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].
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].
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.
In this manuscript we will use the term "utility" for all scores generated with preference based instruments although.
The four induction factors include (i) recognition of advertised food products (advertisement recognition), (ii) liking the food advertisements on TV (favourite advertisement), (iii) purchase requests induced by TVFA (purchase request), and (iv) product preference generated by TVFA (product preference).
However, some agencies, and NICE in particular, recommend utility values generated using a generic preference-based measure and for the purposes of comparability across health technology assessment undertaken across different conditions and patient populations recommend the use of EQ-5D in particular [ 14].
It generates a preference based utility scale (range 0.29 to 1) that was derived using the preference elicitation technique Standard Gamble.
