Kelci Miclaus, Senior Manager, Advanced Analytics R & D, JMP Life Sciences, SAS Institute Inc.
Translational research (from basic research to applied fields) and clinical research are expensive.In order to understand health outcomes and illnesses, researchers typically collect maximum data on maximum endpoints, taking into account costs and time.This is further motivated by a limited understanding of the genomic basis of the disease and good clinical research practice protocols to assess safety and operational integrity as well as the effectiveness of new therapies. It is attached.In the “life cycle” of life science data, the amount of data can be a heavy burden in the analysis and transmission of results (though traditional methods of creating hundreds of tables are neither efficient nor effective) ).In this session, we will focus on visualizations to communicate results in a typical case study analysis in life science research.About summarizing and visualizing data and graphing to convey the results of complex statistical analysis,
I will explain in detail.Examples include running Volcano plots, Manhattan plots, safety and efficacy distribution summaries in clinical trials, laboratory diagnostic trends (waterfall plots, shifts) of thousands to millions of statistical models using genomic data Plots, spaghetti plots, lasagna plots, swimmer plots), and detection of abnormalities in clinical trial integrity.All examples were created using JMP's graph builder and showcase its advantages and flexibility.
