Objective To improve communication of harm in publications of randomised controlled trials via the development of recommendations for visually presenting harm outcomes. Design Consensus study. Setting 15 clinical trials units registered with the UK Clinical Research Collaboration, an academic population health department, Roche Products, and The BMJ . Participants Experts in clinical trials: 20 academic statisticians, one industry statistician, one academic health economist, one data graphics designer, and two clinicians. Main outcome measures A methodological review of statistical methods identified visualisations along with those recommended by consensus group members. Consensus on visual recommendations was achieved (at least 60% of the available votes) over a series of three meetings with participants. The participants reviewed and critically appraised candidate visualisations against an agreed framework and voted on whether to endorse each visualisation. Scores marginally below this threshold (50-60%) were revisited for further discussions and votes retaken until consensus was reached. Results 28 visualisations were considered, of which 10 are recommended for researchers to consider in publications of main research findings. The choice of visualisations to present will depend on outcome type (eg, binary, count, time-to-event, or continuous), and the scenario (eg, summarising multiple emerging events or one event of interest). A decision tree is presented to assist trialists in deciding which visualisations to use. Examples are provided of each endorsed visualisation, along with an example interpretation, potential limitations, and signposting to code for implementation across a range of standard statistical software. Clinician feedback was incorporated into the explanatory information provided in the recommendations to aid understanding and interpretation. Conclusions Visualisations provide a powerful tool to communicate harms in clinical trials, offering an alternative perspective to the traditional frequency tables. Increasing the use of visualisations for harm outcomes in clinical trial manuscripts and reports will provide clearer presentation of information and enable more informative interpretations. The limitations of each visualisation are discussed and examples of where their use would be inappropriate are given. Although the decision tree aids the choice of visualisation, the statistician and clinical trial team must ultimately decide the most appropriate visualisations for their data and objectives. Trialists should continue to examine crude numbers alongside visualisations to fully understand harm profiles. The datasets used in this analysis are available from GlaxoSmithKline via ClinicalStudyDataRequest.com, but restrictions apply to the availability of these data, which were used under licence for the current study. The synthetic dataset example is available for download in the Stata aedot and aevolcano command packages.

中文翻译：

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可视化随机对照试验出版物中的危害：共识和建议


目标 通过制定直观呈现伤害结果的建议，改善随机对照试验出版物中伤害的沟通。设计共识研究。设置 15 个在英国临床研究合作组织、学术人口健康部门、罗氏产品和 BMJ 注册的临床试验单位。参与者 临床试验专家：20 名学术统计学家、1 名行业统计学家、1 名学术健康经济学家、1 名数据图形设计师和 2 名临床医生。主要结果指标 对统计方法的方法学审查确定了可视化以及共识小组成员推荐的可视化。通过与参与者举行的一系列三场会议，就视觉推荐达成了共识（至少 60% 的可用选票）。参与者根据商定的框架审查和严格评估候选可视化，并投票决定是否认可每个可视化。略低于此阈值（50-60%）的分数将被重新审视以进行进一步讨论并重新投票，直至达成共识。结果 考虑了 28 个可视化，其中建议研究人员在主要研究成果的出版物中考虑其中 10 个。要呈现的可视化的选择将取决于结果类型（例如，二进制、计数、事件时间或连续）和场景（例如，总结多个新出现的事件或一个感兴趣的事件）。提供决策树来帮助试验者决定使用哪些可视化。提供了每个认可的可视化的示例，以及示例解释、潜在限制以及用于在一系列标准统计软件中实施的代码的路标。 临床医生的反馈已纳入建议中提供的解释性信息中，以帮助理解和解释。结论 可视化提供了一个强大的工具来传达临床试验中的危害，为传统频率表提供了另一种视角。在临床试验手稿和报告中增加对伤害结果可视化的使用将提供更清晰的信息呈现并实现更丰富的解释。讨论了每种可视化的局限性，并给出了不适当使用它们的示例。尽管决策树有助于可视化的选择，但统计学家和临床试验团队必须最终决定最适合其数据和目标的可视化。试验人员应继续检查粗略数字和可视化结果，以充分了解危害概况。本分析中使用的数据集可通过 ClinicalStudyDataRequest.com 从 GlaxoSmithKline 获得，但这些数据的可用性受到限制，这些数据是在当前研究的许可下使用的。合成数据集示例可在 Stata aedot 和 aevolcano 命令包中下载。