Traditional approaches to medical product development rely on generating pages upon pages of analysis results to describe the safety and effectiveness of novel therapies. Study teams struggle to understand and communicate the story hidden within the data to their colleagues. First and foremost, with the high cost of conducting translational clinical research, it is common to collect as much data as possible on as many endpoints as possible. This phenomenon is further reinforced due to our limited understanding of biological mechanisms and pathways, including the potential genomic underpinnings of a disease or treatment response. For example, we may have a clear understanding for how a novel therapy induces an efficacious response, but there is typically limited knowledge into the downstream effects of the drug to other body systems. A second challenge to communication lies in the increased use of sensitivity analyses to assess the consistency and robustness of study results to varying assumptions. Given the volume of data to review and the variety of analyses to perform, it should come as no surprise that clear insight is often out of reach. In this environment, the traditional means of data summary – tables and listings – are ineffective for gaining insight; visualization is the key to effective communication for the modern clinical trialist.
Ben Shneiderman stated that “the purpose of visualization is insight.” Therefore, the goal of this short course is to describe data visualization techniques to aid in the understanding and communication of results from applications in clinical trials and genomics research. Numerous practical illustrations and examples from the literature will be presented. To be accessible to a wide audience, this course will focus on principles and interpretation, and limit technical jargon. At least 2/3 of the course will focus on case studies specific to clinical trials, while the remainder will be spent on examples in genomics.
Who should attend?
At the conclusion of this course, participants should be able to:
- Describe the transition from traditional methods of data analysis to visual approaches
- Interpret life science data using one or more data visualizations
- Assess the strengths and limitations of various graphical techniques
- Explain the “data story” of numerous clinical research examples using data visualization techniques