Interrupted Time Series for Assessing the Causality of Intervention Effects. Part 35 of a Series on Evaluating...

The gold standard for evaluating interventions in medicine and health care is the randomized controlled trial (RCT). In practice, however, RCTs may be difficult to conduct because of high costs, ethical aspects, or practical considerations. This is particularly true of studies on the population level, e.g., for the evaluation of health policy measures.

We present a type of study design called the interrupted time series (ITS) and its critical interpretation, with several illustrative examples. This discussion is based on selected meth - odological publications.

ITS are suitable for the assessment of interventions with a clear point of intervention (interruption). They are analyzed with the statistical methods of time-series analysis. One strength of ITS is that they can be used to estimate an immediate effect as well as a gradually developing effect. Under certain assumptions, the findings of an ITS analysis can be interpreted causally. The main assumption underlying an ITS is that the trend after the intervention would have been exactly the same as the trend before the intervention if the intervention had not taken place and all other conditions had remained unchanged. A further assumption is that there should be no differences in the pre- versus postintervention phases in the subjects or other entities being tested (e.g., hospitals) that might affect the measured endpoints (e.g., differences in mean age affecting measured mortality). Moreover, the intervention periods must be properly distinct from one another in order to prevent biased effect estimates. The robustness of the assumptions should also be checked with sensitivity analyses.

As long as all sources of bias have been avoided and the findings are both plausible and robust, the effects revealed by ITS can be interpreted as causal. ITS may serve as an alternative method for evaluating intervention effects when an RCT cannot be performed.