Predicting SF-6D from the European Organization for Treatment and Research of Cancer Quality of Life Questionnaire scores in patients with colorectal cancer.

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. 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. 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. 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.

Wong CK ，Lam CL ，Wan YF ，Rowen D ... -  《-》

Mapping the Functional Assessment of Cancer Therapy-general or -Colorectal to SF-6D in Chinese patients with colorectal neoplasm.

To map Functional Assessment of Cancer Therapy-General (FACT-G) and Functional Assessment of Cancer Therapy-Colorectal (FACT-C) subscale scores onto six-dimensional health state short form (derived from short form 36 health survey) (SF-6D) preference-based values in patients with colorectal neoplasm, with and without adjustment for clinical and demographic characteristics. These results can then be applied to studies that have used FACT-G or FACT-C to predict SF-6D utility values to inform economic evaluation. Ordinary least square regressions were estimated mapping FACT-G and FACT-C onto SF-6D by using cross-sectional data of 537 Chinese subjects with different stages of colorectal neoplasm. Mapping functions for SF-6D preference-based values were developed separately for FACT-G and FACT-C in four sequential models for addition of variables: 1) main-effect terms, 2) squared terms, 3) interaction terms, and 4) clinical and demographic variables. Predictive performance in each model was assessed by the R(2), adjusted R(2), predicted R(2), information criteria (Akaike information criteria and Bayesian information criteria), the root mean square error, the mean absolute error, and the proportions of absolute error within the threshold of 0.05 and 0.10. Models including FACT variables and clinical and demographic variables had the best predictive performance measured by using R(2) (FACT-G: 59.98%; FACT-C: 60.43%), root mean square error (FACT-G: 0.086; FACT-C: 0.084), and mean absolute error (FACT-G: 0.065; FACT-C: 0.065). The FACT-C-based mapping function had better predictive ability than did the FACT-G-based mapping function. Models mapping FACT-G and FACT-C onto SF-6D reached an acceptable degree of precision. Mapping from the condition-specific measure (FACT-C) had better performance than did mapping from the general cancer measure (FACT-G). These mapping functions can be applied to FACT-G or FACT-C data sets to estimate SF-6D utility values for economic evaluation of medical interventions for patients with colorectal neoplasm. Further research assessing model performance in independent data sets and non-Chinese populations are encouraged.

Wong CK ，Lam CL ，Rowen D ，McGhee SM ，Ma KP ，Law WL ，Poon JT ，Chan P ，Kwong DL ，Tsang J ... -  《-》

Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments.

Kontodimopoulos N ，Aletras VH ，Paliouras D ，Niakas D ... -  《-》

Mapping EORTC-QLQ-C30 and QLQ-CR29 onto EQ-5D-5L in Colorectal Cancer Patients.

Ameri H ，Yousefi M ，Yaseri M ，Nahvijou A ，Arab M ，Akbari Sari A ... -  《-》

Quality of life measures (EORTC QLQ-C30 and SF-36) as predictors of survival in palliative colorectal and lung cancer patients.

Grande GE ，Farquhar MC ，Barclay SI ，Todd CJ ... -  《-》