| 王佳敏,刘秋萍,巩超,张明露,高培,唐迅,胡永华.离散事件模拟模型在流行病学筛查成本效果分析中的应用[J].Chinese journal of Epidemiology,2023,44(3):463-469 |
| 离散事件模拟模型在流行病学筛查成本效果分析中的应用 |
| Application of discrete event simulation model in analysis on cost-effectiveness of epidemiology screening |
| Received:July 25, 2022 |
| DOI：10.3760/cma.j.cn112338-20220725-00659 |
| KeyWord: 筛查 离散事件模拟 成本效果分析 |
| English Key Word: Screening Discrete event simulation Cost-effectiveness analysis |
| FundProject:国家自然科学基金（81973132，81872695） |
| Author Name | Affiliation | E-mail | | Wang Jiamin | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Liu Qiuping | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Gong Chao | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Zhang Minglu | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Gao Pei | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Tang Xun | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | tangxun@bjmu.edu.cn | | Hu Yonghua | Medical Informatics Center, Peking University, Beijing 100191, China | yhhu@bjmu.edu.cn |
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| Abstract: |
| 离散事件模拟（DES）模型基于个体水平的数据，通过模拟发生在不同时间的各离散事件的情况来反映疾病发生发展过程，不仅可以处理个体特征对疾病进展的影响，由于其不设定固定的周期，对事件发生时间的估计相比状态转换模型更精确，特别适用于考虑时间维度的复杂疾病筛查及干预策略的长期效果评价。本文介绍了DES模型的基本原理、构建步骤、分析方法及相关注意事项。结合在英国≥65岁的女性人群中开展的一项腹主动脉瘤筛查成本效果分析的研究实例，从模型构建、模型分析和结果解读等方面，详细讨论了DES模型在复杂疾病筛查成本效果分析中实际应用的要点。DES模型通过建立离散事件发生时间的分布函数，实现对事件发生时间的精确估计，越来越多地应用于需考虑时间维度的复杂疾病筛查策略评价。在建模过程中应注意清晰展现模型结构和模拟过程，并遵循相应的报告规范开展成本效果分析，以保证研究的透明度和可重复性。 |
| English Abstract: |
| Discrete event simulation (DES) model is based on individual data, by which discrete events over time are simulated to reflect disease progression. The effects of individual characteristics on disease progression could be considered in the DES model. Moreover, unlike state-transition models, DES model without setting of fixed cycle can contribute to more accurate estimation of event time, especially in the evaluation of the long-term effectiveness of screening strategies for complex diseases in which time dimension needs to be considered. This article introduces the general principles, construction steps, analytic methods and other relevant issues of the DES model. Based on a research case of estimating the cost-effectiveness of screening for abdominal aortic aneurysms in women aged 65 years and above in the United Kingdom, key points in applications of the DES model in analysis on effectiveness of complex disease screening are discussed in detail, including model construction and analysis and interpretation of the results. DES model can predict occurring time of discrete events accurately by establishing the distribution function of their occurring time and is increasingly used to evaluate the screening strategies for complex diseases in which time dimension needs to be considered. In the construction of DES model, it is necessary to pay close attention to the clear presentation of model structure and simulation process and follow the relevant reporting specification to conduct cost-effectiveness analysis to ensure the transparency and repeatability of the research. |
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