Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Heterogen Behandlings-effekt Interrupted Time Series (HTE-ITS)× | Avbruten tidsserieanalys (ITS)× | |
|---|---|---|
| Ämnesområde | Kausal inferens | Kausal inferens |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2000s–2010s | 2002 |
| Upphovsperson≠ | Extensions of Shadish, Cook & Campbell (2002) ITS framework; HTE formulation developed by Lopez Bernal and colleagues | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Typ≠ | Quasi-experimental segmented regression with subgroup moderation | Quasi-experimental segmented regression |
| Ursprungskälla≠ | 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 ↗ |
| Alias≠ | HTE-ITS, Subgroup ITS, Effect-modifier ITS, Segmented ITS with interaction | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Närliggande≠ | 4 | 5 |
| Sammanfattning≠ | Heterogeneous Treatment Effect Interrupted Time Series extends the standard ITS design to detect whether an intervention's effect on a time series differs systematically across subgroups or in response to unit-level moderators. Where ordinary ITS yields a single level-change and slope-change estimate, HTE-ITS adds interaction terms for a moderating variable, revealing who benefits more or less from the intervention and by how much. | 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. |
| ScholarGateDatamängd ↗ |
|
|