Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Adaptativo de Riesgos Competitivos× | Diseño Adaptativo de Ensayos× | |
|---|---|---|
| Campo≠ | Epidemiología | Investigación clínica |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1999 (foundational Fine-Gray model); adaptive extensions 2000s–2010s | 1990s-2000s |
| Autor original≠ | Fine & Gray (subdistribution hazard, 1999); adaptive extensions by Beyersmann, Schumacher and colleagues | Stephen Pocock, Christopher Jennison, and statistical methodologists; FDA formalized guidance 2019 |
| Tipo≠ | Statistical survival analysis with adaptive interim monitoring | Research Design |
| 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 ↗ | Pocock, S. J. (2005). Current issues in the design and interpretation of clinical trials. BMJ, 330(7500), 1118–1121. link ↗ |
| Alias≠ | adaptive Fine-Gray analysis, group-sequential competing risks, adaptive subdistribution hazard analysis, competing risks adaptive design | adaptive trial, adaptive design, response-adaptive randomization, RAR |
| Relacionados≠ | 2 | 1 |
| Resumen≠ | Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study to be modified mid-course (e.g., sample size reassessment, early stopping) based on accumulating competing-risk data while maintaining pre-specified type I error control. | An adaptive trial design allows pre-specified modifications to the trial based on interim data—such as sample size re-estimation, stopping for futility or efficacy, dropping ineffective arms, or shifting randomization ratios toward better-performing treatments. Developed systematically in the 1990s–2000s by statisticians like Pocock and Jennison, and formalized by the FDA in 2019, adaptive designs accelerate drug development, reduce exposure to ineffective treatments, and improve efficiency without inflating false-positive rates when properly executed. |
| ScholarGateConjunto de datos ↗ |
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