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| Régression de Cox mult centrique à risques proportionnels× | Analyse de survie× | |
|---|---|---|
| Domaine≠ | Épidémiologie | Statistiques de recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1972 (Cox model); multicenter applications formalized 1980s–1990s | 1958 |
| Auteur d'origine≠ | D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology | Edward L. Kaplan and Paul Meier |
| Type≠ | Semi-parametric survival regression for clustered data | 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. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Alias≠ | multicenter Cox regression, multisite Cox PH model, stratified Cox model across centers, multicenter survival regression | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Multicenter Cox proportional hazards regression extends the classic Cox PH model to studies conducted at two or more clinical sites or centers. It estimates the effect of predictors on time-to-event outcomes while explicitly accounting for clustering within centers, between-center heterogeneity, and potential differences in baseline hazard across sites. This design is standard practice in large multicenter RCTs and observational cohort studies in oncology, cardiology, and other clinical fields. | 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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