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| Paneldaten-Modell für marginale Struktur (MSM)× | Marginales Strukturelles Modell (MSM)× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr | 2000 | 2000 |
| Urheber | James M. Robins, Miguel A. Hernan, Babette Brumback | James M. Robins, Miguel A. Hernan, Babette Brumback |
| Typ≠ | Causal model for time-varying treatments | Causal model / semiparametric weighting |
| Wegweisende Quelle | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Aliasnamen | MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | A panel data marginal structural model (MSM) uses inverse probability of treatment weighting (IPTW) across multiple time periods to estimate the causal effect of a time-varying treatment, while appropriately adjusting for time-varying confounders that are themselves affected by prior treatment — a bias source that conventional regression cannot handle. | 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. |
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