, Volume 26, Issue 4, pp 297–310 | Cite as

The Use of QALY Weights for QALY Calculations

A Review of Industry Submissions Requesting Listing on the Australian Pharmaceutical Benefits Scheme 2002–4
  • Paul A. Scuffham
  • Jennifer A. Whitty
  • Andrew Mitchell
  • Rosalie Viney
Original Research Article



QALYs combine survival and health-related quality of life (QOL) into a single index, enabling judgements about the relative value for money of healthcare interventions.


To investigate the methods used for estimating QALY weights included in submissions by industry for listing of their products on the Australian Pharmaceutical Benefits Scheme.

Study design

Retrospective descriptive review of submissions considered by the Pharmaceutical Benefits Advisory Committee (PBAC) 2002–4.

Data sources

The database of submissions considered at PBAC meetings was obtained from the Pharmaceutical Evaluation Section of the Australian Government Department of Health and Ageing. Further information on each included submission was obtained in the form of the Pharmaceutical Evaluation Section commentary (expert report) on the submission.


Submissions to the PBAC over 2002–4 presenting QALYs as an outcome measure were reviewed to identify the methods used to obtain preference-based QALY weights. Information was analyzed according to the approach taken to obtain QALY weights (multi-attribute utility instrument [MAUI], health state valuation [HSV] experiment for scaling the health states, or non-preference-based approach); the population from whom the QALY weights were obtained; the appropriateness of the population for the instrument; the recommendation made by the PBAC; and the main indicated category for use of the pharmaceutical.

The approach and the population were classified as ‘more appropriate’ and ‘less appropriate’. The ‘more appropriate’ approaches were where a MAUI was administered to patients who were currently experiencing the health states being valued, or when an HSV experiment was undertaken in either the general population to value a health state derived from clinical and QOL studies or a population of patients to value their own health state. All other approaches were considered ‘less appropriate’.


MAUIs were used in 39% of approaches reporting QALYs; the most frequently used MAUI was the EQ-5D. HSV experiments were used in 36% of the approaches and generally drawn from the published literature. Non-preference based approaches (24%) included rating scales, mapping transformations and consensus opinions. Responses from patients were used in 58% of the approaches, followed by healthcare professionals and investigators (24% and 9%, respectively). Healthcare professionals and investigators’ responses were frequently used in non-preference-based approaches. Submissions for nervous system, infectious disease and neoplasms disease areas were less likely to have presented QALY weights derived from a ‘more appropriate’ approach. Of the approaches using ‘more appropriate’ populations and techniques, 56% were rejected by the PBAC compared with 66% of those using ‘less appropriate’ approaches.


The variability in the quality of QALY weights is troubling. The PBAC guidelines that applied over the period studied neither encouraged nor discouraged cost-utility analyses and provided only brief guidance on how QALY studies should be conducted. A consistent approach to the application of standard methods should be used when the QALY is used to inform decisions on resource allocation. The new PBAC guidelines released in 2006 provide more extensive guidance on derivation of QALY estimates and are more encouraging of the presentation of cost-utility analysis. MAUIs offer a straightforward approach to obtaining QALY weights, and ideally should be used routinely in relevant comparative randomized trials to assess patients’ health states.


Health State Valuation Pharmaceutical Benefit Advisory Committee QALY Weight State Preference Experiment QALY Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



No sources of funding were used to assist in the preparation of this study. The authors have no conflicts of interest that are directly relevant to the content of this study. At the time the data were collected, Andrew Mitchell was the Secretary and Rosalie Viney was a member of the Economics Subcommittee of the Pharmaceutical Benefits Advisory Committee.


