So sánh phương pháp

	Mô hình Sinh tồn Tham số Linh hoạt (Royston-Parmar)	Mô hình rủi ro cạnh tranh Fine-Gray
Lĩnh vực	Phân tích sống còn	Thống kê
Họ	Survival analysis	Hypothesis test
Năm ra đời	2002	1999
Người khởi xướng	Royston, P. & Parmar, M.K.B.	Jason P. Fine & Robert J. Gray
Loại	Parametric survival regression model	Subdistribution hazard regression
Công trình gốc	Royston, P. & Parmar, M.K.B. (2002). Flexible Parametric Proportional-Hazards and Proportional-Odds Models for Censored Survival Data, with Application to Prognostic Modelling and Estimation of Treatment Effects. Statistics in Medicine, 21(15), 2175–2197.	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	flexible parametric model, restricted cubic spline survival model, stpm2, Esnek Parametrik Survival Modeli (Royston-Parmar)	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli
Liên quan	8	5
Tóm tắt	The Royston-Parmar model, introduced by Royston and Parmar in 2002, is a modern parametric approach to survival analysis that replaces the rigid distributional assumptions of classical models with a restricted cubic spline fitted to the log-cumulative-hazard scale. It combines the interpretability of a fully parametric model with the flexibility to capture non-standard hazard shapes, and it supports proportional-hazards, accelerated failure-time, and proportional-odds link functions.	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.