Comparer des méthodes

	Cox à méta-analyse	Estimateur de Kaplan-Meier
Domaine	Épidémiologie	Statistique
Famille	Process / pipeline	Survival analysis
Année d'origine	1998–2007	1958
Auteur d'origine	Parmar, Torri & Stewart; Tierney et al.	Edward L. Kaplan and Paul Meier
Type	Meta-analytic survival model	Nonparametric estimator
Source fondatrice	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.
Alias	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
Apparentées	3	2
Résumé	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.