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Related Experiment Video

Updated: Feb 8, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
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Quantifying Eligibility Pattern Shifts: a Data-Driven Paradigm for Early Risk Detection in Clinical Trials.

Atanu Bhattacharjee1, Ayon Mukherjee2

  • 1Division of Population Health and Genomics, University of Dundee, Dundee, United Kingdom.

Therapeutic Innovation & Regulatory Science
|February 6, 2026
PubMed
Summary

This study introduces a novel framework to monitor patient eligibility in clinical trials, enhancing risk-based monitoring (RBM) by detecting enrollment pattern shifts for improved trial integrity.

Keywords:
Adaptive OversightBaseline Inclusion CriteriaBayesian Monitoring FrameworkCentralized MonitoringClinical Trial Quality AssuranceEligibility HeterogeneityEnrollment Pattern ShiftRisk-Based Monitoring (RBM)Shiny Decision-Support ToolSite-Level Risk Assessment

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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.