76,760 research outputs found
Cluster detection and risk estimation for spatio-temporal health data
In epidemiological disease mapping one aims to estimate the spatio-temporal
pattern in disease risk and identify high-risk clusters, allowing health
interventions to be appropriately targeted. Bayesian spatio-temporal models are
used to estimate smoothed risk surfaces, but this is contrary to the aim of
identifying groups of areal units that exhibit elevated risks compared with
their neighbours. Therefore, in this paper we propose a new Bayesian
hierarchical modelling approach for simultaneously estimating disease risk and
identifying high-risk clusters in space and time. Inference for this model is
based on Markov chain Monte Carlo simulation, using the freely available R
package CARBayesST that has been developed in conjunction with this paper. Our
methodology is motivated by two case studies, the first of which assesses if
there is a relationship between Public health Districts and colon cancer
clusters in Georgia, while the second looks at the impact of the smoking ban in
public places in England on cardiovascular disease clusters
Spatial clustering of average risks and risk trends in Bayesian disease mapping
Spatiotemporal disease mapping focuses on estimating the spatial pattern in disease risk across a set of nonoverlapping areal units over a fixed period of time. The key aim of such research is to identify areas that have a high average level of disease risk or where disease risk is increasing over time, thus allowing public health interventions to be focused on these areas. Such aims are well suited to the statistical approach of clustering, and while much research has been done in this area in a purely spatial setting, only a handful of approaches have focused on spatiotemporal clustering of disease risk. Therefore, this paper outlines a new modeling approach for clustering spatiotemporal disease risk data, by clustering areas based on both their mean risk levels and the behavior of their temporal trends. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland
Effect fusion using model-based clustering
In social and economic studies many of the collected variables are measured
on a nominal scale, often with a large number of categories. The definition of
categories is usually not unambiguous and different classification schemes
using either a finer or a coarser grid are possible. Categorisation has an
impact when such a variable is included as covariate in a regression model: a
too fine grid will result in imprecise estimates of the corresponding effects,
whereas with a too coarse grid important effects will be missed, resulting in
biased effect estimates and poor predictive performance.
To achieve automatic grouping of levels with essentially the same effect, we
adopt a Bayesian approach and specify the prior on the level effects as a
location mixture of spiky normal components. Fusion of level effects is induced
by a prior on the mixture weights which encourages empty components.
Model-based clustering of the effects during MCMC sampling allows to
simultaneously detect categories which have essentially the same effect size
and identify variables with no effect at all. The properties of this approach
are investigated in simulation studies. Finally, the method is applied to
analyse effects of high-dimensional categorical predictors on income in
Austria
A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer
A common characteristic of environmental epidemiology is the multi-dimensional aspect of exposure patterns, frequently reduced to a cumulative exposure for simplicity of analysis. By adopting a flexible Bayesian clustering approach, we explore the risk function linking exposure history to disease. This approach is applied here to study the relationship between different smoking characteristics and lung cancer in the framework of a population based case control study
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