Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многоцентровый анализ конкурирующих рисков× | Когортное исследование с участием нескольких центров× | |
|---|---|---|
| Область | Эпидемиология | Эпидемиология |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Автор метода≠ | 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) |
| Тип≠ | Survival / time-to-event statistical analysis | Observational longitudinal study |
| Основополагающий источник≠ | 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 |
| Другие названия | 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 |
| Связанные≠ | 4 | 6 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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