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Exploring the Research Decision Space: The Expected Value of Information for Sequential Research Designs

Author

Listed:
  • Susan Griffin

    (Centre for Health Economics, University of York, York, United Kingdom, scg3@york.ac.uk)

  • Nicky J. Welton

    (Department of Community Based Medicine, University of Bristol, Bristol, United Kingdom)

  • Karl Claxton

    (Centre for Health Economics, University of York, York, United Kingdom)

Abstract

Purpose. To investigate the expected value of partial perfect information (EVPPI) and the research decisions it can address. Methods. Expected value of information (EVI) analysis assesses the expected gain in net benefit from further research. Where the expected value of perfect information (EVPI) exceeds the costs of additional research, EVPPI can be used to identify parameters that contribute most to the EVPI and parameters with no EVPPI that may be disregarded as targets for further research. Recently, it was noted that parameters with low EVPPI for a one-off research design may be associated with high EVPPI when considered as part of a sequential design. This article examines the characteristics and role of conditional and sequential EVPPI in EVI analysis. Results. The calculation of EVPPI is demonstrated for single parameters, groups of parameters, and conditional and sequential EVPPI. Conditional EVPPI is the value of perfect information about one parameter, conditional on having obtained perfect information about another. Sequential EVPPI is the value of perfect information for a sequential research design to investigate first one parameter, then another. Conditional EVPPI differs from the individual EVPPI for a single parameter. Sequential EVPPI includes elements from the joint EVPPI for the parameters and the EVPPI for the first parameter in sequence. Sequential designs allow abandonment of research on the second parameter on the basis of additional information obtained on the first. Conclusions. The research decision space addressed by EVI analyses can be widened by incorporating sequential EVPPI to assess sequential research designs.

Suggested Citation

  • Susan Griffin & Nicky J. Welton & Karl Claxton, 2010. "Exploring the Research Decision Space: The Expected Value of Information for Sequential Research Designs," Medical Decision Making, , vol. 30(2), pages 155-162, March.
    • Handle: RePEc:sae:medema:v:30:y:2010:i:2:p:155-162
    DOI: 10.1177/0272989X09344746
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    References listed on IDEAS

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    1. Hung‐po Chao & Stephen C. Peck & Y. Steve Wan, 1994. "Managing Uncertainty: The Tropospheric Ozone Challenge," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 465-475, August.
    2. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    3. 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.
    4. Fumie Yokota & Kimberly M. Thompson, 2004. "Value of Information Analysis in Environmental Health Risk Management Decisions: Past, Present, and Future," Risk Analysis, John Wiley & Sons, vol. 24(3), pages 635-650, June.
    5. 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.
    6. Alan Brennan & Samer Kharroubi & Anthony O'Hagan & Jim Chilcott, 2007. "Calculating Partial Expected Value of Perfect Information via Monte Carlo Sampling Algorithms," Medical Decision Making, , vol. 27(4), pages 448-470, July.
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