手法を比較

	メタアナリシス的比例ハザード回帰	Individual Patient Data Meta-Analysis
分野	疫学	エビデンス統合
系統	Process / pipeline	Process / pipeline
提唱年	1998–2007	1990s
提唱者	Parmar, Torri & Stewart; Tierney et al.	Cochrane Collaboration, Pioneered by Stewart & Clarke
種類	Meta-analytic survival model	Method
原典	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.	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.
別名	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
関連	3	1
概要	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.