Salīdzināt metodes

	Robust Interrupted Time Series Analysis	Paneļdatu pārtraukto laika rindu analīze
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2010s	2000s–2010s
Autors	Bernal, Cummins & Gasparrini; Linden (robust extensions)	Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)
Tips	Quasi-experimental segmented regression with robust inference	Quasi-experimental causal inference
Pirmavots	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.	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.
Citi nosaukumi	robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA	panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series
Saistītās	5	5
Kopsavilkums	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.	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.