Can synthetic controls improve causal inference in interrupted time series evaluations of public health interventions?

AbstractInterrupted time series designs are a valuable quasi-experimental approach for evaluating public health interventions. Interrupted time series extends a single group pre-post comparison by using multiple time points to control for underlying trends. But history bias —confounding by unexpected events occurring at the same time of the intervention—threatens the validity of this design and limits causal inference. Synthetic control methodology, a popular data-driven technique for deriving a control series from a pool of unexposed populations, is increasingly r ecommended. In this paper, we evaluate if and when synthetic controls can strengthen an interrupted time series design. First, we summarize the main observational study designs used in evaluative research, highlighting their respective uses, strengths, biases and design extensions for addressing the se biases. Second, we outline when the use of synthetic controls can strengthen interrupted time series studies and when their combined use may be problematic. Third, we provide recommendations for using synthetic controls in interrupted time series and, using a real-world example, we illustrate the potential pitfalls of using a data-driven approach to identify a suitable control series. Finally, we emphasize the importance of theoretical approaches for informing study design and argue that synthetic control methods are not always well suited for generating a counterfactual that minimizes crit ical threats to interr...
Source: International Journal of Epidemiology - Category: Epidemiology Source Type: research