So sánh phương pháp

	Phân tích Rủi ro Cạnh tranh Hồi cứu	Phân tích Kaplan-Meier
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1978 (cause-specific); 1999 (subdistribution/Fine-Gray)	1958
Người khởi xướng	Fine & Gray (subdistribution model); Prentice et al. (cause-specific framework)	Edward L. Kaplan and Paul Meier
Loại	Retrospective observational survival analysis	Nonparametric survival estimator
Công trình gốc	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.
Tên gọi khác	retrospective CRA, competing risks survival analysis (retrospective), cause-specific hazard analysis (retrospective), subdistribution hazard analysis (retrospective)	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
Liên quan	4	5
Tóm tắt	Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the probability of each event type while accounting for the fact that occurrence of one event permanently precludes the others. Widely used in oncology, cardiology, and transplant medicine where administrative or registry records are the data source.	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.