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考虑到不同的赋值方法对效用值可能产生的影响,对效用值进行Meta分析是否合适?

Is Meta-Analysis for Utility Values Appropriate Given the Potential Impact Different Elicitation Methods Have on Values?

作者信息

Peasgood Tessa, Brazier John

机构信息

School of Health and Related Research, University of Sheffield, Regent Court, Regent Street, Sheffield, S1 4DA, UK.

出版信息

Pharmacoeconomics. 2015 Nov;33(11):1101-5. doi: 10.1007/s40273-015-0310-y.

DOI:10.1007/s40273-015-0310-y
PMID:26133293
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4607715/
Abstract

A growing number of published articles report estimates from meta-analysis or meta-regression on health state utility values (HSUVs), with a view to providing input into decision-analytic models. Pooling HSUVs is problematic because of the fact that different valuation methods and different preference-based measures (PBMs) can generate different values on exactly the same clinical health state. Existing meta-analyses of HSUVs are characterised by high levels of heterogeneity, and meta-regressions have identified significant (and substantial) impacts arising from the elicitation method used. The use of meta-regression with few utility values and inclusion criteria that extend beyond the required utility value has not helped. There is the potential to explore greater use of mapping between different PBMs and valuation methods prior to data synthesis, which could support greater use of pooling values. Researchers wishing to populate decision-analytic models have a responsibility to incorporate all high-quality evidence available. In relation to HSUVs, greater understanding of the differences between different methods and greater consistency of methodology is required before this can be achieved.

摘要

越来越多已发表的文章报道了来自荟萃分析或荟萃回归的健康状态效用值(HSUVs)估计值,旨在为决策分析模型提供输入。汇总HSUVs存在问题,因为不同的评估方法和不同的基于偏好的测量方法(PBMs)在完全相同的临床健康状态下可能产生不同的值。现有的HSUVs荟萃分析的特点是异质性程度高,并且荟萃回归已经确定了所使用的诱导方法产生的显著(且实质性)影响。使用效用值较少的荟萃回归以及超出所需效用值的纳入标准并无帮助。在数据合成之前,有可能探索更多地使用不同PBMs和评估方法之间的映射,这可以支持更多地使用汇总值。希望填充决策分析模型的研究人员有责任纳入所有可用的高质量证据。就HSUVs而言,在实现这一点之前,需要对不同方法之间的差异有更深入的理解,并使方法更加一致。