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A comprehensive set of postestimation measures to enrich interrupted time-series analysis

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  • Ariel Linden

    (Linden Consulting Group, LLC)

Abstract

While the primary goal of interrupted time-series analysis (ITSA) is to evaluate whether there is a change in the level or trend of an outcome following an interruption (for example, policy change, intervention initiation), a series of additional measures may be relevant to the analysis. In this article, I seek to fill a gap in the ITSA literature by describing a comprehensive set of measures that can be computed following ITSA models, including those that fulfill the primary goal and those that provide supplementary information about trends. These measures can be calculated using the itsa command; this article therefore serves as a com- plement to “Conducting interrupted time-series analysis for single and multiple group comparisons” (Linden, 2015, Stata Journal 15: 480–500), which introduced the itsa command. Specific ITSA postestimation measures described in this ar- ticle include individual trend lines, comparisons between multiple interventions, and comparisons with a counterfactual.

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  • Ariel Linden, 2017. "A comprehensive set of postestimation measures to enrich interrupted time-series analysis," Stata Journal, StataCorp LP, vol. 17(1), pages 73-88, March.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:1:p:73-88
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    1. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    2. Wayne F. Velicer & John Harrop, 1983. "The Reliability and Accuracy of Time Series Model Identification," Evaluation Review, , vol. 7(4), pages 551-560, August.
    3. Ariel Linden, 2015. "Conducting interrupted time-series analysis for single- and multiple-group comparisons," Stata Journal, StataCorp LP, vol. 15(2), pages 480-500, June.
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