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Converting the SF-12 into the EQ-5D

An Empirical Comparison of Methodologies

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Abstract

Background: For cost-utility analysis, analysts need a measure that summarizes health-status utilities in a single index of health-related quality of life (HR-QOL). It is common to find in clinical studies that only an HR-QOL profile measure such as the SF-36 is included, but not the summary HR-QOL index. Therefore, the economist’s usual practice is to reprocess the profile data into a single index format. Several ‘after-market’ tools are available to convert the SF-36 or SF-12 into a single form with or without utility-weighting metric property. However, there has been no consensus with regard to a regression method that should be recommended for such a mapping task.

Objective: To report on the performance of different regression methods that have previously been applied to the conversion of SF-12 data in the analysis of a single common dataset. The mapping between the SF-12 and EQ-5D is the focus.

Methods: The data were adopted from the Medical Expenditure Panel Survey 2003 where 19 678 adults completed both EQ-5D and SF-12 questionnaires. Four econometric techniques, namely ordinary least squares (OLS), censored least absolute deviation, multinomial logit model and two-part model regressions were investigated together with two main types of model specifications: item-based and summary score-based. The performance of each examined model was judged by various criteria, including its estimated mean, the size of mean absolute error and the number of errors.

Results: Among four compared econometric techniques, OLS regression was the most accurate model in estimating the group mean. Models with item-based model specification performed better than those with summary scorebased regardless of the chosen econometric technique. Nevertheless, the accuracy of OLS deteriorates in older and less healthy subgroups. The results also suggested that the two-part model, which addresses the heterogeneity issue, performs better in these vulnerable subgroups.

Conclusions: None of the mapping methods included in the current study are suitable for estimating at the individual level. The methodology exemplified here has wider applicability and might just as readily be applied to other members of the SF family or indeed to other profile measures of HR-QOL. However, it is recommended that a preference-based, single index measure of HR-QOL should be included in the clinical studies for the purpose of economic evaluation.

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Notes

  1. To assist researchers wishing to undertake such a mapping task, all algorithms developed in the current study (in STATA format) are available online.[46]

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Acknowledgements

Paul Kind is Principal Investigator for Quality Outcomes (QUO), which specializes in the use of outcome measures including EQ-5D. L-H Chuang has no conflicts of interest that are directly relevant to the content of this study.

No sources of funding were used to assist in the preparation of this study.

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Authors and Affiliations

  1. Outcomes Research Group, Centre for Health Economics, University of York, York, YO10 5DD, UK

    Ling-Hsiang Chuang MSc & Paul Kind

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  1. Ling-Hsiang Chuang MSc
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Correspondence to Ling-Hsiang Chuang MSc.

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Chuang, LH., Kind, P. Converting the SF-12 into the EQ-5D. Pharmacoeconomics 27, 491–505 (2009). https://doi.org/10.2165/00019053-200927060-00005

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