PharmacoEconomics

, Volume 23, Issue 6, pp 529–536

Incorporation of uncertainty in health economic modelling studies

Authors

  • Anthony O’Hagan
    • Centre for Bayesian Statistics in Health Economics, Department of Probability and StatisticsUniversity of Sheffield
    • Department of Probability and StatisticsUniversity of Sheffield
  • Christopher McCabe
    • Centre for Bayesian Statistics in Health Economics, Department of Probability and StatisticsUniversity of Sheffield
    • Health Economics and Decision ScienceUniversity of Sheffield
  • Ron Akehurst
    • Centre for Bayesian Statistics in Health Economics, Department of Probability and StatisticsUniversity of Sheffield
    • Health Economics and Decision ScienceUniversity of Sheffield
  • Alan Brennan
    • Centre for Bayesian Statistics in Health Economics, Department of Probability and StatisticsUniversity of Sheffield
    • Health Economics and Decision ScienceUniversity of Sheffield
  • Andrew Briggs
    • Health Economics Research CentreUniversity of Oxford
  • Karl Claxton
    • Centre for Health EconomicsUniversity of York
  • Elisabeth Fenwick
    • Centre for Health EconomicsUniversity of York
  • Dennis Fryback
    • University of Wisconsin Medical School
  • Mark Sculpher
    • Centre for Health EconomicsUniversity of York
  • David Spiegelhalter
    • MRC Biostatistics UnitUniversity of Cambridge
  • Andrew Willan
    • Public Health SciencesUniversity of Toronto
Leading Article

DOI: 10.2165/00019053-200523060-00001

Cite this article as:
O’Hagan, A., McCabe, C., Akehurst, R. et al. Pharmacoeconomics (2005) 23: 529. doi:10.2165/00019053-200523060-00001

Abstract

In a recent leading article in PharmacoEconomics, Nuijten described some methods for incorporating uncertainty into health economic models and for utilising the information on uncertainty regarding the cost effectiveness of a therapy in resource allocation decision-making. His proposals are found to suffer from serious flaws in statistical and health economic reasoning.

Nuijten’s suggestions for incorporating uncertainty: (a) wrongly interpret the p-value as the probability that the null hypothesis is true; (b) represent this probability wrongly by truncating the input distribution; and (c) in the specific example of an antiparkinsonian drug uses a completely inappropriate p-value of 0.05 when the null hypothesis would, in reality, be emphatically disproved by the data.

His suggestions regarding minimum important differences in cost effectiveness: (a) introduce areas of indifference that suggest inappropriate reliance on cost minimisation while failing to recognise that decisions should be based on expected costs versus benefits; and (b) offer no guidance on how the probabilities associated with these areas could be used in decision-making. Furthermore, Nuijten’s model for Parkinson’s disease is over-simplified to the point of providing a bad example of modelling practice, which may mislead the readers of PharmacoEconomics.

The rationale for this paper is to ensure that readers do not apply inappropriate analyses as a result of following the proposals contained in Nuijten’s paper. In addition to a detailed critique of Nuijten’s proposals, we provide brief summaries of the currently accepted best practice in cost-effectiveness decision-making under uncertainty.

Copyright information

© Adis Data Information BV 2005