Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Marginaal Structureel Model (MSM)× | Inverse Probability of Treatment Weighting (IPW / IPTW)× | |
|---|---|---|
| Vakgebied | Causale inferentie | Causale inferentie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan | 2000 | 2000 |
| Grondlegger≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Robins, Hernán & Brumback |
| Type≠ | Causal model / semiparametric weighting | Causal inference weighting estimator |
| Oorspronkelijke bron≠ | 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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Aliassen≠ | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Verwant | 5 | 5 |
| Samenvatting≠ | 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. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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