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Calculating Partial Expected Value of Perfect Information via Monte Carlo Sampling Algorithms

Author

Listed:
  • Alan Brennan

    (School of Health and Related Research, The University of Sheffield, Sheffield, England, a.brennan@sheffield.ac.uk)

  • Samer Kharroubi

    (Department of Mathematics, University of York, Heslington, York, England)

  • Anthony O'Hagan

    (Department of Probability and Statistics, The University of Sheffield, Sheffield, England)

  • Jim Chilcott

    (School of Health and Related Research, The University of Sheffield, Sheffield, England)

Abstract

Partial expected value of perfect information (EVPI) calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational approaches. This article examines the computation of partial EVPI estimates via Monte Carlo sampling algorithms. The mathematical definition shows 2 nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalized Monte Carlo sampling algorithm uses nested simulation with an outer loop to sample parameters of interest and, conditional upon these, an inner loop to sample remaining uncertain parameters. Alternative computation methods and shortcut algorithms are discussed and mathematical conditions for their use considered. Maxima of Monte Carlo estimates of expectations are biased upward, and the authors show that the use of small samples results in biased EVPI estimates. Three case studies illustrate 1) the bias due to maximization and also the inaccuracy of shortcut algorithms 2) when correlated variables are present and 3) when there is nonlinearity in net benefit functions. If relatively small correlation or nonlinearity is present, then the shortcut algorithm can be substantially inaccurate. Empirical investigation of the numbers of Monte Carlo samples suggests that fewer samples on the outer level and more on the inner level could be efficient and that relatively small numbers of samples can sometimes be used. Several remaining areas for methodological development are set out. A wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analyzing research priorities.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:medema:v:27:y:2007:i:4:p:448-470
    DOI: 10.1177/0272989X07302555
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    References listed on IDEAS

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    Cited by:

    1. Straub, Daniel & Ehre, Max & Papaioannou, Iason, 2022. "Decision-theoretic reliability sensitivity," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Mehdi Najafzadeh & Carlo Marra & Eleni Galanis & David Patrick, 2009. "Cost Effectiveness of Herpes Zoster Vaccine in Canada," PharmacoEconomics, Springer, vol. 27(12), pages 991-1004, December.
    3. Yunpeng Sun & Daniel W. Apley & Jeremy Staum, 2011. "Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation," Operations Research, INFORMS, vol. 59(4), pages 998-1007, August.
    4. Wei Fang & Zhenru Wang & Michael B. Giles & Chris H. Jackson & Nicky J. Welton & Christophe Andrieu & Howard Thom, 2022. "Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information," Medical Decision Making, , vol. 42(2), pages 168-181, February.
    5. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    6. Brett Houlding & Frank P. A. Coolen & Donnacha Bolger, 2015. "A Conjugate Class of Utility Functions for Sequential Decision Problems," Risk Analysis, John Wiley & Sons, vol. 35(9), pages 1611-1622, September.
    7. 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.
    8. 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.

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