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Parametric cost-effectiveness inference with skewed data


  • Bebu, Ionut
  • Luta, George
  • Mathew, Thomas
  • Kennedy, Paul A.
  • Agan, Brian K.


Comparing treatment effects while taking into account the associated costs is an important goal of cost-effectiveness analyses. Several cost-effectiveness measures have been proposed to quantify these comparisons, including the incremental cost-effectiveness ratio (ICER) and the incremental net benefit (INB). Various approaches have been proposed for constructing confidence intervals for ICER and INB, including parametric methods (e.g. based on the Delta method or on Fieller’s method), nonparametric methods (e.g. various bootstrap methods), as well as Bayesian methods. Skewed data are usually the norm in cost-effectiveness analyses, and accurate parametric confidence intervals in this context are lacking. Confidence intervals for both ICER and INB are constructed using the concept of a generalized pivotal quantity, which can be derived for various combinations of normal, lognormal, and other skewed distributions for costs and effectiveness. The proposed methodology is straightforward in terms of computation and implementation even in the presence of covariates, and the resulting confidence intervals compared favorably with existing methods in a simulation study. The approach is illustrated using data from three randomized trials.

Suggested Citation

  • Bebu, Ionut & Luta, George & Mathew, Thomas & Kennedy, Paul A. & Agan, Brian K., 2016. "Parametric cost-effectiveness inference with skewed data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 210-220.
  • Handle: RePEc:eee:csdana:v:94:y:2016:i:c:p:210-220
    DOI: 10.1016/j.csda.2015.08.017

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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.
    2. Richard M. Nixon & David Wonderling & Richard D. Grieve, 2010. "Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared," Health Economics, John Wiley & Sons, Ltd., vol. 19(3), pages 316-333.
    3. Gamage, Jinadasa & Mathew, Thomas & Weerahandi, Samaradasa, 2004. "Generalized p-values and generalized confidence regions for the multivariate Behrens-Fisher problem and MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 177-189, January.
    4. Hannig, Jan & Iyer, Hari & Patterson, Paul, 2006. "Fiducial Generalized Confidence Intervals," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 254-269, March.
    5. Anthony O'Hagan & John W. Stevens, 2003. "Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?," Health Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 33-49.
    6. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.
    7. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis," NBER Technical Working Papers 0227, National Bureau of Economic Research, Inc.
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    Cost-effectiveness; ICER; INB; GPQ; Skewed data;


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