Agenda:
Interrupted Time Series (ITS) studies are commonly used to evaluate public health and policy interventions when randomisation is impractical or infeasible; for example, examining the effects of mass media campaigns on the use of methamphetamine among young adults. In an ITS study, measurements on a group of individuals (e.g., community) are taken repeatedly both before and after the intervention. The key benefit of the ITS design is that any secular trend in the period before the intervention can be accounted for when estimating the impact of the intervention. Several effect measures can be used to characterise both short and long-term effects of the intervention (e.g., immediate level-change and long-term level-change). Meta-analysis of these effect estimates can usefully inform decision-making.

This Cochrane Learning Live webinar aims to equip review authors with knowledge to incorporate results from ITS studies in their reviews. It will provide an introduction on how to analyse ITS studies using segmented linear regression models and how to meta-analyse the resulting effect estimates. In particular, the presenters will discuss the complexities that arise when analysing time series data (e.g. autocorrelation, seasonality), and issues that can arise when meta-analysing results from ITS studies.

The session is of particular interest to review authors who'd like to incorporate results from ITS studies in a review.