title: Reducing Monte Carlo error in the Bayesian estimation of risk ratios using log-binomial regression models.
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 owing to the constraints that must be imposed on these coefficients. Bayesian methods allow a straightforward approach for log-binomial regression models and produce smaller mean squared errors in the estimation of risk ratios than the frequentist methods, and the posterior inferences can be obtained using the software WinBUGS. However, Markov chain Monte Carlo methods implemented in WinBUGS can lead to large Monte Carlo errors in the approximations to the posterior inferences because 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.
authors.affiliation: CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain.|Servicio de Epidemiología, Consejería de Sanidad y Política Social, Ronda de Levante 11, E30008, Murcia, Spain.|Instituto Murciano de Investigación Biosanitaria Virgen de la Arrixaca, IMIB, Murcia, Spain.|Departamento de Ciencias Sociosanitarias, Universidad de Murcia, Murcia, Spain.
authors.forename: Diego
authors.initials: D
authors.lastname: Salmerón
authors.affiliation: Departamento de Estadística e Investigación Operativa, Universidad de Murcia, Espinardo, E30100, Spain.
authors.forename: Juan A
authors.initials: JA
authors.lastname: Cano
authors.affiliation: CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain.|Servicio de Epidemiología, Consejería de Sanidad y Política Social, Ronda de Levante 11, E30008, Murcia, Spain.|Instituto Murciano de Investigación Biosanitaria Virgen de la Arrixaca, IMIB, Murcia, Spain.
authors.forename: María D
authors.initials: MD
authors.lastname: Chirlaque
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D015982:Bias; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016015:Logistic Models; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000453:epidemiology* / Q000401:mortality; D016017:Odds Ratio; D016012:Poisson Distribution; D011336:Probability; D012044:Regression Analysis; D016019:Survival Analysis
keywords: Bayesian inference; Markov chain Monte Carlo; Monte Carlo error; binomial regression models