A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases

Knorr‐Held, Leonhard; Best, Nicola

doi:10.1111/1467-985x.00187

Cited by 226 publications

(271 citation statements)

References 23 publications

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“…The shared component model can separate the underlying risk surface for each crime into a shared and a crime-specific component [61]. However, when the shared component model is extended to the joint analysis of more than two types of crime, the number of possible permutations of shared and specific components may rapidly become prohibitive [61]. Moreover, the interpretation of a shared component in such cases is more difficult [51].…”

Section: Discussionmentioning

confidence: 99%

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Liu

Zhu

2017

IJGI

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A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

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Section: Discussionmentioning

confidence: 99%

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Liu

Zhu

2017

IJGI

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“…The second framework used joint modeling where there were shared random effects representing some common unmeasured or unknown risk factors among all of the population groups. This joint modeling framework has been used to map one disease for multiple population groups or multiple diseases that have common risk factors (Richardson et al 2006;Knorr-Held and Best 2001;Held et al 2005;Downing et al2008;Tassone et al 2009;Wheeler et al 2008). We compared a total of seven models including two separate models and five joint models.…”

Section: Area-based Ses Measurementioning

confidence: 99%

Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000–2007

Yin

Madden

et al. 2014

J Geogr Syst

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Abstract:Lung cancer is the second most commonly diagnosed cancer in both men and women in Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not been fully studied. Hierarchical Bayesian models are used here to explore the spatio-temporal patterns of lung cancer incidence risk by race and gender in Georgia for the period of 2000-2007. With the census tract level as the spatial scale and the 2-year period aggregation as the temporal scale, we compare a total of seven Bayesian spatio-temporal models including two under a separate modeling framework and five under a joint modeling framework. One joint model outperforms others based on the deviance information criterion. Results show that the northwest region of Georgia has consistently high lung cancer incidence risk for all population groups during the study period. In addition, there are inverse relationships between the socioeconomic status and the lung cancer incidence risk among all Georgian population groups, and the relationships in males are stronger than those in females. By mapping more reliable variations in lung cancer incidence risk at a relatively fine spatio-temporal scale for different Georgian population groups, our study aims to better support healthcare performance assessment, etiological hypothesis generation, and health policy making.

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“…Another meeting on disease clusters and ecological studies included various Bayesian developments [264][265][266]. A read paper showed a very detailed examination of projections of the AIDS epidemic [267], and another used hierarchical modelling to combine evidence on air pollution and daily mortality in the 20 largest U.S. cities [268].…”

Section: -2001mentioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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SUMMARYThis review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

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A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases

Cited by 226 publications

References 23 publications

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000–2007

Bayesian statistics in medicine: a 25 year review

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