Creating effective interrupted time series graphs: Review and recommendations

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 visualisation 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 standardisation and improvement in the display of ITS data, and facilitate re-use of the data in systematic reviews and meta-analyses.

[1]  Edward R. Tufte Visual explanations: images and quantities, evidence and narrative , 1997 .

[2]  Nathan Yau,et al.  Visualize This: The FlowingData Guide to Design, Visualization, and Statistics , 2011 .

[3]  Jeremy M Grimshaw,et al.  Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: A review. , 2020, Journal of clinical epidemiology.

[4]  J. Grimshaw,et al.  Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: protocol for a review , 2019, BMJ Open.

[5]  Craig R Ramsay,et al.  INTERRUPTED TIME SERIES DESIGNS IN HEALTH TECHNOLOGY ASSESSMENT: LESSONS FROM TWO SYSTEMATIC REVIEWS OF BEHAVIOR CHANGE STRATEGIES , 2003, International Journal of Technology Assessment in Health Care.

[6]  D. Redelmeier,et al.  Hospital Readmissions Following Physician Call System Change: A Comparison of Concentrated and Distributed Schedules. , 2016, The American journal of medicine.

[7]  M. Boers Designing effective graphs to get your message across , 2018, Annals of the rheumatic diseases.

[8]  Antonio Gasparrini,et al.  Evaluating the Impact of Florida’s “Stand Your Ground” Self-defense Law on Homicide and Suicide by Firearm: An Interrupted Time Series Study , 2017, JAMA internal medicine.

[9]  C. Ramsay,et al.  Methodology and reporting characteristics of studies using interrupted time series design in healthcare , 2019, BMC Medical Research Methodology.

[10]  Joseph W. McKean,et al.  Identifying Autocorrelation Generated by Various Error Processes in Interrupted Time-Series Regression Designs , 2007 .

[11]  Ariel Linden,et al.  Using forecast modelling to evaluate treatment effects in single‐group interrupted time series analysis , 2018, Journal of evaluation in clinical practice.

[12]  Chandler Stolp,et al.  The Visual Display of Quantitative Information , 1983 .

[13]  Daniel D. Drevon,et al.  Intercoder Reliability and Validity of WebPlotDigitizer in Extracting Graphed Data , 2017, Behavior modification.

[14]  Julia Kastner,et al.  Show Me The Numbers Designing Tables And Graphs To Enlighten , 2016 .

[15]  Muhammad Mamdani,et al.  Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. , 2015, Journal of clinical epidemiology.

[16]  D. Kleinbaum,et al.  Modelling interrupted time series to evaluate prevention and control of infection in healthcare , 2012, Epidemiology and Infection.

[17]  William S. Cleveland The elements of graphing data , 1980 .

[18]  D. Lane,et al.  Designing better graphs by including distributional information and integrating words, numbers, and images. , 2009, Psychological methods.

[19]  E. Tufte Beautiful Evidence , 2006 .