Bayesian multivariate disease mapping and ecological regression with errors in covariates: Bayesian estimation of DALYs and ‘preventable’ DALYs

2009 ◽  
Vol 28 (9) ◽  
pp. 1369-1385 ◽  
Author(s):  
Ying C. MacNab
Keyword(s):  
Bayesian Estimation ◽  
Disease Mapping ◽  
Ecological Regression ◽  
Multivariate Disease Mapping

scholarly journals On the convenience of heteroscedasticity in highly multivariate disease mapping

Test ◽  
2019 ◽  
Vol 28 (4) ◽  
pp. 1229-1250 ◽  
Author(s):  
F. Corpas-Burgos ◽  
P. Botella-Rocamora ◽  
M. A. Martinez-Beneito
Keyword(s):  
Disease Mapping ◽  
Multivariate Disease Mapping

A unifying modeling framework for highly multivariate disease mapping

2015 ◽  
Vol 34 (9) ◽  
pp. 1548-1559 ◽  
Author(s):  
P. Botella-Rocamora ◽  
M.A. Martinez-Beneito ◽  
S. Banerjee
Keyword(s):  
Disease Mapping ◽  
Modeling Framework ◽  
Multivariate Disease Mapping

On Gaussian Markov random fields and Bayesian disease mapping

2010 ◽  
Vol 20 (1) ◽  
pp. 49-68 ◽  
Author(s):  
Ying C MacNab
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Spatial Interaction ◽  
Spatial Regression ◽  
Disease Mapping ◽  
Model Framework ◽  
Gaussian Markov Random Fields ◽  
Markov Random ◽  
Mapping Models ◽  
Multivariate 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.

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