Methoden vergelijken

	Dynamische Onderbroken Tijdreeks	Onderbroken Tijdreeks (ITS) Analyse
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2002–2017	2002
Grondlegger	Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Type	Quasi-experimental time-series design	Quasi-experimental segmented regression
Oorspronkelijke bron	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.	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.
Aliassen	Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Verwant	4	5
Samenvatting	Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how an intervention's impact evolves across time periods, making it especially suited to public health, health services research, and policy evaluation where effects accumulate gradually or wear off after initial impact.	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.