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| Pragmatisk overlevelsesanalyse× | Kaplan-Meier-analyse× | |
|---|---|---|
| Fagområde | Epidemiologi | Epidemiologi |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | Conceptual framework: 1967; widespread application: 1990s–2000s | 1958 |
| Ophavsperson≠ | Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward | Edward L. Kaplan and Paul Meier |
| Type≠ | Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings | Nonparametric survival estimator |
| Oprindelig kilde≠ | Ford, I., & Norrie, J. (2016). Pragmatic Trials. New England Journal of Medicine, 375(5), 454–463. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Aliasser | real-world survival analysis, pragmatic time-to-event analysis, effectiveness survival analysis, PSA | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Relaterede | 5 | 5 |
| Resumé≠ | Pragmatic survival analysis applies time-to-event statistical methods within pragmatic or real-world settings, estimating how long patients survive, remain event-free, or retain treatment benefit under conditions of routine clinical practice. Unlike explanatory survival analyses conducted under tightly controlled trial conditions, the pragmatic variant embraces the heterogeneity, treatment switching, non-adherence, and competing events that characterise real-world patient populations, prioritising external validity over internal precision. | 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. |
| ScholarGateDatasæt ↗ |
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