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Eliciting SF-6Dv2 health state utilities using an anchored best-worst scaling technique

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  • Osman, Ahmed M.Y.
  • Wu, Jing
  • He, Xiaoning
  • Chen, Gang

Abstract

There is an increasing interest in using ordinal data collection methods, such as the best-worst scaling (BWS), to develop preference-based tariffs (value sets) for health-related quality of life instruments, yet the evidence on their performance is limited. This paper proposed to use an anchored BWS technique (in which the state of “death” served as an anchoring state) to directly develop a utility weight that lies on a scale anchored at 0 = death and 1 = full health for the Simplified Chinese version of the Short Form 6 Dimension version 2 (SF-6Dv2). An online panel from the general population of Mainland China completed an online survey between 20th July and 19th August, 2019 and 463 respondents were included in the main analysis. The Conditional Logit (CL) model, which assumes a homogeneous preference, as well as a Hierarchical Bayes (HB) model, which accounts for preference heterogeneity, were used to analyze the BWS data. The model performances were evaluated based on monotonicity and model-fit statistics. The majority of respondents indicated that the BWS questions were easy to understand and complete. Initial analyses suggested that the best and worst choices should not be pooled together. Based on model fit statistics of separated estimations and previous literature on health state valuation studies using BWS, the best choices were used for developing the final algorithm. The HB estimates were found to have better model performance than the CL estimates. This study provides an essential insight into using an anchored BWS approach in health state valuation. Furthermore, it demonstrates the advantage of using HB compared to the traditional CL model in producing preference values.

Suggested Citation

  • Osman, Ahmed M.Y. & Wu, Jing & He, Xiaoning & Chen, Gang, 2021. "Eliciting SF-6Dv2 health state utilities using an anchored best-worst scaling technique," Social Science & Medicine, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:socmed:v:279:y:2021:i:c:s0277953621003506
    DOI: 10.1016/j.socscimed.2021.114018
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