Introduction to analysis and meta-analysis of interrupted time series studies with continuous outcomes

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 aimed to equip review authors with knowledge to incorporate results from ITS studies in their reviews. It provided 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 discussed 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 was of particular interest to review authors who'd like to incorporate results from ITS studies in a review.

The session was delivered in July 2024 and below you will find the videos from the webinar, together with the accompanying slides to download [PDF].

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Presenter Bios

Dr Elizabeth Korevaar, Methods in Evidence Synthesis Unit, Monash University, Australia. 
Elizabeth is a Biostatistician and she undertakes research on statistical methods used to analyse ITS studies, and meta-analyse their results. In addition, she provides biostatistical support for systematic reviews.

Dr Simon Turner, Methods in Evidence Synthesis Unit, Monash University, Australia.
Simon is a Biostatistician and he undertakes biostatistical and methodological research on ITS and meta-analysis. He is also a biostatistical collaborator on ITS studies and systematic reviews.