Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Многопериодни прекъснати времеви редове× | Анализ на прекъснати времеви редове (ITS)× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2000s-2015 | 2002 |
| Създател≠ | Extended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Тип≠ | Quasi-experimental time series regression | Quasi-experimental segmented regression |
| Основополагащ източник≠ | Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750. DOI ↗ | 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 ↗ |
| Други названия≠ | multi-period ITS, multiple-interruption ITS, segmented time series with multiple breakpoints, MITS | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Свързани | 5 | 5 |
| Резюме≠ | Multi-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS estimates the level change and slope change attributable to each intervention while controlling for the underlying secular trend and for the effects of earlier interruptions. | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
| ScholarGateНабор от данни ↗ |
|
|