In our daily study practice, we identified the need for a concise and straightforward tool to help study teams extract significant information from the latest EDC raw data. However, study teams often find themselves overwhelmed by the vast amount of EDC data and overly complex data review tools. To address this, we developed a one-click R-Shiny App that bridges EDC data directly to actionable outputs, enabling the detection of critical signals related to data quality.
The goal of this one-click R-Shiny App is to resolve the following challenges:
1. Provide a global dashboard that accommodates all company-level studies.
2. Detect patterns between the overall population and the China subgroup in safety data, end-of-treatment, end-of-study, protocol deviations, and biomarker sample collection.
3. Extract the most critical exception listings.
4. Address the issue of variable names lacking direct access to variable labels.
Keting LV
Daiichi Sankyo
Transforming EDC Data into Practical Insights: The Power of a One-Click R-Shiny App
In our daily study practice, we identified the need for a concise and straightforward tool to help study teams extract significant information from the latest EDC raw data. However, study teams often find themselves overwhelmed by the vast amount of EDC data and overly complex data review tools. To address this, we developed a one-click R-Shiny App that bridges EDC data directly to actionable outputs, enabling the detection of critical signals related to data quality. The goal of this one-click R-Shiny App is to resolve the following challenges 1. Provide a global dashboard that accommodates all company-level studies. 2. Detect patterns between the overall population and the China subgroup in safety data, end-of-treatment, end-of-study, protocol deviations, and biomarker sample collection. 3. Extract the most critical exception listings. 4. Address the issue of variable names lacking direct access to variable labels.