication:
Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models

dc.contributor.author	Salmerón, D.
dc.contributor.author	Cano, J.A.
dc.contributor.author	Chirlaque López, María Dolores
dc.contributor.department	Ciencias Sociosanitarias
dc.date.accessioned	2024-03-01T07:52:19Z
dc.date.available	2024-03-01T07:52:19Z
dc.date.issued	2015
dc.description	© 2015 JohnWiley & Sons, Ltd.. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0 This document is the accepted version of a published work that appeared in final form in Statistics in Medicine	es
dc.description.abstract	In cohort studies binary outcomes are very often analyzed by logistic regression. However, it is well-known that when the goal is to estimate a risk ratio, the logistic regression is inappropriate if the outcome is common. In these cases, a log-binomial regression model is preferable. On the other hand, the estimation of the regression coefficients of the log-binomial model is difficult due to the constraints that must be imposed on these coefficients. Bayesian methods allow a straightforward approach for log-binomial regression models, produce smaller mean squared errors in the estimation of risk ratios than the frequentist methods, and the posterior inferences can be obtained using the softwareWinBUGS. However,Markov chainMonte Carlo (MCMC) methods implemented inWinBUGS can lead to largeMonte Carlo errors in the approximations to the posterior inferences since they produce correlated simulations and the accuracy of the approximations are inversely related to this correlation. To reduce correlation and to improve accuracy, we propose a reparameterization based on a Poisson model and a sampling algorithm coded in R.	es
dc.format	application/pdf	es
dc.format.extent	16	es
dc.identifier.citation	Statistics in Medicine,. Volume 34, Issue 19 pp. 2755-2767
dc.identifier.doi	https://doi.org/10.1002/sim.6527
dc.identifier.issn	Print: 0277-6715
dc.identifier.issn	Electronic: 1097-0258
dc.identifier.uri	http://hdl.handle.net/10201/139790
dc.language	eng	es
dc.publisher	Wiley	es
dc.relation	This research was supported by the S´eneca Foundation Programme for the Generation of Excellence Scientific Knowledge under Project 15220/PI/10.	es
dc.relation.publisherversion	https://onlinelibrary.wiley.com/doi/10.1002/sim.6527	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Binomial regression models	es
dc.subject	Markov chain Monte Carlo	es
dc.subject	Monte Carlo error	es
dc.subject	Bayesian inference	es
dc.title	Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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