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

Joel Brown, Johan Dubbeldam, Mark Broom and Peter Bayer ()

No 22-1291, TSE Working Papers from Toulouse School of Economics (TSE)

Keywords: 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 are facing in a numerical example and calculate the value lost to a cohort who choose 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. (search for similar items in EconPapers)
Date: 2022-01-21
New Economics Papers: this item is included in nep-hea
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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)
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