So sánh phương pháp

	Phân tích rủi ro cạnh tranh tiền cứu	Phân tích Kaplan-Meier
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1978–1999 (foundational frameworks; prospective application standard by 2000s)	1958
Người khởi xướng	Fine & Gray (subdistribution hazard model, 1999); Prentice, Kalbfleisch et al. (cause-specific hazard, 1978)	Edward L. Kaplan and Paul Meier
Loại	Observational analytic study with event-time statistical 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	prospective CRA, prospective subdistribution hazard analysis, prospective cause-specific hazard analysis, forward-looking competing events analysis	KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve
Liên quan	4	5
Tóm tắt	Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing outcomes. It combines the temporal clarity of prospective cohort follow-up with the statistical rigor of competing risks methodology to avoid the overestimation inherent in standard Kaplan-Meier curves when multiple event types are present.	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.