The use of controls in interrupted time series studies of public health interventions

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.