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| 贝叶斯生态学研究× | 生态学研究× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1991–2000s (Besag 1991 for spatial priors; Lawson 2001 for disease mapping framework) | 19th century (Snow 1854); formalised mid-20th century |
| 提出者≠ | Andrew Lawson; Julian Besag (spatial Bayesian foundations) | Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues |
| 类型≠ | Observational epidemiological design with Bayesian statistical framework | Observational epidemiological study |
| 开创性文献≠ | Lawson, A. B. (2013). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (2nd ed.). CRC Press. ISBN: 978-1466504813 | Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81. DOI ↗ |
| 别名 | Bayesian ecological analysis, Bayesian disease mapping, Bayesian ecological regression, Bayesian spatial ecological study | aggregate study, correlational study, ecological correlation study, population-level study |
| 相关≠ | 3 | 5 |
| 摘要≠ | 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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