Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis retrospectivo de riesgos competitivos× | Análisis de Kaplan-Meier× | |
|---|---|---|
| Campo | Epidemiología | Epidemiología |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1978 (cause-specific); 1999 (subdistribution/Fine-Gray) | 1958 |
| Autor original≠ | Fine & Gray (subdistribution model); Prentice et al. (cause-specific framework) | Edward L. Kaplan and Paul Meier |
| Tipo≠ | Retrospective observational survival analysis | Nonparametric survival estimator |
| Fuente seminal≠ | 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 ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias | retrospective CRA, competing risks survival analysis (retrospective), cause-specific hazard analysis (retrospective), subdistribution hazard analysis (retrospective) | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the probability of each event type while accounting for the fact that occurrence of one event permanently precludes the others. Widely used in oncology, cardiology, and transplant medicine where administrative or registry records are the data source. | 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. |
| ScholarGateConjunto de datos ↗ |
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