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Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching

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  • Anna Heath
  • Ioanna Manolopoulou
  • Gianluca Baio

Abstract

Background. The Expected Value of Sample Information (EVSI) is used to calculate the economic value of a new research strategy. Although this value would be important to both researchers and funders, there are very few practical applications of the EVSI. This is due to computational difficulties associated with calculating the EVSI in practical health economic models using nested simulations. Methods. We present an approximation method for the EVSI that is framed in a Bayesian setting and is based on estimating the distribution of the posterior mean of the incremental net benefit across all possible future samples, known as the distribution of the preposterior mean. Specifically, this distribution is estimated using moment matching coupled with simulations that are available for probabilistic sensitivity analysis, which is typically mandatory in health economic evaluations. Results. This novel approximation method is applied to a health economic model that has previously been used to assess the performance of other EVSI estimators and accurately estimates the EVSI. The computational time for this method is competitive with other methods. Conclusion. We have developed a new calculation method for the EVSI which is computationally efficient and accurate. Limitations. This novel method relies on some additional simulation so can be expensive in models with a large computational cost.

Suggested Citation

  • Anna Heath & Ioanna Manolopoulou & Gianluca Baio, 2018. "Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching," Medical Decision Making, , vol. 38(2), pages 163-173, February.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:2:p:163-173
    DOI: 10.1177/0272989X17738515
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    References listed on IDEAS

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    1. 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, November.
    2. Rochelle P Walensky & Robin Wood & Andrea L Ciaranello & A David Paltiel & Sarah B Lorenzana & Xavier Anglaret & Adam W Stoler & Kenneth A Freedberg & for the CEPAC-International Investigators, 2010. "Scaling Up the 2010 World Health Organization HIV Treatment Guidelines in Resource-Limited Settings: A Model-Based Analysis," PLOS Medicine, Public Library of Science, vol. 7(12), pages 1-12, December.
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    4. 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.
    5. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    6. 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.
    7. Karl Claxton, 1999. "Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 269-274, May.
    8. 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.
    9. Mark Strong & Jeremy E. Oakley & Alan Brennan, 2014. "Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 34(3), pages 311-326, April.
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    Cited by:

    1. Mathyn Vervaart & Eline Aas & Karl P. Claxton & Mark Strong & Nicky J. Welton & Torbjørn Wisløff & Anna Heath, 2023. "General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations," Medical Decision Making, , vol. 43(5), pages 595-609, July.
    2. Anna Heath, 2022. "Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation," Medical Decision Making, , vol. 42(5), pages 626-636, July.
    3. 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.
    4. Anna Heath & Mark Strong & David Glynn & Natalia Kunst & Nicky J. Welton & Jeremy D. Goldhaber-Fiebert, 2022. "Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial," Medical Decision Making, , vol. 42(2), pages 143-155, February.
    5. Haitham Tuffaha, 2021. "Value of Information Analysis: Are We There Yet?," PharmacoEconomics - Open, Springer, vol. 5(2), pages 139-141, June.

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