Σύγκριση μεθόδων

	Ανάλυση Ανταγωνιστικών Κινδύνων με Ζευγοποίηση	Αντιστοίχιση Βαθμολογίας Προδιάθεσης
Πεδίο	Επιδημιολογία	Ερευνητική Στατιστική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1999 (Fine-Gray model); extended to matched designs ~2010s	1983
Δημιουργός	Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework)	Paul Rosenbaum and Donald Rubin
Τύπος	Observational survival analysis with matching and competing events	Method
Θεμελιώδης πηγή	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Εναλλακτικές ονομασίες	matched Fine-Gray analysis, propensity-matched competing risks, matched cause-specific hazard analysis, matched subdistribution hazard analysis	PSM, propensity score weighting, covariate balance
Συναφείς	4	3
Σύνοψη	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.