השוואת שיטות

	ניתוח סיכונים מתחרים רב-מרכזי	רגרסיית סיכונים יחסיים מרובי מרכזים
תחום	אפידמיולוגיה	אפידמיולוגיה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s	1972 (Cox model); multicenter applications formalized 1980s–1990s
הוגה השיטה	Fine & Gray (subdistribution hazard model); Prentice et al. (cause-specific hazard model)	D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology
סוג	Survival / time-to-event statistical analysis	Semi-parametric survival regression for clustered data
מקור מכונן	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.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
כינויים	multicenter CRA, multi-site competing risks, multicenter cumulative incidence analysis, polycentric competing risks study	multicenter Cox regression, multisite Cox PH model, stratified Cox model across centers, multicenter survival regression
קשורות	4	4
תקציר	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.	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.