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Two‐Level Resampling As A Novel Method For The Calculation Of The Expected Value Of Sample Information In Economic Trials

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  • Mohsen Sadatsafavi
  • Carlo Marra
  • Stirling Bryan

Abstract

It has already been pointed out that the bootstrap can be used to calculate the expected value of perfect information (EVPI) when individual‐level data from a randomized controlled trial (RCT) is at hand. However, as mentioned by others, it is not clear if and how such a method can be extended to calculate the expected value of sample information (EVSI). In this article, we provide a nonparametric definition for EVPI and EVSI, which is based on considering the entire population distribution as the uncertain entity for which the current RCT provides partial information. This enables a theoretical justification for using the bootstrap for EVPI calculation and allows us to propose a two‐level resampling method for EVSI calculation. What is considered as the sampling unit in this algorithm can range from the individual level net benefits to the full panel of the RCT data for an individual, enabling the analyst to decide on a trade‐off between computational efficiency and comprehensiveness in value of information analysis. As such, we argue that this method, in addition to being consistent with the popular bootstrap method of RCT‐based economic evaluations, is a flexible approach for EVPI and EVSI calculations. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Mohsen Sadatsafavi & Carlo Marra & Stirling Bryan, 2013. "Two‐Level Resampling As A Novel Method For The Calculation Of The Expected Value Of Sample Information In Economic Trials," Health Economics, John Wiley & Sons, Ltd., vol. 22(7), pages 877-882, July.
    • Handle: RePEc:wly:hlthec:v:22:y:2013:i:7:p:877-882
    DOI: 10.1002/hec.2869
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    References listed on IDEAS

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    1. Andrew H. Briggs & David E. Wonderling & Christopher Z. Mooney, 1997. "Pulling cost‐effectiveness analysis up by its bootstraps: A non‐parametric approach to confidence interval estimation," Health Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 327-340, July.
    2. Simon Eckermann & Andrew R. Willan, 2007. "Expected value of information and decision making in HTA," Health Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 195-209, February.
    3. Andrew R. Willan & Simon Eckermann, 2010. "Optimal clinical trial design using value of information methods with imperfect implementation," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 549-561, May.
    4. James C. Felli & Gordon B. Hazen, 1998. "Sensitivity Analysis and the Expected Value of Perfect Information," Medical Decision Making, , vol. 18(1), pages 95-109, January.
    5. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.
    6. Anthony O'Hagan & John W. Stevens, 2003. "Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?," Health Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 33-49, January.
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