Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Retrospektiv kohortstudie× | Overlevelsesanalyse× | |
|---|---|---|
| Fagfelt≠ | Epidemiologi | Forskningsstatistikk |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | Mid-20th century (widely formalized 1950s–1970s) | 1958 |
| Opphavsperson≠ | Systematic use attributed to early 20th-century occupational epidemiology; formalized in modern epidemiological theory by Brian MacMahon and others | Edward L. Kaplan and Paul Meier |
| Type≠ | Observational analytic study | Method |
| Opprinnelig kilde≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias≠ | historical cohort study, non-concurrent cohort study, retrospective follow-up study, historical prospective study | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Relaterte≠ | 6 | 3 |
| Sammendrag≠ | A retrospective cohort study assembles a group of individuals who share a common starting point and reconstructs their exposure history and subsequent outcomes entirely from pre-existing records. Because the data have already been collected before the study begins, the design is far faster and cheaper than a prospective cohort; however, the researcher must work with whatever information was recorded at the time rather than collecting purpose-built measurements. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
| ScholarGateDatasett ↗ |
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