方法对比
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| 实用剂量-效应分析× | 生存分析× | |
|---|---|---|
| 领域≠ | 流行病学 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s (formalized in pragmatic trial context) | 1958 |
| 提出者≠ | Rooted in pharmacoepidemiology and pragmatic trial methodology; PRECIS framework by Thorpe et al. (2009) | Edward L. Kaplan and Paul Meier |
| 类型≠ | Observational or experimental quantitative method | Method |
| 开创性文献≠ | Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301–1309. 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≠ | real-world dose-response analysis, pragmatic exposure-response study, dose-response in pragmatic trials, effectiveness dose-response analysis | Kaplan-Meier analysis, Cox regression, TTE analysis |
| 相关≠ | 4 | 3 |
| 摘要≠ | Pragmatic dose-response analysis quantifies how varying levels of an exposure or treatment relate to clinical outcomes under real-world conditions. By embedding dose-response questions within pragmatic study designs — broad eligibility criteria, routine care settings, and heterogeneous populations — it bridges the gap between controlled pharmacological dose-finding and the messy variability of everyday clinical practice. The approach is especially valued when the goal is to establish or refine optimal dosing guidance from evidence that reflects actual patient populations. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
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