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| Analyse pragmatique de Kaplan-Meier× | Analyse de Kaplan-Meier× | |
|---|---|---|
| Domaine | Épidémiologie | Épidémiologie |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1958 (estimator); pragmatic application formalized 1967 onward | 1958 |
| Auteur d'origine≠ | Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967) | Edward L. Kaplan and Paul Meier |
| Type≠ | Non-parametric survival estimator within pragmatic study design | Nonparametric survival estimator |
| Source fondatrice | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias | pragmatic KM analysis, real-world Kaplan-Meier, pragmatic survival curve estimation, KM analysis in pragmatic trials | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Apparentées | 5 | 5 |
| Résumé≠ | Pragmatic Kaplan-Meier analysis applies the non-parametric Kaplan-Meier product-limit estimator to time-to-event data collected under real-world or pragmatic conditions — diverse populations, routine clinical care, minimal exclusions, and standard-of-care comparators. Unlike explanatory trials designed to isolate a treatment effect under ideal conditions, pragmatic designs accept real-world heterogeneity, and the resulting survival curves reflect the effectiveness of an intervention as it actually performs in clinical practice. | 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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