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创建有效的中断时间序列图:综述与建议。

Creating effective interrupted time series graphs: Review and recommendations.

机构信息

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.

出版信息

Res Synth Methods. 2021 Jan;12(1):106-117. doi: 10.1002/jrsm.1435. Epub 2020 Jul 22.

DOI:10.1002/jrsm.1435
PMID:32657532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7818488/
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.

摘要

简介

中断时间序列(ITS)研究可用于评估中断(如干预或暴露)的影响。此类研究的数据特别适合可视化显示,并且当清晰地描绘时,可以清楚地显示中断的短期和长期影响。此外,精心构建的图形允许使用数字化软件提取数据,这可以方便地将其纳入系统评价和荟萃分析中。

目的

我们提供了用于绘制 ITS 数据的建议,研究了 ITS 研究中呈现的图形的属性,并提供了使用我们的建议进行的示例。

方法和结果

从开创性的数据可视化资源中改编了适用于 ITS 研究的图形建议。改编后的建议涵盖了数据点、趋势线、中断、附加线和一般图形组件的绘制。我们评估了最近发表的(2013-2017 年)ITS 研究的 217 个图形是否符合我们的建议,发现 130 个图形(60%)具有明显不同的数据点,100 个图形(46%)具有趋势线,161 个图形(74%)具有明确界定的中断。只有 72 个(33%)图形可以进行准确的数据提取(需要与轴刻度标记对齐的明显点和允许解释点的标签)。

结论

我们发现许多 ITS 图形不符合我们的建议,可以通过简单的更改进行改进。我们提出的建议旨在实现 ITS 数据显示的更大标准化和改进,并方便在系统评价和荟萃分析中重新使用这些数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/0581f3525efa/JRSM-12-106-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/36a79ac349cb/JRSM-12-106-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/85cfca5550ed/JRSM-12-106-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/01f49e833637/JRSM-12-106-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/5ac52300f22f/JRSM-12-106-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/11d66ff822c3/JRSM-12-106-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/041e5a1c1133/JRSM-12-106-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/0581f3525efa/JRSM-12-106-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/36a79ac349cb/JRSM-12-106-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/85cfca5550ed/JRSM-12-106-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/01f49e833637/JRSM-12-106-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/5ac52300f22f/JRSM-12-106-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/11d66ff822c3/JRSM-12-106-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/041e5a1c1133/JRSM-12-106-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/7818488/0581f3525efa/JRSM-12-106-g007.jpg

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