विधियों की तुलना करें

	बहु-केंद्र कॉक्स आनुपातिक जोखिम	कैप्लान-मेयर विश्लेषण
क्षेत्र	महामारी विज्ञान	महामारी विज्ञान
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1972 (Cox model); multicenter applications formalized 1980s–1990s	1958
प्रवर्तक	D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology	Edward L. Kaplan and Paul Meier
प्रकार	Semi-parametric survival regression for clustered data	Nonparametric survival estimator
मौलिक स्रोत	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.
उपनाम	multicenter Cox regression, multisite Cox PH model, stratified Cox model across centers, multicenter survival regression	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
संबंधित	4	5
सारांश	Multicenter Cox proportional hazards regression extends the classic Cox PH model to studies conducted at two or more clinical sites or centers. It estimates the effect of predictors on time-to-event outcomes while explicitly accounting for clustering within centers, between-center heterogeneity, and potential differences in baseline hazard across sites. This design is standard practice in large multicenter RCTs and observational cohort studies in oncology, cardiology, and other clinical fields.	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.