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Regresión de Supervivencia Bayesiana×Regresión de Riesgos Proporcionales de Cox×
CampoEstadísticaSupervivencia
FamiliaRegression modelSurvival analysis
Año de origen1990s–20011972
Autor originalIbrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al.Cox, D. R.
TipoBayesian parametric/semiparametric regressionSemi-parametric hazard regression model
Fuente seminalIbrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗
AliasBayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time modelcox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu
Relacionados53
ResumenBayesian Survival Regression combines parametric or semiparametric survival models — such as Weibull, log-normal, or Cox proportional hazards — with Bayesian inference. Instead of point estimates, it produces full posterior distributions for regression coefficients and the baseline hazard, naturally handling censored observations and incorporating prior knowledge about event times or covariate effects.Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.
ScholarGateConjunto de datos
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  1. v1
  2. 2 Fuentes
  3. PUBLISHED

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ScholarGateComparar métodos: Bayesian Survival regression · Cox Regression. Recuperado el 2026-06-18 de https://scholargate.app/es/compare