Abstract
This paper studies the performance of case 2 best-worst scaling (BWS) when it is applied to a mix of positive and negative attributes, for example in studying treatments characterized by both benefits and harms. Intuitively, such a mix of positive and negative attributes leads to dominance. We analytically show that dominance leads to infinitely large differences between the parameter estimates for the positive versus negative attributes. The results from a simulation study confirm our analytical results: parameter values of the attributes could not be accurately recovered. When only a single positive attribute was used, even the relative ordering of the attribute level preferences was not identified. As a result, case 2 BWS can be used to elicit preferences if only good (positive) or only bad (negative) attributes are included in the choice tasks, but not for both since dominance will impact parameter estimation and therefore decision-making.
Original language | English |
---|---|
Article number | 100325 |
Number of pages | 14 |
Journal | Journal of Choice Modelling |
Volume | 41 |
Early online date | 1 Oct 2021 |
DOIs | |
Publication status | Published - 9 Oct 2021 |
Bibliographical note
Funding Information:Financial support for this study was provided both by the Research Excellence Initiative-Erasmus Choice Modelling Centre grant from the Erasmus University Rotterdam as well as the Patient Preferences in Benefit-Risk Assessments during the Drug Life Cycle (PREFER) project from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115966. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA . The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report.
Publisher Copyright:
© 2021 The Authors