विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मेटा-एनालिटिक प्रतिस्पर्धी जोखिम विश्लेषण× | मेटा-विश्लेषणात्मक कोहोर्ट अध्ययन× | |
|---|---|---|
| क्षेत्र | महामारी विज्ञान | महामारी विज्ञान |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2000s–2010s (formalized as a pooled approach) | 1980s–1990s (formalized practice) |
| प्रवर्तक≠ | Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward) | Developed iteratively through epidemiological meta-analysis literature; Greenland, Berlin, Colditz among key contributors |
| प्रकार≠ | Systematic review / meta-analysis | Quantitative synthesis / observational epidemiology |
| मौलिक स्रोत≠ | Riley, R. D., Hayden, J. A., Steyerberg, E. W., et al. (2013). Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research. PLOS Medicine, 10(2), e1001380. DOI ↗ | Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309. DOI ↗ |
| उपनाम≠ | meta-analysis of competing risks, pooled competing risks analysis, systematic review competing risks | cohort meta-analysis, pooled cohort analysis, meta-analysis of cohort studies, prospective cohort meta-analysis |
| संबंधित≠ | 5 | 2 |
| सारांश≠ | Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, specialized pooling strategies are required that respect the subdistribution hazard structure introduced by Fine and Gray and the distinction between cause-specific and all-cause hazard models. | A meta-analytic cohort study systematically identifies, appraises, and statistically pools the findings of two or more independent cohort studies addressing the same exposure-outcome relationship. By combining large prospective datasets, it provides more precise risk estimates than any single cohort alone, makes dose-response patterns detectable, and enables subgroup analyses across diverse populations. It is the design of choice when cohort-level evidence exists but individual studies are underpowered or inconsistent. |
| ScholarGateडेटासेट ↗ |
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