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Value-based clinical trials: selecting trial lengths and recruitment rates in different regulatory contexts

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  • Andres Alban
  • Stephen E. Chick
  • Martin Forster

Abstract

Health systems are placing increasing emphasis on improving the design and operation of clinical trials, with a view to increasing the rate of innovation and adoption of health technologies in a ‘value-based’ world. We present a value-based, Bayesian decision-theoretic model of a two-armed clinical trial and health technology adoption decision in which the recruitment rate and duration of the recruitment period are optimised. We account for a wide range of regulatory and practical contexts, addressing questions of how health is valued (considering discounting, the horizon of an adoption decision, and the endogenisation of outcomes for patients in the trial), and the state of clinical practice prior to commencement of the trial (we consider both exploratory trials for pharmaceutical research and pragmatic trials which compare technologies currently in use). We apply the model using research and treatment cost data from an existing trial and health technology assessment and challenge traditional perceptions concerning the efficiency, length and knowledge that may be gained from clinical research when trial teams are charged with delivering ‘value’ efficiently.

Suggested Citation

  • Andres Alban & Stephen E. Chick & Martin Forster, 2020. "Value-based clinical trials: selecting trial lengths and recruitment rates in different regulatory contexts," Discussion Papers 20/01, Department of Economics, University of York.
    • Handle: RePEc:yor:yorken:20/01
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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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    More about this item

    Keywords

    Bayesian sequential experimentation; Randomised clinical trials; Health technology assessment;
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