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

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

Bayer, Péter (Esplanade de l’université, Toulouse)
Brown, Joel S. (Lee Moffitt Cancer Center and Research Institute)
Dubbeldam, J.L.A. (TU Delft Mathematical Physics)
Broom, Mark (City University London)

2022

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.

Cancer therapy
Dynamic optimization
Markov decision processes
Quality of life

http://resolver.tudelft.nl/uuid:1b81547a-8459-4e9b-b188-33200068389c

https://doi.org/10.1016/j.jtbi.2022.111237

2023-07-01

0022-5193

Journal of Theoretical Biology, 551-552

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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© 2022 Péter Bayer, Joel S. Brown, J.L.A. Dubbeldam, Mark Broom