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].
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].
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.
I should emphasize that I am not claiming – as some of my liberal nationalist colleagues do – that nationhood is the only possible basis for achieving progressive ends, or that there are no alternative ways of generating converging preferences on political units, or generating a solidaristic ethics of social membership.
However, also here we want to stress, as we did before for coding-region DNA, that the very high preference-occurrence R2s observed when non-genic and intronic GC content is intermediate do not imply that the forces generating motif preferences do not shape non-genic and intronic DNA whose GC content is clearly higher or lower than 0.5.
Methods for generating health preferences are based on the development of decision theory.
It generates a preference based utility scale (range 0.29 to 1) that was derived using the preference elicitation technique Standard Gamble.
