পদ্ধতির তুলনা করুন

	মেটা-অ্যানালিটিক সারভাইভাল অ্যানালাইসিস	ব্যক্তিগত রোগী তথ্য মেটা-বিশ্লেষণ
ক্ষেত্র	মহামারীবিদ্যা	প্রমাণ সংশ্লেষণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1990s–2000s (formalized ~1998)	1990s
প্রবর্তক	Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group	Cochrane Collaboration, Pioneered by Stewart & Clarke
ধরন	Quantitative synthesis / meta-analytic method	Method
মৌলিক উৎস	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.	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.
অপর নাম	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
সম্পর্কিত	4	1
সারসংক্ষেপ	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.