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Multidimensional Thresholding for Individual-Level Preference Elicitation.

Sebastian Heidenreich1, Douwe Postmus2, Tommi Tervonen3

  • 1Evidera, London, England, UK.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|March 1, 2024
PubMed
Summary
This summary is machine-generated.

Multidimensional thresholding (MDT) is a new method for collecting health preference data. MDT precisely recovers individual preference weights, even with small sample sizes and large attribute differences.

Keywords:
analysisdesignmultidimensional thresholdingpreference elicitation

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Area of Science:

  • Health economics
  • Decision analysis
  • Psychometrics

Background:

  • Existing methods for collecting health preference data lack detailed guidance on novel designs and analysis.
  • Accurate elicitation of individual-level health preferences is crucial for healthcare decision-making.

Purpose of the Study:

  • To introduce and detail the design and analysis of Multidimensional Thresholding (MDT), a novel method for health preference data collection.
  • To demonstrate the utility of MDT in generating preference information for multiattribute utility functions.

Main Methods:

  • MDT employs a two-step process: initial attribute importance ranking followed by systematic trade-off questions.
  • Hit-and-Run sampling is utilized for the precise estimation of preference weights.
  • A computational experiment was conducted to compare various MDT designs.

Main Results:

  • MDT successfully generates preference information applicable to individual-level multiattribute utility functions.
  • The computational experiment confirmed MDT's high precision in recovering preference weights.
  • A specific MDT design exhibited superior precision, particularly when attribute importance varied significantly.

Conclusions:

  • MDT is a valuable tool for preference elicitation, especially in scenarios with limited sample sizes.
  • Further research is recommended to refine MDT, potentially by removing the initial ranking step, to broaden its applicability.