Data Science Solution to Event Prediction in Outsourced Clinical Trial Models.

Area of Science:

• Clinical research
• Statistical modeling
• Data science in healthcare

Background:

• Late-phase clinical trials are frequently outsourced to Contract Research Organizations (CROs), with sponsors retaining ultimate risk and accountability.
• Statistical tasks performed by CROs often require revalidation by in-house sponsor teams, creating potential inefficiencies and trust gaps.

Purpose of the Study:

• To present a technological approach for standardized event prediction in clinical trials.
• To introduce a dynamic web application built around an R-package designed to foster transparency and trust between CROs and in-house statisticians.
• To demonstrate the benefits of standardization in improving communication and reliability for time-to-event prediction, particularly in oncology.

Main Methods:

• Development of a dynamic web application integrated with an R-package for standardized event prediction.
• Focus on key features including a short learning curve, interactivity, reproducibility, and data diagnostics.
• Application motivated by time-to-event prediction in oncology clinical trials.

Main Results:

• The developed tool offers reliable event predictions, simplifying communication and increasing trust through transparency.
• Demonstrated clear benefits of standardization for both CROs and sponsor companies.
• The application supports exploration, communication, sensitivity analysis, and standard report generation.

Conclusions:

• Standardized event prediction tools can significantly enhance collaboration and efficiency in outsourced clinical trial statistics.
• The presented web application provides a transparent and reproducible method for time-to-event predictions, particularly beneficial in oncology.
• Adoption of such tools can lead to improved data integrity and streamlined workflows between research organizations.