Assessment of Successful Randomization Through a Machine Learning and Visualization Tool for Pre-treatment Symptoms: Examples from CCTG/AGITG CO.17 and CO.20 Trials

Patients in randomized controlled trials (RCTs) must be successfully randomized to reduce or eliminate bias. Because pre-treatment symptoms have prognostic significance in cancer patients, qualitative and quantitative tools were developed to assess similarity of baseline pre-treatment symptoms across different treatment arms of RCTs as one measure of randomization success. Clinician-reported symptom data from two colorectal cancer RCTs, CO.20 and CO.17, were used to demonstrate the utility of a qualitative visualization tool and quantitative machine learning K-means tool, which grouped patients into clusters using baseline symptoms. Qualitatively, reflection bar graphs (RBGs) visualized potential imbalances in baseline symptoms (i) across treatment arms and (ii) by corresponding patient clusters identified within each treatment arm. RBGs found that the treatment arms for both RCTs had similar symptom profiles, while the lack of significant differences in the proportions of patients in each cluster across treatment arms further confirmed successful randomization. This paper details the creation of visualization, machine-learning, and statistical tools to compare baseline symptoms across RCT treatment arms, demonstrating that the CO.20 and CO.17 trials were successfully randomized by baseline symptoms and are comparable. These tools can therefore be implemented easily to ensure an extra layer of quality assurance of the randomization process for study assessment.