Main description:

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.

Features:

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.

Contents:

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