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| Retrospektiv Kaplan-Meier Analyse× | Kaplan-Meier-analyse× | |
|---|---|---|
| Fagområde | Epidemiologi | Epidemiologi |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1958 (method); retrospective application standard in clinical research since 1970s–1980s) | 1958 |
| Ophavsperson | Edward L. Kaplan and Paul Meier | Edward L. Kaplan and Paul Meier |
| Type≠ | Non-parametric survival analysis applied to historical data | Nonparametric survival estimator |
| Oprindelig kilde | 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 ↗ |
| Aliasser | retrospective KM analysis, retrospective survival curve estimation, historical Kaplan-Meier, retrospective KM estimator | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Relaterede | 5 | 5 |
| Resumé≠ | Retrospective Kaplan-Meier analysis applies the Kaplan-Meier product-limit estimator to time-to-event data drawn from existing records — medical charts, registries, or administrative databases — rather than from a prospectively followed cohort. The method estimates the probability of surviving (or remaining event-free) beyond any given time point while accounting for participants whose follow-up ended before the event occurred (censored observations). It is among the most commonly reported analyses in clinical oncology, cardiology, and surgery. | 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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