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Comparing the Incomparable? A Systematic Review of Competing Techniques for Converting Descriptive Measures of Health Status into QALY-Weights

Author

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  • Duncan Mortimer

    (Centre for Health Economics, Faculty of Business & Economics, Monash University, Melbourne, Australia, duncan.mortimer@buseco.monash.edu.au)

  • Leonie Segal

    (Centre for Health Economics, Faculty of Business & Economics, Monash University, Melbourne, Australia)

Abstract

Background . Algorithms for converting descriptive measures of health status into quality-adjusted life year (QALY)—weights are now widely available, and their application in economic evaluation is increasingly commonplace. The objective of this study is to describe and compare existing conversion algorithms and to highlight issues bearing on the derivation and interpretation of the QALY-weights so obtained. Methods . Systematic review of algorithms for converting descriptive measures of health status into QALY-weights. Results . The review identified a substantial body of literature comprising 46 derivation studies and 16 studies that provided evidence or commentary on the validity of conversion algorithms. Conversion algorithms were derived using 1 of 4 techniques: 1) transfer to utility regression, 2) response mapping, 3) effect size translation, and 4) “revaluing†outcome measures using preference-based scaling techniques. Although these techniques differ in their methodological/theoretical tradition, data requirements, and ease of derivation and application, the available evidence suggests that the sensitivity and validity of derived QALY-weights may be more dependent on the coverage and sensitivity of measures and the disease area/patient group under evaluation than on the technique used in derivation. Conclusions . Despite the recent proliferation of conversion algorithms, a number of questions bearing on the derivation and interpretation of derived QALY-weights remain unresolved. These unresolved issues suggest directions for future research in this area. In the meantime, analysts seeking guidance in selecting derived QALY-weights should consider the validity and feasibility of each conversion algorithm in the disease area and patient group under evaluation rather than restricting their choice to weights from a particular derivation technique.

Suggested Citation

  • Duncan Mortimer & Leonie Segal, 2008. "Comparing the Incomparable? A Systematic Review of Competing Techniques for Converting Descriptive Measures of Health Status into QALY-Weights," Medical Decision Making, , vol. 28(1), pages 66-89, January.
  • Handle: RePEc:sae:medema:v:28:y:2008:i:1:p:66-89
    DOI: 10.1177/0272989X07309642
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    References listed on IDEAS

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    Cited by:

    1. Harindra C. Wijeysundera & George Tomlinson & Colleen M. Norris & William A. Ghali & Dennis T. Ko & Murray D. Krahn, 2011. "Predicting EQ-5D Utility Scores from the Seattle Angina Questionnaire in Coronary Artery Disease," Medical Decision Making, , vol. 31(3), pages 481-493, May.
    2. Seamus Kent & Alastair Gray & Iryna Schlackow & Crispin Jenkinson & Emma McIntosh, 2015. "Mapping from the Parkinson’s Disease Questionnaire PDQ-39 to the Generic EuroQol EQ-5D-3L," Medical Decision Making, , vol. 35(7), pages 902-911, October.
    3. Vlaev, Ivo, 2012. "How different are real and hypothetical decisions? Overestimation, contrast and assimilation in social interaction," Journal of Economic Psychology, Elsevier, vol. 33(5), pages 963-972.
    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. Peter P. Wakker, 2008. "Lessons Learned by (from?) an Economist Working in Medical Decision Making," Medical Decision Making, , vol. 28(5), pages 690-698, September.
    6. Gang Chen & Munir A. Khan & Angelo Iezzi & Julie Ratcliffe & Jeff Richardson, 2016. "Mapping between 6 Multiattribute Utility Instruments," Medical Decision Making, , vol. 36(2), pages 160-175, February.
    7. Kelvin K. W. Chan & Andrew R. Willan & Michael Gupta & Eleanor Pullenayegum, 2014. "Underestimation of Uncertainties in Health Utilities Derived from Mapping Algorithms Involving Health-Related Quality-of-Life Measures," Medical Decision Making, , vol. 34(7), pages 863-872, October.
    8. Nicholas Mitsakakis & Karen E. Bremner & George Tomlinson & Murray Krahn, 2020. "Exploring the Benefits of Transformations in Health Utility Mapping," Medical Decision Making, , vol. 40(2), pages 183-197, February.
    9. McCarthy, Ian M., 2016. "Eliminating composite bias in treatment effects estimates: Applications to quality of life assessment," Journal of Health Economics, Elsevier, vol. 50(C), pages 47-58.

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