Porovnat metody

	Robustní analýza přerušených časových řad	Analýza přerušených časových řad (ITS)
Obor	Kauzální inference	Kauzální inference
Rodina	Regression model	Regression model
Rok vzniku	2010s	2002
Tvůrce	Bernal, Cummins & Gasparrini; Linden (robust extensions)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Typ	Quasi-experimental segmented regression with robust inference	Quasi-experimental segmented regression
Původní zdroj	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.	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.
Další názvy	robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Příbuzné	5	5
Shrnutí	Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation.	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.