Methoden vergelijken

	Bayesian Kaplan-Meier Analyse	Cox Proportional Hazards
Vakgebied	Epidemiologie	Epidemiologie
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	1976	1972
Grondlegger	Susarla & Van Ryzin (Bayesian nonparametric survival estimation)	Sir David Roxbee Cox
Type	Bayesian nonparametric survival analysis	Semi-parametric regression model
Oorspronkelijke bron	Susarla, V., & Van Ryzin, J. (1976). Nonparametric Bayesian estimation of survival curves from incomplete observations. Journal of the American Statistical Association, 71(356), 897–902.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Aliassen	Bayesian survival curve estimation, Bayesian nonparametric survival analysis, Dirichlet process Kaplan-Meier, BKM	Cox regression, Cox PH model, proportional hazards model, CPH
Verwant	4	5
Samenvatting	Bayesian Kaplan-Meier analysis extends the classical Kaplan-Meier estimator by placing a prior distribution over the survival function and updating it with observed time-to-event data to obtain a full posterior distribution for the survival curve. This approach, rooted in Susarla and Van Ryzin's 1976 Dirichlet-process framework, yields credible intervals rather than confidence intervals and enables coherent incorporation of prior clinical knowledge, making it particularly valuable in small-sample or early-phase clinical settings.	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.