Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping. (July 2015)

• Record Type: Journal Article
• Authors: Carroll, R.; Lawson, A.B.; Faes, C.; Kirby, R.S.; Aregay, M.; Watjou, K.
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 14/15(2015)
• Page Start: 45
• Page End: 54
• Publication Date: 2015-07
• Subjects: Bayesian -- Spatial -- Integrated Nested Laplace Approximation -- BRugs -- Poisson -- MCMC; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2015.08.001
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: The default priors in INLA are not suitable in the disease mapping framework. We explore many options for improving INLA results. INLA's main advantage over OpenBUGS is computation time. INLA is unable to use hyperparameters as flexibly as in OpenBUGS. INLA and OpenBUGS can produce comparable results with appropriate alterations. Abstract: The recently developed R package INLA (Integrated Nested Laplace Approximation) is becoming a more widely used package for Bayesian inference. The INLA software has been promoted as a fast alternative to MCMC for disease mapping applications. Here, we compare the INLA package to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. We focus on the Poisson data model commonly used for disease mapping. Ultimately, INLA is a computationally efficient way of implementing Bayesian methods and returns nearly identical estimates for fixed parameters in comparison to OpenBUGS, but falls short in recovering the true estimates for the random effects, their precisions, and model goodness of fit measures under the default settings. We assumed default settings for ground truth parameters, and through altering these default settings in our simulation study, we were able to recover estimates comparable to those produced in OpenBUGS under the same assumptions.