Salīdzināt metodes

	Elastīgais parametriskais izdzīvošanas modelis (Royston-Parmar)	Paātrinātās atteices laika (AFT) modelis
Nozare	Dzīvildze	Dzīvildze
Saime	Survival analysis	Survival analysis
Izcelsmes gads	2002	1992
Autors	Royston, P. & Parmar, M.K.B.	Wei, L. J. (seminal review 1992); origins in parametric survival literature
Tips	Parametric survival regression model	Parametric survival regression model
Pirmavots	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.	Wei, L. J. (1992). The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11(14–15), 1871–1879.
Citi nosaukumi	flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar)	AFT model, parametric survival regression, Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)
Saistītās	8	3
Kopsavilkums	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.	The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how covariates shift the hazard rate, AFT models express the covariate effect as an acceleration or deceleration of the time axis itself.