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How do Design Characteristics Affect Respondent Engagement? Assessing Attribute Non-attendance in Discrete Choice

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Attribute overlap in discrete choice experiments (DCEs) improves respondent engagement and reduces non-attendance. Modified Fedorov designs using Ngene or SAS are recommended for better health state value set development.

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Area of Science:

  • Health Economics
  • Psychometrics
  • Survey Methodology

Background:

  • Discrete Choice Experiments (DCEs) are widely used for developing health-related quality-of-life (HRQoL) value sets.
  • Respondent heuristics, such as attribute non-attendance (ANA), can impact the accuracy of these value sets.
  • Attribute level overlap in DCE tasks may enhance respondent engagement and simplify task completion.

Purpose of the Study:

  • To compare the effectiveness of DCE designs with and without attribute level overlap.
  • To assess how different design construction methods influence respondent engagement and ANA.
  • To evaluate the impact of ANA on derived health state utility values.

Main Methods:

  • A multi-arm DCE using the EQ-5D-5L instrument was conducted in the Australian general population.
  • Designs with varying levels of attribute overlap were compared based on respondent engagement, quantified by inferred ANA using latent class models.
  • Utility decrements were estimated using all respondents versus only those attending to all attributes.

Main Results:

  • Inclusion of attribute overlap significantly increased full attendance rates from 22.3-28.4% to 28.2-54.2%.
  • Modified Fedorov designs (Ngene, SAS) with overlap demonstrated higher full attendance rates compared to other designs.
  • Attribute importance varied significantly before and after excluding respondents based on ANA analysis.

Conclusions:

  • Modified Fedorov designs incorporating attribute overlap effectively reduce ANA and enhance respondent engagement in DCE studies.
  • Attribute non-attendance analysis serves as a valuable quality control tool for respondent selection in health valuation.
  • These findings support improved methods for developing accurate EQ-5D-5L value sets.