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| Regresión de Supervivencia por Riesgos Proporcionales de Cox Multicéntrica× | Análisis de Kaplan-Meier× | |
|---|---|---|
| Campo | Epidemiología | Epidemiología |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1972 (Cox model); multicenter applications formalized 1980s–1990s | 1958 |
| Autor original≠ | D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology | Edward L. Kaplan and Paul Meier |
| Tipo≠ | Semi-parametric survival regression for clustered data | Nonparametric survival estimator |
| Fuente seminal≠ | 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 | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. |
| ScholarGateConjunto de datos ↗ |
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