Disease mapping with WinBUGS and MLwiN
Author(s)
Bibliographic Information
Disease mapping with WinBUGS and MLwiN
(Statistics in practice)
Wiley, c2003
Available at 10 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliography (p. 267-273) and index
Description and Table of Contents
Description
Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages - such as WinBUGS and MLwiN - are now easy to implement in practice.
Provides an introduction to Bayesian and multilevel modelling in disease mapping.
Adopts a practical approach, with many detailed worked examples.
Includes introductory material on WinBUGS and MLwiN.
Discusses three applications in detail - relative risk estimation, focused clustering, and ecological analysis.
Suitable for public health workers and epidemiologists with a sound statistical knowledge.
Supported by a Website featuring data sets and WinBUGS and MLwiN programs.
Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.
Table of Contents
Preface. Notation.
0.1 Standard notation for multilevel modelling.
0.2 Spatial multiple-membership models and the MMMC notation.
0.3 Standard notation for WinBUGS models.
1. Disease mapping basics.
1.1 Disease mapping and map reconstruction.
1.2 Disease map restoration.
2. Bayesian hierarchical modelling.
2.1 Likelihood and posterior distributions.
2.2 Hierarchical models.
2.3 Posterior inference.
2.4 Markov chain Monte Carlo methods.
2.5 Metropolis and Metropolis-Hastings algorithms.
2.6 Residuals and goodness of fit.
3. Multilevel modelling.
3.1 Continuous response models.
3.2 Estimation procedures for multilevel models.
3.3 Poisson response models.
3.4 Incorporating spatial information.
3.5 Discussion.
4. WinBUGS basics.
4.1 About WinBUGS.
4.2 Start using WinBUGS.
4.3 Specification of the model.
4.4 Model fitting.
4.5 Scripts.
4.6 Checking convergence.
4.7 Spatial modelling: GeoBUGS.
4 .8 Conclusions.
5. MLwiN basics.
5.1 About MLwiN.
5.2 Getting started.
5.3 Fitting statistical models.
5.4 MCMC estimation in MLwiN.
5.5 Spatial modelling.
5.6 Conclusions.
6. Relative risk estimation.
6.1 Relative risk estimation using WinBUGS.
6.2 Spatial prediction.
6.3 An analysis of the Ohio dataset using MLwiN.
7. Focused clustering: the analysis of putative health hazards.
7.1 Introduction.
7.2 Study design.
7.3 Problems of inference.
7.4 Modelling the hazard exposure risk.
7.5 Models for count data.
7.6 Bayesian models.
7.7 Focused clustering in WinBUGS.
7.8 Focused clustering in MLwiN.
8. Ecological analysis.
8.1 Introduction.
8.2 Statistical models.
8.3 WinBUGS analyses of ecological datasets.
8.4 MLwiN analyses of ecological datasets.
9. Spatially-correlated survival analysis.
9.1 Survival analysis in WinBUGS.
9.2 Survival analysis in MLwiN.
10. Epilogue.
Appendix 1: WinBUGS code for focused clustering models.
A.1: Falkirk example.
A.2: Ohio example.
Appendix 2: S-Plus function for conversion to GeoBUGS format.
Bibliography.
Index.
by "Nielsen BookData"