A comprehensive set of postestimation measures to enrich interrupted time-series analysis
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 complement 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 article include individual trend lines, comparisons between
multiple interventions, and comparisons with a counterfactual.
View all articles by this author:
Ariel Linden
View all articles with these keywords:
itsa, interrupted time-series analysis, quasiexperimental designs, causal inference, counterfactual
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