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Expected value of sample information for Weibull survival data

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  • Alan Brennan
  • Samer A. Kharroubi

Abstract

Expected value of sample information (EVSI) involves simulating data collection, Bayesian updating, and re‐examining decisions. Bayesian updating in Weibull models typically requires Markov chain Monte Carlo (MCMC). We examine five methods for calculating posterior expected net benefits: two heuristic methods (data lumping and pseudo‐normal); two Bayesian approximation methods (Tierney & Kadane, Brennan & Kharroubi); and the gold standard MCMC. A case study computes EVSI for 25 study options. We compare accuracy, computation time and trade‐offs of EVSI versus study costs. Brennan & Kharroubi (B&K) approximates expected net benefits to within ±1% of MCMC. Other methods, data lumping (+54%), pseudo‐normal (−5%) and Tierney & Kadane (+11%) are less accurate. B&K also produces the most accurate EVSI approximation. Pseudo‐normal is also reasonably accurate, whilst Tierney & Kadane consistently underestimates and data lumping exhibits large variance. B&K computation is 12 times faster than the MCMC method in our case study. Though not always faster, B&K provides most computational efficiency when net benefits require appreciable computation time and when many MCMC samples are needed. The methods enable EVSI computation for economic models with Weibull survival parameters. The approach can generalize to complex multi‐state models and to survival analyses using other smooth parametric distributions. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:hlthec:v:16:y:2007:i:11:p:1205-1225:a
    DOI: 10.1002/hec.1217
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    Cited by:

    1. Hawre Jalal & Fernando Alarid-Escudero, 2018. "A Gaussian Approximation Approach for Value of Information Analysis," Medical Decision Making, , vol. 38(2), pages 174-188, February.
    2. Hawre Jalal & Jeremy D. Goldhaber-Fiebert & Karen M. Kuntz, 2015. "Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling," Medical Decision Making, , vol. 35(5), pages 584-595, July.
    3. Anna Heath & Ioanna Manolopoulou & Gianluca Baio, 2019. "Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression," Medical Decision Making, , vol. 39(4), pages 347-359, May.
    4. Anna Heath & Natalia Kunst & Christopher Jackson & Mark Strong & Fernando Alarid-Escudero & Jeremy D. Goldhaber-Fiebert & Gianluca Baio & Nicolas A. Menzies & Hawre Jalal, 2020. "Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies," Medical Decision Making, , vol. 40(3), pages 314-326, April.
    5. Andrew Willan & Simon Eckermann, 2012. "Value of Information and Pricing New Healthcare Interventions," PharmacoEconomics, Springer, vol. 30(6), pages 447-459, June.
    6. Claire McKenna & Karl Claxton, 2011. "Addressing Adoption and Research Design Decisions Simultaneously," Medical Decision Making, , vol. 31(6), pages 853-865, November.
    7. 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.
    8. Hendrik Koffijberg & Claire Rothery & Kalipso Chalkidou & Janneke Grutters, 2018. "Value of Information Choices that Influence Estimates: A Systematic Review of Prevailing Considerations," Medical Decision Making, , vol. 38(7), pages 888-900, October.
    9. Mark Strong & Jeremy E. Oakley & Alan Brennan & Penny Breeze, 2015. "Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 35(5), pages 570-583, July.
    10. Nicolas A. Menzies, 2016. "An Efficient Estimator for the Expected Value of Sample Information," Medical Decision Making, , vol. 36(3), pages 308-320, April.
    11. 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.

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