ABSTRACT
This chapter aims to estimate the risk of lung cancer in Ohio, USA, from 1968 to 1988 using the R-integrated nested Laplace approximation (INLA) package. It describes a spatio-temporal model and detail the required steps to obtain the disease risk estimates using R-INLA. The lung cancer data and the Ohio map are obtained from the SpatialEpiApp package. The data contain the number of lung cancer cases and population stratified by gender and race in each of the Ohio counties from 1968 to 1988. The chapter presents a neighborhood matrix needed to define the spatial random effect. It shows how to create static and interactive maps and time plots of the standardized incidence ratios and disease risk estimates using the ggplot2 and plotly packages, and how to generate animated maps showing disease risk for each year with the ganimate package.
