Salīdzināt metodes

	Saskaņotā Kaplan-Meier analīze	Koksa proporcionālo risku modelis
Nozare	Epidemioloģija	Epidemioloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1958 (KM); matched application formalized 1980s–2000s	1972
Autors	Kaplan & Meier (KM method, 1958); matching extensions developed through propensity score methods (Rosenbaum & Rubin, 1983)	Sir David Roxbee Cox
Tips	Nonparametric survival analysis with observational confounder control	Semi-parametric regression model
Pirmavots	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.
Citi nosaukumi	KM analysis in matched cohorts, propensity-matched survival curves, matched survival analysis, paired Kaplan-Meier	Cox regression, Cox PH model, proportional hazards model, CPH
Saistītās	6	5
Kopsavilkums	Matched Kaplan-Meier analysis estimates and compares survival functions in groups that have been pre-balanced through individual or propensity-score matching. By applying the Kaplan-Meier product-limit estimator to matched cohorts or matched pairs, investigators can visualize time-to-event outcomes while controlling for confounders that would otherwise distort treatment or exposure comparisons in observational data.	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.