Toulouse School of Economics, 1 Esplanade de l'université 31080 Toulouse, France.
Department of Integrated Mathematical Oncology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL 33612, United States.
J Theor Biol. 2022 Nov 7;551-552:111237. doi: 10.1016/j.jtbi.2022.111237. Epub 2022 Aug 6.
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.
这篇论文开发并分析了一个用于癌症治疗的马尔可夫链模型。癌症治疗被建模为患者的马尔可夫决策问题，目标是最大化患者贴现后的预期生命质量年数。患者根据疾病的进展和自身偏好来决定治疗的持续时间。我们通过这个模型获得了一个强大的分析决策工具，患者可以通过它来选择他们喜欢的治疗策略。我们通过一个数值例子来说明患者的权衡取舍，并计算了在次优策略下患者群体所损失的价值。在第二个模型中，患者可以选择包括药物假期。通过延迟治疗，患者暂时放弃治疗的收益，以延迟其副作用。我们得到了一个分析工具，允许对最佳延迟时间进行数值近似。
