AIM To identify unique clusters of patients based on their concerns in using analgesia for cancer pain and predictors of the cluster membership.METHODS This was a 3-mo prospective observational study(n = 207).Patients were included if they were adults(≥ 18 years), diagnosed with solid tumors or multiple myelomas, and had at least one prescription of around the clock pain medication for cancer or cancer-treatment-related pain.Patients were recruited from two outpatient medical oncology clinics within a large health system in Philadelphia.A choice-based conjoint(CBC) analysis experiment was used to elicit analgesic treatment preferences(utilities).Patients employed trade-offs based on five analgesic attributes(percent relief from analgesics, type of analgesic, type of sideeffects, severity of side-effects, out of pocket cost).Patients were clustered based on CBC utilities using novel adaptive statistical methods.Multiple logistic regression was used to identify predictors of cluster membership.RESULTS The analyses found 4 unique clusters: Most patients made trade-offs based on the expectation of pain relief(cluster 1, 41%).For a subset, the main underlying concern was type of analgesic prescribed, i.e., opioid vs non-opioid(cluster 2, 11%) and type of analgesic side effects(cluster 4, 21%), respectively.About one in four made trade-offs based on multiple concerns simultaneously including pain relief, type of side effects, and severity of side effects(cluster 3, 27.5%).In multivariable analysis, to identify predictors of cluster membership, clinical and socioeconomic factors(education, health literacy, income, social support) rather than analgesic attitudes and beliefs were found important; only the belief, i.e., pain medications can mask changes in health or keep you from knowing what is going on in your body was found significant in predicting two of the four clusters [cluster 1(-); cluster 4(+)].CONCLUSION Most patients appear to be driven by a single salient concern in using analgesia for cancer pain.Addressing these concerns, perhaps through real time clinical assessments, may improve patients’ analgesic adherence patterns and cancer pain outcomes. AIM To identify unique clusters of patients based on their concerns in using analgesia for cancer pain and predictors of the cluster membership. METHODS This was a 3-mo prospective observational study (n = 207). Patients were included if they were adults years), diagnosed with solid tumors or multiple myelomas, and had at least one prescription of around the clock pain medication for cancer or cancer-treatment-related pain. Patients were recruited from two outpatient medical oncology clinics within a large health system in Philadelphia. A choice-based conjoint (CBC) analysis experiment was used to elicit analgesic treatment preferences (utilities). Patients employed trade-offs based on five analgesic attributes (percent relief from analgesics, type of analgesic, type of side effects, severity of side-effects , out of pocket cost). Pats were clustered based on CBC utilities using novel adaptive statistical methods. Multiple logistic regression was used to identify predictors of cluster me mbership.RESULTS The analyzes found 4 unique clusters: Most patients made trade-offs based on the expectation of pain relief (cluster 1, 41%). For a subset, the main underlying concern was type of analgesic prescribed, ie, opioid vs non -opioid (cluster 2, 11%) and type of analgesic side effects (cluster 4, 21%), respectively. About one in four made trade-offs based on multiple concerns simultaneously including pain relief, type of side effects, and severity of side effects (cluster 3, 27.5%). In multivariable analysis, to identify predictors of cluster membership, clinical and socioeconomic factors (education, health literacy, income, social support) rather than analgesic attitudes and beliefs were found important; only the belief, ie, pain medications can mask changes in health or keep you from knowing knowing what is going on in your body was found significant in predicting two of the four clusters [cluster 1 (-); cluster 4 (+)]. CONCLUSION Most patients appear to be driven by a single salient conc ern in using analgesia for cancer pain. Add stressing these concerns, perhaps through real time clinical assessments, may improve patients’ analgesic adherence patterns and cancer pain outcomes.