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| Retrospektiivinen tapausselostus× | Retrospektiivinen tapaus-verrokkitutkimus× | |
|---|---|---|
| Tieteenala | Epidemiologia | Epidemiologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | Long-standing practice; codified in EBM frameworks during 1990s–2000s | 1950s–1960s (formal methodology) |
| Kehittäjä≠ | Clinical medicine tradition (no single originator); formalized in evidence-based medicine literature | Jerome Cornfield; formalized by Brian MacMahon and others in mid-20th-century epidemiology |
| Tyyppi≠ | Observational descriptive study design | Observational analytical study |
| Alkuperäislähde≠ | Kooistra, B., Dijkman, B., Einhorn, T. A., & Bhandari, M. (2009). How to design a good case series. Journal of Bone and Joint Surgery, 91(Suppl 3), 21–26. DOI ↗ | Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195029338 |
| Rinnakkaisnimet | retrospective case series, chart review case series, historical case series, medical records case series | case-control study, retrospective case-referent study, case-referent design, trohoc study |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | A retrospective case series is an observational study that systematically describes the clinical features, treatments, and outcomes of a defined group of patients by examining pre-existing medical records or administrative data. It looks backward in time — data have already been recorded before the study begins. With no control group, no randomization, and no prospective follow-up, it sits near the base of the evidence hierarchy but remains one of the most practical and frequently published study designs in clinical medicine. | A retrospective case-control study identifies individuals who already have an outcome of interest (cases) and a comparable group without it (controls), then looks backward in time using existing records to determine prior exposure to a suspected risk factor. The primary measure of association is the odds ratio. This design is especially efficient for studying rare diseases or outcomes with long latency periods, since the outcome has already occurred before the study begins. |
| ScholarGateAineisto ↗ |
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