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| Analisi delle cause competitive con abbinamento× | Abbinamento del punteggio di propensione× | |
|---|---|---|
| Campo≠ | Epidemiologia | Statistica per la ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1999 (Fine-Gray model); extended to matched designs ~2010s | 1983 |
| Ideatore≠ | Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework) | Paul Rosenbaum and Donald Rubin |
| Tipo≠ | Observational survival analysis with matching and competing events | Method |
| Fonte seminale≠ | 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. DOI ↗ | 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. DOI ↗ |
| Alias≠ | matched Fine-Gray analysis, propensity-matched competing risks, matched cause-specific hazard analysis, matched subdistribution hazard analysis | PSM, propensity score weighting, covariate balance |
| Correlati≠ | 4 | 3 |
| Sintesi≠ | 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. |
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