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Sunday, June 23 • 9:00am - 12:30pm
SC24: #24: Data Visualization in the Life Sciences

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Component Type: Tutorial
CE: ACPE 3.25 Application UAN: 0286-0000-19-504-L04-P; CME 3.25; IACET 3.25; RN 3.25

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. An additional registration fee is required for all preconference short courses. Back to DIA 2019 Short Courses

Who should attend?

This short course is designed to be accessible to a wide audience, it will focus on principles, limit technical jargon, and interpret numerous examples of data visualization using data from the life sciences literature. The audience may include any individual interested in developing their skills for more efficient interpretation and communication of various aspects of study design and analysis.


Learning Objectives

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.



Speakers
avatar for Kelci Miclaus

Kelci Miclaus

Sr Manager, Advanced Analytics R&D, SAS Institute Inc., JMP Division
Dr. Kelci Miclaus is Advanced Analytics Sr. Manager for the JMP Life Sciences division at SAS Institute where she manages the R&D team and develops statistical features for JMP Genomics and JMP Clinical software. She joined SAS in 2006 and holds a PhD in Statistics from North Carolina... Read More →
avatar for Richard C. Zink

Richard C. Zink

Senior Director, Data Management and Statistics, TARGET Pharmasolutions Inc
Richard C. Zink is Senior Director of Data Management and Statistics at TARGET PharmaSolutions. He is the 2019 Chair of the Biopharmaceutical Section of the American Statistical Association, host of the Biopharmaceutical Section Statistics Podcast, and Associate Editor for the DIA... Read More →


Sunday June 23, 2019 9:00am - 12:30pm
Room 5AB San Diego Convention Center 111 W Harbor Drive, San Diego, CA 92101 USA
  • Level Intermediate
  • Featured Topics Real World Evidence
  • Credit Type ACPE, CME, IACET, RN
  • Tags Tutorial