विधियों की तुलना करें

	Bayesian Cox Proportional Hazards	कैप्लान-मेयर विश्लेषण
क्षेत्र	महामारी विज्ञान	महामारी विज्ञान
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1972 (Cox); Bayesian formulation developed through the 1990s	1958
प्रवर्तक	D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001)	Edward L. Kaplan and Paul Meier
प्रकार	Bayesian semiparametric survival regression	Nonparametric survival estimator
मौलिक स्रोत	Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer. ISBN: 978-0387952772	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
उपनाम	Bayesian CPH, Bayesian survival regression, Bayesian semiparametric hazard model, Bayesian partial likelihood survival model	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
संबंधित	4	5
सारांश	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.	Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research.