Comparer des méthodes

	Analyse de survie appariée	Cox proportional hazards
Domaine	Épidémiologie	Épidémiologie
Famille	Process / pipeline	Process / pipeline
Année d'origine	1983 (propensity-score matching); applied to survival outcomes throughout 1990s–2000s	1972
Auteur d'origine	Building on Kaplan & Meier (1958) and Cox (1972); matching framework formalised in observational study design literature (Rosenbaum & Rubin, 1983)	Sir David Roxbee Cox
Type	Observational study analytic method	Semi-parametric regression model
Source fondatrice	Austin, P. C. (2014). Graphical assessments of the balance of propensity score matched samples: A SAS macro. Journal of Statistical Software, 58(7), 1-29. Also see Austin, P. C. (2017). A tutorial on multilevel survival analysis: Methods, models and applications. International Statistical Review, 85(2), 185-203.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Alias	matched time-to-event analysis, propensity-matched survival analysis, matched Kaplan-Meier analysis, paired survival analysis	Cox regression, Cox PH model, proportional hazards model, CPH
Apparentées	4	5
Résumé	Matched survival analysis combines a matching design — typically propensity score matching or exact matching on key covariates — with time-to-event methods such as Kaplan-Meier estimation and the Cox proportional hazards model. By pairing treated and control subjects who are similar on observed confounders before estimating survival curves or hazard ratios, the approach reduces confounding bias in non-randomised studies and produces more credible comparisons of event-free survival between exposure groups.	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.