เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Heterogeneous Treatment Effect Marginal Structural Model× | แบบจำลองโครงสร้างส่วนเพิ่ม (Marginal Structural Model: MSM)× | |
|---|---|---|
| สาขาวิชา | การอนุมานเชิงสาเหตุ | การอนุมานเชิงสาเหตุ |
| ตระกูล | Regression model | Regression model |
| ปีกำเนิด≠ | 2000–2010s | 2000 |
| ผู้ริเริ่ม≠ | Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010s | James M. Robins, Miguel A. Hernan, Babette Brumback |
| ประเภท≠ | Causal inference / weighted regression with effect modification | Causal model / semiparametric weighting |
| แหล่งต้นตำรับ | 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 ↗ |
| ชื่อเรียกอื่น | HTE-MSM, heterogeneous MSM, subgroup MSM, effect-modified marginal structural model | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| ที่เกี่ยวข้อง | 5 | 5 |
| สรุป≠ | The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatment with effect modifiers in the weighted outcome model, the approach produces subgroup-specific or continuous causal effect estimates from observational data. | 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. |
| ScholarGateชุดข้อมูล ↗ |
|
|