Сравнение методов

	Маргинальная структурная модель (MSM)	G-computation (Параметрическая G-формула)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2000	1986
Автор метода	James M. Robins, Miguel A. Hernan, Babette Brumback	James M. Robins
Тип	Causal model / semiparametric weighting	Parametric causal effect estimation
Основополагающий источник	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. (1986). A new approach to causal inference in mortality studies with sustained exposure periods: application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.
Другие названия	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model	G-formula, Parametric G-formula, Standardization
Связанные	5	2
Сводка	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.	G-computation is a causal inference method for estimating the effect of an intervention or treatment on an outcome from observational data. Developed by James M. Robins in 1986, it provides a parametric approach to standardization that can handle time-varying exposures and confounders. The method estimates what the population outcome would be under different intervention scenarios by utilizing fitted outcome models.