قارن الطرق

	تحليل المخاطر المتنافسة التكيفي	تحليل البقاء
المجال	علم الأوبئة	إحصاء البحث
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1999 (foundational Fine-Gray model); adaptive extensions 2000s–2010s	1958
صاحب الطريقة	Fine & Gray (subdistribution hazard, 1999); adaptive extensions by Beyersmann, Schumacher and colleagues	Edward L. Kaplan and Paul Meier
النوع	Statistical survival analysis with adaptive interim monitoring	Method
المصدر التأسيسي	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.
الأسماء البديلة	adaptive Fine-Gray analysis, group-sequential competing risks, adaptive subdistribution hazard analysis, competing risks adaptive design	Kaplan-Meier analysis, Cox regression, TTE analysis
ذات صلة	2	3
الملخص	Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study to be modified mid-course (e.g., sample size reassessment, early stopping) based on accumulating competing-risk data while maintaining pre-specified type I error control.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.