Valuation of SF-6Dv2 Health States in China Using Time Trade-off and Discrete-Choice Experiment with a Duration Dimension

Objectives Our objective was to generate a value set for the SF-6Dv2 using time trade-off (TTO) and a discrete-choice experiment with a duration dimension (DCETTO) in China. Methods A large representative sample of the Chinese general population was recruited from eight provinces/municipalities in China, stratified by age, sex, education level, and proportion of urban/rural residence. Respondents completed eight TTO tasks and ten DCETTO tasks during face-to-face interviews. Ordinary least squares (OLS), random-effects, fixed-effects, and Tobit models were used for TTO data, and conditional logit and mixed logit models were used for DCETTO. The monotonicity of model coefficients and the consistency of the predicted values according to intraclass correlation coefficient (ICC), mean absolute difference (MAD), and mean squared difference (MSD) were compared between the two approaches. Results In total, 3320 respondents (50.3% male; range 18–90 years) were recruited. The random-effects model and the conditional logit model were preferred for the TTO and DCETTO, respectively. The TTO values ranged from − 0.277 to 1, with 927 (4.94%) states considered as worse than dead (WTD). The corresponding range for DCETTO was − 0.535 to 1, with a higher WTD of 8.50%. DCETTO presented minor non­monotonicity with the coefficients in two dimensions. Values from the two approaches were highly consistent (ICC 0.9804, MAD 0.0588, MSD 0.0055), albeit those with DCETTO were slightly lower than those with TTO. The value set generated by TTO was preferred given the better monotonicity and the statistical significance of coefficients. Conclusions The Chinese value set for the SF-6Dv2 was established based on the TTO approach, but the DCETTO also performed well. Minor issues of non­monotonicity did present for DCETTO. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-020-00997-1.