We used SAS 9.2 (Statistical Analysis System, 2009) to conduct the data reduction and cluster analysis, and the Partitioning Around Medoids (PAM) package in the R language (Calinski and Harabasz, 1974; Kaufman and Rousseuw, 1990) for the k-medoids method. After determining the final number of clusters, we characterized the resultant clusters using 33 variables reflecting demographics, opioid use behaviors, and related non-opioid use behaviors. The characteristics of each cluster were used to label the clusters. GEE Wald Type 3 χ2-tests were used to determine whether the clusters differed significantly on these variables. We used Bonferroni correction (p<0.05/33 = 0.0015) to avoid inflating the Type I error rate.
