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。
| 自适应生存分析× | Cox比例风险模型× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s (formalized ~2000–2006) | 1972 |
| 提出者≠ | Bauer, Posch, and collaborators (adaptive design framework); Lachin & Foulkes (event-driven survival trial foundations) | Sir David Roxbee Cox |
| 类型≠ | Adaptive statistical design for time-to-event outcomes | Semi-parametric regression model |
| 开创性文献≠ | Bauer, P., & Posch, M. (2004). Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections. Statistics in Medicine, 23(8), 1333–1353. link ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| 别名 | adaptive time-to-event analysis, adaptive event-driven trial analysis, adaptive hazard modeling, ASA | Cox regression, Cox PH model, proportional hazards model, CPH |
| 相关≠ | 3 | 5 |
| 摘要≠ | Adaptive survival analysis integrates adaptive clinical trial design with time-to-event statistical methods, allowing pre-specified modifications to sample size, event targets, or allocation ratios at interim stages based on accumulating survival data. It is widely used in oncology, cardiovascular, and infectious disease research where the primary endpoint is a hazard-based outcome such as progression-free survival or all-cause mortality. | The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research. |
| ScholarGate数据集 ↗ |
|
|