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 a such as WinBUGS and MLwiN a 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 a 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.New York: John Wiley aamp; Sons, Inc. Robert, C. P. and G. Casella (1999). Monte Carlo ... Bayesian computation via the Gibbs Sampler and related Markov chain Monte Carlo methods. Journal of the Royal ... WinBugs User Manual. Version 1.4.
|Title||:||Disease Mapping with WinBUGS and MLwiN|
|Author||:||Andrew B. Lawson, William J. Browne, Carmen L. Vidal Rodeiro|
|Publisher||:||John Wiley & Sons - 2003-10-31|