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| 후향적 3상 임상시험× | 생존 분석× | |
|---|---|---|
| 분야≠ | 역학 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Late 20th century (ICH E8 1997; widespread retrospective Phase III use from 1990s onward) | 1958 |
| 창시자≠ | Regulatory framework codified by ICH E8/E9 (1997–1998); retrospective application developed through post-marketing and registry practice | Edward L. Kaplan and Paul Meier |
| 유형≠ | Retrospective comparative clinical study | Method |
| 원전≠ | Friedman, L. M., Furberg, C. D., & DeMets, D. L. (2010). Fundamentals of Clinical Trials (4th ed.). Springer. ISBN: 978-1441915856 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| 별칭≠ | retrospective Phase III study, historical Phase III trial, Phase III retrospective analysis, retrospective comparative efficacy trial | Kaplan-Meier analysis, Cox regression, TTE analysis |
| 관련≠ | 5 | 3 |
| 요약≠ | A retrospective Phase III clinical trial evaluates the comparative efficacy and safety of an intervention against a control using data that were collected before the study was designed. Rather than enrolling new patients prospectively, researchers analyze existing records — from registries, hospital databases, or historical trial archives — to address a Phase III-level question: does Treatment A outperform the current standard of care in a large, representative patient population? This design is used when prospective enrollment is infeasible, unethical, or when historical data are sufficiently complete to support a rigorous comparison. | 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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