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| Kaplan-Meier 분석× | 생존 분석× | |
|---|---|---|
| 분야≠ | 역학 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도 | 1958 | 1958 |
| 창시자 | Edward L. Kaplan and Paul Meier | Edward L. Kaplan and Paul Meier |
| 유형≠ | Nonparametric survival estimator | Method |
| 원전 | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| 별칭≠ | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve | Kaplan-Meier analysis, Cox regression, TTE analysis |
| 관련≠ | 5 | 3 |
| 요약≠ | 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. | 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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