Comparer des méthodes

	Régression bayésienne de survie	Modèle bayésien à effets mixtes
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1990s–2001	1990s–2000s (modern Bayesian MCMC era)
Auteur d'origine	Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al.	Gelman, Hill, and the broader Bayesian hierarchical modeling tradition
Type	Bayesian parametric/semiparametric regression	Bayesian regression model
Source fondatrice	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891
Alias	Bayesian time-to-event regression, Bayesian parametric survival model, Bayesian survival analysis, Bayesian accelerated failure time model	Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model
Apparentées	5	5
Résumé	Bayesian 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.	The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously.