विधियों की तुलना करें

	मिलान की गई प्रतिस्पर्धी जोखिम विश्लेषण	कॉक्स प्रोपोर्शनल हैज़र्ड्स
क्षेत्र	महामारी विज्ञान	महामारी विज्ञान
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1999 (Fine-Gray model); extended to matched designs ~2010s	1972
प्रवर्तक	Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework)	Sir David Roxbee Cox
प्रकार	Observational survival analysis with matching and competing events	Semi-parametric regression model
मौलिक स्रोत	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.
उपनाम	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
संबंधित	4	5
सारांश	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.