Comparar métodos

	Modelo de Riesgos Competitivos de Fine-Gray	Regresión de Cox con covariables dependientes del tiempo
Campo	Estadística	Supervivencia
Familia	Hypothesis test	Survival analysis
Año de origen	1999	1972
Autor original	Jason P. Fine & Robert J. Gray	Cox, D. R. (extended formulation by Therneau & Grambsch)
Tipo	Subdistribution hazard regression	Semi-parametric hazard regression model
Fuente seminal	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.	Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.
Alias	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli	time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu
Relacionados	5	4
Resumen	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.	Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.