Linganisha mbinu

	Regressioni ya Kuishi ya Weibull ya Parametric	Mfumo wa Matukio Yanayoshindana wa Fine-Gray
Nyanja	Uchanganuzi wa Uhai	Takwimu
Familia	Survival analysis	Hypothesis test
Mwaka wa asili	1951	1999
Mwanzilishi	Waloddi Weibull	Jason P. Fine & Robert J. Gray
Aina	Fully parametric survival regression model	Subdistribution hazard regression
Chanzo asilia	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.	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.
Majina mbadala	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma	competing risks regression, subdistribution hazard model, Fine-Gray model, Fine-Gray Competing Risks Modeli
Zinazohusiana	4	5
Muhtasari	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.	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.