Comparer des méthodes

	Analyse des risques concurrents ajustés aux risques	Cox proportional hazards
Domaine	Épidémiologie	Épidémiologie
Famille	Process / pipeline	Process / pipeline
Année d'origine	1999 (subdistribution hazard model); cause-specific hazard framework earlier	1972
Auteur d'origine	Jason Fine and Robert Gray	Sir David Roxbee Cox
Type	Regression model for time-to-event data with competing events	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.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Alias	competing risks regression, subdistribution hazard model, cause-specific hazard analysis, Fine-Gray model	Cox regression, Cox PH model, proportional hazards model, CPH
Apparentées	4	5
Résumé	Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confounders, the method yields unbiased estimates of the absolute probability — the cumulative incidence function — of each event type over time in the presence of competing events.	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.