ArticleStatistical methods in medical research2012
A Bayesian latent model with spatio-temporally varying coefficients in low birth weight incidence data.
Article in Statistical methods in medical research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Bayesian spatially-clustered coefficient model with temporal structures for hepatitis A data in South Korea.Journal of applied statistics · 2026Article
- Looking at the big picture: demonstrating benefits of Bayesian latent cluster spatio-temporal analysis for understanding maternal prescription opioids misuse.BMC public health · 2025Article
- Exploring the risk factors of COVID-19 Delta variant in the United States based on Bayesian spatio-temporal analysis.Transboundary and emerging diseases · 2022Article
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6 authors.
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Abstract
In spatial epidemiology studies, the effects of covariates on adverse health outcomes could vary over space and time so examining the spatio-temporally varying effects is useful. In particular, the association between covariates and health outcomes could have locally different temporal patterns. In this article, we develop a Bayesian spatio-temporal latent model to identify spatial clusters in each of which covariate effects have homogeneous temporal patterns as well as estimate heterogeneous temporal effects of covariates depending on spatial groups. We compare the proposed model to several alternative models to assess the performance of the proposed model in terms of a range of model assessment measures. Low birth weight incidence data in Georgia for the years 1997-2006 are used.
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