Linganisha mbinu

	Uchambuzi wa Hatari Shikani za Nyuma	Mfumo wa Cox Proportional Hazards
Nyanja	Epidemiolojia	Epidemiolojia
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1978 (cause-specific); 1999 (subdistribution/Fine-Gray)	1972
Mwanzilishi	Fine & Gray (subdistribution model); Prentice et al. (cause-specific framework)	Sir David Roxbee Cox
Aina	Retrospective observational survival analysis	Semi-parametric regression model
Chanzo asilia	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.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Majina mbadala	retrospective CRA, competing risks survival analysis (retrospective), cause-specific hazard analysis (retrospective), subdistribution hazard analysis (retrospective)	Cox regression, Cox PH model, proportional hazards model, CPH
Zinazohusiana	4	5
Muhtasari	Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the probability of each event type while accounting for the fact that occurrence of one event permanently precludes the others. Widely used in oncology, cardiology, and transplant medicine where administrative or registry records are the data source.	The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.