Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ конкурирующих рисков с подбором пар× | Взвешивание по обратной вероятности лечения (IPW / IPTW)× | |
|---|---|---|
| Область≠ | Эпидемиология | Причинно-следственный вывод |
| Семейство≠ | Process / pipeline | Regression model |
| Год появления≠ | 1999 (Fine-Gray model); extended to matched designs ~2010s | 2000 |
| Автор метода≠ | Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework) | Robins, Hernán & Brumback |
| Тип≠ | Observational survival analysis with matching and competing events | Causal inference weighting estimator |
| Основополагающий источник≠ | Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Другие названия≠ | matched Fine-Gray analysis, propensity-matched competing risks, matched cause-specific hazard analysis, matched subdistribution hazard analysis | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Matched competing risks analysis combines subject-level matching (e.g., propensity-score matching) with competing risks survival methods to estimate the cause-specific or subdistribution hazard of an event of interest while accounting for competing events that preclude the occurrence of that event. It is widely used in clinical and epidemiological observational studies where patients may die from causes other than the primary outcome of interest, and where treatment groups differ on baseline confounders. | 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. |
| ScholarGateНабор данных ↗ |
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