Sammenlign metoder

	Robust Marginal Structural Model	Marginal Structural Model (MSM)
Fagfelt	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Opprinnelsesår	2000–2004	2000
Opphavsperson	Robins, Hernán & Brumback; robustness extensions by Scharfstein, Rotnitzky, Lunceford & Davidian	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Causal inference / weighted regression	Causal model / semiparametric weighting
Opprinnelig kilde	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	robust MSM, doubly-robust MSM, sandwich-SE MSM, robust IPTW marginal structural model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relaterte	6	5
Sammendrag	Robust Marginal Structural Models (robust MSMs) extend the standard MSM framework — which uses inverse probability of treatment weighting to handle time-varying confounding — by pairing IPTW estimation with sandwich (robust) standard errors or doubly-robust estimators. This combination yields valid causal estimates and reliable inference even when the outcome regression model is mildly misspecified or weights are moderately variable.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.