Сравнение методов

	Гибкая параметрическая модель выживаемости (Royston-Parmar)	Байесовский анализ выживаемости
Область	Анализ выживаемости	Байесовские методы
Семейство	Survival analysis	Bayesian methods
Год появления	2002	2001
Автор метода	Royston, P. & Parmar, M.K.B.	Ibrahim, Chen & Sinha
Тип	Parametric survival regression model	Bayesian time-to-event model
Основополагающий источник	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.
Другие названия	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
Связанные	8	4
Сводка	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.