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Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
Published on: September 16, 2015
Incorporation of uncertainty in health economic modelling studies
Anthony O'Hagan1, Christopher McCabe, Ron Akehurst
1Centre for Bayesian Statistics in Health Economics, Department of Probability and Statistics, University of Sheffield, Sheffield, UK.
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.
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