04859nam 22007334a 450 991083105790332120230617024602.01-280-27039-X97866102703920-470-34164-50-470-85605-X0-470-85606-8(CKB)111087027100668(EBL)164843(OCoLC)475873648(SSID)ssj0000139885(PQKBManifestationID)11151259(PQKBTitleCode)TC0000139885(PQKBWorkID)10028525(PQKB)11769699(MiAaPQ)EBC164843(EXLCZ)9911108702710066820030604d2003 uy 0engur|n|---|||||txtccrDisease mapping with WinBUGS and MLwiN[electronic resource] /Andrew B. Lawson, William J. Browne, Carmen L. Vidal RodeiroChichester, West Sussex, England ;Hoboken, NJ J. Wileyc20031 online resource (293 p.)Statistics in practiceDescription based upon print version of record.0-470-85604-1 Includes bibliographical references (p. 267-273) and index.Disease Mapping with WinBUGS and MLwiN; 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 modelling3.1 Continuous response models3.2 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 WinBUGS6.2 Spatial prediction6.3 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 WinBUGS9.2 Survival analysis in MLwiN10 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; IndexDisease 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 mStatistics in practice.Medical mappingMedical geographyMapsData processingEpidemiologyStatistical methodsEpidemiologyData processingPublic health surveillanceMedical mapping.Medical geographyMapsData processing.EpidemiologyStatistical methods.EpidemiologyData processing.Public health surveillance.614614.4202855369615.4/2/0727Lawson Andrew(Andrew B.)149118Browne William J(William John),1972-1667885Vidal Rodeiro Carmen L1667886MiAaPQMiAaPQMiAaPQBOOK9910831057903321Disease mapping with WinBUGS and MLwiN4028059UNINA