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

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Author Info

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

Abstract

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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File URL: http://www.york.ac.uk/media/economics/documents/discussionpapers/2000/0039.pdf
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Bibliographic Info

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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Related research

Keywords: stochastic CEA; Bayesian decision theory; value of information.;

References

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  1. 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.
  2. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost-effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
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Citations

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Cited by:
  1. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225.
  2. Sung, Hwansoo & Shortle, James S., 2006. "The Expected Value of Sample Information Analysis for Nonpoint Water Quality Management," 2006 Annual meeting, July 23-26, Long Beach, CA 21296, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  3. Douglas Coyle, 2003. "Determining the optimal combinations of mutually exclusive interventions: a response to Hutubessy and colleagues," Health Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 159-162.
  4. David Cohen & Mirella F Longo & John Williams & Wai-yee Cheung & Hayley Hutchings & I.T. Russell, 2003. "Estimating the marginal value of 'better' research output: 'designed' versus 'routine' data in randomised controlled trials," Health Economics, John Wiley & Sons, Ltd., vol. 12(11), pages 959-974.
  5. Doug Coyle & Jeremy Oakley, 2008. "Estimating the expected value of partial perfect information: a review of methods," The European Journal of Health Economics, Springer, vol. 9(3), pages 251-259, August.
  6. Douglas Coyle & Martin J. Buxton & Bernie J. O'Brien, 2003. "Stratified cost-effectiveness analysis: a framework for establishing efficient limited use criteria," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 421-427.
  7. Bas Groot Koerkamp & M. G. Myriam Hunink & Theo Stijnen & Milton C. Weinstein, 2006. "Identifying key parameters in cost-effectiveness analysis using value of information: a comparison of methods," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 383-392.

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