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Disease Mapping with WinBUGS & MLwiN de AB Lawson

Disease Mapping with WinBUGS & MLwiN: Statistics in Practice

Autor AB Lawson
en Limba Engleză Hardback – 7 aug 2003
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.

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Specificații

ISBN-13: 9780470856048
ISBN-10: 0470856041
Pagini: 296
Dimensiuni: 165 x 241 x 21 mm
Greutate: 0.54 kg
Ediția:New.
Editura: Wiley
Seria Statistics in Practice

Locul publicării:Chichester, United Kingdom

Public țintă

An essential resource for researchers, practitioners and graduate students from statistics, public health and epidemiology with a sound statistical knowledge.

Notă biografică

Descriere

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.