Lowering the 'floor' of the SF-6D scoring algorithm using a lottery equivalent method

摘要

This paper presents a new scoring algorithm for the SF-6D, one of the most popular preference-based health status measures. Previous SF-6D value sets have a minimum (a floor), which is substantially higher than the lowest value generated by the EQ-5D model. Our algorithm expands the range of SF-6D utility scores in such a way that the floor is significantly lowered. We obtain the wider range because of the use of a lottery equivalent method through which preferences from a representative sample of Spanish general population are elicited.