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Extending Multidimensional Thresholding to Include Categorical Attributes.

Jennifer A Whitty1, Nicolas Krucien2, Caitlin Thomas2

  • 1PPD Evidera Patient-Centered Research, Thermo Fisher Scientific London, England, UK; Norwich Medical School, University of East Anglia, Norwich, England, UK; School of Pharmacy, University of Queensland, Brisbane, QLD, Australia.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|June 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to include categorical attributes in multidimensional thresholding (MDT) models. The approach allows for categorical data alongside continuous variables with minimal precision loss, expanding MDT applications.

Keywords:
analysiscategorical attributedesignmultidimensional thresholdingpreference elicitation

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Area of Science:

  • Decision Sciences
  • Marketing Science
  • Psychometrics

Background:

  • Multidimensional thresholding (MDT) is a valuable tool for eliciting individual preferences.
  • Current MDT models assume continuous attributes, limiting their applicability with categorical data.

Purpose of the Study:

  • To present a novel framework for incorporating categorical attributes into MDT.
  • To outline a process for designing MDT studies with a mix of categorical and continuous attributes.

Main Methods:

  • Categorical attribute ranking and point allocation for scoring.
  • Ranking attribute scale swing importance.
  • Thresholding exercises for continuous attributes.
  • Trading categorical attribute swings against continuous attribute swings.

Main Results:

  • Inclusion of categorical attributes in MDT is feasible with a modest loss of precision.
  • Precision loss is dependent on the categorical attribute's importance and rank.
  • The number of continuous attributes and choice tasks minimally impacted precision.

Conclusions:

  • The proposed framework successfully extends MDT to include categorical attributes.
  • This method is particularly useful when categorical attributes are not the primary driver of preferences.
  • Further research in applied settings is recommended.