OBJECTIVES: To evaluate the relationship between the average out of pocket cost (OPC) paid and the total prescription volume (TRx) for oral antineoplastics compared to a market basket of commonly utilized oral drugs.  Is drug utilization more sensitive to OPC changes when there is greater competition and/or when the ailments treated by those drugs are less acute?  We hypothesized an inverse proportional relationship between OPC and TRx.    METHODS: We obtained monthly OPC data and TRx data from SDI’s VONA and VOPA databases from January 2007-April 2009.  We compared eight oral antineoplastics (capecitabine, imatinib, lenalidomide, thalidomide, sunitinib, erlotinib, temozolomide, dasatinib) to a market basket of eight commonly utilized oral drugs (celecoxib, sitagliptin, rosuvastatin, fenofibrate, ramipril, simvastatin, atorvastatin, amlodipine).  We calculated the correlation coefficient (r2) for the relationships between a drug’s average OPC and its TRx.  RESULTS: Two of the eight cancer drugs (erlotinib and dasatinib) showed any remarkable correlations (r2 = 0.17 and 0.29, respectively) while the other six all had correlation coefficients less than 0.10.  Five of the market basket drugs had a correlation coefficient greater than 0.10.  Four of those had negative correlations between OPC and TRx: simvastatin (r2=0.71), sitagliptin (r2=0.64), amlodipine (r2=0.59), atorvastatin (r2 = 0.14).  Interestingly, ramipril had a significant positive correlation between OPC and TRx (r2 = 0.51). CONCLUSIONS: Antineoplastic utilization appears to be inelastic with respect to OPC fluctuations vis-à-vis the chosen market basket. This observation may be a function of the severity of disease, the lack of treatment options or both. In contrast, changes in OPC for products in the general market basket, notably simvastin and sitagliptin, precipitated significant changes in drug utilization.  Broader treatment options and specifically generic competition may contribute to this finding.  Further research is warranted to track these relationships in a prospective, multi-factorial manner in order to better infer a cause-effect relationship.