Salīdzināt metodes

	Smalkā-Graja konkurento risku modelis	Koksa proporcionālo risku modelis
Nozare	Statistika	Epidemioloģija
Saime	Hypothesis test	Process / pipeline
Izcelsmes gads	1999	1972
Autors	Jason P. Fine & Robert J. Gray	Sir David Roxbee Cox
Tips	Subdistribution hazard regression	Semi-parametric 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.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Citi nosaukumi	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli	Cox regression, Cox PH model, proportional hazards model, CPH
Saistītās	5	5
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.	The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.