The current paradigm for analyzing clinical trial data is cumbersome: it is an inefficient, slow, and expensive process. Several rounds of iterations between the main programmer and the validation programmer are usually needed to thoroughly explore the data. Furthermore, clinicians and statisticians often would like to explore the data themselves but lack a robust and flexible platform to carry out this data exploration. For instance, they may need to inspect an endpoint for patients with certain genetic markers, to analyze biomarker data, or to create table summaries. To meet these needs, we created tidyCDISC, an open source Shiny application that can be used to generate custom tables, statistics, and figures. The application has three modules: a drag and drop table generator, a graphical population explorer, and a patient history explorer. We've taken a modularized approach to our package to ensure the application can be easily expanded upon to include further analyses and figures. By sharing our application as an open source solution, we hope to help other scientists with similar problems as well as promote collaboration in the pharmaceutical industry.
Maya Gans | Marly Gotti
Cytel | Biogen
tidyCDISC an Open Source Platform in R to Analyze Clinical Trial Data
The current paradigm for analyzing clinical trial data is cumbersome it is an inefficient, slow, and expensive process. Several rounds of iterations between the main programmer and the validation programmer are usually needed to thoroughly explore the data. Furthermore, clinicians and statisticians often would like to explore the data themselves but lack a robust and flexible platform to carry out this data exploration. For instance, they may need to inspect an endpoint for patients with certain genetic markers, to analyze biomarker data, or to create table summaries. To meet these needs, we created tidyCDISC, an open source Shiny application that can be used to generate custom tables, statistics, and figures. The application has three modules a drag and drop table generator, a graphical population explorer, and a patient history explorer. We've taken a modularized approach to our package to ensure the application can be easily expanded upon to include further analyses and figures. By sharing our application as an open source solution, we hope to help other scientists with similar problems as well as promote collaboration in the pharmaceutical industry.