手法を比較

	リスク調整済み生態学的研究	リスク調整コホート研究
分野	疫学	疫学
系統	Process / pipeline	Process / pipeline
提唱年	1980s–1990s	Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s)
提唱者	Extension of ecological study methodology; risk adjustment concepts formalized by Morgenstern (1982) and developed further in health outcomes research	Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century
種類	Observational ecological design with statistical confounding control	Observational epidemiological study design with statistical confounding control
原典	Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344.	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
別名	risk-adjusted ecological analysis, confounder-adjusted ecological study, ecological regression with risk adjustment, adjusted area-level study	adjusted cohort study, covariate-adjusted cohort, risk-controlled prospective study, propensity-adjusted cohort
関連	4	4
概要	A risk-adjusted ecological study is an observational epidemiological design that examines associations between exposures and outcomes measured at the group or area level — such as regions, hospitals, or countries — while statistically controlling for known risk factors also measured at that level. By incorporating risk adjustment through ecological regression or standardization, the design reduces (though cannot eliminate) confounding from group-level variables, enabling more valid comparisons across populations or settings.	A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial.