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Statistical Methods in Spatial Epidemiology

ISBN-10: 0471975729

ISBN-13: 9780471975724

Edition: 2001

Authors: Andrew B. Lawson

List price: $140.00
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Spatial epidemiology considers the statistical aspects of disease development not only in relation to time, but in particular to geographical location. It also includes a range of applications for spatial epidemiology in medical statistics.
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Book details

List price: $140.00
Copyright year: 2001
Publisher: John Wiley & Sons, Incorporated
Publication date: 3/23/2001
Binding: Hardcover
Pages: 298
Size: 6.25" wide x 9.00" long x 0.75" tall
Weight: 0.594
Language: English

Preface and Acknowledgements to Second Edition
Preface and Acknowledgements
The Nature of Spatial Epidemiology
Definitions, Terminology and Data Sets
Map Hypotheses and Modelling Approaches
Definitions and Data Examples
Case event data
Count data
Further Definitions
Control events and processes
Census tract information
Clustering definitions
Some Data Examples
Case event examples
Count data examples
Scales of Measurement and Data Availability
Small Scale
Large Scale
Rate Dependence
Data Quality and the Ecological Fallacy
Edge Effects
Geographical Representation and Mapping
Introduction and Definitions
Maps and Mapping
Statistical maps and mapping
Object process mapping
Geostatistical mapping
Statistical Accuracy
Mapping Issues Related to Aggregated Data
Basic Models
Sampling Considerations
Likelihood-Based and Bayesian Approaches
Point Event Models
Point process models and applications
The basic Poisson process model
Hybrid models and regionalisation
Bayesian models and random effects
MAP estimation, empirical Bayes and full Bayesian analysis
Bivariate/multivariate models
Hidden structure and mixture models
Space-time extensions
Count Models
Standard models
Random-effect extensions
Hidden structure and mixture models
Space-time extensions
Exploratory Approaches, Parametric Estimation and Inference
Exploratory Methods
Cartographic issues
Case event mapping
Count mapping
Parameter Estimation
Case event likelihood models
Count event likelihood models
Bayesian models
Residual Diagnostics
Hypothesis Testing
Edge Effects
Edge effects in case events
Edge effects in counts
Edge weighting schemes and MCMC methods
The Tuscany example
Important Problems in Spatial Epidemiology
Small Scale: Disease Clustering
Definition of Clusters and Clustering
Modelling Issues
Hypothesis Tests for Clustering
General non-specific clustering
Specific clustering
Space-Time Clustering
Modelling issues
Hypothesis testing
Clustering Examples
Humberside example
Larynx cancer example
Count data clustering example
Space-time clustering examples
Other Methods Related to Clustering
Small Scale: Putative Sources of Hazard
Study Design
Retrospective and prospective studies
Study region design
Replication and controls
Problems of Inference
Exploratory techniques
Modelling the Hazard Exposure Risk
Models for Case Event Data
Hypothesis tests
Diagnostic techniques
A Case Event Example
Models for Count Data
Hypothesis tests
A Count Data Example
Other Directions
Multiple disease analysis
Space-time modelling
Space-time exploratory analysis
Space-time Bayesian analysis
Large Scale: Disease Mapping
Simple Statistical Representation
Crude rates
Standardised mortality/morbidity ratios, standardisation and relative risk surfaces
Exploratory mapping methods
Basic Models
Likelihood models
Random effects and Bayesian models
Advanced Methods
Non-parametric methods
Incorporating spatially correlated heterogeneity
Case event modelling
Model Variants and Extensions
Semiparametric modelling
Geographically weighted regression
Mixture models
Approximate Methods
Multivariate Methods
Evaluation of Model Performance
Hypothesis Testing in Disease Mapping
First-order effects
Second-order and variance effects
Space-Time Disease Mapping
Spatial Survival and Longitudinal Data
Spatial survival analysis
Spatial longitudinal analysis
Spatial multiple event modelling
Disease Mapping: Case Studies
Eastern Germany
Ohio respiratory cancer
Ecological Analysis and Scale Change
Ecological Analysis: Introduction
Small-Scale Modelling Issues
Hypothesis tests
Ecological aggregation effects
Changes of Scale and MAUP
MAUP: the modifiable areal unit problem
Large-scale issues
A Simple Example: Sudden Infant Death in North Carolina
A Case Study: Malaria and IDDM
Infectious Disease Modelling
General Model Development
Spatial Model Development
Count data
Individual-level data
Modelling Special Cases for Individual-Level Data
Proportional hazards interpretation
Subgroup modifications
Cluster function specification
Survival Analysis with Spatial Dependence
Individual-Level Data Example
Distribution of susceptibles S(x, t)
The spatial distance function h
The function g
Fitting the model
Revised model
Underascertainment and Censoring
Large Scale: Surveillance
Process Control Methodology
Spatio-Temporal Modelling
S-T Monitoring
Fixed spatial and temporal frame
Fixed spatial frame and dynamic temporal frame
Syndromic Surveillance
Multivariate-Multifocus Surveillance
Bayesian Approaches
Bayesian alarm functions, Bayes factors and syndromic analyses
Computational Considerations
Infectious Diseases
Monte Carlo Testing, Parametric Bootstrap and Simulation Envelopes
Nuisance Parameters and Test Statistics
Monte Carlo Tests
Null Hypothesis Simulation
Spatial case
Spatio-temporal case
Parametric Bootstrap
Bayesian spatial models
Spatio-temporal case
Simulation Envelopes
Markov Chain Monte Carlo Methods
Metropolis and Metropolis-Hastings Algorithms
Metropolis algorithm
Metropolis-Hastings extension
The Gibbs sampler
M-H versus Gibbs algorithms
Algorithms and Code
Data Exploration
Likelihood and Bayesian Models
Likelihood Models
Case event data
Count data
Bayesian Hierarchical Models
Case event data
Count data
Space-Time Analysis
Data exploration
Likelihood models
Bayesian models
Infectious disease models
Glossary of Estimators
Case Event Estimators
Tract Count Estimators
Spatial statistical tools
Geographical information systems