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A Markovian decision model of adaptive cancer treatment and quality of life

Author

Listed:
  • Peter Bayer

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Joel S. Brown
  • Johan Dubbeldam
  • Mark Broom

Abstract

This paper develops and analyzes a Markov chain model for the treatment of cancer. Cancer therapy is modeled as the patient's Markov Decision Problem, with the objective of maximizing the patient's discounted expected quality of life years. Patients make decisions on the duration of therapy based on the progression of the disease as well as their own preferences. We obtain a powerful analytic decision tool through which patients may select their preferred treatment strategy. We illustrate the tradeoffs patients in a numerical example and calculate the value lost to a cohort in suboptimal strategies. In a second model patients may make choices to include drug holidays. By delaying therapy, the patient temporarily forgoes the gains of therapy in order to delay its side effects. We obtain an analytic tool that allows numerical approximations of the optimal times of delay.

Suggested Citation

  • Peter Bayer & Joel S. Brown & Johan Dubbeldam & Mark Broom, 2022. "A Markovian decision model of adaptive cancer treatment and quality of life," Post-Print hal-03767027, HAL.
    • Handle: RePEc:hal:journl:hal-03767027
    DOI: 10.1016/j.jtbi.2022.111237
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