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Dollars May Not Buy as Many QALYs as We Think:

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  • Dennis G. Fryback
  • William F. Lawrence JR

Abstract

The scale of health state quality that should be used to compute quality-adjusted life years (QALYs) ranges from 0 (death) to 1.0 (excellent health); this is called the "Q" scale. But many cost-utility analyses (CUAs) in the literature use the upper anchor of the scale to denote only the absence of the particular health condition under investigation, and weight the disease state proportional to this endpoint; these are called "q" scales. Computations using q -scale health-state weights ignore the fact that the average patient is still subject to chronic and acute conditions comorbid with the condition being analyzed; the absence of a particular condition is not in general the same as excellent health, i.e., the Q scale is longer than a q scale. CUAs based on q scales yield 'qALYs." Incremental $/qALY ratios are generally lower than $/QALY ratios; in the example presented, $/qALY must be inflated by about 15% to yield $/QALY. Other CUAs correctly weight disease states using the Q scale, but erroneously assign a quality weight of 1.0 to absence of the disease in the CUA computations. The results of such analyses are called "NP-QALYs," as the correction factor to compute QALYs is not a simple proportional adjustment. The authors suggest that analysts doing cost-utility analyses without access to primary data from treated patients use average age-specific health-related quality-of-life weights from population-based studies to represent the state of not having a particular disease. Consumers of CUAs should closely examine the nature of the QALYs in any published analyses before making decisions based on their results. Key words: QALY; quality-adjusted life years; utility; quality-of-life assessment; cost-utility analysis; cost-effectiveness methodology. (Med Decis Making 1997;17:276-284)

Suggested Citation

  • Dennis G. Fryback & William F. Lawrence JR, 1997. "Dollars May Not Buy as Many QALYs as We Think:," Medical Decision Making, , vol. 17(3), pages 276-284, July.
    • Handle: RePEc:sae:medema:v:17:y:1997:i:3:p:276-284
    DOI: 10.1177/0272989X9701700303
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    Cited by:

    1. Bleichrodt, Han & Crainich, David & Eeckhoudt, Louis, 2003. "The effect of comorbidities on treatment decisions," Journal of Health Economics, Elsevier, vol. 22(5), pages 805-820, September.
    2. Stefan Felder & Thomas Mayrhofer, 2011. "Higher-Order Risk Preferences – Consequences for Test and Treatment Thresholds and Optimal Cutoffs," Ruhr Economic Papers 0287, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    3. Arthur Attema & Yvette Edelaar-Peeters & Matthijs Versteegh & Elly Stolk, 2013. "Time trade-off: one methodology, different methods," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(1), pages 53-64, July.
    4. Bleichrodt, Han & Crainich, David & Eeckhoudt, Louis, 2003. "Comorbidities and the willingness to pay for health improvements," Journal of Public Economics, Elsevier, vol. 87(11), pages 2399-2406, October.
    5. Malek B Hannouf & Chander Sehgal & Jeffrey Q Cao & Joseph D Mocanu & Eric Winquist & Gregory S Zaric, 2012. "Cost-Effectiveness of Adding Cetuximab to Platinum-Based Chemotherapy for First-Line Treatment of Recurrent or Metastatic Head and Neck Cancer," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-9, June.
    6. Martijn S Visser & Sankha Amarakoon & Tom Missotten & Reinier Timman & Jan J Busschbach, 2017. "SF-6D utility values for the better- and worse-seeing eye for health states based on the Snellen equivalent in patients with age-related macular degeneration," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-9, February.
    7. Laura McCullagh & Cathal Walsh & Michael Barry, 2012. "Value-of-Information Analysis to Reduce Decision Uncertainty Associated with the Choice of Thromboprophylaxis after Total Hip Replacement in the Irish Healthcare Setting," PharmacoEconomics, Springer, vol. 30(10), pages 941-959, October.
    8. Henry A. Glick & Daniel Polsky & Richard J. Willke & Kevin A. Schulman, 1999. "A Comparison of Preference Assessment Instruments Used in a Clinical Trial," Medical Decision Making, , vol. 19(3), pages 265-275, August.
    9. Ara, R & Brazier, JE, 2010. "Using health state utility values from the general population to approximate baselines in decision analytic models when condition specific data are not available," MPRA Paper 29946, University Library of Munich, Germany.
    10. Bleichrodt, Han & Herrero, Carmen & Pinto, Jose Luis, 2002. "A proposal to solve the comparability problem in cost-utility analysis," Journal of Health Economics, Elsevier, vol. 21(3), pages 397-403, May.
    11. repec:zbw:rwirep:0287 is not listed on IDEAS
    12. Ara, Roberta & Brazier, John, 2009. "Populating an economic model with health state utility values: moving towards better practice," MPRA Paper 29896, University Library of Munich, Germany.
    13. Brouwer, Werner B. F. & van Exel, N. Job A. & Stolk, Elly A., 2005. "Acceptability of less than perfect health states," Social Science & Medicine, Elsevier, vol. 60(2), pages 237-246, January.
    14. Felder, Stefan & Mayrhofer, Thomas, 2011. "Higher-Order Risk Preferences – Consequences for Test and Treatment Thresholds and Optimal Cutoffs," Ruhr Economic Papers 287, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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