Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Мультицентровий аналіз конкуруючих ризиків× | Багатоцентрове когортне дослідження× | |
|---|---|---|
| Галузь | Епідеміологія | Епідеміологія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s | Mid-to-late 20th century (widespread adoption 1970s–1990s) |
| Автор методу≠ | 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 |
| Основоположне джерело≠ | 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 |
| Інші назви | 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 |
| Пов'язані≠ | 4 | 6 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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