Comparer des méthodes

	Analyse robuste des séries chronologiques interrompues	Séries chronologiques interrompues dynamiques
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010s	2002–2017
Auteur d'origine	Bernal, Cummins & Gasparrini; Linden (robust extensions)	Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini
Type	Quasi-experimental segmented regression with robust inference	Quasi-experimental time-series design
Source fondatrice	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.	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.
Alias	robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA	Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS
Apparentées	5	4
Résumé	Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation.	Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how an intervention's impact evolves across time periods, making it especially suited to public health, health services research, and policy evaluation where effects accumulate gradually or wear off after initial impact.