เปรียบเทียบวิธี

	Bayesian Cox Regression	การถดถอยแบบรอดชีพ
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Regression model	Regression model
ปีกำเนิด	1972 (Cox PH); 2001 (Bayesian treatment)	1980s
ผู้ริเริ่ม	Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001)	Kalbfleisch & Prentice; Cox & Oakes
ประเภท	Survival regression	Parametric survival model
แหล่งต้นตำรับ	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576
ชื่อเรียกอื่น	Bayesian Cox PH model, Bayesian proportional hazards model, Bayesian survival regression, BCox	accelerated failure time model, AFT model, parametric survival model, time-to-event regression
ที่เกี่ยวข้อง	6	3
สรุป	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.	Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood.