Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз Каплана-Майєра з поправкою на ризик× | Аналіз Каплана-Майєра× | |
|---|---|---|
| Галузь | Епідеміологія | Епідеміологія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2001–2004 (formal statistical framework for weighted KM curves) | 1958 |
| Автор методу≠ | Conceptual basis: Kaplan & Meier (1958); risk-adjustment via IPTW formalised by Hernán, Brumback & Robins (2001), with practical implementation by Cole & Hernán (2004) | Edward L. Kaplan and Paul Meier |
| Тип≠ | Adjusted non-parametric survival method | Nonparametric survival estimator |
| Основоположне джерело≠ | Cole, S. R., & Hernan, M. A. (2004). Adjusted survival curves with inverse probability weights. Computer Methods and Programs in Biomedicine, 75(1), 45–49. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Інші назви | weighted Kaplan-Meier, IPTW-adjusted Kaplan-Meier, propensity-score-weighted survival curves, adjusted survival curves | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Risk-adjusted Kaplan-Meier analysis combines the non-parametric Kaplan-Meier estimator with inverse probability of treatment weighting (IPTW) or similar risk-adjustment procedures to produce survival curves that are comparable across groups as if the groups had identical distributions of baseline confounders. It is the observational-study analogue of plotting survival curves from a randomised trial. | 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. |
| ScholarGateНабір даних ↗ |
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