ArticleEnvironmetrics2017
Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.
Article in Environmetrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of Bayesian spatial-temporal models on cancer incidence and mortality.International journal of public health · 2020Pooled it
- Spatial Patterns and Epidemiological Drivers of Foot-and-Mouth Disease Outbreaks in Uganda.Transboundary and emerging diseases · 2026Article
- A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.Spatial and spatio-temporal epidemiology · 2023Article
- Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity.Spatial statistics · 2022Article
- Spatial Analysis of Shared Risk Factors between Pleural and Ovarian Cancer Mortality in Lombardy (Italy).International journal of environmental research and public health · 2022Article
- Estimating the burden of the opioid epidemic for adults and adolescents in Ohio counties.Biometrics · 2021Article
- Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping.Biostatistics (Oxford, England) · 2019Article
- Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All.Clinical colorectal cancer · 2019Article
- Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.International journal of environmental research and public health · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.