Linganisha mbinu

	Mchoro wa Kimuundo wa Pembezoni wa Athari Tofauti za Matibabu (HTE-MSM)	Mfumo wa Kielelezo wa Uhusiano (MSM)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000–2010s	2000
Mwanzilishi	Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010s	James M. Robins, Miguel A. Hernan, Babette Brumback
Aina	Causal inference / weighted regression with effect modification	Causal model / semiparametric weighting
Chanzo asilia	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.
Majina mbadala	HTE-MSM, heterogeneous MSM, subgroup MSM, effect-modified marginal structural model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Zinazohusiana	5	5
Muhtasari	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.