A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$

Abstract

Spatial and spatio-temporal disease mapping models are widely used for the analysis of registry data and usually formulated in a hierarchical Bayesian framework. Explanatory variables can be included by a so-called ecological regression. It is possible to assume both a linear and a nonparametric association between disease incidence and the explanatory variable. Integrated nested Laplace approximations (INLA) can be used as a tool for Bayesian inference. INLA is a promising alternative to Markov chain Monte Carlo (MCMC) methods which provides very accurate results within short computational time. It is shown in this paper, how parameter estimates for well-known spatial and spatio-temporal models can be obtained by running INLA directly in R using the package INLA. Selected R code is shown. An emphasis is given to the inclusion of an explanatory variable. Cases of Coxiellosis among Swiss cows from 2005 to 2008 are used for illustration. The number of stillborn calves is included as time-varying covariate. Additionally, various aspects of INLA such as model choice criteria, computer time, accuracy of the results and usability of the R package are discussed.

Trends in Socioeconomic Inequalities in Ischemic Heart Disease Mortality in Small Areas of Nine Spanish Cities from 1996 to 2007 Using Smoothed ANOVA

“…[45][46][47] This approach has been implemented within the R software 48 through the INLA package. 49 For each city and for the two sexes, we generated estimates based on the different components into which the common and differential patterns had been decomposed.…”

Spatial and spatio-temporal models with R-INLA

“…We assume δ i ∼ Normal(0, τ δ ), but other specification can be used, e.g. a conditional autoregressive structure, see Bernardinelli et al (1995), Schrödle and Held (2011a) for a detailed description.…”

Improvement of Fertilization Rates of In Vitro Cultured Human Embryos by Exposure to Sound Vibrations

“…A Bayesian focus was used to make all the inferences by using Integrated Nested Laplace approximations (INLA) [28,29]. All the analyses were completed using the R-Project for statistical computing, version 2.15.2 [29] and the R-INLA package (The R-INLA project, http://r-inla.org/home).…”

“…All the analyses were completed using the R-Project for statistical computing, version 2.15.2 [29] and the R-INLA package (The R-INLA project, http://r-inla.org/home).…”