Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection

This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model's ability in relation to cluster detection. To pursue this evaluatio...

Full description

Saved in:
Bibliographic Details
Published in:Environmental and ecological statistics 2010-03, Vol.17 (1), p.73-95
Main Authors: Hossain, Md. Monir, Lawson, Andrew B
Format: Article
Language:English
Subjects:
Biomedical and Life Sciences
Cancer
Chemistry and Earth Sciences
Cluster
Computer Science
Datasets
Diagnostic
Disease
Ecology
Epidemiology
Health hazards
Health risks
Health Sciences
Life Sciences
Local likelihood
Math. Appl. in Environmental Science
MCMC
Medicine
Mixture
Mortality
Physics
Public health
Q1
Q3
Risk assessment
Space-time
Spacetime
Statistics for Engineering
Statistics for Life Sciences
Studies
Theoretical Ecology/Statistics
Throat cancer
USA, Georgia
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model's ability in relation to cluster detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. The proposed criteria are based on posterior estimates (e.g., misclassification rate) and some are based on post-hoc analysis of posterior samples (e.g., exceedance probability). In addition, we examine more conventional model fit criteria including mean square error (MSE). We illustrate the methodology with a real ST dataset, Georgia throat cancer mortality data for the years 1994-2005, and a simulated dataset where different levels and shapes of clusters are embedded. Overall, it is found that conventional SREST models fair well in ST cluster detection and in goodness-of-fit, while for extreme risk detection the local likelihood ST model does best.
ISSN:1352-8505
1573-3009
1352-8505
DOI:10.1007/s10651-008-0102-z