方法对比
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| Meta-analytic Survival Analysis× | 个体患者数据荟萃分析× | |
|---|---|---|
| 领域≠ | 流行病学 | 证据综合 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s (formalized ~1998) | 1990s |
| 提出者≠ | Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group | Cochrane Collaboration, Pioneered by Stewart & Clarke |
| 类型≠ | Quantitative synthesis / meta-analytic method | Method |
| 开创性文献≠ | 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 ↗ | Stewart, L. A., Clarke, M. J., & Cochrane IPD Meta-analysis Methods Group. (2015). Practical methodology of meta-analyses (including IPD) of randomised trials reporting time to event data. Cochrane Database of Systematic Reviews, 2015(10), MR000027. link ↗ |
| 别名≠ | meta-analysis of time-to-event data, pooled survival analysis, IPD survival meta-analysis, aggregate survival meta-analysis | IPD Meta-Analysis, Participant-Level Data Synthesis, One-Stage Meta-Analysis |
| 相关≠ | 4 | 1 |
| 摘要≠ | 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. | Individual patient data meta-analysis (IPD-MA) is a systematic synthesis method where researchers obtain and analyze raw data at the patient level from multiple randomized controlled trials, rather than relying on published summary statistics (aggregate data). Pioneered by the Cochrane Collaboration and formalized by Stewart, Clarke, and Riley, IPD-MA is considered the gold standard for evidence synthesis because it enables consistent outcome definition across trials, robust subgroup analysis, and detection of treatment-covariate interactions. Though time-intensive and resource-demanding, IPD-MA provides the most reliable estimates of intervention effects and is preferred for critical clinical decisions, particularly for identifying which patients benefit most from treatment. |
| ScholarGate数据集 ↗ |
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