Σύγκριση μεθόδων

	Δυναμική Διακοπτόμενη Χρονοσειρά	Ανάλυση Διακοπτόμενης Χρονοσειράς (ITS)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2002–2017	2002
Δημιουργός	Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Τύπος	Quasi-experimental time-series design	Quasi-experimental segmented regression
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Συναφείς	4	5
Σύνοψη	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.