Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresia Cox cu Hazarduri Proporționale Prospectivă× | Analiza Kaplan-Meier× | |
|---|---|---|
| Domeniu | Epidemiologie | Epidemiologie |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1972 (Cox model); widespread prospective application from late 1970s | 1958 |
| Autorul original≠ | David R. Cox (model); applied prospectively in large cohort studies from 1970s onward | Edward L. Kaplan and Paul Meier |
| Tip≠ | Semi-parametric survival regression applied to prospectively collected time-to-event data | Nonparametric survival estimator |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | prospective Cox regression, Cox PH prospective study, prospective survival regression, prospective hazard modeling | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | 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. | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. |
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