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

Peter Bayer, Joel S. Brown, Johan Dubbeldam and Mark Broom

Post-Print from HAL

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.

Keywords: Markov decision processes; Cancer therapy: Dynamic optimization; Quality of life (search for similar items in EconPapers)
Date: 2022-11
References: Add references at CitEc
Citations:

Published in Journal of Theoretical Biology, 2022, 551-552, ⟨10.1016/j.jtbi.2022.111237⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: A Markovian decision model of adaptive cancer treatment and quality of life (2022)
Working Paper: A Markovian decision model of adaptive cancer treatment and quality of life (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03767027

DOI: 10.1016/j.jtbi.2022.111237

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().