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Related Experiment Videos

Re-scaling social preference data: implications for modelling.

Irina Cleemput1, Paul Kind, Katrien Kesteloot

  • 1Belgian Knowledge Centre for Health Care, Brussels, Belgium. irina.cleemput@kenniscentrum.fgov.be

The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care
|March 11, 2005
PubMed
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This study found that using the median of raw visual analogue scale (VAS) scores, rather than the mean, significantly reduces respondent exclusion when valuing health states for cost-utility analysis. The median approach offers a more inclusive method for deriving social health status preference values.

Area of Science:

  • Health Economics
  • Psychometrics
  • Social Science

Background:

  • Cost-utility analysis relies on generic health status indexes, typically valuing full health as 1 and death as 0.
  • Visual analogue scales (VAS) are used to elicit social preference weights for health states, but raw scores often deviate from the 0-1 scale.
  • Rescaling individual VAS scores to a 0-1 range can exclude respondents who do not value the endpoints of death or full health.

Purpose of the Study:

  • To investigate alternative methods for rescaling VAS scores in health state valuation to minimize respondent exclusion.
  • To compare the impact of rescaling timing (before or after aggregation) and central tendency measure (mean vs. median) on health state valuations.
  • To determine which methodological choice—rescaling timing or central tendency measure—has a greater influence on social health status preference values.

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Main Methods:

  • Utilized data from a postal valuation survey (n=722) in Belgium assessing EQ-5D health states.
  • Compared four models: (a) mean of rescaled values, (b) median of rescaled values, (c) median of raw values then rescaled, and (d) mean of raw values then rescaled.
  • Analyzed exclusion rates, health state rankings, valuations, and incremental value differences for each model.

Main Results:

  • Rescaling after aggregation resulted in exclusion rates below 5%, compared to over 20% when rescaling before aggregation.
  • Significant differences in health state valuations were observed across all models.
  • Differences in health state rankings were more pronounced between models using different measures of central tendency than between models with different rescaling timings.

Conclusions:

  • The choice of central tendency measure (median vs. mean) is more influential on social health status preference values than the timing of rescaling.
  • Using the median of original VAS valuations is theoretically appealing and offers a lower respondent exclusion rate.
  • Further research on diverse datasets is recommended to validate these findings regarding health state valuation methods.