Data visualisation has become a hot topic within the biostatistics space, especially over the past few years. The PSI conference in Gothenburg earlier this year showed us all just how hot it is, as there was a considerable improvement and uptick in the use of data visualisations across all presentations and posters at the conference. A great example of these presentations came from our very own Huw Wilson and Sarah Robson  – How to Influence Decisions with Your Data Visualisations. With the rise of RWE/RWD, the need to interpret and share clear insight with an increasingly diverse audience (i.e. not just fellow Statisticians) is vital. Key decision makers require fast, efficient insight in an increasingly fast-paced clinical trial journey. Here at Veramed, we understand the important insight data visualisation can bring, saving companies time, money and other costly mistakes.

For many years, one session at the PSI conference has been dedicated to data visualisation. Each year, turn up to nearly each of these sessions, but the rise of data visualisation has raised this topic in recent years. This core group of enthusiasts developed the visualisation special interest group (VIS SIG).

At the PSI conference in Gothenburg this year, the VIS SIG team were able to promote and facilitate data visualisation in-person for the first time. This resulted in 4 high-quality presentations covering various aspects of data visualisation.

Many of these presentations included examples from the flagship event of the VIS SIG – the Wonderful Wednesday Webinar (WWW) series. This group, which meets monthly, has now been running for over two years and has created a treasure of data visualisation examples, each of which highlight different aspects of data visualisation principles and approaches (check the VIS SIG blog to explore this treasure). All of these examples use typical data sets and their challenges from clinical trials and other data sources that we, as Statisticians, in the pharmaceutical industry work with.

The data visualisation session – which for the first time happened in the largest room – ended with a panel discussion including many questions from the audience. The large and active attendance underpins the increasing importance of data visualisation as a critical methodological area for Statisticians in pharma. 

What does all this reflect? The uptake in conversation around data visualisation was just the beginning. We’re seeing an increasingly large swing towards innovation in this field, whereby new apps and tools are being developed to better interpret data than ever before. One such example can be seen in the new R Shiny App we developed here at Veramed. The R Shiny app is a great example of how tools can empower Statisticians, not only to program small apps for selected data visualisations, but also streamline them into automating the entire data analysis process, as exemplified through our work in Network Meta Analysis.

As we see it, data visualisation is moving forward in two major ways:

  1. Reactive visualisations. Statisticians will place more focus on generating reactive visualisations, producing tools in the form of apps, as opposed to just the visualisations as an end product.
  2. Customization. Data visualisation customization will gain a much stronger role. It can not be ignored that the strength of a visualisation not only depends on technical details, but also on how well it is tailored to the intended target audience the presenter is communicating with.  

Stay tuned for our upcoming webinar on ‘Data Visualisation in clinical trials: how to make faster, more accurate decisions in 2023’.