Comparer des méthodes

	Régression de Cox à risques proportionnels ajustés sur le risque	Appariement par score de propension
Domaine	Épidémiologie	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1972 (Cox model); risk adjustment widespread from 1980s	1983
Auteur d'origine	D. R. Cox (base model); risk-adjustment as routine practice formalised through clinical epidemiology literature from the 1980s onward	Paul Rosenbaum and Donald Rubin
Type	Multivariable survival regression	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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	adjusted Cox regression, multivariable Cox model, covariate-adjusted survival analysis, risk-adjusted survival model	PSM, propensity score weighting, covariate balance
Apparentées	5	3
Résumé	Risk-adjusted Cox proportional hazards regression extends the classical Cox (1972) survival model by simultaneously entering known confounders — age, sex, comorbidities, disease severity — into the model alongside the exposure of primary interest. This adjustment isolates the independent effect of the exposure on the hazard of an event, producing hazard ratios (HRs) that are not distorted by baseline differences between comparison groups. It is the most widely used method for multivariable survival analysis in clinical and epidemiological research.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.