Comparar métodos

	Séries Temporais Interrompidas para Avaliação de Políticas	Análise de Séries Temporais Interrompidas (ITS)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)	2002
Autor original	Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tipo	Quasi-experimental causal design	Quasi-experimental segmented regression
Fonte seminal	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.	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.
Outros nomes	ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Relacionados	4	5
Resumo	Interrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.	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.