Quantifying Parameter Uncertainty in EQ-5D-3L Value Sets and Its Impact on Studies That Use the EQ-5D-3L to Measure Health Utility

Eleanor M. Pullenayegum, Kelvin K. W. Chan and Feng Xie

Medical Decision Making, 2016, vol. 36, issue 2, 223-233

Abstract: Background . Parameter uncertainty in EQ-5D value sets is routinely ignored. Sources of parameter uncertainty include uncertainty in the estimated regression coefficients of the scoring algorithm and uncertainty that arises from the need to use a nonsaturated functional form when creating the scoring algorithm. We hypothesize that this latter source is the major contributor to parameter uncertainty in the value sets. Methods . We used data from the United States EQ-5D-3L valuation study to assess the extent of parameter uncertainty in the value set. We refitted the US scoring algorithm to quantify contributors to the mean square prediction errors and used a Bayesian approach to estimate the predictive distribution of the mean utilities. The impact of parameter uncertainty in the value set was assessed using survey data. Results . Parameter uncertainty in the estimated regression coefficients explained 16% of the mean squared prediction error; uncertainty in the functional form explained the remaining 84%. The median width of the 95% credible intervals for the mean utilities was 0.15. In estimating mean utility in our survey population, parameter uncertainty in the value set was responsible for 93% of the total variance, with sampling variation in the survey population being responsible for the remaining 7%. Conclusion . EQ-5D-3L value sets are estimated subject to considerable parameter uncertainty; the median credible interval width is large compared with reported values of the minimum important difference for the EQ-5D-3L, which have been reported to be as small as 0.03. Other countries’ scoring algorithms are based on smaller studies and are hence subject to greater uncertainty. This uncertainty should be accounted for when using EQ-5D health utilities in economic evaluations.

Keywords: health utility; parameter uncertainty; Bayesian methods (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X15591966 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:36:y:2016:i:2:p:223-233

DOI: 10.1177/0272989X15591966

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().