In clinical trials, harms (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 six different approaches for visualizing harms-Dot Plot, Stacked Bar Chart, Volcano Plot, Heatmap, Treemap, and Tendril Plot. We considered binary events using individual participant data (IPD) from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and 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 are able to present different dimensions of all harms observed in trials. Except for the Tendril plot, all other plots do not require IPD. The Dot Plot and Volcano Plot are favoured as visualization approaches to present an overall summary of harms data. Our value assessment found the Dot Plot and Volcano Plot were favoured by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.