On Gaussian Markov random fields and Bayesian disease mapping.
We discuss the nature of Gaussian Markov random fields (GMRFs) as they are typically formulated via full conditionals, also named conditional autoregressive or CAR formulations, to represent small area relative risks ensemble priors within a Bayesian hierarchical model framework for statistical inference in disease mapping and spatial regression. We present a partial review on GMRF/CAR and multivariate GMRF prior formulations in univariate and multivariate disease mapping models and communicate insights into various prior characteristics for representing disease risks variability and 'spatial interaction.' We also propose convolution prior modifications to the well known BYM model for attainment of identifiability and Bayesian robustness in univariate and multivariate disease mapping and spatial regression. Several illustrative examples of disease mapping and spatial regression are presented.
MacNab
Ying C
YC
Division of Epidemiology and Biostatistics, School of Population and Public Health, University of British Columbia, Canada. ymacnab@interchange.ubc.ca
D000063:Accidents, Traffic / Q000706:statistics & numerical data; D000293:Adolescent; D001499:Bayes Theorem; D001955:British Columbia / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D015999:Multivariate Analysis; D016011:Normal Distribution; D012044:Regression Analysis; D018570:Risk Assessment / Q000706:statistics & numerical data; D017062:Small-Area Analysis; D055815:Young Adult