Sammenlign metoder

	Multicenter analyse af konkurrerende risici	Kaplan-Meier-analyse
Fagområde	Epidemiologi	Epidemiologi
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s	1958
Ophavsperson	Fine & Gray (subdistribution hazard model); Prentice et al. (cause-specific hazard model)	Edward L. Kaplan and Paul Meier
Type	Survival / time-to-event statistical analysis	Nonparametric survival estimator
Oprindelig kilde	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.
Aliasser	multicenter CRA, multi-site competing risks, multicenter cumulative incidence analysis, polycentric competing risks study	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
Relaterede	4	5
Resumé	Multicenter competing risks analysis is a time-to-event method applied across multiple clinical centers to estimate the probability of a specific event of interest when other mutually exclusive events — competing risks — can preclude its occurrence. By pooling data from diverse sites, it achieves the sample sizes needed to model rare events and enables assessment of center-level variation in cumulative incidence and covariate effects.	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.