Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| G-изчисление (параметрична G-формула)× | Претегляне с обратна вероятност на лечението (IPW / IPTW)× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1986 | 2000 |
| Създател≠ | James M. Robins | Robins, Hernán & Brumback |
| Тип≠ | Parametric causal effect estimation | Causal inference weighting estimator |
| Основополагащ източник≠ | Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods: application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Други названия≠ | G-formula, Parametric G-formula, Standardization | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Свързани≠ | 2 | 5 |
| Резюме≠ | G-computation is a causal inference method for estimating the effect of an intervention or treatment on an outcome from observational data. Developed by James M. Robins in 1986, it provides a parametric approach to standardization that can handle time-varying exposures and confounders. The method estimates what the population outcome would be under different intervention scenarios by utilizing fitted outcome models. | 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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