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Background Discrete choice experiments (DCEs) are widely used to elicit health state preferences. However, additional information is required to transform values to a scale with dead valued at 0 and full health valued at 1. This paper presents DCE-VAS, an understandable and easy anchoring method with low participant burden based on the visual analogue scale (VAS). Methods Responses from 1450 members of the UK general public to a discrete choice experiment (DCE) were analysed using mixed logit models. Latent scale valuations were anchored to a full health = 1, dead = 0 scale using participants’ VAS ratings of three states including the dead. The robustness of results was examined. This included a filtering procedure with the influence each individual respondent had on valuation being calculated, and those whose influence was more than two standard deviations away from the mean excluded. Results Coefficients in all models were in the expected direction and statistically significant. Excluding respondents who self-reported not understanding the VAS task did not significantly influence valuation, but excluding a small number who valued 33333 extremely low did. However, after eight respondents were removed via the filtering procedure, valuations were robust to removing other participants. Conclusion DCE-VAS is a feasible way of anchoring DCE results to a 0–1 anchored scale with low additional respondent burden.

Additional file 1. Appendix 1. Annual space-time clusters of Cryptosporidiosis in Ireland from 2008 to 2017; Appendix 2. Space-time clusters of Cryptosporidiosis in Ireland during 2008.

Objectives To model the relationship between EQ-5D-3L and EQ-5D-5L and examine how differences impact on cost-effectiveness in case studies. Methods We used two datasets that included both EQ-5D-3L and EQ-5D-3L from the same respondents. The EuroQoL dataset (n=3551) included patients with different diseases and a healthy cohort. The National Databank (NDB) dataset included patients with rheumatoid disease (n=5205). We estimated a system of ordinal regressions in each dataset using copula models, to link responses to the 3L instrument to 5L and its tariff, and vice versa. Results were applied to nine cost-effectiveness studies. Results Best-fitting models differed between EuroQoL and NDB datasets in terms of the explanatory variables, copulas and coefficients. In both cases the coefficients of the covariates and latent factor between -3L and -5L were significantly different, indicating that the two instruments are not a uniform realignment of the response levels for most dimensions. In the case studies, moving from 3L to 5L caused a decrease of up to 87% in incremental QALYs gained from effective technologies in almost all cases. ICERs increased, often substantially. Conversely, one technology with a significant mortality gain saw increased incremental QALYs. Conclusion 5L shifts mean utility scores up the utility scale towards full health and compresses them into a smaller range, compared to -3L. Improvements in quality of life are valued less using 5L than with 3L. 3L and 5L can produce substantially different estimates of cost effectiveness. There is no simple proportional adjustment that can be made to reconcile these differences.