The Assessment of Quality of Life - 6 Dimensions (AQoL-6D), a generic preference-based measure, is an appealing alternative to EQ-5D-5L for assessing health status in patients with chronic heart failure (HF), given its expanded scope. However, without a Malaysian value set, the AQoL-6D cannot generate health state utility values (HSUVs) to support local economic evaluations. This study intended to develop algorithms for predicting EQ-5D-5L HSUVs from AQoL-6D in an HF population.
Cross-sectional data from a multicenter cohort of 419 HF outpatients were used. Both direct and indirect mapping approaches were attempted using 5 sets of explanatory variables and 8 models (ordinary least squares, Tobit, censored least absolute deviations, generalized linear model, 2-part model [TPM], beta regression-based model, adjusted limited dependent variable mixture model, and multinomial ordinal regression [MLOGIT]). The models’ predictive performance was assessed through 10-fold cross-validated mean absolute error [MAE] and root mean squared error [RMSE]). Potential prediction bias was also examined graphically. The best-performing models, with the lowest RMSE and no bias, were then identified.
Among the models evaluated, TPM, which included age, sex, and 5 AQoL-6D dimension scores as predictors, appears to be the best-performing model for directly predicting EQ-5D-5L HSUVs from AQoL-6D. TPM yielded the lowest MAE (0.0802) and RMSE (0.1116), and demonstrated predictive accuracy for HSUVs >0.2 without significant bias. A MLOGIT model developed for response mapping had suboptimal predictive accuracy.
This study developed potentially useful mapping algorithms for generating Malaysian EQ-5D-5L HSUVs from AQoL-6D responses among patients with HF when direct EQ-5D-5L data are unavailable.