Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de survie prospective× | Analyse de Kaplan-Meier× | |
|---|---|---|
| Domaine | Épidémiologie | Épidémiologie |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1958–1972 (foundational methods); prospective design emphasis formalized by 1980s | 1958 |
| Auteur d'origine≠ | Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology | Edward L. Kaplan and Paul Meier |
| Type≠ | Longitudinal observational or experimental study design with time-to-event analysis | Nonparametric survival estimator |
| Source fondatrice≠ | Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer. ISBN: 978-1441966452 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias | prospective time-to-event analysis, prospective failure-time analysis, forward-looking survival study, prospective event-time study | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Apparentées | 5 | 5 |
| Résumé≠ | Prospective survival analysis is a longitudinal study design in which participants are enrolled before the event of interest occurs, followed forward in time under standardised conditions, and analysed using survival-analytic methods to estimate the time until a defined clinical endpoint — such as death, disease recurrence, or treatment failure. Because data are collected prospectively, exposure and covariate information are recorded before outcomes are known, substantially reducing recall and selection bias relative to retrospective approaches. | 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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