Methoden vergelijken

	Marginaal Structureel Model voor Heterogene Behandeleffecten (HTE-MSM)	Marginaal Structureel Model (MSM)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2000–2010s	2000
Grondlegger	Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010s	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Causal inference / weighted regression with effect modification	Causal model / semiparametric weighting
Oorspronkelijke bron	Robins, J. M., Hernan, 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.
Aliassen	HTE-MSM, heterogeneous MSM, subgroup MSM, effect-modified marginal structural model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Verwant	5	5
Samenvatting	The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatment with effect modifiers in the weighted outcome model, the approach produces subgroup-specific or continuous causal effect estimates from observational data.	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.