Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza Pragmatică a Supraviețuirii× | Analiza supraviețuirii× | |
|---|---|---|
| Domeniu≠ | Epidemiologie | Statistică pentru cercetare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | Conceptual framework: 1967; widespread application: 1990s–2000s | 1958 |
| Autorul original≠ | Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward | Edward L. Kaplan and Paul Meier |
| Tip≠ | Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings | Method |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative≠ | real-world survival analysis, pragmatic time-to-event analysis, effectiveness survival analysis, PSA | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | 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. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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