Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.
Features:
- Review of R graphics relevant to spatial health data
- Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data
- Bayesian Computation and goodness-of-fit
- Review of basic Bayesian disease mapping models
- Spatio-temporal modeling with MCMC and INLA
- Special topics include multivariate models, survival analysis,
