Comparer des méthodes

	Cox proportional hazards	Régression logistique
Domaine	Épidémiologie	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1972	1958
Auteur d'origine	Sir David Roxbee Cox	David Roxbee Cox
Type	Semi-parametric regression model	Method
Source fondatrice	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	Cox regression, Cox PH model, proportional hazards model, CPH	logit model, binomial logistic regression, LR
Apparentées	5	3
Résumé	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.