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| 적응형 생존 분석× | Kaplan-Meier 추정량× | |
|---|---|---|
| 분야≠ | 역학 | 통계학 |
| 계열≠ | Process / pipeline | Survival analysis |
| 기원 연도≠ | 2000s (formalized ~2000–2006) | 1958 |
| 창시자≠ | Bauer, Posch, and collaborators (adaptive design framework); Lachin & Foulkes (event-driven survival trial foundations) | Edward L. Kaplan and Paul Meier |
| 유형≠ | Adaptive statistical design for time-to-event outcomes | Nonparametric estimator |
| 원전≠ | 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 ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| 별칭 | adaptive time-to-event analysis, adaptive event-driven trial analysis, adaptive hazard modeling, ASA | KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator |
| 관련≠ | 3 | 2 |
| 요약≠ | 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 Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research. |
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