So sánh phương pháp

	Phân tích rủi ro cạnh tranh theo phương pháp meta-analytic	Mô hình rủi ro cạnh tranh Fine-Gray
Lĩnh vực	Dịch tễ học	Thống kê
Họ	Process / pipeline	Hypothesis test
Năm ra đời	2000s–2010s (formalized as a pooled approach)	1999
Người khởi xướng	Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward)	Jason P. Fine & Robert J. Gray
Loại	Systematic review / meta-analysis	Subdistribution hazard regression
Công trình gốc	Riley, R. D., Hayden, J. A., Steyerberg, E. W., et al. (2013). Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research. PLOS Medicine, 10(2), e1001380.	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.
Tên gọi khác	meta-analysis of competing risks, pooled competing risks analysis, systematic review competing risks	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli
Liên quan	5	5
Tóm tắt	Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, specialized pooling strategies are required that respect the subdistribution hazard structure introduced by Fine and Gray and the distinction between cause-specific and all-cause hazard models.	The Fine-Gray model is a semiparametric regression method for survival data in which two or more mutually exclusive event types compete to occur first. Proposed by Fine and Gray in 1999, it models the subdistribution hazard of each event type directly, allowing covariates to be linked to the cumulative incidence function (CIF) — the quantity that actually answers 'what is the probability of experiencing event type k by time t?'. It corrects the well-known shortcoming of standard Cox regression, which ignores competing events and thereby overestimates cause-specific probabilities.