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).