Porównaj metody

	Analiza pragmatyczna Kaplana-Meiera	Model Coxa z proporcjonalnym hazardem
Dziedzina	Epidemiologia	Epidemiologia
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1958 (estimator); pragmatic application formalized 1967 onward	1972
Twórca	Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967)	Sir David Roxbee Cox
Typ	Non-parametric survival estimator within pragmatic study design	Semi-parametric regression model
Źródło pierwotne	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Inne nazwy	pragmatic KM analysis, real-world Kaplan-Meier, pragmatic survival curve estimation, KM analysis in pragmatic trials	Cox regression, Cox PH model, proportional hazards model, CPH
Pokrewne	5	5
Podsumowanie	Pragmatic Kaplan-Meier analysis applies the non-parametric Kaplan-Meier product-limit estimator to time-to-event data collected under real-world or pragmatic conditions — diverse populations, routine clinical care, minimal exclusions, and standard-of-care comparators. Unlike explanatory trials designed to isolate a treatment effect under ideal conditions, pragmatic designs accept real-world heterogeneity, and the resulting survival curves reflect the effectiveness of an intervention as it actually performs in clinical practice.	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.