Flexible Bayesian hierarchical spatial modelling in disease mapping.
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Date
2022
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Abstract
The Gaussian Intrinsic Conditional Autoregressive (ICAR) spatial model, which usually
has two components, namely an ICAR for spatial smoothing and standard random
effects for non-spatial heterogeneity, is used to estimate spatial distributions of
disease risks. The normality assumption in this model may not always be correct and
misspecification of the distribution of random effects could result in biased estimation
of the spatial distribution of disease risk, which could lead to misleading conclusions
and policy recommendations. Limited research studies have been done where the estimation
of the spatial distributions of diseases under the ICAR-normal model were
compared to those obtained from fitting ICAR-nonnormal model. The results from
these studies indicated that the ICAR-nonnormal models performed better than the
ICAR-normal in terms of accuracy, efficiency and predictive capacity. However, these
efforts have not fully addressed the effect on the estimation of spatial distributions
under flexible specification of ICAR models in disease mapping.
The overall aim of this PhD thesis is to develop approaches that relax the normality
assumption that is often used in modeling and fitting of ICAR models in the estimation
of spatial patterns of diseases. In particular, the thesis considers the skew-normal
and skew-Laplace distributions under the univariate, and skew-normal for the multivariate
specifications to estimate the spatial distributions of either univariable or
multivariable areal data. The thesis also considers non-parametric specification of the
multivariate spatial effects in the ICAR model, which is a novel extension of an earlier
work. The estimation of the models was done using Bayesian statistical approaches.
The performances of our suggested alternatives to the ICAR-normal model are evaluated
by simulating studies as well as with practical application to the estimation of
district-level distribution of HIV prevalence and treatment coverage using health survey
data in South Africa. Results from the simulation studies and analysis of real data
demonstrated that our approaches performed better in the prediction of spatial distributions
for univariable and multivariable areal data in disease mapping approaches.
This PhD work shows the limitations of relying on the ICAR-normal model for the
estimations of spatial distributions for all spatial analyses, even when the data could
be asymmetric and non-normal. In such scenarios, skewed-ICAR and nonparametric
ICAR approaches could provide better and unbiased estimation of the spatial pattern
of diseases.
Description
Doctoral Degree. University of KwaZulu-Natal, Durban.