Salīdzināt metodes
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| Metaanalītiskā Koksa proporciju bīstamības× | Individuālu Pacientu Datu Meta-analīze× | |
|---|---|---|
| Nozare≠ | Epidemioloģija | Pierādījumu sintēze |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1998–2007 | 1990s |
| Autors≠ | Parmar, Torri & Stewart; Tierney et al. | Cochrane Collaboration, Pioneered by Stewart & Clarke |
| Tips≠ | Meta-analytic survival model | Method |
| Pirmavots≠ | 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 ↗ | 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 ↗ |
| Citi nosaukumi≠ | pooled Cox regression meta-analysis, meta-Cox model, survival meta-analysis, Cox PH pooling | IPD Meta-Analysis, Participant-Level Data Synthesis, One-Stage Meta-Analysis |
| Saistītās≠ | 3 | 1 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
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