Furthermore, by modelling the underlying trend, ITS also controls for within-group characteristics that tend to change only slowly over time, secular changes, random fluctuations from one time point to the next and regression to the mean. 2 Because the evaluation is based on observing a single population over time, the ITS design is free from problems due to between-group differences, such as selection bias or unmeasured confounders. By collecting data at regular intervals over time, a pre-post comparison can be made while accounting for underlying trends in the outcome. 2 This design is generally applied to natural experiments with an intervention introduced at a known point in time. 1 Interrupted time series (ITS) is an increasingly popular design that adopts a different approach whereby comparisons are instead made across time within a single population. Interrupted time series, quasi-experimental design, evaluation, controls, time series, natural experiments IntroductionĮvaluation of public health interventions normally relies on comparing the outcome of interest in a population exposed to an intervention with that in an external control group not subject to the same intervention.
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. 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 range of different types of controls can be used with interrupted time series designs, each of which has associated strengths and limitations. 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. However, the basic interrupted time series design cannot exclude confounding due to co-interventions or other events occurring around the time of the intervention. This has the advantage that selection bias and confounding due to between-group differences are limited. 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.
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