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| Regresja przeżycia Coxa z proporcjonalnymi hazardami – spojrzenie w przyszłość× | Analiza przeżycia× | |
|---|---|---|
| Dziedzina≠ | Epidemiologia | Statystyka w badaniach |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1972 (Cox model); widespread prospective application from late 1970s | 1958 |
| Twórca≠ | David R. Cox (model); applied prospectively in large cohort studies from 1970s onward | Edward L. Kaplan and Paul Meier |
| Typ≠ | Semi-parametric survival regression applied to prospectively collected time-to-event data | Method |
| Źródło pierwotne≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. 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≠ | prospective Cox regression, Cox PH prospective study, prospective survival regression, prospective hazard modeling | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | Prospective Cox proportional hazards regression combines a forward-looking cohort design — in which participants are enrolled before outcomes occur and followed over time — with Cox's semi-parametric survival model. The method estimates how baseline covariates measured at enrollment influence the rate at which participants experience a time-to-event outcome, while preserving the temporal direction required for causal inference. It is one of the most widely used analytical frameworks in clinical epidemiology and chronic disease 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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