विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| प्रैग्मैटिक सर्वाइवल एनालिसिस× | उत्तरजीविता विश्लेषण× | |
|---|---|---|
| क्षेत्र≠ | महामारी विज्ञान | अनुसंधान सांख्यिकी |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | Conceptual framework: 1967; widespread application: 1990s–2000s | 1958 |
| प्रवर्तक≠ | Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward | Edward L. Kaplan and Paul Meier |
| प्रकार≠ | Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings | Method |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम≠ | real-world survival analysis, pragmatic time-to-event analysis, effectiveness survival analysis, PSA | Kaplan-Meier analysis, Cox regression, TTE analysis |
| संबंधित≠ | 5 | 3 |
| सारांश≠ | 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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