  1. 1.
    Australian Government Department of Health and Ageing. Schedule of pharmaceutical benefits for approved pharmacists and medical practitioners. Canberra (ACT): Commonwealth of Australia, 2006Google Scholar
  2. 2.
    Pharmaceutical Benefits Advisory Committee. Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (version 4.1) 2006 [online]. Available from URL: [Accessed 2007 Jan 6]Google Scholar
  3. 3.
    Bleichrodt H, Diecidue E, Quiggin J. Equity weights in the allocation of health care: the rank-dependent QALY model. J Health Econ 2004; 23 (1): 157–171PubMedCrossRefGoogle Scholar
  4. 4.
    National Institute for Health and Clinical Excellence. Guide to the methods of technology appraisal. London: NICE, 2004 AprilGoogle Scholar
  5. 5.
    Gold MR, Patrick DL, Torrance GW, et al. Identifying and valuing outcomes. In: Gold MR, Siegel JE, Russell LB, et al., editors. Cost-effectiveness in health and medicine. New York: Oxford University Press, 1996: 82–134Google Scholar
  6. 6.
    Kind P. US FDA guidance: apropros of PROs. Pharmacoeconomics 2006; 24 (9): 833–836PubMedCrossRefGoogle Scholar
  7. 7.
    US Department of Health and Human Services Food and Drug Administration. Draft guidance for industry of patient-reported outcome measures: use in medical product development to support labeling claims [online]. Available from URL: [Accessed 2006 Dec 8]
  8. 8.
    Dolan P. Output measures and valuation in health. In: Drummond M, McGuire A, editors. Economic evaluation in health care: merging theory with practice. Oxford: Oxford University Press, 2001: 46–67Google Scholar
  9. 9.
    Bleichrodt H, Quiggin J. Life-cycle preferences over consumption and health: when is cost-effectiveness analysis equivalent to cost-benefit analysis? J Health Econ 1999; 18: 681–708PubMedCrossRefGoogle Scholar
  10. 10.
    Bleichrodt H, Wakker P, Johannesson M. Characterizing QALYs by risk neutrality. J Risk Uncert 1997; 15: 107–114CrossRefGoogle Scholar
  11. 11.
    Pliskin J, Shephard D, Weinstein M. Utility functions for life years and health status. Operation Res 1980; 28: 206–224CrossRefGoogle Scholar
  12. 12.
    Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ 2002; 21: 271–292PubMedCrossRefGoogle Scholar
  13. 13.
    Brazier JE, Roberts J. The estimation of a preference-based measure of health from the SF-12. Med Care 2004 Sep; 42 (9): 851–859PubMedCrossRefGoogle Scholar
  14. 14.
    Kind P, Dolan P, Gudex C, et al. Variations in population health status: results from a United Kingdom national questionnaire survey. Brit Med J 1998; 316 (7133): 736–741PubMedCrossRefGoogle Scholar
  15. 15.
    Shaw R, Johnson J, Coons SJ. US valuation of the EQ-5D health states: development and testing the Dl valuation model. Med Care 2005; 43 (3): 203–220PubMedCrossRefGoogle Scholar
  16. 16.
    Feeny D, Furlong W, Torrance GW, et al. Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Med Care 2002 Feb; 40 (2): 113–128PubMedCrossRefGoogle Scholar
  17. 17.
    Torrance GW, Feeny DH, Furlong WJ, et al. Multiattribute utility function for a comprehensive health status classification system: health utilities index mark 2. Med Care 1996 Jul; 34 (7): 702–722PubMedCrossRefGoogle Scholar
  18. 18.
    Hawthorne G, Richardson J, Day NA. A comparison of the assessment of quality of life (AQoL) with four other generic utility instruments. Ann Med 2001; 33: 358–370PubMedCrossRefGoogle Scholar
  19. 19.
    Hawthorne G, Richardson J, Osborne R. The assessment of quality of life (AQoL) instrument: a psychometric measure of health related quality of life. Qual Life Res 1999; 8: 209–224PubMedCrossRefGoogle Scholar
  20. 20.
    Brazier J, Deverill M. A checklist forjudging preference-based measures of health related quality of life. Health Econ 1999; 8: 41–51PubMedCrossRefGoogle Scholar
  21. 21.
