विधियों की तुलना करें

	जोखिम-समायोजित प्रतिस्पर्धी जोखिम विश्लेषण	कैप्लान-मीयर अनुमानक
क्षेत्र	महामारी विज्ञान	सांख्यिकी
परिवार	Process / pipeline	Survival analysis
उद्भव वर्ष	1999 (subdistribution hazard model); cause-specific hazard framework earlier	1958
प्रवर्तक	Jason Fine and Robert Gray	Edward L. Kaplan and Paul Meier
प्रकार	Regression model for time-to-event data with competing events	Nonparametric estimator
मौलिक स्रोत	Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
उपनाम	competing risks regression, subdistribution hazard model, cause-specific hazard analysis, Fine-Gray model	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator
संबंधित	4	2
सारांश	Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confounders, the method yields unbiased estimates of the absolute probability — the cumulative incidence function — of each event type over time in the presence of competing events.	The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.