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Bayesian Disease Mapping

Hierarchical Modeling in Spatial Epidemiology, Second Edition

By Andrew B. Lawson

Chapman and Hall/CRC – 2013 – 396 pages

Series: Chapman & Hall/CRC Interdisciplinary Statistics

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    978-1-46-650481-3
    March 18th 2013

Description

Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets.

New to the Second Edition

  • Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance
  • Expanded material on case event modeling and spatiotemporal analysis
  • New and updated examples
  • Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models

In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.

Reviews

Praise for the Previous Edition

This book provides a technical grounding in spatial models while maintaining a strong grasp on applied epidemiological problems. … A welcome effort is made to clarify concepts which might, in other texts, have been skimmed over in a rush to fit models. … From the start, the concepts are illustrated with disease mapping examples, including R and WinBUGS code. … The book has relatively few errors … I recommend the book. It taught me new ideas and clarified existing ones. I shall continue to use it and I expect it to be useful for other statisticians with an interest in spatial analysis.

Journal of the Royal Statistical Society, Series A, April 2011

The readers who would like to get a big picture of hierarchical modeling in spatial epidemiology in a quick fashion will find this book very useful. This book covers a range of topics in hierarchical modeling for spatial epidemiological data and provides a practical, comprehensive, and up-to-date overview of the use of spatial statistics in epidemiology. … useful for readers to track down the topics of interests and see the varieties of up-to-date modeling techniques in spatial epidemiology or, more generally, spatial binary or count data. The author also lists the reference following each method for further information.

—Hongfei Li, Technometrics, November 2010

Lawson begins by building a solid Bayesian background … The remaining seven chapters provide a thorough review of modeling relative risk … Lawson provides well-written reviews of many topics and many aspects of those topics are covered in his reviews. The literature cited is huge and diverse, showing the current importance of the subjects covered. One can also gain hands-on training in analysis and visual presentations … by following carefully the detailed introduction to R and WinBUGS given in the book. Many important data sets used in the book are available online…

International Statistical Review (2009), 77, 2

This book is an excellent reference for intermediate learners of Bayesian disease mapping … many of the methodologies discussed in this book are applicable not only to spatial epidemiology but also to many other fields that utilize spatial data.

—J. Law, Biometrics, June 2009

Contents

BACKGROUND

Introduction

Datasets

Bayesian Inference and Modeling

Likelihood Models

Prior Distributions

Posterior Distributions

Predictive Distributions

Bayesian Hierarchical Modeling

Hierarchical Models

Posterior Inference

Exercises

Computational Issues

Posterior Sampling

Markov Chain Monte Carlo Methods

Metropolis and Metropolis-Hastings Algorithms

Perfect Sampling

Posterior and Likelihood Approximations

Exercises

Residuals and Goodness-of-Fit

Model GOF Measures

General Residuals

Bayesian Residuals

Predictive Residuals and the Bootstrap

Interpretation of Residuals in a Bayesian Setting

Pseudo Bayes Factors and Marginal Predictive Likelihood

Other Diagnostics

Exercises

THEMES

Disease Map Reconstruction and Relative Risk Estimation

An Introduction to Case Event and Count Likelihoods

Specification of the Predictor in Case Event and Count Models

Simple Case and Count Data Models with Uncorrelated Random Effects

Correlated Heterogeneity Models

Convolution Models

Model Comparison and Goodness-of-Fit Diagnostics

Alternative Risk Models

Edge Effects

Exercises

Disease Cluster Detection

Cluster Definitions

Cluster Detection using Residuals

Cluster Detection using Posterior Measures

Cluster Models

Edge Detection and Wombling

Regression and Ecological Analysis

Basic Regression Modeling

Missing Data

Non-Linear Predictors

Confounding and Multi-Colinearity

Geographically Dependent Regression

Variable Selection

Ecological Analysis: The General Case of Regression

Biases and Misclassification Error

Putative Hazard Modeling

Case Event Data

Aggregated Count Data

Spatiotemporal Effects

Multiple Scale Analysis

Modifiable Areal Unit Problem (MAUP)

Misaligned Data Problem (MIDP)

Multivariate Disease Analysis

Notation for Multivariate Analysis

Two Diseases

Multiple Diseases

Spatial Survival and Longitudinal Analyses

General Issues

Spatial Survival Analysis

Spatial Longitudinal Analysis

Extensions to Repeated Events

Spatiotemporal Disease Mapping

Case Event Data

Count Data

Alternative Models

Infectious Diseases

Disease Map Surveillance

Surveillance Concepts

Temporal Surveillance

Spatial and Spatiotemporal Surveillance

Appendices

Author Bio

Andrew B. Lawson is a professor of biostatistics and eminent scholar in the Division of Biostatistics and Epidemiology in the College of Medicine at the Medical University of South Carolina. He is an ASA fellow and an advisor in disease mapping and risk assessment for the World Health Organization. Dr. Lawson has published over 100 journal papers and eight books and is the founding editor of Spatial and Spatio-temporal Epidemiology. He received a PhD in spatial statistics from the University of St. Andrews. His research interests include the analysis of clustered disease maps, spatial and spatio-temporal disease surveillance, nutritional measurement error, and Bayesian latent variable and SEM modeling.

Name: Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition (Hardback)Chapman and Hall/CRC 
Description: By Andrew B. Lawson. Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments,...
Categories: Statistics for the Biological Sciences, Statistics & Computing, Epidemiology