Comparing the Value of Data Visualization Methods for Communicating Harms in Clinical Trials.

IF 5.2 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Riaz Qureshi, Xiwei Chen, Carsten Goerg, Evan Mayo-Wilson, Stephanie Dickinson, Lilian Golzarri-Arroyo, Hwanhee Hong, Rachel Phillips, Victoria Cornelius, Mara Mc Adams DeMarco, Eliseo Guallar, Tianjing Li
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引用次数: 0

Abstract

In clinical trials, harms (i.e., adverse events) are often reported by simply counting the number of people who experienced each event. Reporting only frequencies ignores other dimensions of the data that are important for stakeholders, including severity, seriousness, rate (recurrence), timing, and groups of related harms. Additionally, application of selection criteria to harms prevents most from being reported. Visualization of data could improve communication of multidimensional data. We replicated and compared the characteristics of 6 different approaches for visualizing harms: dot plot, stacked bar chart, volcano plot, heat map, treemap, and tendril plot. We considered binary events using individual participant data from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and a group of content experts. We produced all figures using R and share the open-source code on GitHub. Most original visualizations propose presenting individual harms (e.g., dizziness, somnolence) alone or alongside higher level (e.g., by body systems) summaries of harms, although they could be applied at either level. Visualizations can present different dimensions of all harms observed in trials. Except for the tendril plot, all other plots do not require individual participant data. The dot plot and volcano plot are favored as visualization approaches to present an overall summary of harms data. Our value assessment found the dot plot and volcano plot were favored by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.

Abstract Image

Abstract Image

比较数据可视化方法在临床试验中危害沟通中的价值。
在临床试验中,危害(即不良事件)通常通过简单地计算经历每种事件的人数来报告。仅报告频率忽略了对利益相关者很重要的数据的其他维度,包括严重程度、严重性、发生率(复发)、时间和相关危害的群体。此外,选择标准对危害的应用阻止了大多数被报告。数据的可视化可以改善多维数据的交流。我们复制并比较了6种不同的危害可视化方法的特点:点图、堆叠条形图、火山图、热图、树状图和卷尾图。我们使用加巴喷丁治疗神经性疼痛的随机试验的个体参与者数据来考虑二元事件。我们使用启发式方法和一组内容专家来评估它们的价值。我们使用R生成了所有的图形,并在GitHub上分享了开源代码。大多数原始的可视化建议单独呈现个体危害(例如,头晕,嗜睡)或与更高层次(例如,身体系统)危害摘要一起,尽管它们可以在任何一个层次上应用。可视化可以呈现试验中观察到的所有危害的不同维度。除卷须图外,其他图均不需要参与者的个人数据。点阵图和火山图是最受欢迎的可视化方法,以呈现危害数据的总体总结。我们的价值评估发现,点图和火山图受到内容专家的青睐。使用可视化报告危害可以改善沟通。试用者可以使用我们提供的代码轻松实现这些方法。
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来源期刊
Epidemiologic Reviews
Epidemiologic Reviews 医学-公共卫生、环境卫生与职业卫生
CiteScore
8.10
自引率
0.00%
发文量
10
期刊介绍: Epidemiologic Reviews is a leading review journal in public health. Published once a year, issues collect review articles on a particular subject. Recent issues have focused on The Obesity Epidemic, Epidemiologic Research on Health Disparities, and Epidemiologic Approaches to Global Health.
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