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| Analisi meta-analitica dei rischi concorrenti× | Analisi di Kaplan-Meier× | |
|---|---|---|
| Campo | Epidemiologia | Epidemiologia |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 2000s–2010s (formalized as a pooled approach) | 1958 |
| Ideatore≠ | Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward) | Edward L. Kaplan and Paul Meier |
| Tipo≠ | Systematic review / meta-analysis | Nonparametric survival estimator |
| Fonte seminale≠ | 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 ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias≠ | meta-analysis of competing risks, pooled competing risks analysis, systematic review competing risks | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. |
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