630 research outputs found
Bayesian hierarchical statistical SIRS models
The classic SIR (susceptible-infectious-recovered) model, has been used extensively to study the dynamical evolution of an infectious disease in a large population. The SIR-susceptible (SIRS) model is an extension of the SIR model to allow modeling imperfect immunity (those who have recovered might become susceptible again). SIR(S) models assume observed counts are “mass balanced.” Here, mass balance means that total count equals the sum of counts of the individual components of the model. However, since the observed counts have errors, we propose a model that assigns the mass balance to the hidden process of a (Bayesian) hierarchical SIRS (HSIRS) model. Another challenge is to capture the stochastic or random nature of an epidemic process in a SIRS. The HSIRS model accomplishes this through modeling the dynam- ical evolution on a transformed scale. Through simulation, we compare the HSIRS model to the classic SIRS (CSIRS) model, a model where it is assumed that the observed counts are mass balanced and the dynamical evolution is deterministic
General Design Bayesian Generalized Linear Mixed Models
Linear mixed models are able to handle an extraordinary range of
complications in regression-type analyses. Their most common use is to account
for within-subject correlation in longitudinal data analysis. They are also the
standard vehicle for smoothing spatial count data. However, when treated in
full generality, mixed models can also handle spline-type smoothing and closely
approximate kriging. This allows for nonparametric regression models (e.g.,
additive models and varying coefficient models) to be handled within the mixed
model framework. The key is to allow the random effects design matrix to have
general structure; hence our label general design. For continuous response
data, particularly when Gaussianity of the response is reasonably assumed,
computation is now quite mature and supported by the R, SAS and S-PLUS
packages. Such is not the case for binary and count responses, where
generalized linear mixed models (GLMMs) are required, but are hindered by the
presence of intractable multivariate integrals. Software known to us supports
special cases of the GLMM (e.g., PROC NLMIXED in SAS or glmmML in R) or relies
on the sometimes crude Laplace-type approximation of integrals (e.g., the SAS
macro glimmix or glmmPQL in R). This paper describes the fitting of general
design generalized linear mixed models. A Bayesian approach is taken and Markov
chain Monte Carlo (MCMC) is used for estimation and inference. In this
generalized setting, MCMC requires sampling from nonstandard distributions. In
this article, we demonstrate that the MCMC package WinBUGS facilitates sound
fitting of general design Bayesian generalized linear mixed models in practice.Comment: Published at http://dx.doi.org/10.1214/088342306000000015 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Joint disease mapping using six cancers in the Yorkshire region of England
OBJECTIVES:
The aims of this study were to model jointly the incidence rates of six smoking related cancers in the Yorkshire region of England, to explore the patterns of spatial correlation amongst them, and to estimate the relative weight of smoking and other shared risk factors for the relevant disease sites, both before and after adjustment for socioeconomic background (SEB).
METHODS:
Data on the incidence of oesophagus, stomach, pancreas, lung, kidney, and bladder cancers between 1983 and 2003 were extracted from the Northern & Yorkshire Cancer Registry database for the 532 electoral wards in the Yorkshire region. Using postcode of residence, each case was assigned an area-based measure of SEB using the Townsend index. Standardised incidence ratios (SIRs) were calculated for each cancer site and their correlations investigated. The joint analysis of the spatial variation in incidence used a Bayesian shared-component model. Three components were included to represent differences in smoking (for all six sites), bodyweight/obesity (for oesophagus, pancreas and kidney cancers) and diet/alcohol consumption (for oesophagus and stomach cancers).
RESULTS:
The incidence of cancers of the oesophagus, pancreas, kidney, and bladder was relatively evenly distributed across the region. The incidence of stomach and lung cancers was more clustered around the urban areas in the south of the region, and these two cancers were significantly associated with higher levels of area deprivation. The incidence of lung cancer was most impacted by adjustment for SEB, with the rural/urban split becoming less apparent. The component representing smoking had a larger effect on cancer incidence in the eastern part of the region. The effects of the other two components were small and disappeared after adjustment for SEB.
CONCLUSIONS:
This study demonstrates the feasibility of joint disease modelling using data from six cancer sites. Incidence estimates are more precise than those obtained without smoothing. This methodology may be an important tool to help authorities evaluate healthcare system performance and the impact of policies
Restricted Covariance Priors with Applications in Spatial Statistics
We present a Bayesian model for area-level count data that uses Gaussian
random effects with a novel type of G-Wishart prior on the inverse
variance--covariance matrix. Specifically, we introduce a new distribution
called the truncated G-Wishart distribution that has support over precision
matrices that lead to positive associations between the random effects of
neighboring regions while preserving conditional independence of
non-neighboring regions. We describe Markov chain Monte Carlo sampling
algorithms for the truncated G-Wishart prior in a disease mapping context and
compare our results to Bayesian hierarchical models based on intrinsic
autoregression priors. A simulation study illustrates that using the truncated
G-Wishart prior improves over the intrinsic autoregressive priors when there
are discontinuities in the disease risk surface. The new model is applied to an
analysis of cancer incidence data in Washington State.Comment: Published at http://dx.doi.org/10.1214/14-BA927 in the Bayesian
Analysis (http://projecteuclid.org/euclid.ba) by the International Society of
Bayesian Analysis (http://bayesian.org/
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
Feasibility and utility of mapping disease risk at the neighbourhood level within a Canadian public health unit: an ecological study
<p>Abstract</p> <p>Background</p> <p>We conducted spatial analyses to determine the geographic variation of cancer at the neighbourhood level (dissemination areas or DAs) within the area of a single Ontario public health unit, Wellington-Dufferin-Guelph, covering a population of 238,326 inhabitants. Cancer incidence data between 1999 and 2003 were obtained from the Ontario Cancer Registry and were geocoded down to the level of DA using the enhanced Postal Code Conversion File. The 2001 Census of Canada provided information on the size and age-sex structure of the population at the DA level, in addition to information about selected census covariates, such as average neighbourhood income.</p> <p>Results</p> <p>Age standardized incidence ratios for cancer and the prevalence of census covariates were calculated for each of 331 dissemination areas in Wellington-Dufferin-Guelph. The standardized incidence ratios (SIR) for cancer varied dramatically across the dissemination areas. However, application of the Moran's I statistic, a popular index of spatial autocorrelation, suggested significant spatial patterns for only two cancers, lung and prostate, both in males (p < 0.001 and p = 0.002, respectively). Employing Bayesian hierarchical models, areas in the urban core of the City of Guelph had significantly higher SIRs for male lung cancer than the remainder of Wellington-Dufferin-Guelph; and, neighbourhoods in the urban and surrounding rural areas of Orangeville exhibited significantly higher SIRs for prostate cancer. After adjustment for age and spatial dependence, average household income attenuated much of the spatial pattern of lung cancer, but not of prostate cancer.</p> <p>Conclusion</p> <p>This paper demonstrates the feasibility and utility of a systematic approach to identifying neighbourhoods, within the area served by a public health unit, that have significantly higher risks of cancer. This exploratory, ecologic study suggests several hypotheses for these spatial patterns that warrant further investigations. To the best of our knowledge, this is the first Canadian study published in the peer-reviewed literature estimating the risk of relatively rare public health outcomes at a very small areal level, namely dissemination areas.</p
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