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Improving the efficiency and relevance of health technology assessent: the role of iterative decision analytic modelling


Author Info

  • Elizabeth Fenwick
  • Karl Claxton

    (Centre for Health Economics, The University of York)

  • Mark Sculpher

    (Centre for Health Economics, The University of York)

  • Andrew Briggs


Decision making in health care involves two sets of related decisions: those concerning appropriate service provision on the basis of existing information; and those concerned with whether to fund additional research to reduce the uncertainty relating to the decision. Information acquisition is not costless, and the allocation of funds to the enhancement of the decision makers’ information set, in a budgetconstrained health service, reduces the ‘pot’ of resources available for health service provision. Hence, a framework is necessary to unify these decisions and ensure that HTA is subject to the same evaluation of efficiency as service provision. A framework is presented which addresses these two sets of decisions through the employment of decision analytic models and Bayesian value of information analysis, early and regularly within the health technology assessment process. The model becomes the vehicle of health technology assessment, managing and directing future research effort on an iterative basis over the lifetime of the technology. This ensures consistency in decision making between service provision, research and development priorities and research methods. Fulfilling the aim of the National Health Service HTA programme, that research is “produced in the most economical way” using “cost effective research protocols”. The proposed framework is applied to the decision concerning the appropriate management of female patients with symptoms of urinary tract infection, which was the subject of a recent NHS HTA call for proposals. A probabilistic model is employed to fully characterise and assess the uncertainty surrounding the decision. The expected value of perfect information (EVPI) is then calculated for the full model, for each individual management strategy and for particular model parameters. Research effort can then be focused on those areas where the cost of uncertainty is high and where additional research is potentially cost-effective. The analysis can be used to identify the most appropriate research protocol and to concentrate research upon particular parameters where more precise estimates would be of most value.

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Bibliographic Info

Paper provided by Centre for Health Economics, University of York in its series Working Papers with number 179chedp.

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Length: 42 pages
Date of creation: May 2000
Date of revision:
Handle: RePEc:chy:respap:179chedp

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Keywords: assessment;


References listed on IDEAS
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  1. James C. Felli & Gordon B. Hazen, 1999. "A Bayesian approach to sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 263-268.
  2. Aaron A. Stinnett & John Mullahy, 1998. "Net Health Benefits: A New Framework for the Analysis of Uncertainty in Cost-Effectiveness Analysis," NBER Technical Working Papers 0227, National Bureau of Economic Research, Inc.
  3. Johannesson, Magnus & Weinstein, Milton C., 1993. "On the decision rules of cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 12(4), pages 459-467, December.
  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. Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost-effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740.
  6. Claxton, K. & Thompson, K. M., 2001. "A dynamic programming approach to the efficient design of clinical trials," Journal of Health Economics, Elsevier, vol. 20(5), pages 797-822, September.
  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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RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography for Economics:
  1. > Economic Development Technological Change, and Growth > Technological Change: Choices and Consequences > Technology Assessment > Health Technology Assessment
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Cited by:
  1. Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848.
  2. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
  3. 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.
  4. Karl Claxton & Mark Sculpher & Tony Culyer, 2007. "Mark versus Luke? Appropriate Methods for the Evaluation of Public Health Interventions," Working Papers 031cherp, Centre for Health Economics, University of York.
  5. Mark J. Sculpher & Karl Claxton & Mike Drummond & Chris McCabe, 2006. "Whither trial-based economic evaluation for health care decision making?," Health Economics, John Wiley & Sons, Ltd., vol. 15(7), pages 677-687.
  6. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost-effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787.
  7. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2005. "The value of implementation and the value of information: combined and uneven development," Working Papers 005cherp, Centre for Health Economics, University of York.
  8. Samuel Shillcutt & Damian Walker & Catherine Goodman & Anne Mills, 2009. "Cost Effectiveness in Low- and Middle-Income Countries," PharmacoEconomics, Springer, vol. 27(11), pages 903-917, November.
  9. Elisabeth Fenwick & Bernie J. O'Brien & Andrew Briggs, 2004. "Cost-effectiveness acceptability curves - facts, fallacies and frequently asked questions," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 405-415.


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