Utilizing network analysis in blinded independent central review for clinical trials as adjudication agreement dashboard

Abstract

Variability in observer performance BICR is common but not well understood and various measures like AR, AAR, RDI help quantify it which leads to multiple complex data points. Network analysis uses mathematically based algorithms to characterize the components of a network of entities and identifying, visualizing, and analysing their relationships. In a network, variables are represented by nodes, the relationships represented by edges between these nodes. The visualization technique involves mapping relationships among entities based on the symmetry or asymmetry of data. Maps from data points generated during double read adjudication study can provide the performance of each reader pair primarily based on AAR. Adjudication data from four oncology clinical trials with 2163 subjects, 16937 post-baseline responses was analyzed. Performance metrics included number of cases, adjudication rate, adjudication agreement rate for each read and reader pair. The data were aggregated and prepared for network analysis in Python-a high-level, cross-platform, and open-sourced programming language released under a GPL-compatible license. Python Software Foundation (PSF), a non-profit organization, holds the copyright. Url-https://www.python.org Version 3.9.0 This graphic visualization provides simplistic organization of a complicated data analysis and supports the quality monitoring process of independent reviews. The tool provides a snapshot of the review performance of all the readers in the trial allowing the study team to investigate and intervene in a timely manner with the intent of supporting robust and accurate data analysis. Network analysis plots for reader performance metrics in BICR provide excellent visual mapping to interpret multiple critical metrics in a single plot which would otherwise require multiple plots and tables. Timely review of these plots during the trial can help demonstrate the effectiveness of interventions as well.