A Markovian decision model of adaptive cancer treatment and quality of life
Peter Bayer (Esplanade de l’université, Toulouse)
Joel S. Brown (Lee Moffitt Cancer Center and Research Institute)
J.L.A. Dubbeldam (TU Delft - Mathematical Physics)
Mark Broom (City University London)
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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.