An interrupted time series study is a type of non-randomised study that allows researchers to quantify the immediate and long-term impact of interruptions like public health policies. For example, the impact of a mass media campaign on HIV testing rates. The results from multiple interrupted time series may be combined using a statistical technique called meta-analysis. However, there are many meta-analysis methods and no research on how these methods perform when combining interrupted time series studies. This thesis describes how interrupted time series are combined in the published literature and evaluates how well the statistical methods perform.