Screen Shot 2015-09-26 at 18.06.32

Gavin Shaddick and James V. Zidek

Chapman and Hall / CRC Press

Website with online resources can be found here.

Discounts available if purchased from the publishers, CRC press. Details can be found here.

Features

  • Explores the interface between environmental epidemiology and spatio-temporal modeling
  • Incorporates many examples that illustrate how spatio-temporal methodology can be used to inform real societal concerns related to the effects of environmental hazards on health
  • Uses the Bayesian approach as an important unifying framework for the integration of spatio-temporal modeling into environmental epidemiology
  • Discusses current research topics, including visualization and mapping, high-dimensional data analysis, the design of monitoring networks, and the effects of preferential sampling
  • Provides details on using specific R packages; other software, including WinBUGS/OpenBUGS; and modern computational methods, such as INLA
  • Includes a summary and exercises at the end of each chapter
  • Offers code, data, examples, course information, and more on a supplementary website

Figure slides are available upon qualifying course adoption.

Summary

Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological Studies

Spatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and environmental epidemiologists, the book links recent developments in spatio-temporal methodology with epidemiological applications. Drawing on real-life problems, it provides the necessary tools to exploit advances in methodology when assessing the health risks associated with environmental hazards. The book’s clear guidelines enable the implementation of the methodology and estimation of risks in practice.

Designed for graduate students in both epidemiology and statistics, the text covers a wide range of topics, from an introduction to epidemiological principles and the foundations of spatio-temporal modeling to new research directions. It describes traditional and Bayesian approaches and presents the theory of spatial, temporal, and spatio-temporal modeling in the context of its application to environmental epidemiology. The text includes practical examples together with embedded R code, details of specific R packages, and the use of other software, such as WinBUGS/OpenBUGS and integrated nested Laplace approximations (INLA). A supplementary website provides additional code, data, examples, exercises, lab projects, and more.

Representing a major new direction in environmental epidemiology, this book—in full color throughout—underscores the increasing need to consider dependencies in both space and time when modeling epidemiological data. Students will learn how to identify and model patterns in spatio-temporal data as well as exploit dependencies over space and time to reduce bias and inefficiency.

Table of Contents

Why spatio-temporal epidemiology?
Overview
Health-exposure models
Dependencies over space and time
Examples of spatio-temporal epidemiological analyses
Bayesian hierarchical models
Spatial data
Good spatio-temporal modelling approaches

Modelling health risks
Overview
Types of epidemiological study
Measures of risk
Standardised mortality ratios (SMRs)
Generalised linear models
Generalised additive models
Generalised estimating equations
Poisson models for count data
Estimating relative risks in relation to exposures
Modelling the cumulative effects of exposure
Logistic models for case-controls studies

The importance of uncertainty
Overview
The wider world of uncertainty
Quantitative uncertainty
Methods for assessing uncertainty
Quantifying uncertainty

Embracing uncertainty: the Bayesian approach
Overview
Introduction to Bayesian inference
Exchangeability
Using the posterior for inference
Predictions
Transformations of parameters
Prior formulation

The Bayesian approach in practice
Overview
Analytical approximations
Markov chain Monte Carlo (MCMC)
Using samples for inference
WinBUGS
INLA

Strategies for modelling
Overview
Contrasts
Hierarchical models
Generalised linear mixed models
Linking exposure and health models
Model selection and comparison
What about the p-value?
Comparison of models—Bayes factors
Bayesian model averaging

Is ‘real’ data always quite so real?
Overview
Missing Values
Measurement error
Preferential sampling

Spatial patterns in disease
Overview
The Markov random field (MRF)
The conditional autoregressive (CAR) model
Spatial models for disease mapping

From points to fields: modelling environmental hazards over space
Overview
A brief history of spatial modelling
Exploring spatial data
Modelling spatial data
Spatial trend
Spatial prediction
Stationary and isotropic spatial processes
Variograms
Fitting variogram models
Kriging
Extensions of simple kriging
A hierarchical model for spatially varying exposures
INLA and spatial modelling in a continuous domain
Non-stationary random fields

Why time also matters
Overview
Time series epidemiology
Time series modelling
Modelling the irregular components
The spectral representation theorem and Bochner’s lemma
Forecasting
State space models
A hierarchical model for temporally varying exposures

The interplay between space and time in exposure assessment
Overview
Strategies
Spatio-temporal models
Dynamic linear models for space and time
An empirical Bayes approach
A hierarchical model for spatio-temporal exposure data
Approaches to modelling non-separable processes

Roadblocks on the way to causality: exposure pathways, aggregation and other sources of bias
Overview
Causality
Ecological bias
Acknowledging ecological bias
Exposure pathways
Personal exposure models

Better exposure measurements through better design
Overview
Design objectives?
Design paradigms
Geometry-based designs
Probability-based designs
Model-based
An entropy-based approach
Implementation challenges

New frontiers
Overview
Non-stationary fields
Physical–statistical modelling
The problem of extreme values

Appendix 1: Distribution theory
Appendix 2: Entropy decomposition 

References

Index

Author index

A Summary and Exercises appear at the end of each chapter.

Advertisements