Quantifying Eligibility Pattern Shifts: a Data-Driven Paradigm for Early Risk Detection in Clinical Trials

Area of Science:

• Clinical trial methodology
• Data-driven analytics in healthcare
• Regulatory science

Background:

• Traditional Risk-Based Monitoring (RBM) often overlooks patient eligibility heterogeneity.
• Existing RBM metrics focus on site performance but not enrollment pattern shifts.

Purpose of the Study:

• To present a data-driven framework for capturing temporal and inter-site shifts in patient baseline inclusion characteristics.
• To introduce novel metrics for quantifying deviations from expected enrollment patterns.

Main Methods:

• Developed a framework incorporating Borderline Inclusion Index and Eligibility Distribution Divergence metrics.
• Utilized a Bayesian composite score to synthesize indicators for prioritizing oversight.
• Operationalized the framework via an interactive Shiny web application for decision support.

Main Results:

• The framework effectively captures temporal and inter-site shifts in eligibility profiles.
• Monitoring eligibility pattern shifts provides an early warning signal for operational or scientific risks.
• The developed metrics and composite score strengthen overall clinical trial integrity.

Conclusions:

• The proposed data-driven framework enhances traditional RBM by focusing on patient eligibility.
• Early detection of enrollment pattern shifts improves clinical trial oversight and integrity.
• The interactive application facilitates centralized RBM implementation and decision-making.