Compară metode

	Regresia Cox cu Hazarduri Proporționale Retrospectivă	Analiza supraviețuirii
Domeniu	Epidemiologie	Statistică pentru cercetare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1972	1958
Autorul original	David R. Cox	Edward L. Kaplan and Paul Meier
Tip	Semi-parametric survival regression	Method
Sursa seminală	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Denumiri alternative	Cox PH regression (retrospective), retrospective Cox survival model, retrospective hazard regression, Cox model on historical data	Kaplan-Meier analysis, Cox regression, TTE analysis
Înrudite	5	3
Rezumat	Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline hazard, making it the dominant analytic tool when the investigator works backward from already-recorded outcomes and exposures.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.