Methoden vergelijken

	Multi-period Propensity Score Weighting	Marginaal Structureel Model (MSM)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2000	2000
Grondlegger	Robins, Hernán, and Brumback (building on Robins' g-computation framework)	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Quasi-experimental causal inference	Causal model / semiparametric weighting
Oorspronkelijke bron	Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Aliassen	longitudinal propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Verwant	5	5
Samenvatting	Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding.	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.