Comparer des méthodes

	Analyse de Kaplan-Meier ajustée au risque	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)
Domaine	Épidémiologie	Inférence causale
Famille	Process / pipeline	Regression model
Année d'origine	2001–2004 (formal statistical framework for weighted KM curves)	2000
Auteur d'origine	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
Type	Adjusted non-parametric survival method	Causal inference weighting estimator
Source fondatrice	Cole, S. R., & Hernan, M. A. (2004). Adjusted survival curves with inverse probability weights. Computer Methods and Programs in Biomedicine, 75(1), 45–49.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	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
Apparentées	5	5
Résumé	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.