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| 메타분석 무작위 대조 시험× | 개별 환자 데이터 메타분석× | |
|---|---|---|
| 분야≠ | 역학 | 근거 합성 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1976 (Glass coinage of meta-analysis); 1993 (Cochrane Collaboration formalization) | 1990s |
| 창시자≠ | Gene V. Glass (meta-analysis method); Cochrane Collaboration (systematic RCT pooling standards) | Cochrane Collaboration, Pioneered by Stewart & Clarke |
| 유형≠ | Quantitative evidence-synthesis design | Method |
| 원전≠ | Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). Wiley-Blackwell. ISBN: 978-1119536628 | Stewart, L. A., Clarke, M. J., & Cochrane IPD Meta-analysis Methods Group. (2015). Practical methodology of meta-analyses (including IPD) of randomised trials reporting time to event data. Cochrane Database of Systematic Reviews, 2015(10), MR000027. link ↗ |
| 별칭≠ | meta-analytic RCT, MA-RCT, meta-analysis of RCTs, pooled randomized trial analysis | IPD Meta-Analysis, Participant-Level Data Synthesis, One-Stage Meta-Analysis |
| 관련≠ | 3 | 1 |
| 요약≠ | A meta-analytic randomized clinical trial is a formal evidence-synthesis method that identifies, appraises, and statistically combines the results of multiple randomized clinical trials addressing the same clinical question. By pooling trial-level data, it produces a single, more precise estimate of treatment effect and quantifies between-trial heterogeneity, sitting at the apex of the evidence hierarchy for evaluating healthcare interventions. | Individual patient data meta-analysis (IPD-MA) is a systematic synthesis method where researchers obtain and analyze raw data at the patient level from multiple randomized controlled trials, rather than relying on published summary statistics (aggregate data). Pioneered by the Cochrane Collaboration and formalized by Stewart, Clarke, and Riley, IPD-MA is considered the gold standard for evidence synthesis because it enables consistent outcome definition across trials, robust subgroup analysis, and detection of treatment-covariate interactions. Though time-intensive and resource-demanding, IPD-MA provides the most reliable estimates of intervention effects and is preferred for critical clinical decisions, particularly for identifying which patients benefit most from treatment. |
| ScholarGate데이터셋 ↗ |
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