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| Retrospektiivinen selviytymisanalyysi× | Retrospektiivinen tapaus-verrokkitutkimus× | |
|---|---|---|
| Tieteenala | Epidemiologia | Epidemiologia |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1970s–1980s (retrospective variant established) | 1950s–1960s (formal methodology) |
| Kehittäjä≠ | Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s | Jerome Cornfield; formalized by Brian MacMahon and others in mid-20th-century epidemiology |
| Tyyppi≠ | Retrospective observational analytical study | Observational analytical study |
| Alkuperäislähde≠ | Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press. ISBN: 978-1439856789 | Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195029338 |
| Rinnakkaisnimet | historical survival study, retrospective time-to-event analysis, retrospective follow-up survival study, archival survival analysis | case-control study, retrospective case-referent study, case-referent design, trohoc study |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | Retrospective survival analysis applies time-to-event statistical methods — most commonly the Kaplan-Meier estimator and Cox proportional hazards regression — to data collected from past records rather than through prospective follow-up. The researcher looks back at medical records, disease registries, or administrative databases to reconstruct each patient's journey from a defined starting point (e.g., diagnosis or surgery) to an outcome of interest (e.g., death, relapse, or hospital readmission), making it a cost-efficient approach for studying prognosis and risk factors when prospective follow-up is not feasible. | 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. |
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