Usporedite metode
Pregledajte odabrane metode jednu uz drugu; retci koji se razlikuju su istaknuti.
| Višeperiodični prekinuti vremenski niz× | Panel Data Interrupted Time Series× | |
|---|---|---|
| Područje | Uzročno zaključivanje | Uzročno zaključivanje |
| Obitelj | Regression model | Regression model |
| Godina nastanka≠ | 2000s-2015 | 2000s–2010s |
| Tvorac≠ | Extended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s) | Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial) |
| Vrsta≠ | Quasi-experimental time series regression | Quasi-experimental causal inference |
| Temeljni izvor≠ | 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 ↗ | 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 ↗ |
| Drugi nazivi | multi-period ITS, multiple-interruption ITS, segmented time series with multiple breakpoints, MITS | panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series |
| Srodne | 5 | 5 |
| Sažetak≠ | 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. | 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. |
| ScholarGateSkup podataka ↗ |
|
|