Comparer des méthodes

	Modèle paramétrique flexible pour la survie (Royston-Parmar)	Régression de survie paramétrique de Weibull
Domaine	Analyse de survie	Analyse de survie
Famille	Survival analysis	Survival analysis
Année d'origine	2002	1951
Auteur d'origine	Royston, P. & Parmar, M.K.B.	Waloddi Weibull
Type	Parametric survival regression model	Fully parametric survival regression model
Source fondatrice	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.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Alias	flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar)	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Apparentées	8	4
Résumé	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.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.