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An Educational Review of the Statistical Issues in Analysing Utility Data for Cost-Utility Analysis

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  • Rachael Hunter

    ()

  • Gianluca Baio
  • Thomas Butt
  • Stephen Morris
  • Jeff Round
  • Nick Freemantle

    ()

Abstract

The aim of cost-utility analysis is to support decision making in healthcare by providing a standardised mechanism for comparing resource use and health outcomes across programmes of work. The focus of this paper is the denominator of the cost-utility analysis, specifically the methodology and statistical challenges associated with calculating QALYs from patient-level data collected as part of a trial. We provide a brief description of the most common questionnaire used to calculate patient level utility scores, the EQ-5D, followed by a discussion of other ways to calculate patient level utility scores alongside a trial including other generic measures of health-related quality of life and condition- and population-specific questionnaires. Detail is provided on how to calculate the mean QALYs per patient, including discounting, adjusting for baseline differences in utility scores and a discussion of the implications of different methods for handling missing data. The methods are demonstrated using data from a trial. As the methods chosen can systematically change the results of the analysis, it is important that standardised methods such as patient-level analysis are adhered to as best as possible. Regardless, researchers need to ensure that they are sufficiently transparent about the methods they use so as to provide the best possible information to aid in healthcare decision making. Copyright Springer International Publishing Switzerland 2015

Suggested Citation

  • Rachael Hunter & Gianluca Baio & Thomas Butt & Stephen Morris & Jeff Round & Nick Freemantle, 2015. "An Educational Review of the Statistical Issues in Analysing Utility Data for Cost-Utility Analysis," PharmacoEconomics, Springer, vol. 33(4), pages 355-366, April.
  • Handle: RePEc:spr:pharme:v:33:y:2015:i:4:p:355-366
    DOI: 10.1007/s40273-014-0247-6
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    References listed on IDEAS

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    1. Andrew R. Willan & Andrew H. Briggs & Jeffrey S. Hoch, 2004. "Regression methods for covariate adjustment and subgroup analysis for non‐censored cost‐effectiveness data," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 461-475, May.
    2. Gerald Richardson & Andrea Manca, 2004. "Calculation of quality adjusted life years in the published literature: a review of methodology and transparency," Health Economics, John Wiley & Sons, Ltd., vol. 13(12), pages 1203-1210, December.
    3. Andrea Manca & Neil Hawkins & Mark J. Sculpher, 2005. "Estimating mean QALYs in trial‐based cost‐effectiveness analysis: the importance of controlling for baseline utility," Health Economics, John Wiley & Sons, Ltd., vol. 14(5), pages 487-496, May.
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    Cited by:

    1. Alexina J. Mason & Manuel Gomes & Richard Grieve & James R. Carpenter, 2018. "A Bayesian framework for health economic evaluation in studies with missing data," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1670-1683, November.

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