OBJECTIVES: To develop a mapping model for estimating six-dimensional health state short form (SF-6D) utility scores from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaires (QLQ-C30 and QLQ-CR29) scores in patients with colorectal cancer (CRC), with and without adjustment for clinical and demographic characteristics. METHODS: Ordinary least squares regression models were applied to a cross-sectional data set of 216 patients with CRC collected from a regional hospital in Hong Kong. Item responses or scale scores of cancer-specific (QLQ-C30) and colorectal-specific health-related quality-of-life (QLQ-CR38/CR29) data and selected demographic and clinical characteristics of patients were used to predict the SF-6D scores. Model goodness of fit was examined by using exploratory power (R(2) and adjusted R(2)), Akaike information criterion, and Bayesian information criterion, and predictive performance was evaluated by using root mean square error, mean absolute error, and Spearman's correlation coefficients between predicted and observed SF-6D scores. Models were validated by using an independent data set of 56 patients with CRC. RESULTS: Both scale and item response models explained more than 67% of the variation in SF-6D scores. The best-performing model based on goodness of fit (R(2) = 75.02%), predictive ability in the estimation (root mean square error = 0.080, mean absolute error = 0.065), and validation data set prediction (root mean square error = 0.103, mean absolute error = 0.081) included variables of main and interaction effects of the QLQ-C30 supplemented by QLQ-CR29 subset scale responses and a demographic (sex) variable. CONCLUSIONS: SF-6D scores can be predicted from QLQ-C30 and QLQ-CR38/CR29 scores with satisfactory precision in patients with CRC. The mapping model can be applied to QLQ-C30 and QLQ-CR38/CR29 data sets to produce utility scores for the appraisal of clinical interventions targeting patients with CRC using economic evaluation.
OBJECTIVES: To develop a mapping model for estimating six-dimensional health state short form (SF-6D) utility scores from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaires (QLQ-C30 and QLQ-CR29) scores in patients with colorectal cancer (CRC), with and without adjustment for clinical and demographic characteristics. METHODS: Ordinary least squares regression models were applied to a cross-sectional data set of 216 patients with CRC collected from a regional hospital in Hong Kong. Item responses or scale scores of cancer-specific (QLQ-C30) and colorectal-specific health-related quality-of-life (QLQ-CR38/CR29) data and selected demographic and clinical characteristics of patients were used to predict the SF-6D scores. Model goodness of fit was examined by using exploratory power (R(2) and adjusted R(2)), Akaike information criterion, and Bayesian information criterion, and predictive performance was evaluated by using root mean square error, mean absolute error, and Spearman's correlation coefficients between predicted and observed SF-6D scores. Models were validated by using an independent data set of 56 patients with CRC. RESULTS: Both scale and item response models explained more than 67% of the variation in SF-6D scores. The best-performing model based on goodness of fit (R(2) = 75.02%), predictive ability in the estimation (root mean square error = 0.080, mean absolute error = 0.065), and validation data set prediction (root mean square error = 0.103, mean absolute error = 0.081) included variables of main and interaction effects of the QLQ-C30 supplemented by QLQ-CR29 subset scale responses and a demographic (sex) variable. CONCLUSIONS: SF-6D scores can be predicted from QLQ-C30 and QLQ-CR38/CR29 scores with satisfactory precision in patients with CRC. The mapping model can be applied to QLQ-C30 and QLQ-CR38/CR29 data sets to produce utility scores for the appraisal of clinical interventions targeting patients with CRC using economic evaluation.