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| Prospektív kohorszvizsgálat× | Túlélemzési módszerek× | |
|---|---|---|
| Tudományterület≠ | Epidemiológia | Kutatási statisztika |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1950s (systematic application); conceptual roots earlier | 1958 |
| Megalkotó≠ | Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks | Edward L. Kaplan and Paul Meier |
| Típus≠ | Observational longitudinal study design | Method |
| Alapmű≠ | 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 ↗ |
| Alternatív nevek≠ | longitudinal cohort study, prospective follow-up study, incidence study, prospective observational cohort | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Kapcsolódó≠ | 6 | 3 |
| Összefoglaló≠ | 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. | 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. |
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