Linganisha mbinu

	Mfumo wa Kunusurika wa Parametrici Unyumbufu (Royston-Parmar)	Uchanganuzi wa Uhai wa Bayesian
Nyanja	Uchanganuzi wa Uhai	Mbinu za Bayes
Familia	Survival analysis	Bayesian methods
Mwaka wa asili	2002	2001
Mwanzilishi	Royston, P. & Parmar, M.K.B.	Ibrahim, Chen & Sinha
Aina	Parametric survival regression model	Bayesian time-to-event model
Chanzo asilia	Royston, P. & Parmar, M.K.B. (2002). Flexible Parametric Proportional-Hazards and Proportional-Odds Models for Censored Survival Data, with Application to Prognostic Modelling and Estimation of Treatment Effects. Statistics in Medicine, 21(15), 2175–2197.	Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.
Majina mbadala	flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar)	bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model
Zinazohusiana	8	4
Muhtasari	The Royston-Parmar model, introduced by Royston and Parmar in 2002, is a modern parametric approach to survival analysis that replaces the rigid distributional assumptions of classical models with a restricted cubic spline fitted to the log-cumulative-hazard scale. It combines the interpretability of a fully parametric model with the flexibility to capture non-standard hazard shapes, and it supports proportional-hazards, accelerated failure-time, and proportional-odds link functions.	Bayesian survival analysis applies Bayesian inference to time-to-event models — Cox proportional hazards, parametric (Weibull, exponential), and cure models. Formalised comprehensively by Ibrahim, Chen and Sinha (2001), the approach encodes prior knowledge about hazard rates and regression coefficients, then updates it with censored survival data to yield posterior hazard ratios and credible intervals rather than single point estimates.