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Bayesian Models with Spatial Correlation Improve the Precision of EQ-5D-5L Value Sets

Author

Listed:
  • Menglu Che

    (Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA)

  • Feng Xie

    (Centre for Health Economics and Policy Analysis, Department of Health Research Methods, Evidence & Impact (HEI), McMaster University, Hamilton, ON, Canada)

  • Stephanie Thomas

    (Sobey School of Business, Saint Mary’s University, Halifax, NS, Canada)

  • Eleanor Pullenayegum

    (Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada)

Abstract

Background Health utilities from value sets for the EQ-5D-5L are commonly used in economic evaluations. We examined whether modeling spatial correlation among health states could improve the precision of the value sets. Methods Using data from 7 EQ-5D-5L valuation studies, we compared the predictive precision of the published linear model, a recently proposed cross-attribute level effects (CALE) model, and 2 Bayesian models with spatial correlation. Predictive precision was quantified through the root mean squared error (RMSE) for out-of-sample predictions of state-level mean utilities on omitting individual states, as well as omitting blocks of states. Results In all 7 countries, on omitting single health states, Bayesian models with spatial correlation improved upon the published linear model: the RMSEs for the originally published models, 0.050, 0.051, 0.060, 0.061, 0.039, 0.050, and 0.087 for Canada, China, Germany, Indonesia, Japan, Korea, and the Netherlands, respectively, could be reduced to 0.043, 0.042, 0.051, 0.054, 0.037, 0.037, and 0.085, respectively. On omitting blocks of health states, Bayesian models with spatial correlation led to smaller RMSEs in 3 countries, while the CALE model led to smaller RMSEs in the remaining 4 countries. Discussion: Bayesian models incorporating spatial correlation and CALE models are promising for improving the precision of value sets for the EQ-5D-5L. The differential performance of the Bayesian models on omitting single states versus blocks of states suggests that designing valuation studies to capture more health states may further improve precision. We suggest that Bayesian and CALE models be considered as candidates when creating value sets and that alternative designs be explored; this is vital as the prediction errors in value sets need to be smaller than the minimal important difference of the instrument. Highlights The accuracy of value sets of multi-attribute utility instruments is typically of the same order of magnitude as the instrument’s minimal important difference and would benefit from improvement. Bayesian models with spatial correlation have been shown to improve value set accuracy in isolated cases. We showed that Bayesian approaches with spatial correlation improved predictive precision in 7 EQ-5D-5L valuation studies. We recommend that Bayesian models incorporating spatial correlation be considered when creating value sets and have provided code for fitting them.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:medema:v:43:y:2023:i:5:p:587-594
    DOI: 10.1177/0272989X231173699
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    References listed on IDEAS

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    1. 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.
    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. Shahriar Shams & Eleanor Pullenayegum, 2019. "Reducing Uncertainty in EQ-5D Value Sets: The Role of Spatial Correlation," Medical Decision Making, , vol. 39(2), pages 91-99, February.
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