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Statistical Alchemy: Conceptual Validity and Mapping to Generate Health State Utility Values

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  • Jeff Round

    (University of Bristol)

  • Annie Hawton

    (University of Exeter
    University of Exeter)

Abstract

Mapping between non-preference- and preference-based health-related quality-of-life instruments has become a common technique for estimating health state utility values for use in economic evaluations. Despite the increased use of mapped health state utility estimates in health technology assessment and economic evaluation, the methods for deriving them have not been fully justified. Recent guidelines aim to standardise reporting of the methods used to map between instruments but do not address fundamental concerns in the underlying conceptual model. Current mapping methods ignore the important conceptual issues that arise when extrapolating results from potentially unrelated measures. At the crux of the mapping problem is a question of validity; because one instrument can be used to predict the scores on another, does this mean that the same preference for health is being measured in actual and estimated health state utility values? We refer to this as conceptual validity. This paper aims to (1) explain the idea of conceptual validity in mapping and its implications; (2) consider the consequences of poor conceptual validity when mapping for decision making in the context of healthcare resource allocation; and (3) offer some preliminary suggestions for improving conceptual validity in mapping.

Suggested Citation

  • Jeff Round & Annie Hawton, 2017. "Statistical Alchemy: Conceptual Validity and Mapping to Generate Health State Utility Values," PharmacoEconomics - Open, Springer, vol. 1(4), pages 233-239, December.
    • Handle: RePEc:spr:pharmo:v:1:y:2017:i:4:d:10.1007_s41669-017-0027-2
    DOI: 10.1007/s41669-017-0027-2
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    References listed on IDEAS

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    1. Brazier, John & Ratcliffe, Julie & Salomon, Joshua & Tsuchiya, Aki, 2016. "Measuring and Valuing Health Benefits for Economic Evaluation," OUP Catalogue, Oxford University Press, edition 2, number 9780198725923.
    2. Tsuchiya, A & Brazier, J & McColl, E & Parkin, D, 2002. "Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices," MPRA Paper 29740, University Library of Munich, Germany.
    3. Rowen, D & Brazier, J & Roberts, J, 2008. "Mapping SF-36 onto the EQ-5D index: how reliable is the relationship?," MPRA Paper 29831, University Library of Munich, Germany.
    4. David Parkin & Nigel Rice & Nancy Devlin, 2010. "Statistical Analysis of EQ-5D Profiles: Does the Use of Value Sets Bias Inference?," Medical Decision Making, , vol. 30(5), pages 556-565, September.
    5. Christopher McCabe & Richard Edlin & David Meads & Chantelle Brown & Samer Kharroubi, 2013. "Constructing Indirect Utility Models: Some Observations on the Principles and Practice of Mapping to Obtain Health State Utilities," PharmacoEconomics, Springer, vol. 31(8), pages 635-641, August.
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    Cited by:

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    3. Fan Yang & Carlos K. H. Wong & Nan Luo & James Piercy & Rebecca Moon & James Jackson, 2019. "Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(8), pages 1195-1206, November.
    4. Ralph Crott, 2018. "Direct Mapping of the QLQ-C30 to EQ-5D Preferences: A Comparison of Regression Methods," PharmacoEconomics - Open, Springer, vol. 2(2), pages 165-177, June.
    5. Richard Huan Xu & Eliza Lai Yi Wong & Jun Jin & Ying Dou & Dong Dong, 2020. "Mapping of the EORTC QLQ-C30 to EQ-5D-5L index in patients with lymphomas," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(9), pages 1363-1373, December.

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