Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Multi-period Interrupted Time Series× | Onderbroken Tijdreeks (ITS) Analyse× | |
|---|---|---|
| Vakgebied | Causale inferentie | Causale inferentie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 2000s-2015 | 2002 |
| Grondlegger≠ | 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) |
| Type≠ | Quasi-experimental time series regression | Quasi-experimental segmented regression |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen≠ | 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 |
| Verwant | 5 | 5 |
| Samenvatting≠ | 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. |
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