Linganisha mbinu

	Mfumo wa Kunusurika wa Parametrici Unyumbufu (Royston-Parmar)	Kikokotozi cha Kuishi cha Kaplan-Meier
Nyanja	Uchanganuzi wa Uhai	Uchanganuzi wa Uhai
Familia	Survival analysis	Survival analysis
Mwaka wa asili	2002	1958
Mwanzilishi	Royston, P. & Parmar, M.K.B.	Kaplan, E. L. & Meier, P.
Aina	Parametric survival regression model	Non-parametric survival estimator
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.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Majina mbadala	flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar)	product-limit estimator, km curve, kaplan-meier sağkalım analizi
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 Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.