Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Mitmeperioodiline pöörd-tõenäosuskaalutamine× | Marginaalne strukturaalne mudel (MSM)× | |
|---|---|---|
| Valdkond | Põhjuslik järeldamine | Põhjuslik järeldamine |
| Perekond | Regression model | Regression model |
| Tekkeaasta | 2000 | 2000 |
| Looja≠ | Robins, Hernan & Brumback | James M. Robins, Miguel A. Hernan, Babette Brumback |
| Tüüp≠ | Weighted causal estimator | Causal model / semiparametric weighting |
| Algallikas | 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 ↗ |
| Rööpnimetused | longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| Seotud≠ | 6 | 5 |
| Kokkuvõte≠ | Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies. | 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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