So sánh phương pháp

	Phân tích chuỗi thời gian bị gián đoạn mạnh mẽ	Phân tích chuỗi thời gian bị gián đoạn (ITS)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2010s	2002
Người khởi xướng	Bernal, Cummins & Gasparrini; Linden (robust extensions)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Loại	Quasi-experimental segmented regression with robust inference	Quasi-experimental segmented regression
Công trình gốc	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.
Tên gọi khác	robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Liên quan	5	5
Tóm tắt	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.