Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Multicéntrico de Riesgos Competitivos× | Estudio de cohortes multicéntrico× | |
|---|---|---|
| Campo | Epidemiología | Epidemiología |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Autor original≠ | Fine & Gray (subdistribution hazard model); Prentice et al. (cause-specific hazard model) | Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992) |
| Tipo≠ | Survival / time-to-event statistical analysis | Observational longitudinal study |
| Fuente seminal≠ | Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Alias | multicenter CRA, multi-site competing risks, multicenter cumulative incidence analysis, polycentric competing risks study | multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study |
| Relacionados≠ | 4 | 6 |
| Resumen≠ | Multicenter competing risks analysis is a time-to-event method applied across multiple clinical centers to estimate the probability of a specific event of interest when other mutually exclusive events — competing risks — can preclude its occurrence. By pooling data from diverse sites, it achieves the sample sizes needed to model rare events and enables assessment of center-level variation in cumulative incidence and covariate effects. | A multicenter cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and population diversity that single-site designs cannot match, making it the workhorse of large-scale epidemiological and clinical research. |
| ScholarGateConjunto de datos ↗ |
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