Salīdzināt metodes

	Konkurējošo risku izdzīvošanas analīze	Smalkā-Graja konkurento risku modelis
Nozare	Dzīvildze	Statistika
Saime	Survival analysis	Hypothesis test
Izcelsmes gads	1999	1999
Autors	Fine, J.P. & Gray, R.J.	Jason P. Fine & Robert J. Gray
Tips	Competing risks survival model	Subdistribution hazard regression
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.	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.
Citi nosaukumi	Rekabet Eden Riskler Analizi, cumulative incidence function, CIF analysis, cause-specific survival analysis	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli
Saistītās	5	5
Kopsavilkums	Competing risks analysis, formalized by Fine and Gray in 1999, is a survival analysis framework for settings where a subject can experience one of several mutually exclusive event types. The key quantity is the cumulative incidence function (CIF), which estimates the probability of a specific event occurring by time t in the presence of the other competing events.	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.