Disease mapping with WinBUGS and MLwiN
著者
書誌事項
Disease mapping with WinBUGS and MLwiN
(Statistics in practice)
Wiley, c2003
大学図書館所蔵 全10件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliography (p. 267-273) and index
内容説明・目次
内容説明
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.
目次
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 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.
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