방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Kaplan-Meier 분석× | 생존 곡선 비교를 위한 로그-순위 검정× | |
|---|---|---|
| 분야≠ | 역학 | 생존분석 |
| 계열≠ | Process / pipeline | Survival analysis |
| 기원 연도≠ | 1958 | 1966 |
| 창시자≠ | Edward L. Kaplan and Paul Meier | Mantel, N. |
| 유형≠ | Nonparametric survival estimator | Non-parametric hypothesis test |
| 원전≠ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ | Mantel, N. (1966). Evaluation of Survival Data and Two New Rank Order Statistics Arising in Its Consideration. Cancer Chemotherapy Reports, 50(3), 163–170. link ↗ |
| 별칭 | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve | Mantel log-rank test, Mantel-Cox test, log-rank sağkalım testi, Log-Rank Testi |
| 관련≠ | 5 | 2 |
| 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. | The log-rank test, developed by Nathan Mantel in 1966, is a non-parametric hypothesis test that compares the overall survival experience of two or more groups throughout the entire follow-up period. It is the standard companion to Kaplan-Meier curves and determines whether observed differences between curves are statistically meaningful. |
| ScholarGate데이터셋 ↗ |
|
|