ScholarGate
Assistant

Comparer des méthodes

Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.

Séries chronologiques interrompues multi-périodes×Séries chronologiques interrompues avec données de panel×
DomaineInférence causaleInférence causale
FamilleRegression modelRegression model
Année d'origine2000s-20152000s–2010s
Auteur d'origineExtended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s)Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)
TypeQuasi-experimental time series regressionQuasi-experimental causal inference
Source fondatriceKontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750. 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 ↗
Aliasmulti-period ITS, multiple-interruption ITS, segmented time series with multiple breakpoints, MITSpanel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series
Apparentées55
RésuméMulti-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS estimates the level change and slope change attributable to each intervention while controlling for the underlying secular trend and for the effects of earlier interruptions.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.
ScholarGateJeu de données
  1. v1
  2. 2 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

Aller à la recherche Télécharger les diapositives

ScholarGateComparer des méthodes: Multi-period Interrupted Time Series · Panel Data Interrupted Time Series. Consulté le 2026-06-19 sur https://scholargate.app/fr/compare