Nan Luo1, Pei Wang, Julian Thumboo, Yee-Wei Lim, Hubertus J M Vrijhoef. 1. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 16 Medical Drive, Block MD3, Singapore, 117597, Singapore, nan_luo@nuhs.edu.sg.
Abstract
OBJECTIVE: The aim of this study was to establish an EQ-5D-3L value set using the time trade-off (TTO) method to elicit the health preferences of the general Singaporean population. METHODS: The values of 80 EQ-5D-3L health states were elicited from a general Singaporean population sample using a TTO method. In face-to-face interviews, participants were asked to value a block of ten health states. Various linear regression models were examined to assess for goodness of fit to the data, at both aggregate and individual levels. Prediction precision was assessed in terms of mean absolute error (MAE), and numbers of prediction errors larger than 0.10 and 0.20. Prediction consistency and bias were also assessed. RESULTS: A total of 456 participants provided data for this study. The N3 model without a constant estimated using the aggregate data exhibited the best fit of the data, predicted values with the least bias, and generated logically consistent values for all 243 EQ-5D-3L health states. The MAE was 0.1137, and 35 of 80 predicted values had errors less than 0.10 in absolute magnitude. Based on this model, the utility values ranged from 0.854 for state 11121 to -0.769 for state 33333. CONCLUSIONS: The EQ-5D-3L value set can be estimated using the TTO method in the multi-cultural, multi-ethnic Singapore. Although the estimation precision is not optimal, the health-state preference values generated in this study are useful to health service researchers in the country before estimates with smaller errors are available.
OBJECTIVE: The aim of this study was to establish an EQ-5D-3L value set using the time trade-off (TTO) method to elicit the health preferences of the general Singaporean population. METHODS: The values of 80 EQ-5D-3L health states were elicited from a general Singaporean population sample using a TTO method. In face-to-face interviews, participants were asked to value a block of ten health states. Various linear regression models were examined to assess for goodness of fit to the data, at both aggregate and individual levels. Prediction precision was assessed in terms of mean absolute error (MAE), and numbers of prediction errors larger than 0.10 and 0.20. Prediction consistency and bias were also assessed. RESULTS: A total of 456 participants provided data for this study. The N3 model without a constant estimated using the aggregate data exhibited the best fit of the data, predicted values with the least bias, and generated logically consistent values for all 243 EQ-5D-3L health states. The MAE was 0.1137, and 35 of 80 predicted values had errors less than 0.10 in absolute magnitude. Based on this model, the utility values ranged from 0.854 for state 11121 to -0.769 for state 33333. CONCLUSIONS: The EQ-5D-3L value set can be estimated using the TTO method in the multi-cultural, multi-ethnic Singapore. Although the estimation precision is not optimal, the health-state preference values generated in this study are useful to health service researchers in the country before estimates with smaller errors are available.
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