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| 메타분석적 Cox 비례 위험 모형× | Kaplan-Meier 추정량× | |
|---|---|---|
| 분야≠ | 역학 | 통계학 |
| 계열≠ | Process / pipeline | Survival analysis |
| 기원 연도≠ | 1998–2007 | 1958 |
| 창시자≠ | Parmar, Torri & Stewart; Tierney et al. | Edward L. Kaplan and Paul Meier |
| 유형≠ | Meta-analytic survival model | Nonparametric estimator |
| 원전≠ | 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 ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| 별칭 | pooled Cox regression meta-analysis, meta-Cox model, survival meta-analysis, Cox PH pooling | KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator |
| 관련≠ | 3 | 2 |
| 요약≠ | 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. | The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research. |
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