Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на съвпадащи конкуриращи се рискове× | Претегляне с обратна вероятност на лечението (IPW / IPTW)× | |
|---|---|---|
| Област≠ | Епидемиология | Причинно-следствено заключение |
| Семейство≠ | Process / pipeline | Regression model |
| Година на възникване≠ | 1999 (Fine-Gray model); extended to matched designs ~2010s | 2000 |
| Създател≠ | 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 |
| Основополагащ източник≠ | 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 ↗ |
| Други 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 |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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 от данни ↗ |
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