    Bleichrodt H. A new explanation for the difference between time trade-off utilities and standard gamble utilities. Health Econ 2002 July; 11 (5): 447–456PubMedCrossRefGoogle Scholar
  22. 22.
    Dolan P. The measurement of health related quality of life for use in resource allocation decisions in health care. In: Culyer A, Newhouse J, editors. Handbook of health economics. Amsterdam: Elsevier, 2000Google Scholar
  23. 23.
    Torrance G. Measurement of health state utilities for economic appraisal: a review. J Health Econ 1986; 5 (1): 1–30PubMedCrossRefGoogle Scholar
  24. 24.
    Torrance G, Furlong W, Feeny D, et al. Multi-attribute preference functions: health utilities index. Pharmacoeconomics 1995; 7: 503–520PubMedCrossRefGoogle Scholar
  25. 25.
    Gold MR, Siegel JE, Russell LB, et al., editors. Cost-effectiveness in health and medicine. New York: Oxford University Press, 1996Google Scholar
  26. 26.
    Fryback DG, Lawrence WF, Martin PA, et al. Predicting quality of well-being scores from the SF-36: results from the Beaver Dam health outcomes study. Med Decis Making 1997; 17: 1–9PubMedCrossRefGoogle Scholar
  27. 27.
    Wong J, Bennett W, Koff R, et al. Pretreatment evaluation of chronic hepatitis C: risks, benefits, and costs. JAMA 1998; 280 (24): 2088–2093PubMedCrossRefGoogle Scholar
  28. 28.
    George B, Harris A, Mitchell A. Cost-effectiveness analysis and the consistency of decision making: evidence from pharmaceutical reimbursement in Australia (1991 to 1996). Pharmacoeconomics 2001; 19 (11): 1103–1109PubMedCrossRefGoogle Scholar
  29. 29.
    Scuffham P. The assessment of pharmaceuticals for government subsidy in Australia: recent developments. J Med Econ 2007; 10: 163–169CrossRefGoogle Scholar
  30. 30.
    Hill SR, Mitchell AS, Henry DA. Problems with the interpretation of pharmacoeconomic analyses: a review of submissions to the Australian Pharmaceutical Benefits Scheme. JAMA 2000 Apr 26; 283 (16): 2116–2121PubMedCrossRefGoogle Scholar
  31. 31.
    Neumann PJ, Zinner DE, Wright JC. Are methods for estimating QALYs in cost-effectiveness analyses improving? Med Decis Making 1997; 17: 402–408PubMedCrossRefGoogle Scholar
  32. 32.
    Bell CM, Chapman RH, Stone PW, et al. An off-the-shelf help list: a comprehensive catalog of preference scores from published cost-utility analysis. Med Decis Making 2001; 21: 288–294PubMedGoogle Scholar
  33. 33.
    Stein K, Fry A, Round A, et al. What value health? A review of health state values used in early technology assessments for NICE. Appl Health Econ Health Policy 2006; 4 (4): 219–228CrossRefGoogle Scholar
  34. 34.
    Richardson G, Manca A. Calculation of quality adjusted life years in the published literature: a review of methodology and transparency. Health Econ 2004; 13: 1203–1210PubMedCrossRefGoogle Scholar
  35. 35.
    Tsuchiya A, Ikeda S, Ikegami N, et al. Estimating an EQ-5D population value set: the case of Japan. Health Econ 2002; 11 (4): 341–353PubMedCrossRefGoogle Scholar
  36. 36.
    Greiner W, Weijnen T, Nieuwenhuizen M, et al. A single European currency for EQ-5D health states: results from a six-country study. Eur J Health Econ 2003; 4: 222–231PubMedCrossRefGoogle Scholar
  37. 37.
    McGregor M, Caro J. QALYs: are they helpful to decision makers? Pharmacoeconomics 2006; 24 (10): 947–952PubMedCrossRefGoogle Scholar
  38. 38.
    Vijan S. Should we abandon QALYs as a resource allocation tool? Pharmacoeconomics 2006; 24 (10): 953–954PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2008

Authors and Affiliations

  • Paul A. Scuffham
    • 1
  • Jennifer A. Whitty
    • 1
  • Andrew Mitchell
    • 2
  • Rosalie Viney
    • 3
  1. 1.School of Medicine, Logan Campus L03 2.43Griffith UniversityMeadowbrookAustralia
  2. 2.Pharmaceutical Benefits Division, Australian Government Department of Health and AgeingCanberraAustralia
  3. 3.Centre for Health Economics Research and Evaluation, Faculty of BusinessUniversity of TechnologySydneyAustralia

Personalised recommendations