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Estimating mean QALYs in trial‐based cost‐effectiveness analysis: the importance of controlling for baseline utility


  • Andrea Manca
  • Neil Hawkins
  • Mark J. Sculpher


In trial‐based cost‐effectiveness analysis baseline mean utility values are invariably imbalanced between treatment arms. A patient's baseline utility is likely to be highly correlated with their quality‐adjusted life‐years (QALYs) over the follow‐up period, not least because it typically contributes to the QALY calculation. Therefore, imbalance in baseline utility needs to be accounted for in the estimation of mean differential QALYs, and failure to control for this imbalance can result in a misleading incremental cost‐effectiveness ratio. This paper discusses the approaches that have been used in the cost‐effectiveness literature to estimate absolute and differential mean QALYs alongside randomised trials, and illustrates the implications of baseline mean utility imbalance for QALY calculation. Using data from a recently conducted trial‐based cost‐effectiveness study and a micro‐simulation exercise, the relative performance of alternative estimators is compared, showing that widely used methods to calculate differential QALYs provide incorrect results in the presence of baseline mean utility imbalance regardless of whether these differences are formally statistically significant. It is demonstrated that multiple regression methods can be usefully applied to generate appropriate estimates of differential mean QALYs and an associated measure of sampling variability, while controlling for differences in baseline mean utility between treatment arms in the trial. Copyright © 2004 John Wiley & Sons, Ltd.

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  • 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.
  • Handle: RePEc:wly:hlthec:v:14:y:2005:i:5:p:487-496
    DOI: 10.1002/hec.944

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    References listed on IDEAS

    1. Daniel Polsky & Henry A. Glick & Richard Willke & Kevin Schulman, 1997. "Confidence Intervals for Cost–Effectiveness Ratios: A Comparison of Four Methods," Health Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 243-252, May.
    2. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    3. 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.
    4. Jeffrey S. Hoch & Andrew H. Briggs & Andrew R. Willan, 2002. "Something old, something new, something borrowed, something blue: a framework for the marriage of health econometrics and cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 11(5), pages 415-430, July.
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