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| Analyse méta-analytique de Kaplan-Meier× | Analyse de survie× | |
|---|---|---|
| Domaine≠ | Épidémiologie | Statistiques de recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2007–2012 (systematic formalization) | 1958 |
| Auteur d'origine≠ | Building on Kaplan & Meier (1958); meta-analytic extension formalized by Tierney et al. (2007) and Guyot et al. (2012) | Edward L. Kaplan and Paul Meier |
| Type≠ | Quantitative meta-analytic method | Method |
| Source fondatrice≠ | Guyot, P., Ades, A. E., Ouwens, M. J., & Welton, N. J. (2012). Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology, 12, 9. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias≠ | KM meta-analysis, pooled Kaplan-Meier analysis, survival meta-analysis, IPD-KM meta-analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Meta-analytic Kaplan-Meier analysis synthesizes time-to-event data across multiple studies by pooling Kaplan-Meier survival estimates, either from reconstructed individual patient data or from summary statistics extracted from published curves. It produces a pooled survival function with confidence bands and enables formal heterogeneity testing across studies, offering higher statistical power and more generalizable survival estimates than any single study alone. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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