Visualising harms in publications of randomised controlled trials: consensus and recommendations

Rachel Phillips* (Corresponding Author), Suzie Cro, Graham Wheeler, Simon Bond, Tim P. Morris, Siobhan Creanor, Catherine Hewitt, Sharon Love, Andre Lopes, Iryna Schlackow, Carrol Gamble, Graeme MacLennan, Chris Habron, Anthony C. Gordon, Nikhil Vergis, Tianjing Li, Riaz Qureshi, Colin C. Everett, Jane Holmes, Amanda KirkhamClare Peckitt, Sarah Pirrie, Norin Ahmed, Laura Collett, Victoria Cornelius

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
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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 TheBMJ. 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.

Original languageEnglish
Article numbere068983
Number of pages13
JournalBMJ (Clinical research ed.)
Early online date16 May 2022
Publication statusPublished - 16 May 2022

Bibliographical note

Funding: RP was funded by a National Institute for Health and Care Research (NIHR) doctoral research fellowship to undertake this work (DRF-2017-10-131). SC is supported by an NIHR advanced fellowship (NIHR300593). This paper presents independent research funded by NIHR. The views expressed are those of the authors and not necessarily those of the NHS, NIHR, or Department of Health and Social Care. Infrastructure support for this research was provided by the Imperial College London NIHR Biomedical Research Centre. TPM was supported by MRC grants MC_UU_00004/07, MC_UU_12023/21 and MC_UU_12023/29. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. The UK Clinical Research Collaboration Registered Clinical Trials Unit Statistics Operations Group supports and endorses this work.

Data Availability Statement

Data sharing: The datasets used in this analysis are available from GlaxoSmithKline via, 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.


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