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Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies

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  • Paolo Pertile
  • Martin Forster
  • Davide La Torre

Abstract

type="main" xml:id="rssa12025-abs-0001"> We present a Bayes sequential economic evaluation model for health technologies in which an investigator has flexibility over the timing of a decision to stop carrying out research and to conclude that one technology is preferred to another on cost-effectiveness grounds. We implement the model by using an evaluation of the treatment of bacterial sinusitis and derive approximations of the optimal stopping rule as a function of accumulated sample size. We compare the performance of the model with existing frequentist and Bayes sequential designs and investigate the sensitivity of the stopping rule to changes in the parameters of the model. Our results suggest that accounting for the dynamic nature of experimentation, together with its economic parameters, should lead to greater efficiency in resource allocation within healthcare systems.

Suggested Citation

  • Paolo Pertile & Martin Forster & Davide La Torre, 2014. "Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 419-438, February.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:419-438
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-2
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    Citations

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    Cited by:

    1. Stephen Chick & Martin Forster & Paolo Pertile, 2017. "A Bayesian decision theoretic model of sequential experimentation with delayed response," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1439-1462, November.
    2. Oluwaseun Sharomi & Tufail Malik, 2017. "Optimal control in epidemiology," Annals of Operations Research, Springer, vol. 251(1), pages 55-71, April.
    3. Sebastian Sund & Lars H. Sendstad & Jacco J. J. Thijssen, 2022. "Kalman filter approach to real options with active learning," Computational Management Science, Springer, vol. 19(3), pages 457-490, July.
    4. Andres Alban & Stephen E. Chick & Martin Forster, 2023. "Value-Based Clinical Trials: Selecting Recruitment Rates and Trial Lengths in Different Regulatory Contexts," Management Science, INFORMS, vol. 69(6), pages 3516-3535, June.
    5. Thijssen, Jacco J.J. & Bregantini, Daniele, 2017. "Costly sequential experimentation and project valuation with an application to health technology assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 202-229.
    6. Williamson, S. Faye & Jacko, Peter & Jaki, Thomas, 2022. "Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    7. Panos Kouvelis & Joseph Milner & Zhili Tian, 2017. "Clinical Trials for New Drug Development: Optimal Investment and Application," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 437-452, July.
    8. Stephen E. Chick & Noah Gans & Özge Yapar, 2022. "Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions," Management Science, INFORMS, vol. 68(7), pages 4919-4938, July.

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