Elsevier

Value in Health

Volume 15, Issue 6, September–October 2012, Pages 971-974
Value in Health

Methodological articles
Using Health State Utility Values in Models Exploring the Cost-Effectiveness of Health Technologies

https://doi.org/10.1016/j.jval.2012.05.003Get rights and content
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Abstract

Background

To improve comparability of economic data used in decision making, some agencies recommend that a particular instrument should be used to measure health state utility values (HSUVs) used in decision-analytic models. The methods used to incorporate HSUVs in models, however, are often methodologically poor and lack consistency. Inconsistencies in the methodologies used will produce discrepancies in results, undermining policy decisions informed by cost per quality-adjusted life-years.

Objective

To provide an overview of the current evidence base relating to populating decision-analytic models with HSUVs.

Findings

Research exploring suitable methods to accurately reflect the baseline or counterfactual HSUVs in decision-analytic models is limited, and while one study suggested that general population data may be appropriate, guidance in this area is poor. Literature describing the appropriateness of different methods used to estimate HSUVs for combined conditions is growing, but there is currently no consensus on the most appropriate methodology. While exploratory analyses suggest that a statistical regression model might improve accuracy in predicted values, the models require validation and testing in external data sets. Until additional research has been conducted in this area, the current evidence suggests that the multiplicative method is the most appropriate technique. Uncertainty in the HSUVs used in decision-analytic models is rarely fully characterized in decision-analytic models and is generally poorly reported.

Conclusions

A substantial volume of research is required before definitive detailed evidence-based practical advice can be provided. As the methodologies used can make a substantial difference to the results generated from decision-analytic models, the differences and lack of clarity and guidance will continue to lead to inconsistencies in policy decision making.

Keywords

EQ-5D
quality of life
SF-36
utility

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This article is based on a technical support document that was funded by the National Institute for Health and Clinical Excellence through its Decision Support Unit. The views, and any errors or omissions, expressed in this article are of the author only.