Small differences in EQ-5D-5L health utility scores were interpreted differently between and within respondents

McClure, Nathan S.; Xie, Feng; Paulden, Mike; Öhinmaa, Arto; Johnson, Jeffrey

doi:10.1016/j.jclinepi.2021.10.019

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…Even with the Bayesian models, the accuracy of the estimated value sets requires improvements. The Bayesian models with spatial correlation have MAEs of the same order of magnitude as reported MIDs for the EQ-5D-5L (which range from 0.05 to 0.1 [6][7][8][9][10][11][12] ). Given our observation that the predictive performance of the Bayesian models decreases as the number of health states used for fitting the models decreases (i.e., on omitting blocks rather than states), we hypothesize that valuing more states, even if it meant fewer observations per state, would lead to better predictive precision.…”

Section: Discussionsupporting

confidence: 54%

“…The accuracy of value sets varies; standard errors for statewise mean utilities for the SF-6D range from 0.03 to 0.06, 2 while root mean square errors (RMSEs) for the EQ-5D-3L range from 0.03 to 0.28. Given that reported minimally important differences (MIDs) range from 0.03 to 0.04 for the SF-6D, 3 0.05 to 0.08 for the EQ-5D-3L, 3–5 and 0.04 to 0.1 for the EQ-5D-5L, 6–12 improvements to the accuracy of these value sets are desirable.…”

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confidence: 99%

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

et al. 2023

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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.

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Section: Discussionsupporting

confidence: 54%

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confidence: 99%

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

et al. 2023

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“…While we collected valuable information with these tools and questions, we could have missed subtleties of mental health and well-being that could be assessed with qualitative work. Moreover, the EQ-5D-5L utility score has been shown to be influenced by the heterogeneity in respondents’ ordinal preferences, which suggested that small differences (less than 0.05) are negligible to support a difference in QoL [ 46 ]. The impact that information bias could have had on our results is difficult to predict and may have been differential.…”

Section: Discussionmentioning

confidence: 99%

Association between Pet Ownership and Mental Health and Well-Being of Canadians Assessed in a Cross-Sectional Study during the COVID-19 Pandemic

Denis-Robichaud

Aenishaenslin

Richard

et al. 2022

IJERPH

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The objective of this cross-sectional study was to assess the association between pet ownership and quality of life (QoL), loneliness, anxiety, stress, overall health, and mental health of Canadians during the COVID-19 pandemic using a One Health perspective. An online bilingual survey was completed by 1500 Canadians in April–May 2021. Socio-demographics, health, QoL, stress and anxiety, loneliness, social support, pet ownership, and attitude towards pets data were collected. Crude and adjusted associations between pet ownership and mental health and well-being indicators were estimated. The 1500 participants were from all provinces and territories, half were women; half of the participants were pet owners by design. The crude association estimates showed that pet owners had poorer QoL, overall health, and mental health than non-pet owners, and were lonelier, more stressed, and more anxious than non-pet owners. Adjusted estimates showed that these associations disappeared with the inclusion of the confounders (socio-economic, demographic, health, and pet-related variables). Our results suggest that there was no association between pet ownership and the mental health and well-being indicators measured in the present study.

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Small differences in EQ-5D-5L health utility scores were interpreted differently between and within respondents

Cited by 2 publications

References 25 publications

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

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

Association between Pet Ownership and Mental Health and Well-Being of Canadians Assessed in a Cross-Sectional Study during the COVID-19 Pandemic

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