图书简介

I. DISEASE MAPPING: THE FOUNDATIONS 1. Introduction Some considerations on this book Notation 2. Some basic ideas of Bayesian inference Bayesian inference Some useful probability distributions Bayesian Hierarchical Models Markov chain Monte Carlo Computing Convergence assessment of MCMC simulations 3. Some essential tools for the practice of Bayesian disease mapping WinBUGS The BUGS language Running models in WinBUGS Calling WinBUGS from R INLA INLA basics Plotting maps in R Some interesting resources in R for disease mapping practitioners 4. Disease mapping from foundations Why disease mapping? Risk measures in epidemiology Risk measures as statistical estimators Disease mapping, the statistical problem Non-spatial smoothing Spatial smoothing Spatial distributions The Intrinsic CAR distribution Some proper CAR distributions Spatial hierarchical models Prior choices in disease mapping models Some computational issues on the BYM model Some illustrative results on real data II. DISEASE MAPPING: TOWARDS MULTIDIMENSIONAL MODELING 5. Ecological Regression Ecological regression: a motivation Ecological regression in practice Some issues to take care of in ecological regression studies Confounding Fallacies in ecological regression The Texas sharpshooter fallacy The ecological fallacy Some particular applications of ecological regression Spatially varying coefficients models Point source modelling 6. Alternative spatial structures CAR-based spatial structures Geostatistical modeling Moving-average based spatial dependence Splines based modeling Modelling of specific features in disease mapping studies Modeling partitions and discontinuities Models for fitting zero excesses 7. Spatio-temporal disease mapping Some general issues in spatio-temporal modelling Parametric temporal modelling Splines-based modelling Non-parametric temporal modelling 8. Multivariate modelling Conditionally specified models Multivariate models as sets of conditional multivariate Distributions Multivariate models as sets of conditional univariate distributions Coregionalization models Factor models, Smoothed ANOVA and other approaches Factor models Smoothed ANOVA Other approaches 9. Multidimensional modelling A brief introduction and review of multidimensional modeling A formal framework for multidimensional modeling Some tools and notation Separable modeling Inseparable modeling Annex 1 Bibliography Index

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