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| 위험 조정 카플란-마이어 분석× | 역확률 가중치 (Inverse Probability Weighting, IPW / IPTW)× | |
|---|---|---|
| 분야≠ | 역학 | 인과추론 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 2001–2004 (formal statistical framework for weighted KM curves) | 2000 |
| 창시자≠ | Conceptual basis: Kaplan & Meier (1958); risk-adjustment via IPTW formalised by Hernán, Brumback & Robins (2001), with practical implementation by Cole & Hernán (2004) | Robins, Hernán & Brumback |
| 유형≠ | Adjusted non-parametric survival method | Causal inference weighting estimator |
| 원전≠ | 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 ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 별칭≠ | weighted Kaplan-Meier, IPTW-adjusted Kaplan-Meier, propensity-score-weighted survival curves, adjusted survival curves | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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