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| Phân tích Kaplan-Meier theo phương pháp tổng hợp meta× | Phân tích sống còn× | |
|---|---|---|
| Lĩnh vực≠ | Dịch tễ học | Thống kê nghiên cứu |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2007–2012 (systematic formalization) | 1958 |
| Người khởi xướng≠ | Building on Kaplan & Meier (1958); meta-analytic extension formalized by Tierney et al. (2007) and Guyot et al. (2012) | Edward L. Kaplan and Paul Meier |
| Loại≠ | Quantitative meta-analytic method | Method |
| Công trình gốc≠ | 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 ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Tên gọi khác≠ | KM meta-analysis, pooled Kaplan-Meier analysis, survival meta-analysis, IPD-KM meta-analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | 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. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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