MARKOV MODELING AND DISCRETE EVENT SIMULATION IN HEALTH CARE: A SYSTEMATIC COMPARISON
MARKOV MODELING AND DISCRETE EVENT SIMULATION IN HEALTH CARE: A SYSTEMATIC COMPARISON
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DOI：
10.1017/s0266462314000117
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发表时间：
2014-04-01
影响因子：
3.2
通讯作者：
Scuffham, Paul
中科院分区：
文献类型：
--
作者：
Standfield, Lachlan;Comans, Tracy;Scuffham, Paul
目的：本研究的目的是评估是否使用马尔可夫模型（MM）或离散事件模拟（DES）的成本效益分析（CEA）可能会改变医疗资源分配decisions.Methods：一个系统的文献检索和审查的经验和非经验的研究比较MM和DES技术中使用的CEA的医疗technologies were conducted.Results：22个相关的出版物被确定。2篇文献从经验上比较了MM和DES模型，1篇文献提出了概念性DES和MM，2篇文献描述了DES共识指南，17篇文献通过作者的经验比较了MM和DES。DES相对于MM的主要优势是能够对有限资源的排队进行建模，捕获个体患者病史，适应复杂性和不确定性，灵活地表示时间，对竞争风险进行建模，并同时适应多个事件。DES相对于MM的缺点是模型过度规范的可能性、增加的数据要求、专门的昂贵软件以及增加的模型开发、验证和计算时间。在个体患者历史是未来事件的重要驱动力的情况下，个体患者模拟技术（如DES）可能优于MM。和通过医疗保健系统中的其他途径转移患者可能是成本效益的驱动因素，DES建模方法可以为决策者提供更准确的信息，以作为资源分配决策的基础。当这些不是成本效益问题的主要特征时，MM仍然是确定新医疗保健干预措施的成本效益的有效，易于验证，简约和准确的方法。
Objectives: The aim of this study was to assess if the use of Markov modeling (MM) or discrete event simulation (DES) for cost-effectiveness analysis (CEA) may alter healthcare resource allocation decisions.Methods: A systematic literature search and review of empirical and non-empirical studies comparing MM and DES techniques used in the CEA of healthcare technologies was conducted.Results: Twenty-two pertinent publications were identified. Two publications compared MM and DES models empirically, one presented a conceptual DES and MM, two described a DES consensus guideline, and seventeen drew comparisons between MM and DES through the authors' experience. The primary advantages described for DES over MM were the ability to model queuing for limited resources, capture individual patient histories, accommodate complexity and uncertainty, represent time flexibly, model competing risks, and accommodate multiple events simultaneously. The disadvantages of DES over MM were the potential for model overspecification, increased data requirements, specialized expensive software, and increased model development, validation, and computational time.Conclusions: Where individual patient history is an important driver of future events an individual patient simulation technique like DES may be preferred over MM. Where supply shortages, subsequent queuing, and diversion of patients through other pathways in the healthcare system are likely to be drivers of cost-effectiveness, DES modeling methods may provide decision makers with more accurate information on which to base resource allocation decisions. Where these are not major features of the cost-effectiveness question, MM remains an efficient, easily validated, parsimonious, and accurate method of determining the cost-effectiveness of new healthcare interventions.