马尔可夫模型
计算机科学
马尔可夫链
马尔可夫过程
马尔可夫决策过程
背景(考古学)
变阶马尔可夫模型
马尔可夫性质
风险分析(工程)
管理科学
机器学习
数学
经济
医学
统计
生物
古生物学
作者
Andrew Briggs,Mark Sculpher
标识
DOI:10.2165/00019053-199813040-00003
摘要
Markov models are often employed to represent stochastic processes, that is, random processes that evolve over time. In a healthcare context, Markov models are particularly suited to modelling chronic disease. In this article, we describe the use of Markov models for economic evaluation of healthcare interventions. The intuitive way in which Markov models can handle both costs and outcomes make them a powerful tool for economic evaluation modelling. The time component of Markov models can offer advantages of standard decision tree models, particularly with respect to discounting. This paper gives a comprehensive description of Markov modelling for economic evaluation, including a discussion of the assumptions on which the type of model is based, most notably the memoryless quality of Markov models often termed the ‘Markovian assumption’. A hypothetical example of a drug intervention to slow the progression of a chronic disease is employed to demonstrate the modelling technique and the possible methods of analysing Markov models are explored. Analysts should be aware of the limitations of Markov models, particularly the Markovian assumption, although the adept modeller will often find ways around this problem.
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