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

TY - JOUR

T1 - Quantifying Eligibility Pattern Shifts

T2 - a Data-Driven Paradigm for Early Risk Detection in Clinical Trials

AU - Bhattacharjee, Atanu

AU - Mukherjee, Ayon

N1 - Copyright: © The Author(s) 2026.

PY - 2026/2/6

Y1 - 2026/2/6

N2 - Traditional Risk-Based Monitoring (RBM) strategies emphasise key risk indicators and site-level performance metrics but seldom address the heterogeneity of patient eligibility profiles. We present a data-driven framework that captures temporal and inter-site shifts in baseline inclusion characteristics. Central to this framework are two new metrics-Borderline Inclusion Index and Eligibility Distribution Divergence-that quantify departures from expected enrolment patterns. A Bayesian composite score synthesises these indicators to prioritise oversight actions. Through simulation experiments and a worked case study, we show that monitoring eligibility pattern shifts offers an early warning signal of operational or scientific risk and strengthens overall trial integrity. We operationalize the framework through an interactive Shiny web application that computes indicator-specific posteriors, generates composite site risk scores, and provides visual decision-support for centralized RBM implementation.

AB - Traditional Risk-Based Monitoring (RBM) strategies emphasise key risk indicators and site-level performance metrics but seldom address the heterogeneity of patient eligibility profiles. We present a data-driven framework that captures temporal and inter-site shifts in baseline inclusion characteristics. Central to this framework are two new metrics-Borderline Inclusion Index and Eligibility Distribution Divergence-that quantify departures from expected enrolment patterns. A Bayesian composite score synthesises these indicators to prioritise oversight actions. Through simulation experiments and a worked case study, we show that monitoring eligibility pattern shifts offers an early warning signal of operational or scientific risk and strengthens overall trial integrity. We operationalize the framework through an interactive Shiny web application that computes indicator-specific posteriors, generates composite site risk scores, and provides visual decision-support for centralized RBM implementation.

KW - Adaptive Oversight

KW - Baseline Inclusion Criteria

KW - Bayesian Monitoring Framework

KW - Centralized Monitoring

KW - Clinical Trial Quality Assurance

KW - Eligibility Heterogeneity

KW - Enrollment Pattern Shift

KW - Risk-Based Monitoring (RBM)

KW - Shiny Decision-Support Tool

KW - Site-Level Risk Assessment

UR - https://www.scopus.com/pages/publications/105029672477

U2 - 10.1007/s43441-026-00918-y

DO - 10.1007/s43441-026-00918-y

M3 - Article

C2 - 41649747

AN - SCOPUS:105029672477

SN - 2168-4790

JO - Therapeutic Innovation and Regulatory Science

JF - Therapeutic Innovation and Regulatory Science

ER -