Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Meta-analytische Kaplan-Meier-Analyse× | Kaplan-Meieranalyse× | |
|---|---|---|
| Vakgebied | Epidemiologie | Epidemiologie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2007–2012 (systematic formalization) | 1958 |
| Grondlegger≠ | 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 | Nonparametric survival estimator |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen | KM meta-analysis, pooled Kaplan-Meier analysis, survival meta-analysis, IPD-KM meta-analysis | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Verwant≠ | 4 | 5 |
| Samenvatting≠ | 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. | 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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