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Bayesian latent modeling of spatio‐temporal variation in small‐area health data

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The analysis of small‐area health data is often a focus of epidemiological research. While it is possible to provide smoothed risk estimates for disease maps, it is often important to consider underlying structure in the risk outcome that is suggested by understanding of the etiology of disease processes. To address this more complex problem, it can be important to consider latent structure in the disease risk. Latent structure can take a variety of forms, from basic random effects to more complex latent variables models. In this paper I will review outline the basic approaches to the problem of latent structure for spatio‐temporal health outcome data and the solutions that Bayesian modeling can offer.

This article is categorized under:

  • Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting
  • Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods
  • Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
  • Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)
Respiratory cancer SIRs for the 88 counties in the state of Ohio for the period 1979–1988
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Respiratory cancer in Ohio counties: Box plots of posterior mean temporal trend
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Respiratory cancer in Ohio counties: Correlated noise posterior mean field
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Respiratory cancer in Ohio counties: Uncorrelated noise posterior mean field
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Low birth weight incidence in the counties of Georgia and South Carolina for the period 1997–2006
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Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting
Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods
Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)
Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory

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