方法对比
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| 元分析 Kaplan-Meier 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) | 1972 |
| 提出者≠ | Building on Kaplan & Meier (1958); meta-analytic extension formalized by Tierney et al. (2007) and Guyot et al. (2012) | Sir David Roxbee Cox |
| 类型≠ | Quantitative meta-analytic method | Semi-parametric regression 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. DOI ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| 别名 | KM meta-analysis, pooled Kaplan-Meier analysis, survival meta-analysis, IPD-KM meta-analysis | Cox regression, Cox PH model, proportional hazards model, CPH |
| 相关≠ | 4 | 5 |
| 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. | The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research. |
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