School of Health and Related Research, University of Sheffield, Sheffield, UK (RA, AJW)
Med Decis Making. 2013 Feb;33(2):139-53. doi: 10.1177/0272989X12455461. Epub 2012 Aug 27.
Analysts frequently estimate the health state utility values (HSUVs) for joint health conditions (JHCs) using data from cohorts with single health conditions. The methods can produce very different results, and there is currently no consensus on the most appropriate technique.
To conduct a detailed critical review of existing empirical literature to gain an understanding of the reasons for differences in results and identify where uncertainty remains that may be addressed by further research.
Of the 11 studies identified, 10 assessed the additive method, 10 the multiplicative method, 7 the minimum method, and 3 the combination model. Two studies evaluated just 1 of the techniques, whereas the others compared results generated using 2 or more. The range of actual HSUVs can influence general findings, and methods are sometimes compared using descriptive statistics that may not be appropriate for assessing predictive ability. None of the methods gave consistently accurate results across the full range of possible HSUVs, and the values assigned to normal health influence the accuracy of the methods.
Within the limitations of the current evidence base, we would advocate the multiplicative method, conditional on adjustment for baseline utility, as the preferred technique to estimate HSUVs for JHCs when using mean values obtained from cohorts with single conditions. We would recommend that a range of sensitivity analyses be performed to explore the effect on results when using the estimated HSUVs in economic models. Although the linear models appeared to give more accurate results in the studies we reviewed, these models require validating in external data before they can be recommended.
分析师经常使用单一健康状况队列的数据来估算关节健康状况的健康状态效用值（HSUVs）。这些方法可能会产生非常不同的结果，目前对于最合适的技术尚无共识。
对现有实证文献进行详细的批判性回顾，以了解结果差异的原因，并确定是否存在不确定性，这些不确定性可以通过进一步研究来解决。
在所确定的 11 项研究中，有 10 项评估了加法方法，10 项评估了乘法方法，7 项评估了最小方法，3 项评估了组合模型。两项研究仅评估了其中一种技术，而其他研究则比较了使用两种或更多种技术生成的结果。实际 HSUVs 的范围可能会影响总体发现，并且方法有时使用可能不适合评估预测能力的描述性统计进行比较。在可能的 HSUVs 全范围内，没有一种方法始终给出准确的结果，并且正常健康状况赋予的数值会影响方法的准确性。
在当前证据基础的限制内，如果要在使用来自单一状况队列的平均值估算 JHC 的 HSUVs 时，我们会支持乘法方法，并在调整基线效用的条件下将其作为首选技术。我们建议进行一系列敏感性分析，以探讨在经济模型中使用估算的 HSUVs 对结果的影响。尽管在我们回顾的研究中线性模型似乎给出了更准确的结果，但在推荐这些模型之前，需要在外部数据中验证它们。
