Disease Mapping with WinBUGS and MLwiN

Disease mapping involves the analysis of geo-referenced diseaseincidence data and has many applications, for example withinresource allocation, cluster alarm analysis, and ecologicalstudies. There is a real need amongst public health workers forsimpler and more efficient tools for the analysis of geo-referenceddisease incidence data. Bayesian and multilevel methods provide therequired efficiency, and with the emergence of software packages- such as WinBUGS and MLwiN - are now easy to implementin practice.* Provides an introduction to Bayesian and multilevel modellingin disease mapping.* Adopts a practical approach, with many detailed workedexamples.* Includes introductory material on WinBUGS and MLwiN.* Discusses three applications in detail - relative riskestimation, focused clustering, and ecological analysis.* Suitable for public health workers and epidemiologists with asound statistical knowledge.* Supported by a Website featuring data sets and WinBUGS andMLwiN programs.Disease Mapping with WinBUGS and MLwiN provides apractical introduction to the use of software for disease mappingfor researchers, practitioners and graduate students fromstatistics, public health and epidemiology who analyse diseaseincidence data.

Preface.Notation.0.1 Standard notation for multilevel modelling.0.2 Spatial multiple-membership models and the MMMCnotation.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 healthhazards.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 GeoBUGSformat.Bibliography.Index.