विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मेटा-एनालिटिक कपलान-मेयर विश्लेषण× | मेटा-विश्लेषणात्मक कॉक्स आनुपातिक खतरे× | |
|---|---|---|
| क्षेत्र | महामारी विज्ञान | महामारी विज्ञान |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2007–2012 (systematic formalization) | 1998–2007 |
| प्रवर्तक≠ | Building on Kaplan & Meier (1958); meta-analytic extension formalized by Tierney et al. (2007) and Guyot et al. (2012) | Parmar, Torri & Stewart; Tierney et al. |
| प्रकार≠ | Quantitative meta-analytic method | Meta-analytic survival model |
| मौलिक स्रोत≠ | Guyot, P., Ades, A. E., Ouwens, M. J., & Welton, N. J. (2012). Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology, 12, 9. DOI ↗ | Tierney, J. F., Stewart, L. A., Ghersi, D., Burdett, S., & Sydes, M. R. (2007). Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 8(1), 16. DOI ↗ |
| उपनाम | KM meta-analysis, pooled Kaplan-Meier analysis, survival meta-analysis, IPD-KM meta-analysis | pooled Cox regression meta-analysis, meta-Cox model, survival meta-analysis, Cox PH pooling |
| संबंधित≠ | 4 | 3 |
| सारांश≠ | Meta-analytic Kaplan-Meier analysis synthesizes time-to-event data across multiple studies by pooling Kaplan-Meier survival estimates, either from reconstructed individual patient data or from summary statistics extracted from published curves. It produces a pooled survival function with confidence bands and enables formal heterogeneity testing across studies, offering higher statistical power and more generalizable survival estimates than any single study alone. | Meta-analytic Cox proportional hazards is a quantitative synthesis technique that pools log hazard ratios from multiple Cox regression survival analyses into a single, more precise estimate of the association between an exposure or treatment and a time-to-event outcome. It combines the inferential power of survival analysis with the evidence-aggregation logic of meta-analysis, making it the standard approach for summarising multi-study survival evidence in clinical and epidemiological research. |
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