方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 多中心竞争风险分析× | Kaplan-Meier分析× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s | 1958 |
| 提出者≠ | Fine & Gray (subdistribution hazard model); Prentice et al. (cause-specific hazard model) | Edward L. Kaplan and Paul Meier |
| 类型≠ | Survival / time-to-event statistical analysis | Nonparametric survival estimator |
| 开创性文献≠ | 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 ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| 别名 | multicenter CRA, multi-site competing risks, multicenter cumulative incidence analysis, polycentric competing risks study | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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. | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. |
| ScholarGate数据集 ↗ |
|
|