Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пространственный анализ прерванных временных рядов× | Анализ прерванных временных рядов (Interrupted Time Series, ITS)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s–2000s | 2002 |
| Автор метода≠ | Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Тип≠ | Quasi-experimental causal inference with spatial adjustment | Quasi-experimental segmented regression |
| Основополагающий источник≠ | McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950 | 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 ↗ |
| Другие названия≠ | Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Spatial Interrupted Time Series (Spatial ITS) extends the classic ITS design to settings where units are geo-referenced and outcomes in one location may spill over into or correlate with outcomes in neighbouring locations. It estimates the causal effect of a discrete intervention on an outcome time series while explicitly modelling geographic autocorrelation, preventing biased standard errors and enabling detection of spatial spillovers. | 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. |
| ScholarGateНабор данных ↗ |
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