Development of mapping algorithms for gastric cancer: translating EORTC QLQ-C30 and QLQ-STO22 to EQ-5D-5L health utilities.

PURPOSE

This study aims to develop a mapping model from the EORTC QLQ-C30 and QLQ-STO22 to the EQ-5D-5L.

METHODS

Data were collected from 1059 gastric cancer patients in mainland China. The conceptual overlap between the EORTC QLQ-C30/QLQ-STO22 and EQ-5D-5L was assessed using the Pearson correlation coefficient. Four regression models were applied to estimate the algorithm: ordinary least squares (OLS), Tobit regression (Tobit), ordered probit regression (Oprobit), and beta mixture regression (Betamix). The independent variables in the models comprised the scale dimension scores of the EORTC QLQ-C30 and QLQ-STO22, squared terms of relevant dimension scores, age, and gender. The generalizability of the models was assessed using random sample validation and five-fold cross-validation. Model performance was evaluated using four primary metrics: root mean squared error (RMSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and absolute error (AE).

RESULTS

The mean health utility value of the EQ-5D-5L was 0.853 (SD = 0.240). The Oprobit3 demonstrated the best performance among all evaluated models, with RMSE = 0.197, MAE = 0.111, AE > 0.05 (%) = 56.84, AE > 0.1 (%) = 28.61, and ICC = 0.703. The predictors included all dimensions of the EORTC QLQ-C30 and QLQ-STO22 questionnaires, as well as age and gender.

CONCLUSIONS

The developed algorithm enables researchers to estimate EQ-5D-5L health utilities based on EORTC QLQ-C30 and QLQ-STO22 scores. This approach facilitates cost-utility analyses in gastric cancer patients when EQ-5D-5L data are unavailable.