Linganisha mbinu

	Mfumo wa Kunusurika wa Parametrici Unyumbufu (Royston-Parmar)	Muundo wa Dawa wa Mchanganyiko
Nyanja	Uchanganuzi wa Uhai	Uchanganuzi wa Uhai
Familia	Survival analysis	Survival analysis
Mwaka wa asili	2002	1949
Mwanzilishi	Royston, P. & Parmar, M.K.B.	Boag, J. W.
Aina	Parametric survival regression model	Parametric mixture survival 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.	Boag, J. W. (1949). Maximum Likelihood Estimates of the Proportion of Patients Cured. Journal of the Royal Statistical Society B, 11(1), 15–53.
Majina mbadala	flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar)	cure fraction model, cure rate model, bounded cumulative hazard model, İyileşme Modeli (Mixture Cure Model)
Zinazohusiana	8	2
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.	The mixture cure model, first proposed by Boag in 1949 for cancer survival data, is a parametric survival model that explicitly accounts for a fraction of subjects who will never experience the event of interest — the so-called cured or immune fraction. It is the appropriate tool whenever the Kaplan-Meier curve levels off into a long, stable plateau rather than continuing to decline, indicating that a proportion of subjects are permanently event-free.