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Meanwhile, indirect preference-based approaches use preferences from other samples, usually from general population, to generate preference index scores for hypothetical health states from a HRQOL instrument [ 8].
The aim of this study is to present a procedure combining two analytical techniques that have not, thus far, featured in the debate: (i) Cluster Analysis (CA) which is used to generate preference subgroups, and (ii) Multi-Criteria Decision Analysis (MCDA) which provides the explicit policy framework for including clustered preferences.
These instruments measure patient preferences and generate preference or utility scores for respondents' health states on a 0 to 1.0 scale where 0 typically equals dead and 1.0 is full health [ 17].
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The aim of this study was to find out whether it was feasible, acceptable, reliable, and valid to use the standard gamble (SG) method to generate preference-based values for the SF-6D in a Chinese population.
We therefore did not generate preferences for each attribute, but for each test-frequency combination, thereby respecting the co-dependency.
On the one hand, it is unlikely that a jump from three to nine types of cue would radically alter valuation mechanisms, and in any case subjects effectively had to order a combined set of 12 cues rather than simply generate preferences within separate sets of three (learned) and nine (described) options.
Floor effects, with high percentages reporting problems, have not been reported to be of the same extent for the EuroQol compared to other generic measures such as SF-6D or HUI3 making it more suitable for generating preference-based index values for use in economic evaluation when the conditions studied are more severe [ 2, 30].
Levy's study sampled both CHB patients and uninfected persons to generate the preference values using hypothetical health states, while our study measured the SF-6D preference values of the actual HRQOL states of the different CHB patient groups.
In the current study, one might expect that the 1- and 10-year time horizons could generate different preference scores.
This may generate a preference for the security of having the psychiatrists initiate treatment and follow for at least a short time.
(A ) Our previous PDI dataset was compiled with the dataset in this study to generate binding preference of the 17 TFs.
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