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| Analyse méta-analytique des risques concurrents× | Modèle à risques concurrents de Fine-Gray× | |
|---|---|---|
| Domaine≠ | Épidémiologie | Statistique |
| Famille≠ | Process / pipeline | Hypothesis test |
| Année d'origine≠ | 2000s–2010s (formalized as a pooled approach) | 1999 |
| Auteur d'origine≠ | Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward) | Jason P. Fine & Robert J. Gray |
| Type≠ | Systematic review / meta-analysis | Subdistribution hazard regression |
| Source fondatrice≠ | Riley, R. D., Hayden, J. A., Steyerberg, E. W., et al. (2013). Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research. PLOS Medicine, 10(2), e1001380. DOI ↗ | 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 ↗ |
| Alias≠ | meta-analysis of competing risks, pooled competing risks analysis, systematic review competing risks | competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli |
| Apparentées | 5 | 5 |
| Résumé≠ | Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, specialized pooling strategies are required that respect the subdistribution hazard structure introduced by Fine and Gray and the distinction between cause-specific and all-cause hazard models. | The Fine-Gray model is a semiparametric regression method for survival data in which two or more mutually exclusive event types compete to occur first. Proposed by Fine and Gray in 1999, it models the subdistribution hazard of each event type directly, allowing covariates to be linked to the cumulative incidence function (CIF) — the quantity that actually answers 'what is the probability of experiencing event type k by time t?'. It corrects the well-known shortcoming of standard Cox regression, which ignores competing events and thereby overestimates cause-specific probabilities. |
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