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| Analiza Kaplana-Meiera w wielu ośrodkach× | Analiza przeżycia× | |
|---|---|---|
| Dziedzina≠ | Epidemiologia | Statystyka w badaniach |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1958 (base method); multicenter designs common from 1970s | 1958 |
| Twórca≠ | Edward L. Kaplan and Paul Meier (method); multicenter application developed through large clinical trial consortia from the 1970s onward | Edward L. Kaplan and Paul Meier |
| Typ≠ | Nonparametric survival analysis in a multicenter setting | Method |
| Źródło pierwotne | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Inne nazwy≠ | pooled Kaplan-Meier, multi-site KM analysis, multicenter survival curve analysis, KM pooled analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | Multicenter Kaplan-Meier analysis applies the Kaplan-Meier nonparametric estimator to time-to-event data collected from two or more clinical centers. By pooling or stratifying data across sites, it estimates survival functions and compares them between treatment groups while accounting for potential center effects, enabling conclusions with greater statistical power and broader generalizability than single-center studies. | 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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