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09:39
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
Published on: November 18, 2019
Hierarchical Bayesian spatiotemporal analysis of revascularization odds using smoothing splines
Giovani L Silva1, C B Dean, Théophile Niyonsenga
1Departamento de Matemática-IST, Universidade Técnica de Lisboa, Lisboa, Portugal. gsilva@math.ist.utl.pt
Statistics in Medicine
|October 19, 2007
Summary
Hierarchical Bayesian models were developed for longitudinal, spatially correlated binomial data to identify temporal trends and regional effects. These models provide smoothed maps and assess model fit for health outcomes.
Area of Science:
- Biostatistics
- Spatial Epidemiology
- Longitudinal Data Analysis
Background:
- Longitudinal data with spatial correlation and over-dispersion present challenges for traditional statistical modeling.
- Understanding regional variations in health outcomes requires sophisticated spatiotemporal analyses.
Purpose of the Study:
- To develop hierarchical Bayesian models for over-dispersed longitudinal spatially correlated binomial data.
- To identify temporal trends and regional effects, producing smoothed spatiotemporal maps.
- To assess model sensitivity to prior assumptions and compare goodness-of-fit mechanisms.
Main Methods:
- Hierarchical Bayesian modeling incorporating random effects for regional correlation.
- Application of smoothing splines for flexible modeling of spatiotemporal odds.
- Markov chain Monte Carlo (MCMC) inference for model fitting.
- Sensitivity analyses and goodness-of-fit assessments.
Main Results:
- The proposed models effectively capture temporal trends and regional variations in binomial data.
- Smoothed maps illustrating regional effects and spatiotemporal patterns were generated.
- The study evaluated the impact of prior assumptions and compared different methods for assessing model fit.
Conclusions:
- Hierarchical Bayesian spline models offer a flexible framework for analyzing complex longitudinal, spatially correlated binomial data.
- These methods are valuable for identifying spatiotemporal patterns in health outcomes, such as revascularization odds.
- The developed approach aids in understanding regional disparities and informing public health strategies.
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