Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Multicéntrico de Kaplan-Meier× | Regresión de Supervivencia por Riesgos Proporcionales de Cox Multicéntrica× | |
|---|---|---|
| Campo | Epidemiología | Epidemiología |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1958 (base method); multicenter designs common from 1970s | 1972 (Cox model); multicenter applications formalized 1980s–1990s |
| Autor original≠ | Edward L. Kaplan and Paul Meier (method); multicenter application developed through large clinical trial consortia from the 1970s onward | D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology |
| Tipo≠ | Nonparametric survival analysis in a multicenter setting | Semi-parametric survival regression for clustered data |
| Fuente seminal≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| Alias | pooled Kaplan-Meier, multi-site KM analysis, multicenter survival curve analysis, KM pooled analysis | multicenter Cox regression, multisite Cox PH model, stratified Cox model across centers, multicenter survival regression |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | Multicenter Kaplan-Meier analysis applies the Kaplan-Meier nonparametric estimator to time-to-event data collected from two or more clinical centers. By pooling or stratifying data across sites, it estimates survival functions and compares them between treatment groups while accounting for potential center effects, enabling conclusions with greater statistical power and broader generalizability than single-center studies. | 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. |
| ScholarGateConjunto de datos ↗ |
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