Literature DB >> 18370565

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 Scuffham1, Jennifer A Whitty, Andrew Mitchell, Rosalie Viney.   

Abstract

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.

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Year:  2008        PMID: 18370565     DOI: 10.2165/00019053-200826040-00003

Source DB:  PubMed          Journal:  Pharmacoeconomics        ISSN: 1170-7690            Impact factor:   4.981


  27 in total

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2.  The estimation of a preference-based measure of health from the SF-36.

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3.  Estimating an EQ-5D population value set: the case of Japan.

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4.  Equity weights in the allocation of health care: the rank-dependent QALY model.

Authors:  Han Bleichrodt; Enrico Diecidue; John Quiggin
Journal:  J Health Econ       Date:  2004-01       Impact factor: 3.883

Review 5.  Multi-attribute preference functions. Health Utilities Index.

Authors:  G W Torrance; W Furlong; D Feeny; M Boyle
Journal:  Pharmacoeconomics       Date:  1995-06       Impact factor: 4.981

6.  A single European currency for EQ-5D health states. Results from a six-country study.

Authors:  Wolfgang Greiner; Tom Weijnen; Martin Nieuwenhuizen; Siem Oppe; Xavier Badia; Jan Busschbach; Martin Buxton; Paul Dolan; Paul Kind; Paul Krabbe; Arto Ohinmaa; David Parkin; Montserat Roset; Harri Sintonen; Aki Tsuchiya; Frank de Charro
Journal:  Eur J Health Econ       Date:  2003-09

7.  US valuation of the EQ-5D health states: development and testing of the D1 valuation model.

Authors:  James W Shaw; Jeffrey A Johnson; Stephen Joel Coons
Journal:  Med Care       Date:  2005-03       Impact factor: 2.983

8.  US FDA guidance: apropos of PROs.

Authors:  Paul Kind
Journal:  Pharmacoeconomics       Date:  2006       Impact factor: 4.981

9.  Should we abandon QALYs as a resource allocation tool?

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Journal:  Pharmacoeconomics       Date:  2006       Impact factor: 4.981

Review 10.  Are methods for estimating QALYs in cost-effectiveness analyses improving?

Authors:  P J Neumann; D E Zinner; J C Wright
Journal:  Med Decis Making       Date:  1997 Oct-Dec       Impact factor: 2.583

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  23 in total

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2.  Utilities and QALYs in health economic evaluations: glossary and introduction.

Authors:  Gianni Virgili; Daniela Koleva; Livio Garattini; Rita Banzi; Gian Franco Gensini
Journal:  Intern Emerg Med       Date:  2010-07-06       Impact factor: 3.397

Review 3.  Cost effectiveness of influenza vaccination in older adults: a critical review of economic evaluations for the 50- to 64-year age group.

Authors:  Anthony T Newall; Heath Kelly; Stuart Harsley; Paul A Scuffham
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

4.  Korean guidelines for pharmacoeconomic evaluation (second and updated version) : consensus and compromise.

Authors:  Seungjin Bae; Soook Lee; Eun Young Bae; Sunmee Jang
Journal:  Pharmacoeconomics       Date:  2013-04       Impact factor: 4.981

5.  Harnessing the potential to quantify public preferences for healthcare priorities through citizens' juries.

Authors:  Jennifer A Whitty; Paul Burton; Elizabeth Kendall; Julie Ratcliffe; Andrew Wilson; Peter Littlejohns; Paul A Scuffham
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6.  Discrepancies between proxy estimates and patient reported, health related, quality of life: minding the gap between patient and clinician perceptions in heart failure.

Authors:  Roslyn A Prichard; Fei-Li Zhao; Julee Mcdonagh; Stephen Goodall; Patricia M Davidson; Phillip J Newton; Ben Farr-Wharton; Christopher S Hayward
Journal:  Qual Life Res       Date:  2021-01-02       Impact factor: 4.147

7.  A comparison of EQ-5D-3L population norms in Queensland, Australia, estimated using utility value sets from Australia, the UK and USA.

Authors:  Susan Clemens; Nelufa Begum; Catherine Harper; Jennifer A Whitty; Paul A Scuffham
Journal:  Qual Life Res       Date:  2014-03-28       Impact factor: 4.147

Review 8.  A review of quality of life instruments used in liver transplantation.

Authors:  Colleen L Jay; Zeeshan Butt; Daniela P Ladner; Anton I Skaro; Michael M Abecassis
Journal:  J Hepatol       Date:  2009-07-28       Impact factor: 25.083

Review 9.  A systematic review of stated preference studies reporting public preferences for healthcare priority setting.

Authors:  Jennifer A Whitty; Emily Lancsar; Kylie Rixon; Xanthe Golenko; Julie Ratcliffe
Journal:  Patient       Date:  2014       Impact factor: 3.883

10.  QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30.

Authors:  M T King; D S J Costa; N K Aaronson; J E Brazier; D F Cella; P M Fayers; P Grimison; M Janda; G Kemmler; R Norman; A S Pickard; D Rowen; G Velikova; T A Young; R Viney
Journal:  Qual Life Res       Date:  2016-01-20       Impact factor: 4.147

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