ArticleStatistics in medicine2017
Cluster detection of spatial regression coefficients.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Assessing and attenuating the impact of selection bias on spatial cluster detection studies.Spatial and spatio-temporal epidemiology · 2024Article
- Article
- A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics.International journal of health geographics · 2022Article
- Exploring the risk factors of COVID-19 Delta variant in the United States based on Bayesian spatio-temporal analysis.Transboundary and emerging diseases · 2022Article
- A Spatiotemporal Analysis of Organ-Specific Lupus Flares in Relation to Atmospheric Variables and Fine Particulate Matter Pollution.Arthritis & rheumatology (Hoboken, N.J.) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying-coefficient regression and spatial clusters in the regression coefficients. For varying-coefficient regression, such as the geographically weighted regression, different regression coefficients are obtained for different spatial units. It is often of interest to the practitioners to identify clusters of spatial units with distinct patterns in a regression coefficient, but there is no formal statistical methodology for that. Rather, cluster identification is often ad-hoc such as by eyeballing the map of fitted regression coefficients and discerning patterns. In this paper, we develop new methodology for spatial cluster detection in the regression setting based on hypotheses testing. We evaluate our methods in terms of power and coverages for true clusters via simulation studies. For illustration, our methodology is applied to a cancer mortality dataset. Copyright © 2016 John Wiley & Sons, Ltd.
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Registered trials
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