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

Author

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  • Eleanor M. Pullenayegum
  • Kelvin K. W. Chan
  • Feng Xie

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.

Suggested Citation

  • Eleanor M. Pullenayegum & Kelvin K. W. Chan & Feng Xie, 2016. "Quantifying Parameter Uncertainty in EQ-5D-3L Value Sets and Its Impact on Studies That Use the EQ-5D-3L to Measure Health Utility," Medical Decision Making, , vol. 36(2), pages 223-233, February.
  • Handle: RePEc:sae:medema:v:36:y:2016:i:2:p:223-233
    DOI: 10.1177/0272989X15591966
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    References listed on IDEAS

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    1. JosÉ Figueira & Salvatore Greco & Matthias Ehrogott, 2005. "Multiple Criteria Decision Analysis: State of the Art Surveys," International Series in Operations Research and Management Science, Springer, number 978-0-387-23081-8, December.
    2. Samer A. Kharroubi & Anthony O'Hagan & John E. Brazier, 2005. "Estimating utilities from individual health preference data: a nonparametric Bayesian method," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 879-895, November.
    3. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
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    Cited by:

    1. Menglu Che & Eleanor Pullenayegum, 2023. "Efficient Designs for Valuation Studies That Use Time Tradeoff (TTO) Tasks to Map Latent Utilities from Discrete Choice Experiments to the Interval Scale: Selection of Health States for TTO Tasks," Medical Decision Making, , vol. 43(3), pages 387-396, April.
    2. Menglu Che & Feng Xie & Stephanie Thomas & Eleanor Pullenayegum, 2023. "Bayesian Models with Spatial Correlation Improve the Precision of EQ-5D-5L Value Sets," Medical Decision Making, , vol. 43(5), pages 587-594, July.
    3. Spyridon Poulimenos & Jeff Round & Gianluca Baio, 2024. "Capturing Valuation Study Sampling Uncertainty in the Estimation of Health State Utility Values Using the EQ-5D-3L," Medical Decision Making, , vol. 44(4), pages 393-404, May.
    4. Samer A. Kharroubi & Yara Beyh & Esmail Abdul Fattah & Tracey Young, 2020. "The Importance of Accounting for Parameter Uncertainty in SF-6D Value Sets and Its Impact on Studies that Use the SF-6D to Measure Health Utility," IJERPH, MDPI, vol. 17(11), pages 1-12, June.

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