Sammenlign metoder

	Cox Regression med tidsvarierende kovariater	Fleksibel parametrisk overlevelsesmodel (Royston-Parmar)
Fagområde	Overlevelsesanalyse	Overlevelsesanalyse
Familie	Survival analysis	Survival analysis
Oprindelsesår	1972	2002
Ophavsperson	Cox, D. R. (extended formulation by Therneau & Grambsch)	Royston, P. & Parmar, M.K.B.
Type	Semi-parametric hazard regression model	Parametric survival regression model
Oprindelig kilde	Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.	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.
Aliasser	time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu	flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar)
Relaterede	4	8
Resumé	Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.	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.