Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Multicentrická kohortová štúdia× | Analýza prežitia× | |
|---|---|---|
| Odbor≠ | Epidemiológia | Štatistika vo výskume |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | Mid-to-late 20th century (widespread adoption 1970s–1990s) | 1958 |
| Tvorca≠ | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) | Edward L. Kaplan and Paul Meier |
| Typ≠ | Observational longitudinal study | Method |
| Pôvodný zdroj≠ | 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 ↗ |
| Ďalšie názvy≠ | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Príbuzné≠ | 6 | 3 |
| Zhrnutie≠ | A multicenter cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and population diversity that single-site designs cannot match, making it the workhorse of large-scale epidemiological 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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