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

Journal article (2022)

Authors

Péter Bayer Esplanade de l’université, Toulouse
Joel S. Brown Lee Moffitt Cancer Center and Research Institute
J.L.A. Dubbeldam Mathematical Physics - EEMCS
Mark Broom City University London
Research Group
Mathematical Physics (EEMCS) (TU Delft)
Copyright: © 2022 Péter Bayer, Joel S. Brown, J.L.A. Dubbeldam, Mark Broom
DOI: https://doi.org/10.1016/j.jtbi.2022.111237
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

Research Group

Mathematical Physics

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.

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