OBJECTIVES: To help facilitate economic evaluations of interventions for treating cancer, we estimated utility indices for the two frequently used cancer-specific (FACT-G and EORTC-QLQ-C30) instruments of quality-of-life, by mapping them onto each of the EQ-5D and SF-6D preference-based-indices.  METHODS: A sample of 367 cancer patients from the Vancouver Cancer Centre completed four health-related-quality-of-life questionnaires (EORTC-QLQ-C30/FACT-G/EQ-5D/SF-6D). The sample was randomly divided to provide development(n=184) and cross-validation(n=183) samples. Models of the relationships between the EORTC-QLQ-C30/FACT-G and each of the preference-based-indices were estimated using regression analyses. We examined three alternative modeling approaches: ordinary-least-squares(OLS); generalized-linear-modeling(GLM) using a Gaussian distribution and log-link; and censored-least-absolute deviations(CLAD). The performance of the models was assessed in terms of how well the responses to the cancer-specific instruments predicted utilities from each of the EQ-5D/SF-6D instruments. RESULTS: The CLAD approach considers the non-normal(left-skew) distribution of the utility scores and their apparent truncation at one. Results from the final models of the three approaches did not differ significantly. Physical, functional and emotional well-being domain-scores of FACT-G significantly predict EQ-5D/SF-6D utility scores. Physical and emotional functioning and pain subscales of the EORTC-QLQ-C30 were significant predictors of the utility scores. Cognitive functioning and insomnia subscales of the EORTC-QLQ-C-30 were significantly associated with the EQ-5D, while the social and role functioning, and fatigue were only significant predictors of the SF-6D utility-index. The addition of age, gender, stage of disease, and ethnicity did not lead to significant improvement in the model. The root mean square error(RMSE) for the SF-6D was lower (0.064), suggesting better predictions than for the EQ-5D(0.098). CONCLUSIONS: There is potential to estimate EQ-5D/SF-6D utilities using responses from the cancer-specific FACT-G/EORTC-QLQ-C-30 measures of quality-of-life, even though the latter were not designed as utility instruments. Our results suggest that it is possible to estimate Quality-Adjusted-Life-Years(QALYs) from studies where only cancer-specific instruments have been administered.