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

Lookup NU author(s): Dr Ayon MukherjeeORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© The Author(s) 2026.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.

Publication metadata

Author(s): Bhattacharjee A, Mukherjee A

Publication type: Article

Publication status: Published

Journal: Therapeutic Innovation and Regulatory Science

Year: 2026

Pages: epub ahead of print

Online publication date: 06/02/2026

Acceptance date: 09/01/2026

Date deposited: 24/02/2026

ISSN (print): 2168-4790

ISSN (electronic): 2168-4804

Publisher: Springer Science and Business Media Deutschland GmbH

URL: https://doi.org/10.1007/s43441-026-00918-y

DOI: 10.1007/s43441-026-00918-y

Data Access Statement: No datasets were generated or analysed during the current study.

PubMed id: 41649747

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