What are the most effective ways to visualize clinical trial data?

1 Define your goals and audience

Before you start creating any data visualization, you need to define your goals and audience. What is the main message or question you want to answer with your data? Who are you trying to inform or persuade with your visualization? Depending on your goals and audience, you may need to choose different types of charts, graphs, tables, or dashboards to display your data. For example, if you want to show the overall results of a trial to a general audience, you may use a simple bar chart or pie chart to compare the outcomes of different groups. However, if you want to show the detailed data of a trial to a technical audience, you may use a more complex plot, such as a forest plot or a Kaplan-Meier curve, to show the effect size and confidence intervals of different variables.

2 Choose the right data and format

Once you have defined your goals and audience, you need to choose the right data and format for your visualization. You need to select the most relevant and accurate data that supports your message or question. You also need to make sure that your data is clean, consistent, and complete. You may need to perform some data preprocessing, such as filtering, aggregating, or transforming your data, to make it suitable for visualization. Additionally, you need to choose the right format for your data, such as numeric, categorical, ordinal, or temporal. The format of your data will determine the type of visualization you can use, as well as the scale, axis, and labels you need to apply.

3 Select the appropriate visualization type

When selecting the right data and format for your visualization, it's important to consider the type of visualization you'll use to display clinical trial data. Histograms and box plots are helpful for showing the distribution and variation of a single variable, while scatter plots and line charts are ideal for demonstrating relationships and trends between multiple variables. Bar charts and pie charts provide a great way to compare and contrast different categories, while heat maps and contour plots can be used to display multiple variables' density and pattern. Finally, forest plots and Kaplan-Meier curves are useful for displaying the effect and survival of different interventions over time. When choosing a visualization type, it's important to consider its clarity, simplicity, accuracy, and aesthetics.

4 Apply the best practices and principles

Once you have chosen the right visualization type, you should apply the best practices and principles of data visualization to create your visualization. This includes using appropriate colors, shapes, and sizes to emphasize and differentiate your data points, groups, and categories. Additionally, clear and consistent labels, titles, legends, and annotations should be used to explain and describe your data and visualization. Moreover, you should use appropriate scales, axes, and grids to organize and align your data and visualization. Lastly, you should use appropriate charts, graphs, tables, or dashboards to combine and arrange your data and visualization. All of these best practices must be applied in order to enhance the readability, usability, and credibility of your visualization.

5 Test and refine your visualization

Finally, after you have created your visualization, you need to test and refine it. To ensure accuracy and reliability, you should check for any errors, inconsistencies or gaps in your data and format. Additionally, you should evaluate the type and design of your visualization for any flaws or weaknesses. To do this, you may need to use analytical tools such as Excel, R, Python or Tableau. Furthermore, it’s important to get feedback from your audience, peers or experts. Ask them questions such as: is the visualization clear and understandable? Is it accurate and reliable? Is it relevant and interesting? Is it appealing and engaging? You should continue to test and refine your visualization until you achieve your goals and satisfy your audience.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?