Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse prospective des risques concurrents× | Cox proportional hazards× | |
|---|---|---|
| Domaine | Épidémiologie | Épidémiologie |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1978–1999 (foundational frameworks; prospective application standard by 2000s) | 1972 |
| Auteur d'origine≠ | Fine & Gray (subdistribution hazard model, 1999); Prentice, Kalbfleisch et al. (cause-specific hazard, 1978) | Sir David Roxbee Cox |
| Type≠ | Observational analytic study with event-time statistical analysis | Semi-parametric regression model |
| Source fondatrice≠ | Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. 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 | prospective CRA, prospective subdistribution hazard analysis, prospective cause-specific hazard analysis, forward-looking competing events analysis | Cox regression, Cox PH model, proportional hazards model, CPH |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing outcomes. It combines the temporal clarity of prospective cohort follow-up with the statistical rigor of competing risks methodology to avoid the overestimation inherent in standard Kaplan-Meier curves when multiple event types are present. | 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. |
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