Salīdzināt metodes

	Analīze ar saskaņotiem konkurējošiem riskiem	Koksa proporcionālo risku modelis
Nozare	Epidemioloģija	Epidemioloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1999 (Fine-Gray model); extended to matched designs ~2010s	1972
Autors	Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework)	Sir David Roxbee Cox
Tips	Observational survival analysis with matching and competing events	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	matched Fine-Gray analysis, propensity-matched competing risks, matched cause-specific hazard analysis, matched subdistribution hazard analysis	Cox regression, Cox PH model, proportional hazards model, CPH
Saistītās	4	5
Kopsavilkums	Matched competing risks analysis combines subject-level matching (e.g., propensity-score matching) with competing risks survival methods to estimate the cause-specific or subdistribution hazard of an event of interest while accounting for competing events that preclude the occurrence of that event. It is widely used in clinical and epidemiological observational studies where patients may die from causes other than the primary outcome of interest, and where treatment groups differ on baseline confounders.	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.