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Multicenter Kaplan-Meier analysis×Coxin suhteellisten vaarojen regressiomalli aikasarja-analyysiin×
TieteenalaEpidemiologiaEpidemiologia
MenetelmäperheProcess / pipelineProcess / pipeline
Syntyvuosi1958 (base method); multicenter designs common from 1970s1972
KehittäjäEdward L. Kaplan and Paul Meier (method); multicenter application developed through large clinical trial consortia from the 1970s onwardSir David Roxbee Cox
TyyppiNonparametric survival analysis in a multicenter settingSemi-parametric regression model
AlkuperäislähdeKaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗
Rinnakkaisnimetpooled Kaplan-Meier, multi-site KM analysis, multicenter survival curve analysis, KM pooled analysisCox regression, Cox PH model, proportional hazards model, CPH
Liittyvät55
TiivistelmäMulticenter Kaplan-Meier analysis applies the Kaplan-Meier nonparametric estimator to time-to-event data collected from two or more clinical centers. By pooling or stratifying data across sites, it estimates survival functions and compares them between treatment groups while accounting for potential center effects, enabling conclusions with greater statistical power and broader generalizability than single-center studies.The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.
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ScholarGateVertaile menetelmiä: Multicenter Kaplan-Meier analysis · Cox proportional hazards. Haettu 2026-06-19 osoitteesta https://scholargate.app/fi/compare