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

The Author(s) 2015. This article is published with open access at Springerlink.com 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 prob-lematic because of the fact that different valuation methods and different preference-based measures (PBMs) can gen-erate 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 syn-thesis, which could support greater use of pooling values. Researchers wishing to populate decision-analytic models have a responsibility to incorporate all high-quality evi-dence available. In relation to HSUVs, greater under-standing of the differences between different methods and greater consistency of methodology is required before this can be achieved. Key Points for Decision Makers Searching and synthesis of health state utility values (HSUVs) to populate decision models should incorporate all good-quality evidence, but the variability of utility scores by elicitation methods generates a problem for pooling values through meta-analysis. Stricter inclusion criteria for meta-regression or meta-analysis of HSUVs may help. There is potential for greater use of mapping algorithms between HSUVs prior to meta-analysis, although careful consideration should be given to the appropriateness of the mapping function and the additional level of uncertainty associated with mapped values.