Interpretation of consensus and recommendations of harms visualization in reports of randomized controlled trials_Chinese Journal of Evidence-Based Medicine

Authors：

 LU Cuncun ¹ , CHEN Zijia ¹ , QIAO Meng ¹ , LEI Chao ¹ , WANG Ziyi ² , SHANG Wenru ³ , ZHANG Qiang ¹ , XIE Yanming ¹ ,  WANG Zhifei ¹

1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China;
2. Evidence Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou 730000, P. R. China;
3. Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, P. R. China;

Corresponding author：

LU Cuncun, Email: cuncunlu2017@163.com; WANG Zhifei, Email: wzhftcm@163.com

Keywords：

Clinical trial; Harms; Adverse events; Visualization

DOI：

10.7507/1672-2531.202305017

Video：

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Abstract Full text Figures/Tables Video References Cited by

Statistical graph is an indispensable part of scientific papers. It is helpful to promote the communication, dissemination, and application of academic achievements by presenting research results intuitively and accurately through standardized and beautiful visual graphs. The safety of a medical intervention is the basic premise of its clinical application, and randomized controlled trial (RCT) as an important design to determine the efficacy and safety of medical interventions, it is extremely important to accurately present the information on the safety outcomes of interventions found therein. However, the research found that the reports of RCTs didn’t adequately use visual graphs to present harms data. In order to promote clinical researchers to better use visual graphs to present harms data, international scholars recently published a consensus study in BMJ, which identified and recommended 10 statistical graphs for presenting harms data in RCTs. In order to facilitate domestic scholars to understand and apply the consensus, this article interprets the consensus and recommendations, and it is expected to provide help for improving the quality of harms visualization in domestic papers of RCTs.

Citation： LU Cuncun, CHEN Zijia, QIAO Meng, LEI Chao, WANG Ziyi, SHANG Wenru, ZHANG Qiang, XIE Yanming, WANG Zhifei. Interpretation of consensus and recommendations of harms visualization in reports of randomized controlled trials. Chinese Journal of Evidence-Based Medicine, 2023, 23(9): 1110-1116. doi: 10.7507/1672-2531.202305017 Copy

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2.	Hehman E, Xie SY. Doing better data visualization. Adv Methods Pract Psychol Sci. 2021, 4(4): 1-18.
3.	Vandemeulebroecke M, Baillie M, Margolskee A, et al. Effective visual communication for the quantitative scientist. CPT Pharmacometrics Syst Pharmacol, 2019, 8(10): 705-719.
4.	Gatto NM, Wang SV, Murk W, et al. Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting. Pharmacoepidemiol Drug Saf, 2022, 31(11): 1140-1152.
5.	林云志, 张隆垚, 陈峰, 等. 医学研究中的统计图形规范. 中华流行病学杂志, 2022, 43(10): 1666-1670.
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7.	Carmeli AB, Meloney L, Bierer BE. Data visualization explorer: a tool for participant representation in pivotal trials of FDA-approved medicinal products. Patterns (N Y), 2023, 4(5): 100713.
8.	O'Donoghue SI, Baldi BF, Clark SJ, et al. Visualization of biomedical data. Ann Rev of Biomedi Data Sci, 2018, 1(1): 275-304.
9.	卢存存, 陈子佳, 张强, 等. 基于真实世界数据的目标试验模拟研究: 现状与展望. 中国循证医学杂志, 2023, 23(4): 492-496.
10.	Chapman SJ, Aldaffaa M, Downey CL, et al. Research waste in surgical randomized controlled trials. Br J Surg, 2019, 106(11): 1464-1471.
11.	Glasziou P, Altman DG, Bossuyt P, et al. Reducing waste from incomplete or unusable reports of biomedical research. Lancet, 2014, 383(9913): 267-276.
12.	辛沂青, 陈泽, 吴云云, 等. 针刺治疗干眼症随机对照试验的报告质量评价. 中国循证医学杂志, 2020, 20(10): 1199-1207.
13.	Loannidis JP, Evans SJ, Gøtzsche PC, et al. Better reporting of harms in randomized trials: an extension of the CONSORT statement. Ann Intern Med, 2004, 141(10): 781-788.
14.	卢存存, 张强, 雷超, 等. 使用队列和常规收集数据开展随机对照试验的报告清单(CONSORT-ROUTINE2021)解读. 中国循证医学杂志, 2022, 22(6): 731-738.
15.	Zhang X, Liang F, Lau CT, et al. STandards for Reporting Interventions in Clinical Trials Of Tuina/Massage (STRICTOTM): extending the CONSORT statement. J Evid Based Med, 2023, 16(1): 68-81.
16.	Mayo-Wilson E, Fusco N, Li T, et al. Harms are assessed inconsistently and reported inadequately part 1: systematic adverse events. J Clin Epidemiol, 2019, 113: 20-27.
17.	胡瑞学, 梁宁, 柴倩云, 等. PRISMA危害报告清单: 改进系统综述中危害的报告(一). 中国药物警戒, 2018, 15(6): 377-384.
18.	Qureshi R, Chen X, Goerg C, et al. Comparing the value of data visualization methods for communicating harms in clinical trials. Epidemiol Rev, 2022, 44(1): 55-66.
19.	Phillips R, Hazell L, Sauzet O, et al. Analysis and reporting of adverse events in randomised controlled trials: a review. BMJ Open, 2019, 9(2): e024537.
20.	Cornelius V, Cro S, Phillips R. Advantages of visualisations to evaluate and communicate adverse event information in randomised controlled trials. Trials, 2020, 21(1): 1028.
21.	Phillips R, Cro S, Wheeler G, et al. Visualising harms in publications of randomised controlled trials: consensus and recommendations. BMJ, 2022, 377: e068983.
22.	Phillips R, Sauzet O, Cornelius V. Statistical methods for the analysis of adverse event data in randomised controlled trials: a scoping review and taxonomy. BMC Med Res Methodol, 2020, 20(1): 288.
23.	Duan R, Tong J, Sutton AJ, et al. Origami plot: a novel multivariate data visualization tool that improves radar chart. J Clin Epidemiol, 2023, 156: 85-94.
24.	Spicer JO, Coleman CG. Creating effective infographics and visual abstracts to disseminate research and facilitate medical education on social media. Clin Infect Dis, 2022, 74(Suppl 3): 14-22.
25.	Guidolin K, Lin J, Zorigtbaatar A, et al. Evaluating the accuracy and design of visual abstracts in academic surgical journals. Ann Surg, 2022, 276(5): 275-283.

