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| Cox proportional hazards× | Survival Analysis× | |
|---|---|---|
| Field≠ | Epidemiology | Research Statistics |
| Family | Process / pipeline | Process / pipeline |
| Year of origin≠ | 1972 | 1958 |
| Originator≠ | Sir David Roxbee Cox | Edward L. Kaplan and Paul Meier |
| Type≠ | Semi-parametric regression model | Method |
| Seminal source≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Aliases≠ | Cox regression, Cox PH model, proportional hazards model, CPH | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Related≠ | 5 | 3 |
| Summary≠ | 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. | 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. |
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