Comparar métodos

	Análise Pragmática de Kaplan-Meier	Cox Proportional Hazards
Área	Epidemiologia	Epidemiologia
Família	Process / pipeline	Process / pipeline
Ano de origem	1958 (estimator); pragmatic application formalized 1967 onward	1972
Autor original	Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967)	Sir David Roxbee Cox
Tipo	Non-parametric survival estimator within pragmatic study design	Semi-parametric regression model
Fonte seminal	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.
Outros nomes	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
Relacionados	5	5
Resumo	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.