Salīdzināt metodes

	Analīze ar saskaņotiem konkurējošiem riskiem	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Epidemioloģija	Cēloņsakarību secināšana
Saime	Process / pipeline	Regression model
Izcelsmes gads	1999 (Fine-Gray model); extended to matched designs ~2010s	2000
Autors	Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework)	Robins, Hernán & Brumback
Tips	Observational survival analysis with matching and competing events	Causal inference weighting estimator
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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	matched Fine-Gray analysis, propensity-matched competing risks, matched cause-specific hazard analysis, matched subdistribution hazard analysis	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.