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| Badanie ekologiczne z uwzględnieniem ryzyka× | Badanie kohortowe z korektą ryzyka× | |
|---|---|---|
| Dziedzina | Epidemiologia | Epidemiologia |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1980s–1990s | Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s) |
| Twórca≠ | 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 |
| Typ≠ | Observational ecological design with statistical confounding control | Observational epidemiological study design with statistical confounding control |
| Źródło pierwotne≠ | Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Inne nazwy | 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 |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | 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. |
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