© 2008 – Chapman and Hall/CRC
362 pages | 115 B/W Illus.
Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease.
The book explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the Metropolis–Hastings algorithm, goodness-of-fit measures, and residual diagnostics. It also focuses on special topics, such as cluster detection; space-time modeling; and multivariate, survival, and longitudinal analyses. The author explains how to apply these methods to disease mapping using numerous real-world data sets pertaining to cancer, asthma, epilepsy, foot and mouth disease, influenza, and other diseases. In the appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation.
Applying Bayesian methods to the modeling of georeferenced health data, Bayesian Disease Mapping proves that the application of these approaches to biostatistical problems can yield important insights into data.
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
Bayesian Inference and Modeling
Bayesian Hierarchical Modeling
Markov Chain Monte Carlo Methods
Metropolis and Metropolis–Hastings Algorithms
Posterior and Likelihood Approximations
Residuals and Goodness-of-Fit
Model Goodness-of-Fit Measures
Predictive Residuals and the Bootstrap
Interpretation of Residuals in a Bayesian Setting
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
Model Comparison and Goodness-of-Fit Diagnostics
Alternative Risk Models
Disease Cluster Detection
Cluster Detection using Residuals
Cluster Detection using Posterior Measures
Edge Detection and Wombling
General Case of Regression
Biases and Misclassification Error
Putative Hazard Models
Multiple Scale Analysis
Modifiable Areal Unit Problem (MAUP)
Misaligned Data Problem (MIDP)
Multivariate Disease Analysis
Notation for Multivariate Analysis
Spatial Survival and Longitudinal Analyses
Spatial Survival Analysis
Spatial Longitudinal Analysis
Extensions to Repeated Events
Spatiotemporal Disease Mapping
Case Event Data
Appendix A: Basic R and WinBUGS
Appendix B: Selected WinBUGS Code
Appendix C: R Code for Thematic Mapping