Bayesian Disease Mapping

In line with the recent growth of Bayesian methods applied to the modeling of geo-referenced health data, "Bayesian Disease Mapping" presents a practical overview of Bayesian modeling and computation in disease mapping. It covers various application areas, including disease map reconstruction, disease cluster detection, multi-scale disease mapping, spatio-temporal models, spatial survival analysis, spatial longitudinal analysis, and latent structure models. This book features a wide range of detailed case studies to illustrate how the methods can be applied. The author implements all examples using R and WinBUGS and provides additional code and datasets available for download on the web.

ISBN 1584888407   Pages 368 ISBN13 9781584888406 (What's this?)   Volumes 1 Publisher Taylor & Francis Inc   Weight (grammes) 635 Imprint Chapman & Hall/CRC   Series ISSN 20 Format Hardback   Series title Chapman & Hall/CRC Interdisciplinary Statistics Series Publication date 04 Aug 2008   Height (mm) 235 Library of Congress 2008022718   Width (mm) 156 DEWEY 614.42   Spine width (mm) 23 DEWEY edition DC22   Academic level Undergraduate, Postgraduate

		List of Tables
I		Background		1
1		Introduction		3
2		Bayesian Inference and Modeling		19
3		Computational Issues		35
4		Residuals and Goodness-of-Fit		55
II		Themes		71
5		Disease Map Reconstruction and Relative Risk Estimation		73
6		Disease Cluster Detection		119
7		Ecological Analysis		151
8		Multiple Scale Analysis		185
9		Multivariate Disease Analysis		201
10		Spatial Survival and Longitudinal Analysis		227
11		Spatiotemporal Disease Mapping		255
A		Basic Rand WinBUGS		283
B		Selected WinBUGS Code		307
C		R Code for Thematic Mapping		319
		References		321
		Index		339

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

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