手法を比較

	ベイジアン生態学的研究	生態学的研究	多層レベルモデリング
分野	疫学	疫学	研究統計
系統	Process / pipeline	Process / pipeline	Process / 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	1992
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	Anthony Bryk and Stephen Raudenbush
種類	Observational epidemiological design with Bayesian statistical framework	Observational epidemiological study	Method
原典	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.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
別名	Bayesian ecological analysis, Bayesian disease mapping, Bayesian ecological regression, Bayesian spatial ecological study	aggregate study, correlational study, ecological correlation study, population-level study	HLM, mixed-effects models, random effects models, MLM
関連	3	5	3
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.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.