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基本説明
The new edition includes new chapters on putative health hazard modeling and spatial and spatio-temporal surveillance, as well as many other changes and additions.
Full Description
Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets.
New to the Second Edition
Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance
Expanded material on case event modeling and spatiotemporal analysis
New and updated examples
Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models
In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.
Contents
BACKGROUND
Introduction
Datasets
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
Perfect Sampling
Posterior and Likelihood Approximations
Exercises
Residuals and Goodness-of-Fit
Model GOF Measures
General Residuals
Bayesian Residuals
Predictive Residuals and the Bootstrap
Interpretation of Residuals in a Bayesian Setting
Pseudo Bayes Factors and Marginal Predictive Likelihood
Other Diagnostics
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
Regression and Ecological Analysis
Basic Regression Modeling
Missing Data
Non-Linear Predictors
Confounding and Multi-Colinearity
Geographically Dependent Regression
Variable Selection
Ecological Analysis: The General Case of Regression
Biases and Misclassification Error
Putative Hazard Modeling
Case Event Data
Aggregated Count Data
Spatiotemporal Effects
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
Disease Map Surveillance
Surveillance Concepts
Temporal Surveillance
Spatial and Spatiotemporal Surveillance
Appendices
