2014
DOI: 10.1177/1098214014527337
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Examining the Internal Validity and Statistical Precision of the Comparative Interrupted Time Series Design by Comparison With a Randomized Experiment

Abstract: Although evaluators often use an interrupted time series (ITS) design to test hypotheses about program effects, there are few empirical tests of the design's validity. We take a randomized experiment on an educational topic and compare its effects to those from a comparative ITS (CITS) design that uses the same treatment group as the experiment but a nonequivalent comparison group that is assessed at six time points before treatment. We estimate program effects with and without matching of the comparison schoo… Show more

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Cited by 79 publications
(68 citation statements)
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“…Controlled interrupted time series designs are among the most robust for causal inference, and accumulating evidence suggests that matching on baseline trend generates effect estimates similar to randomized clinical trials. 38 …”
Section: Discussionmentioning
confidence: 99%
“…Controlled interrupted time series designs are among the most robust for causal inference, and accumulating evidence suggests that matching on baseline trend generates effect estimates similar to randomized clinical trials. 38 …”
Section: Discussionmentioning
confidence: 99%
“…We also matched on baseline quarterly numbers of high-priority (defined below) primary care and specialist visits, preventable acute diabetes complication visits (defined below), ED visits, hospitalization days, and baseline quarterly total out-of-pocket spending per member. 8,9 Compared with the unmatched sample, our propensity score matching approach increased the similarity of the HDHP and control groups with respect to age, gender, neighborhood poverty level, morbidity score, baseline outpatient copayment, and employer size (Table 1). Our final group included 12,084 HDHP members with diabetes and their 1:1 matched controls.…”
Section: Methodsmentioning
confidence: 99%
“…The second method is comparative/controlled interrupted time series (CITS). The method has been proved to be valid and precise, and is considered more flexible than DD, rendering it superior under certain conditions. More specifically, while the DD method assumes that outcome trends are similar, that is, “common,” between the intervention and the non‐intervention groups, the CITS method relaxes the assumption and allows those trends to diverge .…”
Section: Methodsmentioning
confidence: 99%