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| Pragmatische Überlebenszeitanalyse× | Prospektive Überlebenszeitanalyse× | |
|---|---|---|
| Fachgebiet | Epidemiologie | Epidemiologie |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | Conceptual framework: 1967; widespread application: 1990s–2000s | 1958–1972 (foundational methods); prospective design emphasis formalized by 1980s |
| Urheber≠ | Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward | Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology |
| Typ≠ | Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings | Longitudinal observational or experimental study design with time-to-event analysis |
| Wegweisende Quelle≠ | Ford, I., & Norrie, J. (2016). Pragmatic Trials. New England Journal of Medicine, 375(5), 454–463. DOI ↗ | Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer. ISBN: 978-1441966452 |
| Aliasnamen | real-world survival analysis, pragmatic time-to-event analysis, effectiveness survival analysis, PSA | prospective time-to-event analysis, prospective failure-time analysis, forward-looking survival study, prospective event-time study |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | 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. |
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