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The relative importance of each attribute was then calculated with respect to the sum of utility ranges.
Table 3 illustrates the standardised, zero-centred partworth utilities for all attribute levels and the attribute utility ranges, which form the basis for quantifying the relative importance of the attributes.
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In these 6 studies, the mean utility ranged from 0.66 to 0.80.
This scoring approach limits the utility range of health states worse than dead to values between 0 and −1.
Within the limited utility range, the Australian Adolescent Tariff generated a wider spread of scores and consistently lower mean values.
Mean absolute error in predictions by EQ-5D utility range increases with severity for our models (0.085 to 0.34) and for existing mapping functions (0.123 to 0.272).
When these cost s were weighted by the individual mean health status (area under the Karnofsky curve), the cost utility ranged from €41167 to €53369 per QALY gained.
The EQ-5D appears to be less sensitive to changes in the upper end of the health utility range (i.e., it exhibits ceiling effects), while the SF-6D appears to be less sensitive in the lower end of the health utility range (i.e., it exhibits floor effects) [ 29].
Table 3 shows mean error, mean absolute error and mean square error of predicted compared to actual utility scores by EQ-5D utility range for existing approaches [ 3, 4].
Overall health-related quality of life of the subjects was good (mean utility ranged from 0.68 (±0.08) to 0.85 ±0.14) over the nine utility measures) and comparable to patients without diabetes.
Table 3 reports mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) of predicted compared to actual utility scores by EQ-5D utility range for the two-part model using the weekly diary data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com