方法对比

	元分析 Kaplan-Meier 分析	荟萃分析 Cox 比例风险模型
领域	流行病学	流行病学
方法族	Process / pipeline	Process / pipeline
起源年份	2007–2012 (systematic formalization)	1998–2007
提出者	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.
类型	Quantitative meta-analytic method	Meta-analytic survival model
开创性文献	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.	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.
别名	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
相关	4	3
摘要	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.