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| Prospektiivinen annos-vastesuhdeanalyysi× | Prospektiivinen kohorttitutkimus× | |
|---|---|---|
| Tieteenala | Epidemiologia | Epidemiologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1965 (Hill's criteria); widely applied through 1980s–present | 1950s (systematic application); conceptual roots earlier |
| Kehittäjä≠ | Bradford Hill (causal criteria including dose-response, 1965); formalized in modern epidemiology by Rothman, Greenland and others | Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks |
| Tyyppi≠ | Analytical epidemiological study design | Observational longitudinal study design |
| Alkuperäislähde | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Rinnakkaisnimet | prospective exposure-response analysis, prospective trend analysis, forward-looking dose-response study, prospective gradient analysis | longitudinal cohort study, prospective follow-up study, incidence study, prospective observational cohort |
| Liittyvät≠ | 4 | 6 |
| Tiivistelmä≠ | Prospective dose-response analysis is an epidemiological approach that measures exposure levels in a defined population before outcomes occur, then quantifies how the risk or magnitude of an outcome changes systematically as exposure increases. By collecting exposure data prospectively, researchers can establish temporal sequence, reduce recall bias, and assess whether a biological gradient — one of Hill's classic causal criteria — exists between the agent of interest and a health outcome. | A prospective cohort study assembles a group of participants who are free of the outcome of interest at baseline, measures their exposures, and then follows them forward in time to record who develops the outcome. By collecting exposure data before outcomes occur, it establishes a clear temporal sequence that supports causal inference — a major advantage over retrospective designs. It is the cornerstone observational method in epidemiology and clinical research. |
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