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| 메타분석 경쟁 위험 분석× | 메타분석 생존 분석× | |
|---|---|---|
| 분야 | 역학 | 역학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2000s–2010s (formalized as a pooled approach) | 1990s–2000s (formalized ~1998) |
| 창시자≠ | Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward) | Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group |
| 유형≠ | Systematic review / meta-analysis | Quantitative synthesis / meta-analytic method |
| 원전≠ | 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 ↗ | Parmar, M. K. B., Torri, V., & Stewart, L. (1998). Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine, 17(24), 2815–2834. DOI ↗ |
| 별칭≠ | meta-analysis of competing risks, pooled competing risks analysis, systematic review competing risks | meta-analysis of time-to-event data, pooled survival analysis, IPD survival meta-analysis, aggregate survival meta-analysis |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | Meta-analytic survival analysis is a quantitative synthesis method that pools hazard ratios and related time-to-event statistics from multiple independent studies to produce a single, more precise estimate of a treatment or exposure effect on survival outcomes such as overall survival, disease-free survival, or time to relapse. It can operate on aggregate published data or on individual patient data (IPD) contributed directly by study investigators. |
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