Marieke G M Weernink1, Catharina G M Groothuis-Oudshoorn2, Maarten J IJzerman2, Janine A van Til2. 1. Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands. Electronic address: m.g.m.weernink@utwente.nl. 2. Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands.
Abstract
OBJECTIVE: The objective of this study was to compare treatment profiles including both health outcomes and process characteristics in Parkinson disease using best-worst scaling (BWS), time trade-off (TTO), and visual analogue scales (VAS). METHODS: From the model comprising of seven attributes with three levels, six unique profiles were selected representing process-related factors and health outcomes in Parkinson disease. A Web-based survey (N = 613) was conducted in a general population to estimate process-related utilities using profile-based BWS (case 2), multiprofile-based BWS (case 3), TTO, and VAS. The rank order of the six profiles was compared, convergent validity among methods was assessed, and individual analysis focused on the differentiation between pairs of profiles with methods used. RESULTS: The aggregated health-state utilities for the six treatment profiles were highly comparable for all methods and no rank reversals were identified. On the individual level, the convergent validity between all methods was strong; however, respondents differentiated less in the utility of closely related treatment profiles with a VAS or TTO than with BWS. For TTO and VAS, this resulted in nonsignificant differences in mean utilities for closely related treatment profiles. CONCLUSIONS: This study suggests that all methods are equally able to measure process-related utility when the aim is to estimate the overall value of treatments. On an individual level, such as in shared decision making, BWS allows for better prioritization of treatment alternatives, especially if they are closely related. The decision-making problem and the need for explicit trade-off between attributes should determine the choice for a method.
OBJECTIVE: The objective of this study was to compare treatment profiles including both health outcomes and process characteristics in Parkinson disease using best-worst scaling (BWS), time trade-off (TTO), and visual analogue scales (VAS). METHODS: From the model comprising of seven attributes with three levels, six unique profiles were selected representing process-related factors and health outcomes in Parkinson disease. A Web-based survey (N = 613) was conducted in a general population to estimate process-related utilities using profile-based BWS (case 2), multiprofile-based BWS (case 3), TTO, and VAS. The rank order of the six profiles was compared, convergent validity among methods was assessed, and individual analysis focused on the differentiation between pairs of profiles with methods used. RESULTS: The aggregated health-state utilities for the six treatment profiles were highly comparable for all methods and no rank reversals were identified. On the individual level, the convergent validity between all methods was strong; however, respondents differentiated less in the utility of closely related treatment profiles with a VAS or TTO than with BWS. For TTO and VAS, this resulted in nonsignificant differences in mean utilities for closely related treatment profiles. CONCLUSIONS: This study suggests that all methods are equally able to measure process-related utility when the aim is to estimate the overall value of treatments. On an individual level, such as in shared decision making, BWS allows for better prioritization of treatment alternatives, especially if they are closely related. The decision-making problem and the need for explicit trade-off between attributes should determine the choice for a method.
Authors: Marieke G M Weernink; Janine A van Til; Catharina G M Groothuis-Oudshoorn; Maarten J IJzerman Journal: Patient Date: 2017-12 Impact factor: 3.883
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