Speaker: Yao Chen , Director of the Medical Statistics Office, Peking University First Hospital
Speaker：Chen Yao, Professor, Peking University First Hospital
Topic: Advantages of Visualizing Clinical Trial Data Analysis Using JMP Clinical
Topics: The Advantages of Using JMP® Clinical for Clinical Data Analysis Visualization
summary:
This paper discusses the application of visualization methods in the analysis of adverse events and laboratory test data in clinical trials. Methods: According to the requirements of the International Conference on Harmonization (ICH) guidelines for drug safety evaluation, we used JMP Clinical to analyze the real adverse events and laboratory test data in clinical trials, and made visualization drawings to compare the differences between groups in the occurrence of adverse events and highlight the adverse events that need to be paid attention to. The time trend was used to compare the distribution between groups to understand the laboratory test data. Results: Multiple pictures were drawn in this paper, such as volcano map, Venn diagram, survival map, relative risk map, box plot, two-dimensional scatter plot, dynamic bubble map and subject personal profile map. Conclusion: Visual evaluation of adverse event data can not only describe the characteristics of adverse events, but also highlight the adverse events that should be paid attention to. Similarly, visual evaluation of laboratory test data can not only describe the characteristics of data, but also highlight the individual data that should be paid attention to. It should be given priority in the analysis of experimental data in clinical trials.
Abstract：
The objective of this presentation is to evaluate a visual approach in the analysis of adverse events and laboratory data from clinical trials. According to the International Conference on Harmonization (ICH) for the drug safety evaluation guide, we use JMP Clinical to analyze real adverse events and laboratory data in clinical trials, and visualize the data. The incidences of adverse events between groups were compared. The important adverse events were identified and studied in detail. We displayed time trends, and compared the distribution between groups to understand the laboratory test data. We illustrated multiple specific graphs and visualizations, such as the volcano plot, Venn diagram, survival plot, relative risk plot, box plot, two-dimensional arrays of scatterplots, dynamic bubble plot and patient profile. Not only can visualization of adverse event data describe characteristics, but it can also highlight which adverse events should receive attention. Similarly, the visualization of laboratory data can describe not only the data characteristics, but also highlight the individual data that should be noticed. Visualization should be recommended for clinical trial data analysis.
