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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 inte...

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Autores principales: Turner, Simon L., Karahalios, Amalia, Forbes, Andrew B., Taljaard, Monica, Grimshaw, Jeremy M., Korevaar, Elizabeth, Cheng, Allen C., Bero, Lisa, McKenzie, Joanne E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818488/
https://www.ncbi.nlm.nih.gov/pubmed/32657532
http://dx.doi.org/10.1002/jrsm.1435
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author Turner, Simon L.
Karahalios, Amalia
Forbes, Andrew B.
Taljaard, Monica
Grimshaw, Jeremy M.
Korevaar, Elizabeth
Cheng, Allen C.
Bero, Lisa
McKenzie, Joanne E.
author_facet Turner, Simon L.
Karahalios, Amalia
Forbes, Andrew B.
Taljaard, Monica
Grimshaw, Jeremy M.
Korevaar, Elizabeth
Cheng, Allen C.
Bero, Lisa
McKenzie, Joanne E.
author_sort Turner, Simon L.
collection PubMed
description 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.
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spelling pubmed-78184882021-01-26 Creating effective interrupted time series graphs: Review and recommendations Turner, Simon L. Karahalios, Amalia Forbes, Andrew B. Taljaard, Monica Grimshaw, Jeremy M. Korevaar, Elizabeth Cheng, Allen C. Bero, Lisa McKenzie, Joanne E. Res Synth Methods Special Issue Papers 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. John Wiley and Sons Inc. 2020-07-22 2021-01 /pmc/articles/PMC7818488/ /pubmed/32657532 http://dx.doi.org/10.1002/jrsm.1435 Text en © 2020 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Special Issue Papers
Turner, Simon L.
Karahalios, Amalia
Forbes, Andrew B.
Taljaard, Monica
Grimshaw, Jeremy M.
Korevaar, Elizabeth
Cheng, Allen C.
Bero, Lisa
McKenzie, Joanne E.
Creating effective interrupted time series graphs: Review and recommendations
title Creating effective interrupted time series graphs: Review and recommendations
title_full Creating effective interrupted time series graphs: Review and recommendations
title_fullStr Creating effective interrupted time series graphs: Review and recommendations
title_full_unstemmed Creating effective interrupted time series graphs: Review and recommendations
title_short Creating effective interrupted time series graphs: Review and recommendations
title_sort creating effective interrupted time series graphs: review and recommendations
topic Special Issue Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818488/
https://www.ncbi.nlm.nih.gov/pubmed/32657532
http://dx.doi.org/10.1002/jrsm.1435
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