MyMedR (Malaysian Medical Repository) is an open-access collection of Malaysian health and biomedical research. The materials are imported from PubMed and MyJurnal. We gratefully acknowledge the permission to reuse the materials from the National Library of Medicine of the United States and the Malaysian Citation Centre. This project is funded by the Academy of Family Physicians of Malaysia. The project team members are CL Teng, CJ Ng, EM Khoo, Mastura Ismail, Abrizah Abdullah, TK Chiew, and Thanaletchumi Dharmalingam.
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METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
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