Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Stimatore di Kaplan-Meier× | Cox proportional hazards× | |
|---|---|---|
| Campo≠ | Statistica | Epidemiologia |
| Famiglia≠ | Survival analysis | Process / pipeline |
| Anno di origine≠ | 1958 | 1972 |
| Ideatore≠ | Edward L. Kaplan and Paul Meier | Sir David Roxbee Cox |
| Tipo≠ | Nonparametric estimator | Semi-parametric regression model |
| Fonte seminale≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| Alias | KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator | Cox regression, Cox PH model, proportional hazards model, CPH |
| Correlati≠ | 2 | 5 |
| Sintesi≠ | The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research. | The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research. |
| ScholarGateInsieme di dati ↗ |
|
|