Bayesian disease mapping : hierarchical modeling in spatial epidemiology

著者

• Lawson, Andrew (Andrew B.)

書誌事項

Bayesian disease mapping : hierarchical modeling in spatial epidemiology

Andrew B. Lawson

（Interdisciplinary statistics）

CRC Press, c2009

注記

"A Chapman & Hall book."

Includes bibliographical references (p. 321-338) and index

内容説明・目次

内容説明

Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the Metropolis-Hastings algorithm, goodness-of-fit measures, and residual diagnostics. It also focuses on special topics, such as cluster detection; space-time modeling; and multivariate, survival, and longitudinal analyses. The author explains how to apply these methods to disease mapping using numerous real-world data sets pertaining to cancer, asthma, epilepsy, foot and mouth disease, influenza, and other diseases. In the appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation. Applying Bayesian methods to the modeling of georeferenced health data, Bayesian Disease Mapping proves that the application of these approaches to biostatistical problems can yield important insights into data.

目次

BACKGROUND Introduction Data Sets Bayesian Inference and Modeling Likelihood Models Prior Distributions Posterior Distributions Predictive Distributions Bayesian Hierarchical Modeling Hierarchical Models Posterior Inference Exercises Computational Issues Posterior Sampling Markov Chain Monte Carlo Methods Metropolis and Metropolis-Hastings Algorithms Gibbs Sampling Perfect Sampling Posterior and Likelihood Approximations Exercises Residuals and Goodness-of-Fit Model Goodness-of-Fit Measures General Residuals Bayesian Residuals Predictive Residuals and the Bootstrap Interpretation of Residuals in a Bayesian Setting Exceedence Probabilities Exercises THEMES Disease Map Reconstruction and Relative Risk Estimation An Introduction to Case Event and Count Likelihoods Specification of the Predictor in Case Event and Count Models Simple Case and Count Data Models with Uncorrelated Random Effects Correlated Heterogeneity Models Convolution Models Model Comparison and Goodness-of-Fit Diagnostics Alternative Risk Models Edge Effects Exercises Disease Cluster Detection Cluster Definitions Cluster Detection using Residuals Cluster Detection using Posterior Measures Cluster Models Edge Detection and Wombling Ecological Analysis General Case of Regression Biases and Misclassification Error Putative Hazard Models Multiple Scale Analysis Modifiable Areal Unit Problem (MAUP) Misaligned Data Problem (MIDP) Multivariate Disease Analysis Notation for Multivariate Analysis Two Diseases Multiple Diseases Spatial Survival and Longitudinal Analyses General Issues Spatial Survival Analysis Spatial Longitudinal Analysis Extensions to Repeated Events Spatiotemporal Disease Mapping Case Event Data Count Data Alternative Models Infectious Diseases Appendix A: Basic R and WinBUGS Appendix B: Selected WinBUGS Code Appendix C: R Code for Thematic Mapping References Index

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA8798961X
• ISBN:
  • 9781584888406
• LCCN: 2008022718
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Boca Raton
• ページ数/冊数: xvii, 344 p.
• 大きさ: 25 cm
• 分類:
  • LCC : RA792.5
  • NLM : 2008 L-045
  • NLM : WA 950
  • DC22 : 614.4/2
• 件名:
  • LCSH : Medical mapping
  • LCSH : Epidemiology -- Statistical methods
  • LCSH : Bayesian statistical decision theory
  • MESH : Epidemiologic Methods
  • MESH : Bayes Theorem
  • MESH : Statistics as Topic
  • MESH : Topography, Medical -- methods
• 親書誌ID:
  • BA28817087