Multi-Dimensional Thresholding for Individual-Level Preference Elicitation

Objectives: Multiple methods are available for collecting health preference information. However, information on the design and analysis of novel methods is limited. This article aims to provide the first introduction into the design and analysis of multidimensional thresholding (MDT). Methods: We introduce MDT as a 2-step approach: First, participants rank the largest possible improvements in all considered attributes by their importance. Second, participants complete a series of systematically combined trade-off questions. Hit-and-Run sampling is used for obtaining preference weights. We also use a computational experiment to compare different MDT designs. Results: The outlined MDT can generate preference information suitable for specifying a multi-attribute utility function at the individual level. The computational experiment demonstrates the method's ability to recover preference weights at a high level of precision. While all designs in the computation experiment perform comparably well on average, the design outlined in the paper stands out with a high level of precision even if differences in relative attribute importance are large. Conclusion: MDT is suitable for preference elicitation, in particular if sample sizes are small. Future research should help improve the methods (e.g., remove the need for an initial ranking) to increase the potential reach of MDT.