%0 Journal Article
%T Development of a Web-Based Interactive Tool for Visualizing Breast Cancer Clinical Trial Tolerability Data.
%A Luu M
%A Gresham G
%A Henry L
%A Kim S
%A Rogatko A
%A Yothers G
%A Hays RD
%A Tighiouart M
%A Ganz PA
%J JCO Clin Cancer Inform
%V 8
%N 0
%D 2024 Jul
%M 39013121
暂无%R 10.1200/CCI.24.00007
%X <B>OBJECTIVE: </B>Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data.<BR><B>METHODS: </B>We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer.<BR><B>RESULTS: </B>To illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data.<BR><B>CONCLUSIONS: </B>We have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.