Salīdzināt metodes

	Smalkā-Graja konkurento risku modelis	Koksas regresija ar laika mainīgiem kovariātiem
Nozare	Statistika	Dzīvildze
Saime	Hypothesis test	Survival analysis
Izcelsmes gads	1999	1972
Autors	Jason P. Fine & Robert J. Gray	Cox, D. R. (extended formulation by Therneau & Grambsch)
Tips	Subdistribution hazard regression	Semi-parametric hazard regression model
Pirmavots	Fine, J.P. & Gray, R.J. (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association, 94(446), 496–509.	Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.
Citi nosaukumi	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli	time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu
Saistītās	5	4
Kopsavilkums	The Fine-Gray model is a semiparametric regression method for survival data in which two or more mutually exclusive event types compete to occur first. Proposed by Fine and Gray in 1999, it models the subdistribution hazard of each event type directly, allowing covariates to be linked to the cumulative incidence function (CIF) — the quantity that actually answers 'what is the probability of experiencing event type k by time t?'. It corrects the well-known shortcoming of standard Cox regression, which ignores competing events and thereby overestimates cause-specific probabilities.	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.