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Simulation sample sizes for Monte Carlo partial EVPI calculations


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  • Oakley, Jeremy E.
  • Brennan, Alan
  • Tappenden, Paul
  • Chilcott, Jim
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    Partial expected value of perfect information (EVPI) quantifies the value of removing uncertainty about unknown parameters in a decision model. EVPIs can be computed via Monte Carlo methods. An outer loop samples values of the parameters of interest, and an inner loop samples the remaining parameters from their conditional distribution. This nested Monte Carlo approach can result in biased estimates if small numbers of inner samples are used and can require a large number of model runs for accurate partial EVPI estimates. We present a simple algorithm to estimate the EVPI bias and confidence interval width for a specified number of inner and outer samples. The algorithm uses a relatively small number of model runs (we suggest approximately 600), is quick to compute, and can help determine how many outer and inner iterations are needed for a desired level of accuracy. We test our algorithm using three case studies.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Health Economics.

    Volume (Year): 29 (2010)
    Issue (Month): 3 (May)
    Pages: 468-477

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    Handle: RePEc:eee:jhecon:v:29:y:2010:i:3:p:468-477

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    Keywords: Economic model Expected value of perfect information Monte Carlo estimation Bayesian decision theory;


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    1. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023.
    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. Meltzer, David, 2001. "Addressing uncertainty in medical cost-effectiveness analysis: Implications of expected utility maximization for methods to perform sensitivity analysis and the use of cost-effectiveness analysis to s," Journal of Health Economics, Elsevier, vol. 20(1), pages 109-129, January.
    4. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310.
    5. Bas Groot Koerkamp & M. G. Myriam Hunink & Theo Stijnen & Milton C. Weinstein, 2006. "Identifying key parameters in cost-effectiveness analysis using value of information: a comparison of methods," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 383-392.
    6. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769.
    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. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225.
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    Cited by:
    1. 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.
    2. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.


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