Statistical Analysis
Our first step grouped patients into clusters with similar mixes of symptom severity using an unsupervised machine learning algorithm (k-means with Euclidean distance). We selected k-means because of its computational efficiency and intuitive visualization of the data points related to their respective clusters.After establishing symptom severity clusters, we compared sociodemographic and clinical variables as well as outcome measures by cluster. To examine the validity of the clustering, we compared the severity attributes of individual items and total severity score by cluster. Differences across clusters were tested using analysis of variance for continuous variables and χClustering was implemented using the Cluster package in R version 4.0 (R Project for Statistical Computing). The remaining statistical analyses were performed in SAS version 9.4 (SAS Institute) and Stata version 16.0 (StataCorp), with 2-tailed 