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Methods: 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 Results: 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. Conclusions: 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. |
