A large proportion of oncology outpatients with bone metastasis report unrelieved pain that significantly interferes with daily functioning and quality of life. However, little is known about the longitudinal pattern of pain intensity and analgesic prescriptions or use. Moreover, despite considerable advantages, the use of sophisticated statistical techniques, such as hierarchical linear modeling (HLM) has not been applied to the study of pain and analgesic outcomes. In a prospective longitudinal study, HLM was used to explore predictors of pain intensity and analgesic prescription and intake at the time of enrollment into the study (intercept) and over the course of 6 weeks (trajectory) in a sample of oncology outpatients with bone metastasis who received standard care for pain. In addition to corroborating known predictors of pain intensity, previously unrecognized variables were found that appear to affect both pain and analgesic outcomes. Importantly, some of the predictors of the trajectories of pain intensity and analgesic use (ie, pain-related distress and Pain Management Index (PMI) scores) are particularly amenable to interventions. Findings from this study suggest that sophisticated statistical modeling can be used in pain research to identify individual risk factors and propose novel targets that can be used to improve pain management in oncology outpatients with bone metastasis. Findings from this study suggest that a large amount of inter-individual variability exists in patients' experiences with cancer pain and analgesic use. Future studies need to elucidate the mechanisms that underlie these differences.