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The Half-Life of Truth: What Are Appropriate Time Horizons for Research Decisions?

Author

Listed:
  • Zoe Philips

    (Centre for Health Economics, University of York, UK, che-teehta@york.ac.uk)

  • Karl Claxton

    (Centre for Health Economics, University of York, UK)

  • Stephen Palmer

    (Centre for Health Economics, University of York, UK)

Abstract

Purpose. To evaluate alternative approaches taken to estimate the population that could benefit from research and to demonstrate that explicitly modeling future change leads to more appropriate estimates of the expected value of information (EVI). Methods. Existing approaches to estimating the population typically focus on the time horizon for decisions, employing seemingly arbitrary estimates of the appropriate horizon. These approaches implicitly use the time horizon as a proxy for future changes in technologies, prices, and information. Different approaches to quantifying the time horizon are explored, in the context of a stylized model, to demonstrate the impact of uncertainty in this estimate on EVI. An alternative approach is developed that explicitly models future changes in technologies, prices, and information and that demonstrates the impact on EVI estimates. Results. Explicitly modeling future changes means that the EVI for the decision problem may increase or decrease over time, but the EVI for the group of parameters that can be evaluated by current research tends to decline. The finite and infinite time horizons for the decision problem represent special cases (e.g., price shock or no changes, respectively). This type of analysis can be used to inform policy decisions relating to the timing of research. Conclusions. The value of information depends on future changes in technologies, prices, and evidence. Finite time horizons for decision problems can be seen as a proxy for the complex and uncertain process of future change. A more explicit approach to modeling these changes could provide a more appropriate basis for calculating EVI, but this raises a number of significant methodological and technical challenges.

Suggested Citation

  • Zoe Philips & Karl Claxton & Stephen Palmer, 2008. "The Half-Life of Truth: What Are Appropriate Time Horizons for Research Decisions?," Medical Decision Making, , vol. 28(3), pages 287-299, May.
  • Handle: RePEc:sae:medema:v:28:y:2008:i:3:p:287-299
    DOI: 10.1177/0272989X07312724
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    References listed on IDEAS

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    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    2. Brennan, Alan & Kharroubi, Samer A., 2007. "Efficient computation of partial expected value of sample information using Bayesian approximation," Journal of Health Economics, Elsevier, vol. 26(1), pages 122-148, January.
    3. 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.
    4. Karl Claxton & Simon Eggington & Laura Ginnelly & Susan Griffin & Christopher McCabe & Zoe Philips & Paul Tappenden & Alan Wailoo, 2005. "A Pilot Study of Value of Information Analysis to Support Research Recommendations for the National Institute for Health and Clinical Excellence," Working Papers 004cherp, Centre for Health Economics, University of York.
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    Cited by:

    1. Olivier Ethgen & Baudouin Standaert, 2012. "Population–versus Cohort–Based Modelling Approaches," PharmacoEconomics, Springer, vol. 30(3), pages 171-181, March.
    2. Karl Claxton & Elisabeth Fenwick & Mark J. Sculpher, 2012. "Decision-making with Uncertainty: The Value of Information," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 51, Edward Elgar Publishing.
    3. Lauren E. Cipriano & Thomas A. Weber, 2018. "Population-level intervention and information collection in dynamic healthcare policy," Health Care Management Science, Springer, vol. 21(4), pages 604-631, December.
    4. Stefano Conti & Karl Claxton, 2008. "Dimensions of design space: a decision-theoretic approach to optimal research design," Working Papers 038cherp, Centre for Health Economics, University of York.
    5. Laura McCullagh & Cathal Walsh & Michael Barry, 2012. "Value-of-Information Analysis to Reduce Decision Uncertainty Associated with the Choice of Thromboprophylaxis after Total Hip Replacement in the Irish Healthcare Setting," PharmacoEconomics, Springer, vol. 30(10), pages 941-959, October.
    6. Susan C. Griffin & Karl P. Claxton & Stephen J. Palmer & Mark J. Sculpher, 2011. "Dangerous omissions: the consequences of ignoring decision uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 20(2), pages 212-224, February.
    7. Karl Claxton & Stephen Palmer & Louise Longworth & Laura Bojke & Susan Griffin & Claire McKenna & Marta Soares & Eldon Spackman & Jihee Youn, 2011. "Uncertainty, evidence and irrecoverable costs: Informing approval, pricing and research decisions for health technologies," Working Papers 069cherp, Centre for Health Economics, University of York.

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