Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Heterogen Behandlings-effekt Interrupted Time Series (HTE-ITS)× | Paneldata-avbruten tidsserieanalys× | |
|---|---|---|
| Ämnesområde | Kausal inferens | Kausal inferens |
| Familj | Regression model | Regression model |
| Ursprungsår | 2000s–2010s | 2000s–2010s |
| Upphovsperson≠ | Extensions of Shadish, Cook & Campbell (2002) ITS framework; HTE formulation developed by Lopez Bernal and colleagues | Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial) |
| Typ≠ | Quasi-experimental segmented regression with subgroup moderation | Quasi-experimental causal inference |
| 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 ↗ | 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 ↗ |
| Alias | HTE-ITS, Subgroup ITS, Effect-modifier ITS, Segmented ITS with interaction | panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series |
| 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. | 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. |
| ScholarGateDatamängd ↗ |
|
|