Clinical trial patient retention simulator. Predicts dropout risk using behavioral decay modeling. Saves CROs millions in trial delays. Enterprise-ready.

This project analyzes clinical trial data from ClinicalTrials.gov to uncover factors influencing trial success and dropout rates. Using Python, Excel, and Power BI, it visualizes trends across phases, sponsors, and durations to improve patient retention and research efficiency.

🧪 Model patient retention in clinical trials, identify risks, and simulate interventions to reduce dropouts and save costs.