Briggs A, Sculpher M
Health Economics Research Centre, Institute of Health Sciences, University of Oxford, England.
Pharmacoeconomics. 1998 Apr;13(4):397-409. doi: 10.2165/00019053-199813040-00003.
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.
马尔可夫模型常被用于表示随机过程，即随时间演变的随机过程。在医疗保健领域，马尔可夫模型特别适合对慢性病进行建模。在本文中，我们描述了马尔可夫模型在医疗保健干预措施经济评估中的应用。马尔可夫模型处理成本和结果的直观方式使其成为经济评估建模的有力工具。马尔可夫模型的时间成分可以提供标准决策树模型的优势，特别是在贴现方面。本文全面描述了用于经济评估的马尔可夫建模，包括对模型类型所基于的假设的讨论，最显著的是马尔可夫模型的无记忆特性，通常称为“马尔可夫假设”。采用一个减缓慢性病进展的药物干预的假设示例来演示建模技术，并探讨分析马尔可夫模型的可能方法。分析师应意识到马尔可夫模型的局限性，特别是马尔可夫假设，尽管熟练的建模者通常会找到解决这个问题的方法。
