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Statistics in Practice Ser.: Disease Mapping with WinBUGS and MLwiN by Andrew B. Lawson, William J. Browne and Carmen L. Vidal Rodeiro (2003, Hardcover)

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DISEASE MAPPING WITH WINBUGS AND MLWIN By Andrew B. Lawson & William J. Browne & Vidal Carmen L. Rodeiro - Hardcover **BRAND NEW**.

About this product

Product Identifiers

PublisherWiley & Sons, Incorporated, John

ISBN-100470856041

ISBN-139780470856048

eBay Product ID (ePID)5921573

Product Key Features

Number of Pages292 Pages

Publication NameDisease Mapping with Winbugs and Mlwin

LanguageEnglish

Publication Year2003

SubjectPathology, Public Health, Probability & Statistics / General, Epidemiology

TypeTextbook

Subject AreaMathematics, Medical

AuthorAndrew B. Lawson, William J. Browne, Carmen L. Vidal Rodeiro

SeriesStatistics in Practice Ser.

FormatHardcover

Dimensions

Item Height0.9 in

Item Weight20 Oz

Item Length9.1 in

Item Width6.4 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2003-053782

Reviews"...written at a level that will be readily accessible to anyone with a modest background in applied statistics." ( Technometrics , February 2005) "The book certainly is a nice addition to my disease mapping books. The book is equally useful for the undergraduate and graduate students as well as public health professionals. ( E-STREAMS , July 2004) "...a good guide and a useful addition for any graduate statistician or epidemiologist..." ( Statistical Methods in Medical Research , No.13 2004) "...outlines the models used in statistical disease mapping, and gives details of how the models can be implemented using two packages..." ( Short Book Reviews , Vol.24, No.3) "Readers... will greatly profit from this book" (International Society of Clinical Biostatistics Dec 2005), "...written at a level that will be readily accessible to anyone with a modest background in applied statistics." ( Technometrics , February 2005) "The book certainly is a nice addition to my disease mapping books. The book is equally useful for the undergraduate and graduate students as well as public health professionals. ( E-STREAMS , July 2004) "...a good guide and a useful addition for any graduate statistician or epidemiologist..." ( Statistical Methods in Medical Research , No.13 2004) "...outlines the models used in statistical disease mapping, and gives details of how the models can be implemented using two packages..." ( Short Book Reviews , Vol.24, No.3) " ... any person, even just beginning his own research in the matter, will greatly profit from this book." ( International Society of Clinical Biostatistics , December 2005) "Readers... will greatly profit from this book" (International Society of Clinical Biostatistics Dec 2005)

Dewey Edition21

Series Volume Number11

IllustratedYes

Dewey Decimal615.4/2/0727

Table Of ContentPreface. 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.

SynopsisDisease 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., 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., 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.

LC Classification NumberRA792.5.L388 2003