Porovnat metody

	Multi-period Interrupted Time Series	Přerušená časová řada s panelovými daty
Obor	Kauzální inference	Kauzální inference
Rodina	Regression model	Regression model
Rok vzniku	2000s-2015	2000s–2010s
Tvůrce	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)
Typ	Quasi-experimental time series regression	Quasi-experimental causal inference
Původní zdroj	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.	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.
Další názvy	multi-period ITS, multiple-interruption ITS, segmented time series with multiple breakpoints, MITS	panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series
Příbuzné	5	5
Shrnutí	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.