Evidence map›Paper›PMID 32657532›Full record

ReviewResearch synthesis methods2021

Creating effective interrupted time series graphs: Review and recommendations.

Simon L Turner, Amalia Karahalios, Andrew B Forbes, Monica Taljaard, Jeremy M Grimshaw, Elizabeth Korevaar, Allen C Cheng, Lisa Bero, Joanne E McKenzie

Abstract readReview
In one paragraph

Review in Research synthesis methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Simon L TurnerSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.ORCID https://orcid.org/0000-0001-9163-4524
Amalia KarahaliosSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Andrew B ForbesSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Monica TaljaardClinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Jeremy M GrimshawClinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Elizabeth KorevaarSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Allen C ChengSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Lisa BeroFaculty of Medicine and Health, School of Pharmacy and Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Joanne E McKenzieSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Funding

Australian Government Research Training Program PhD Scholarship (Elizabeth Korevaar)Australian Government Research Training Program PhD Scholarship (Simon Turner)Canadian Institute of Health Research Foundation Grant 143269National Health and Medical Research Council 1068732National Health and Medical Research Council 1143429National Health and Medical Research Council 1145273
6 · The paper itself

Abstract

introductionInterrupted 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.

aimWe provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. METHODS AND

resultsGraphing 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.

conclusionWe 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.

Indexed as

Data VisualizationInterrupted Time Series AnalysisComputer GraphicsHumansMeta-Analysis as TopicSoftwareSystematic Reviews as Topicdata visualizationdisplay of datagraphinterrupted time seriesmeta-analysissystematic review

Identifiers

PMID32657532
PMCPMC7818488

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.