Methoden vergleichen

	Meta-analytische Cox-Proportional-Hazards	Kaplan-Meier-Schätzer
Fachgebiet	Epidemiologie	Statistik
Familie	Process / pipeline	Survival analysis
Entstehungsjahr	1998–2007	1958
Urheber	Parmar, Torri & Stewart; Tierney et al.	Edward L. Kaplan and Paul Meier
Typ	Meta-analytic survival model	Nonparametric estimator
Wegweisende Quelle	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.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Aliasnamen	pooled Cox regression meta-analysis, meta-Cox model, survival meta-analysis, Cox PH pooling	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator
Verwandt	3	2
Zusammenfassung	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.	The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.