Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Modeli Makrostrukural Hapësinor× | Modeli Strukturor i Shumës (MSM)× | |
|---|---|---|
| Fusha | Inferenca kauzale | Inferenca kauzale |
| Familja | Regression model | Regression model |
| Viti i origjinës≠ | 2000s–2010s | 2000 |
| Krijuesi≠ | Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature | James M. Robins, Miguel A. Hernan, Babette Brumback |
| Lloji≠ | Causal inference / spatial weighting | Causal model / semiparametric weighting |
| Burimi themelues | 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 ↗ |
| Emërtime të tjera | Spatial MSM, Geospatial MSM, Spatial IPW-MSM, Space-time marginal structural model | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| Të lidhura≠ | 6 | 5 |
| Përmbledhja≠ | The Spatial Marginal Structural Model (Spatial MSM) extends the classical marginal structural model to settings where units are geographically distributed and spatial dependencies — such as neighborhood spillovers, clustering, and spatial confounding — may bias causal estimates. It estimates causal effects of spatially varying exposures by constructing inverse probability weights that account for both individual covariates and spatial location, then fitting a weighted outcome model in the resulting pseudo-population. | 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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