Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Устойчив анализ на прекъснати времеви редове× | Панелни прекъснати времеви редове (Panel Data Interrupted Time Series)× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2010s | 2000s–2010s |
| Създател≠ | Bernal, Cummins & Gasparrini; Linden (robust extensions) | Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial) |
| Тип≠ | Quasi-experimental segmented regression with robust inference | Quasi-experimental causal inference |
| Основополагащ източник≠ | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ | Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| Други названия | robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA | panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series |
| Свързани | 5 | 5 |
| Резюме≠ | Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation. | Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention. |
| ScholarGateНабор от данни ↗ |
|
|