Salīdzināt metodes

	Kopīgais modelis longitudināliem datiem un notikuma iestāšanās laikam	Koksas regresija ar laika mainīgiem kovariātiem
Nozare	Dzīvildze	Dzīvildze
Saime	Survival analysis	Survival analysis
Izcelsmes gads	2004	1972
Autors	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Cox, D. R. (extended formulation by Therneau & Grambsch)
Tips	Semiparametric regression model	Semi-parametric hazard regression model
Pirmavots	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.
Citi nosaukumi	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu
Saistītās	5	4
Kopsavilkums	The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through shared random effects. It resolves two major problems that simpler approaches cannot handle: informative dropout from longitudinal studies and the endogeneity of time-varying biomarkers used as covariates in a Cox model.	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.