Summarization and visualization are essential for interpreting statistical analysis results, but coding programs takes a certain amount of time. However, in interactive discussions, there are situations where it is desirable to understand data/statistical analysis results in a timely manner. In such situations, dynamic summarization and visualization can be expected to promote discussion and speed up decision-making. The statistical analysis software R provides the Shiny package that enables this dynamic activity. In our presentation, we will introduce a method to create dynamic summary tables and Kaplan-Meier plots when subgroup variables and parameters are changed, focusing particularly on survival data, using the Shiny package.
Takumi Imamura | Takahiro Hasegawa
Shionogi | Shionogi
Dynamic Creation of Kaplan-Meier Plots and Summary Measure Tables for Survival Data with R Shiny
Summarization and visualization are essential for interpreting statistical analysis results, but coding programs takes a certain amount of time. However, in interactive discussions, there are situations where it is desirable to understand data/statistical analysis results in a timely manner. In such situations, dynamic summarization and visualization can be expected to promote discussion and speed up decision-making. The statistical analysis software R provides the Shiny package that enables this dynamic activity. In our presentation, we will introduce a method to create dynamic summary tables and Kaplan-Meier plots when subgroup variables and parameters are changed, focusing particularly on survival data, using the Shiny package.