Methoden vergleichen

	Risikoadjustierte Cox-Proportional-Hazards-Regression	Kaplan-Meier-Schätzer
Fachgebiet	Epidemiologie	Statistik
Familie	Process / pipeline	Survival analysis
Entstehungsjahr	1972 (Cox model); risk adjustment widespread from 1980s	1958
Urheber	D. R. Cox (base model); risk-adjustment as routine practice formalised through clinical epidemiology literature from the 1980s onward	Edward L. Kaplan and Paul Meier
Typ	Multivariable survival regression	Nonparametric estimator
Wegweisende Quelle	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Aliasnamen	adjusted Cox regression, multivariable Cox model, covariate-adjusted survival analysis, risk-adjusted survival model	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator
Verwandt	5	2
Zusammenfassung	Risk-adjusted Cox proportional hazards regression extends the classical Cox (1972) survival model by simultaneously entering known confounders — age, sex, comorbidities, disease severity — into the model alongside the exposure of primary interest. This adjustment isolates the independent effect of the exposure on the hazard of an event, producing hazard ratios (HRs) that are not distorted by baseline differences between comparison groups. It is the most widely used method for multivariable survival analysis 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.