Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Panel Data Interrupted Time Series× | Uchanganuzi wa Mfululizo wa Wakati Uliokatizwa (ITS)× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2000s–2010s | 2002 |
| Mwanzilishi≠ | Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Aina≠ | Quasi-experimental causal inference | Quasi-experimental segmented regression |
| Chanzo asilia≠ | 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. DOI ↗ | 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. DOI ↗ |
| Majina mbadala≠ | panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. | 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. |
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