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Bayesian Value-of-Information Analysis: An Application to a Policy Model of Alzheimer's Disease

  • K. Claxton
  • P. J. Neumannn
  • S. S. Araki
  • M. C. Weinstein

A framework is presented which distinguishes the conceptually separate decisions of which treatment strategy is optimal from the question of whether more information is required to inform this choice in the future. The authors argue that the choice of treatment strategy should be based on expected utility and the only valid reason to characterise the uncertainty surrounding outcomes of interest is to establish the value of acquiring additional information. A Bayesian decision theoretic approach is demonstrated though a probabilistic analysis of a published policy model of Alzheimer’s disease. The expected value of perfect information is estimated for the decision to adopt a new pharmaceutical for the population of US Alzheimer’s disease patients. This provides an upper bound on the value of additional research. The value of information is also estimated for each of the model inputs. This analysis can focus future research by identifying those parameters where more precise estimates would be most valuable, and indicating whether an experimental design would be required. We also discuss how this type of analysis can also be used to design experimental research efficiently (identifying optimal sample size and optimal sample allocation) based on the marginal cost and marginal benefit of sample information. Value-of-information analysis can provide a measure of the expected payoff from proposed research, which can be used to set priorities in research and development. It can also inform an efficient regulatory framework for new health care technologies: an analysis of the value of information would define when a claim for a new technology should be deemed “substantiated” and when evidence should be considered “competent and reliable” when it is not cost-effective to gather anymore information.

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Paper provided by Department of Economics, University of York in its series Discussion Papers with number 00/39.

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Handle: RePEc:yor:yorken:00/39
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  1. Pratt, John W & Zeckhauser, Richard J, 1996. "Willingness to Pay and the Distribution of Risk and Wealth," Journal of Political Economy, University of Chicago Press, vol. 104(4), pages 747-63, August.
  2. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
  3. Karl Claxton, 1999. "Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 269-274.
  4. Karl Claxton & John Posnett, . "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
  5. Garber, Alan M. & Phelps, Charles E., 1997. "Economic foundations of cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 16(1), pages 1-31, February.
  6. McClellan, Mark & Newhouse, Joseph P., 1997. "The marginal cost-effectiveness of medical technology: A panel instrumental-variables approach," Journal of Econometrics, Elsevier, vol. 77(1), pages 39-64, March.
  7. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost-effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261.
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