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Calculation of quality adjusted life years in the published literature: a review of methodology and transparency

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  • Gerald Richardson

    (Centre for Health Economics, University of York, UK)

  • Andrea Manca

    (Centre for Health Economics, University of York, UK)

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    Abstract

    Economic evaluations alongside randomised controlled trials (RCTs) are increasingly being designed to prospectively collect patient-specific resource use and preference-based health status (utility) data. This paper examines the ways in which preference-based health status (utility) data are used to generate quality adjusted life years (QALYs). A literature review was carried out which identified 23 published cost utility analyses suitable for inclusion. The methodology employed to calculate QALYs was not always consistent, as well as being poorly reported. The use of different methodologies in the calculation of QALYs may influence the magnitude and direction of results of evaluations. Analysts need to be consistent and fully transparent in the methodology chosen to calculate QALYs. Copyright © 2004 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/hec.901
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

    Volume (Year): 13 (2004)
    Issue (Month): 12 ()
    Pages: 1203-1210

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    Handle: RePEc:wly:hlthec:v:13:y:2004:i:12:p:1203-1210

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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    1. Nicholas Graves & Damian Walker & Rosalind Raine & Andrew Hutchings & Jennifer A. Roberts, 2002. "Cost data for individual patients included in clinical studies: no amount of statistical analysis can compensate for inadequate costing methods," Health Economics, John Wiley & Sons, Ltd., vol. 11(8), pages 735-739.
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    Cited by:
    1. Miriam Krieger & Thomas Mayrhofer, 2012. "Patient Preferences and Treatment Thresholds under Diagnostic Risk – An Economic Laboratory Experiment," Ruhr Economic Papers 0321, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    2. Mayrhofer, Thomas & Krieger, Miriam, 2012. "Patient Preferences and Treatment Thresholds under Diagnostic Risk: An Economic Laboratory Experiment," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62033, Verein für Socialpolitik / German Economic Association.
    3. Miguel A. Negr�n & Francisco J. Vázquez-Polo, 2006. "Bayesian cost-effectiveness analysis with two measures of effectiveness: the cost-effectiveness acceptability plane," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 363-372.
    4. Moreno, E. & Girón, F.J. & Martínez, M.L. & Vázquez-Polo, F.J. & Negrín, M.A., 2013. "Optimal treatments in cost-effectiveness analysis in the presence of covariates: Improving patient subgroup definition," European Journal of Operational Research, Elsevier, vol. 226(1), pages 173-182.
    5. Christine McDonough & Anna Tosteson, 2007. "Measuring Preferences for Cost-Utility Analysis," PharmacoEconomics, Springer, vol. 25(2), pages 93-106, February.
    6. Negri­n, Miguel A. & Vázquez-Polo, Francisco-José, 2008. "Incorporating model uncertainty in cost-effectiveness analysis: A Bayesian model averaging approach," Journal of Health Economics, Elsevier, vol. 27(5), pages 1250-1259, September.
    7. Thomas Reinhold & Claudia Witt & Susanne Jena & Benno Brinkhaus & Stefan Willich, 2008. "Quality of life and cost-effectiveness of acupuncture treatment in patients with osteoarthritis pain," The European Journal of Health Economics, Springer, vol. 9(3), pages 209-219, August.

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