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| Retrospective Cox proportional hazards× | Analisi di Kaplan-Meier× | |
|---|---|---|
| Campo | Epidemiologia | Epidemiologia |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1972 | 1958 |
| Ideatore≠ | David R. Cox | Edward L. Kaplan and Paul Meier |
| Tipo≠ | Semi-parametric survival regression | Nonparametric survival estimator |
| Fonte seminale≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias | Cox PH regression (retrospective), retrospective Cox survival model, retrospective hazard regression, Cox model on historical data | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Correlati | 5 | 5 |
| Sintesi≠ | Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline hazard, making it the dominant analytic tool when the investigator works backward from already-recorded outcomes and exposures. | 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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