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Abstract
Interrupted time series analysis differs from most other intervention study designs
in that it involves a before-after comparison within a single population, rather than
a comparison with a control group. This has the advantage that selection bias and
confounding due to between-group differences are limited. However, the basic interrupted
time series design cannot exclude confounding due to co-interventions or other events
occurring around the time of the intervention. One approach to minimizse potential
confounding from such simultaneous events is to add a control series so that there
is both a before-after comparison and an intervention-control group comparison. A
range of different types of controls can be used with interrupted time series designs,
each of which has associated strengths and limitations. Researchers undertaking controlled
interrupted time series studies should carefully consider a priori what confounding
events may exist and whether different controls can exclude these or if they could
introduce new sources of bias to the study. A prudent approach to the design, analysis
and interpretation of controlled interrupted time series studies is required to ensure
that valid information on the effectiveness of health interventions can be ascertained.