Interrupted time series (ITS) analyses

In interrupted time series (ITS) studies data are collected at multiple time points before and
after an intervention in order to detect whether or not the intervention had a significantly
greater effect than any underlying secular trend (1). ITS studies may be acceptable for
inclusion in EPOC systematic reviews if appropriately analysed or if re-analysed.
The preferred method to analyse ITS studies is a statistical comparison of time trends before
and after the intervention (see Figure1). Time series analysis using ARIMA models is one way
of analysing the data, but there are a number of statistical techniques that can be used
depending on the characteristics of the data, the number of data points available and
whether autocorrelation is present (3).
However, inappropriate analyses of time series data are consistently identified in original
papers evaluating quality improvement strategies, which often have used a simple beforeafter
comparison of the intervention effect at a single intervention site (2). Solely comparing
the means before and after an intervention, without taking into account any secular trends,
may result in overestimations (or underestimations) of the intervention effect (3).