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

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


BACKGROUND: 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.

OBJECTIVE: 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.

METHODS: 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'.

RESULTS: 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.

CONCLUSIONS: 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.

Original languageEnglish
Pages (from-to)297-310
Number of pages14
Issue number4
Publication statusPublished - Apr 2008


  • Australia
  • Cost-Benefit Analysis
  • Health Services Research
  • Humans
  • Policy Making
  • Quality-Adjusted Life Years
  • Resource Allocation
  • Retrospective Studies

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