Creating effective interrupted time series graphs: Review and recommendations. (22nd July 2020)

• Record Type: Journal Article
• Authors: Turner, Simon L.; Karahalios, Amalia; Forbes, Andrew B.; Taljaard, Monica; Grimshaw, Jeremy M.; Korevaar, Elizabeth; Cheng, Allen C.; Bero, Lisa; McKenzie, Joanne E.
• Other Names: Chaimani Anna guestEditor.; Nikolakopoulou Adriani guestEditor.
• Is Part Of: Research synthesis methods. Volume 12:Number 1(2021)
• Page Start: 106
• Page End: 117
• Publication Date: 2020-07-22
• Subjects: data visualization -- display of data -- graph -- interrupted time series -- meta‐analysis -- systematic review; Research -- Methodology -- Periodicals; Research -- Statistical methods -- Periodicals; 507.2
• DOI: 10.1002/jrsm.1435
• Languages: English
• ISSNs: 1759-2879

Abstract

Abstract : Introduction: Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short‐ and long‐term impact of an interruption. Further, well‐constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta‐analyses. Aim: We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. Methods and results: Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013‐2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. Conclusion: We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re‐use of the data in systematic reviews and meta‐analyses. Highlights: Application of data visualization recommendations can improve quality of interrupted time series graphs. Well‐designed graphs accurately depict time series data, any impact of the interruption, and the results of the analysis. Well‐designed graphs facilitate data extraction for use in systematic reviews and reproducibility. An assessment of graphs included in interrupted time series studies (published between 2013 and 2017) found that graphs often do not meet core graphing recommendations.