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ベイジアン生態学的研究×生態学的研究×
分野疫学疫学
系統Process / pipelineProcess / pipeline
提唱年1991–2000s (Besag 1991 for spatial priors; Lawson 2001 for disease mapping framework)19th century (Snow 1854); formalised mid-20th century
提唱者Andrew Lawson; Julian Besag (spatial Bayesian foundations)Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues
種類Observational epidemiological design with Bayesian statistical frameworkObservational epidemiological study
原典Lawson, A. B. (2013). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (2nd ed.). CRC Press. ISBN: 978-1466504813Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81. DOI ↗
別名Bayesian ecological analysis, Bayesian disease mapping, Bayesian ecological regression, Bayesian spatial ecological studyaggregate study, correlational study, ecological correlation study, population-level study
関連35
概要A Bayesian ecological study combines the group-level observational design of classical ecological epidemiology with Bayesian hierarchical modelling. Rather than treating disease rates as fixed quantities, it places prior distributions over latent spatial or temporal effects — commonly using the Besag-York-Mollié (BYM) convolution prior — and updates beliefs from aggregate data to produce posterior maps of disease risk, smoothed rate estimates, and credible intervals for ecological associations between exposures and outcomes.An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or evaluate hypotheses about population-level associations between risk factors and disease.
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ScholarGate手法を比較: Bayesian Ecological Study · Ecological Study. 2026-06-15に以下より取得 https://scholargate.app/ja/compare