Publication: Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models
Authors
Salmerón, D. ; Cano, J.A. ; Chirlaque López, María Dolores
item.page.secondaryauthor
item.page.director
Publisher
Wiley
publication.page.editor
publication.page.department
DOI
https://doi.org/10.1002/sim.6527
item.page.type
info:eu-repo/semantics/article
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
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.
publication.page.subject
Citation
Statistics in Medicine,. Volume 34, Issue 19 pp. 2755-2767
item.page.embargo
Collections
Ir a Estadísticas
Este ítem está sujeto a una licencia Creative Commons. http://creativecommons.org/licenses/by-nc-nd/4.0/