Abstract
OBJECTIVES: Economic evaluations often use preference-based value sets (tariffs) for health-related quality of life to quantify health effects. For wellbeing at the end of life, issues beyond health-related quality of life may be important. Therefore, the ICECAP Supportive Care Measure (ICECAP-SCM), based on the capability approach, was developed. A validated German ICECAP-SCM version was published recently. However, tariffs for the German ICECAP-SCM are not available. Therefore, the aim was to determine tariffs for the ICECAP-SCM based on preferences of the German general population. METHODS: An online sample of 2996 participants completed a best-worst scaling (BWS) and a discrete choice experiment (DCE). BWSs required participants to choose the best and worst statement within the same capability state, whereas DCEs required participants to trade-off between two capability states. First, BWS and DCE data were analyzed separately. Subsequently, combined data were analyzed using scale-adjusted conditional logit latent class models. Models were selected based on the stability of solutions and the Bayesian information criterion. RESULTS: The two latent class model was identified to be optimal for the BWS, DCE, and combined data, and was used to derive tariffs for the ICECAP-SCM capability states. BWS data captured differences in ICECAP-SCM scale levels, whereas DCE data additionally explained interactions between the seven ICECAP-SCM attributes. DISCUSSION: The German ICECAP-SCM tariffs can be used in addition to health-related quality of life to quantify effectiveness in economic evaluations. The tariffs based on BWS data were similar for Germany and the UK, whereas the tariffs based on combined data varied. We would recommend to use tariffs based on combined data in German evaluations. However, only results on BWS data are comparable between Germany and the UK, so that tariffs based on BWS data should be used when comparing results between Germany and the UK.