Kelci Miclaus, Senior Manager Advanced Analytics R&D, JMP Life Sciences, SAS
Conducting translational and clinical research comes with a big price tag. Given costs and time considerations in studies for understanding health outcomes and disease, researchers commonly collect as much data on as many endpoints as possible. This is further motivated by our limited understanding of the genomic underpinnings of disease and good clinical research practice protocols to assess not only efficacy of new therapeutics, but safety and operational integrity. In the life science data “life cycle,” the data volume poses challenges to analyze and communicate results; traditional practices that produce hundreds of tables are neither efficient nor effective. This presentation will focus on visualization to communicate results in several case study analyses typical of life science research. We highlight both data summary visualization techniques and graphs to communicate the results of complex statistical analyses. Examples include volcano plots and Manhattan plots when performing thousands to millions of statistical models with genomic data, distributional summaries of safety and efficacy in clinical trials, laboratory findings trends (waterfall plots, shift plots, spaghetti plots, lasagna plots, swimmer plots) and clinical operational integrity anomaly detection. All examples will be presented with Graph Builder, showcasing the strength and flexibility of this key JMP platform.