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How Sensitive Are Cost-Effectiveness Analyses to Choice of Parametric Distributions?

Author

Listed:
  • Simon G. Thompson

    (MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK, simon.thompson@mrc-bsu.cam.ac.uk)

  • Richard M. Nixon

    (MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK)

Abstract

Background . Cost-effectiveness analyses of clinical trial data are based on assumptions about the distributions of costs and effects. Cost data usually have very skewed distributions and can be difficult to model. The authors investigate whether choice of distribution can make a difference to the conclusions drawn. Methods . The authors compare 3 distributions for cost data—normal, gamma, and lognormal—using similar parametric models for the cost-effectiveness analyses. Inferences on the cost-effectiveness plane are derived, together with cost-effectiveness acceptability curves. These methods are applied to data from a trial of rapid magnetic resonance imaging (rMRI) investigation in patients with low back pain. Results . The gamma and lognormal distributions fitted the cost data much better than the normal distribution. However, in terms of inferences about cost-effectiveness, it was the normal and gamma distributions that gave similar results. Using the lognormal distribution led to the conclusion that rMRI was cost-effective for a range of willingness-to-pay values where assuming a gamma or normal distribution did not. Conclusions . Conclusions from cost-effectiveness analyses are sensitive to choice of distribution and, in particular, to how the upper tail of the cost distribution beyond the observed data is modeled. How well a distribution fits the data is an insufficient guide to model choice. A sensitivity analysis is therefore necessary to address uncertainty about choice of distribution.

Suggested Citation

  • Simon G. Thompson & Richard M. Nixon, 2005. "How Sensitive Are Cost-Effectiveness Analyses to Choice of Parametric Distributions?," Medical Decision Making, , vol. 25(4), pages 416-423, July.
    • Handle: RePEc:sae:medema:v:25:y:2005:i:4:p:416-423
    DOI: 10.1177/0272989X05276862
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    References listed on IDEAS

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    1. Partha Deb & James F. Burgess, Jr., 2003. "A Quasi-experimental Comparison of Econometric Models for Health Care Expenditures," Economics Working Paper Archive at Hunter College 212, Hunter College Department of Economics.
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    Cited by:

    1. Noémi Kreif & Richard Grieve & M. Zia Sadique, 2013. "Statistical Methods For Cost‐Effectiveness Analyses That Use Observational Data: A Critical Appraisal Tool And Review Of Current Practice," Health Economics, John Wiley & Sons, Ltd., vol. 22(4), pages 486-500, April.
    2. Thompson, Simon G. & Nixon, Richard M. & Grieve, Richard, 2006. "Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study," Journal of Health Economics, Elsevier, vol. 25(6), pages 1015-1028, November.
    3. Caterina Conigliani, 2008. "A bayesian model averaging approach with non-informative priors for cost-effectiveness analyses in health economics," Departmental Working Papers of Economics - University 'Roma Tre' 0094, Department of Economics - University Roma Tre.
    4. Moreno, Elías & Girón, F.J. & Vázquez-Polo, F.J. & NegrI´n, M.A., 2010. "Optimal healthcare decisions: Comparing medical treatments on a cost-effectiveness basis," European Journal of Operational Research, Elsevier, vol. 204(1), pages 180-187, July.
    5. Andrea Gabrio & Michael J. Daniels & Gianluca Baio, 2020. "A Bayesian parametric approach to handle missing longitudinal outcome data in trial‐based health economic evaluations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 607-629, February.
    6. Daniel P Beavers & James D Stamey, 2018. "Bayesian sample size determination for cost-effectiveness studies with censored data," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
    7. Alexina J. Mason & Manuel Gomes & James Carpenter & Richard Grieve, 2021. "Flexible Bayesian longitudinal models for cost‐effectiveness analyses with informative missing data," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3138-3158, December.
    8. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    9. Caterina Conigliani & Andrea Tancredi, 2006. "Comparing parametric and semi-parametric approaches for bayesian cost-effectiveness analyses in health economics," Departmental Working Papers of Economics - University 'Roma Tre' 0064, Department of Economics - University Roma Tre.
    10. Bruce Y Lee & Sarah M McGlone & Rachel R Bailey & Ann E Wiringa & Shanta M Zimmer & Kenneth J Smith & Richard K Zimmerman, 2010. "To Test or to Treat? An Analysis of Influenza Testing and Antiviral Treatment Strategies Using Economic Computer Modeling," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-11, June.
    11. 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.
    12. 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.
    13. Denise Bijlenga & Gouke J. Bonsel & Erwin Birnie, 2011. "Eliciting willingness to pay in obstetrics: comparing a direct and an indirect valuation method for complex health outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 20(11), pages 1392-1406, November.
    14. Daniele Bregantini, 2014. "Don’t Stop ’Til You Get Enough: a quickest detection approach to HTA," Discussion Papers 14/04, Department of Economics, University of York.
    15. Caterina Conigliani & Andrea Tancredi, 2009. "A Bayesian model averaging approach for cost‐effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 18(7), pages 807-821, July.
    16. Lemoine, Coralie & Loubière, Sandrine & Boucekine, Mohamed & Girard, Vincent & Tinland, Aurélie & Auquier, Pascal, 2021. "Cost-effectiveness analysis of housing first intervention with an independent housing and team support for homeless people with severe mental illness: A Markov model informed by a randomized controlle," Social Science & Medicine, Elsevier, vol. 272(C).
    17. 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.

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