Comparar métodos

	Cox Proporcional de Riesgos Bayesiano	Modelo de Riesgos Proporcionales de Cox
Campo	Epidemiología	Epidemiología
Familia	Process / pipeline	Process / pipeline
Año de origen	1972 (Cox); Bayesian formulation developed through the 1990s	1972
Autor original	D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001)	Sir David Roxbee Cox
Tipo	Bayesian semiparametric survival regression	Semi-parametric regression model
Fuente seminal	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Alias	Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model	Cox regression, Cox PH model, proportional hazards model, CPH
Relacionados	4	5
Resumen	The Bayesian Cox proportional hazards model combines Cox's classical semiparametric survival regression with Bayesian inference, replacing point estimates and p-values with full posterior distributions over regression coefficients. It handles right-censored time-to-event outcomes, quantifies uncertainty about hazard ratios in probabilistic terms, and allows the incorporation of prior clinical or historical knowledge directly into the analysis.	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.