Vertaile menetelmiä

	Bayesilainen Coxin regressio	Bayesiläinen sekoitettujen malli
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1972 (Cox PH); 2001 (Bayesian treatment)	1990s–2000s (modern Bayesian MCMC era)
Kehittäjä	Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001)	Gelman, Hill, and the broader Bayesian hierarchical modeling tradition
Tyyppi	Survival regression	Bayesian regression model
Alkuperäislähde	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
Rinnakkaisnimet	Bayesian Cox PH model, Bayesian proportional hazards model, Bayesian survival regression, BCox	Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model
Liittyvät	6	5
Tiivistelmä	Bayesian Cox regression combines the Cox proportional hazards model for time-to-event data with Bayesian inference. Instead of point estimates, it produces full posterior distributions over the hazard ratios, naturally incorporating prior knowledge and providing coherent uncertainty quantification even with small samples or informative censoring.	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.