1. 张杰. R语言数据可视化之美. 北京: 电子工业出版社, 2019.
2. Hehman E, Xie SY. Doing better data visualization. Adv Methods Pract Psychol Sci. 2021, 4(4): 1-18.
3. Vandemeulebroecke M, Baillie M, Margolskee A, et al. Effective visual communication for the quantitative scientist. CPT Pharmacometrics Syst Pharmacol, 2019, 8(10): 705-719.
4. Gatto NM, Wang SV, Murk W, et al. Visualizations throughout pharmacoepidemiology study planning, implementation, and reporting. Pharmacoepidemiol Drug Saf, 2022, 31(11): 1140-1152.
5. 林云志, 张隆垚, 陈峰, 等. 医学研究中的统计图形规范. 中华流行病学杂志, 2022, 43(10): 1666-1670.
6. Gelman A, Vehtari A. What are the most important statistical ideas of the past 50 years? J Am Stat Assoc, 2021: 1-29.
7. Carmeli AB, Meloney L, Bierer BE. Data visualization explorer: a tool for participant representation in pivotal trials of FDA-approved medicinal products. Patterns (N Y), 2023, 4(5): 100713.
8. O'Donoghue SI, Baldi BF, Clark SJ, et al. Visualization of biomedical data. Ann Rev of Biomedi Data Sci, 2018, 1(1): 275-304.
9. 卢存存, 陈子佳, 张强, 等. 基于真实世界数据的目标试验模拟研究: 现状与展望. 中国循证医学杂志, 2023, 23(4): 492-496.
10. Chapman SJ, Aldaffaa M, Downey CL, et al. Research waste in surgical randomized controlled trials. Br J Surg, 2019, 106(11): 1464-1471.
11. Glasziou P, Altman DG, Bossuyt P, et al. Reducing waste from incomplete or unusable reports of biomedical research. Lancet, 2014, 383(9913): 267-276.
12. 辛沂青, 陈泽, 吴云云, 等. 针刺治疗干眼症随机对照试验的报告质量评价. 中国循证医学杂志, 2020, 20(10): 1199-1207.
13. Loannidis JP, Evans SJ, Gøtzsche PC, et al. Better reporting of harms in randomized trials: an extension of the CONSORT statement. Ann Intern Med, 2004, 141(10): 781-788.
14. 卢存存, 张强, 雷超, 等. 使用队列和常规收集数据开展随机对照试验的报告清单(CONSORT-ROUTINE2021)解读. 中国循证医学杂志, 2022, 22(6): 731-738.
15. Zhang X, Liang F, Lau CT, et al. STandards for Reporting Interventions in Clinical Trials Of Tuina/Massage (STRICTOTM): extending the CONSORT statement. J Evid Based Med, 2023, 16(1): 68-81.
16. Mayo-Wilson E, Fusco N, Li T, et al. Harms are assessed inconsistently and reported inadequately part 1: systematic adverse events. J Clin Epidemiol, 2019, 113: 20-27.
17. 胡瑞学, 梁宁, 柴倩云, 等. PRISMA危害报告清单: 改进系统综述中危害的报告(一). 中国药物警戒, 2018, 15(6): 377-384.
18. Qureshi R, Chen X, Goerg C, et al. Comparing the value of data visualization methods for communicating harms in clinical trials. Epidemiol Rev, 2022, 44(1): 55-66.
19. Phillips R, Hazell L, Sauzet O, et al. Analysis and reporting of adverse events in randomised controlled trials: a review. BMJ Open, 2019, 9(2): e024537.
20. Cornelius V, Cro S, Phillips R. Advantages of visualisations to evaluate and communicate adverse event information in randomised controlled trials. Trials, 2020, 21(1): 1028.
21. Phillips R, Cro S, Wheeler G, et al. Visualising harms in publications of randomised controlled trials: consensus and recommendations. BMJ, 2022, 377: e068983.
22. Phillips R, Sauzet O, Cornelius V. Statistical methods for the analysis of adverse event data in randomised controlled trials: a scoping review and taxonomy. BMC Med Res Methodol, 2020, 20(1): 288.
23. Duan R, Tong J, Sutton AJ, et al. Origami plot: a novel multivariate data visualization tool that improves radar chart. J Clin Epidemiol, 2023, 156: 85-94.
24. Spicer JO, Coleman CG. Creating effective infographics and visual abstracts to disseminate research and facilitate medical education on social media. Clin Infect Dis, 2022, 74(Suppl 3): 14-22.
25. Guidolin K, Lin J, Zorigtbaatar A, et al. Evaluating the accuracy and design of visual abstracts in academic surgical journals. Ann Surg, 2022, 276(5): 275-283.

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