Bayesian Disease Mapping: Hierarchical Modeling in Spatial EpidemiologyFocusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and |
Contents
Introduction | 3 |
Bayesian Inference and Modeling | 19 |
Computational Issues | 35 |
Residuals and GoodnessofFit | 55 |
Themes | 71 |
Disease Map Reconstruction and Relative Risk Estimation | 73 |
Disease Cluster Detection | 119 |
Ecological Analysis | 151 |
Multivariate Disease Analysis | 201 |
Spatial Survival and Longitudinal Analysis | 227 |
Spatiotemporal Disease Mapping | 255 |
Basic R and WinBUGS | 283 |
Selected WinBUGS Code | 307 |
R Code for Thematic Mapping | 319 |
| 321 | |
| 339 | |
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Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology Andrew B. Lawson No preview available - 2008 |
Common terms and phrases
algorithm analysis approach assumed asthma autologistic models Bayesian models binary binomial cluster component computed consider converged convolution model COPD count data county level covariates credible interval dataset defined denotes dependence Diggle disease mapping displays distance estimate example exceedence probability expected rates FIGURE fixed formulation function gamma gamma distribution Gaussian prior distribution Gaussian process Georgia given Hence hierarchy hyperprior individual level intensity iteration Lawson likelihood model linear model linear predictor logistic matrix McMC measures misalignment model fit mortality multivariate observed oral cancer outcome parameter vector Poisson process population possible posterior average posterior distribution posterior expected posterior sample prior distribution pseudolikelihood putative source random effect model random effects region regression relative risk residual respiratory cancer sampler simple simulation small area South Carolina space-time spatial correlation specification temporal term uncorrelated usually values variable variance variation WinBUGS zero-mean