Comparer des méthodes

	Analyse bayésienne des risques concurrents	Cox proportional hazards
Domaine	Épidémiologie	Épidémiologie
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s)	1972
Auteur d'origine	Various; Bayesian formulation advanced by Gelfand, Dey, Larson, and Dinse among others	Sir David Roxbee Cox
Type	Bayesian survival/time-to-event model	Semi-parametric regression model
Source fondatrice	Larson, M. G., & Dinse, G. E. (1985). A mixture model for the regression analysis of competing risks data. Applied Statistics, 34(3), 201–211.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Alias	Bayesian cause-specific hazard model, Bayesian subdistribution hazard model, BCRA, Bayesian cumulative incidence analysis	Cox regression, Cox PH model, proportional hazards model, CPH
Apparentées	3	5
Résumé	Bayesian competing risks analysis is a time-to-event method for settings where subjects can fail from more than one mutually exclusive cause — such as death from cancer versus death from cardiovascular disease — and prior knowledge or small-sample uncertainty makes a Bayesian framework advantageous. It extends classical competing risks models (cause-specific hazards and cumulative incidence functions) by placing probability distributions over unknown parameters and updating those distributions with observed data, yielding full posterior inference for each failure type.	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.