Salīdzināt metodes

	Saskaņotā Koksa proporcionālo risku modelēšana	Kaplan-Meier analīze
Nozare	Epidemioloģija	Epidemioloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1972 (Cox model); matched extension widely adopted 1970s–1980s	1958
Autors	D. R. Cox (Cox model, 1972); stratification extension for matched designs by subsequent methodologists including D. C. Thomas	Edward L. Kaplan and Paul Meier
Tips	Semi-parametric survival regression for matched data	Nonparametric survival estimator
Pirmavots	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.
Citi nosaukumi	stratified Cox regression, conditional Cox model, matched survival analysis, Cox model for matched pairs	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
Saistītās	4	5
Kopsavilkums	Matched Cox proportional hazards is a survival analysis method that extends the Cox regression model to appropriately handle data arising from matched study designs — matched cohorts or matched case-control studies with time-to-event outcomes. By stratifying the partial likelihood by matched set, the method eliminates confounding from matching factors without estimating their baseline hazard, yielding valid hazard ratio estimates that are free from matching-induced bias.	Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research.