Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered.
- Discusses the very latest developments on multivariate and multidimensional mapping.
- Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches.
- Balances epidemiological and statistical points-of-view.
- Requires no previous knowledge of disease mapping.
- Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets.
- Supplies R code for the examples in the book so that they can be reproduced by the reader.
About the Authors:
Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master.
Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.
I. DISEASE MAPPING: THE FOUNDATIONS
Some considerations on this book
2. Some basic ideas of 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
The BUGS language
Running models in WinBUGS
Calling WinBUGS from R
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
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
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
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
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
Factor models, Smoothed ANOVA and other approaches
9. Multidimensional modelling
A brief introduction and review of multidimensional modeling
A formal framework for multidimensional modeling
Some tools and notation