Objective: An updated version of the SF-6D Classification System (SF-6Dv2) has been developed, and utility value sets are required. The aim of this study was to test the development of a United Kingdom SF-6Dv2 value set, and address limitations of the existing SF-6D value set (which results in a narrow range of utilities). This was done using 2 discrete-choice experiment (DCE) tasks. Interactions and preference heterogeneity were also investigated.
Research design and subjects: An online sample of respondents (n=3014) completed 10 DCE with duration choice sets from an efficient design of 300 (Design 1) and 2 DCE with duration choice sets including immediate death from a set of 60 (Design 2). Conditional logit regression was used to estimate value set models with and without interactions. We investigated preference heterogeneity using latent class models.
Results: Models including ordered coefficients within each dimension were developed, with the favored model including an additional interaction term when one dimension was at the most severe level. Value sets differed across Designs 1 and 2. Design 1 models had a wider utility range and a higher proportion of negative values. The most important dimensions were pain, mental health, and physical functioning. Preference heterogeneity was apparent, with a 2-class model describing the data.
Conclusions: We developed and applied a protocol to value the SF-6Dv2 using DCE. The results provide a provisional value set for use in resource allocation. The protocol can be applied internationally. Further work should investigate how to account for preference heterogeneity in value set production.