| 刘秋萍,高培,唐迅,胡永华.马尔可夫模型在流行病学筛查成本效果分析中的应用[J].Chinese journal of Epidemiology,2021,42(4):728-734 |
| 马尔可夫模型在流行病学筛查成本效果分析中的应用 |
| Applications of Markov model for cost-effectiveness analysis of screening in epidemiology |
| Received:July 29, 2020 |
| DOI：10.3760/cma.j.cn112338-20200729-00993 |
| KeyWord: 马尔可夫模型 筛查 成本效果分析 |
| English Key Word: Markov model Screening Cost-effectiveness analysis |
| FundProject:国家自然科学基金（81973132，81961128006，81872695）；北京市自然科学基金（7182084） |
| Author Name | Affiliation | E-mail | | Liu Qiuping | 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: |
| 流行病学筛查的成本效果分析是进行公共卫生决策的重要依据。本文介绍了马尔可夫模型（Markov model）的基本原理、构建步骤、分析方法和相关注意事项。通过中国社区人群中开展的原发性开角型青光眼筛查成本效果分析的实例，从模型构建、参数设置、软件应用、基础分析、敏感性分析和结果解读等方面，详细讨论了马尔可夫模型用于筛查成本效果分析的要点。后续研究应注意马尔可夫模型参数的准确性和研究结果的透明度，并且需要按照相应的报告规范开展流行病学筛查的成本效果分析，以便更好地为公共卫生决策提供科学证据。 |
| English Abstract: |
| Cost-effectiveness analysis of screening in epidemiology is essential for public health decision-making. This paper describes the general principles, basic steps involved in implementation, analytic methods and other related issues of Markov model. Based on a practical research case of evaluating the cost-effectiveness of primary open-angle glaucoma screening in a Chinese population, key points in applications of Markov model for cost-effectiveness analysis of screening were discussed in detail, including model development, parameters definition, available software, base case analysis, sensitivity analysis and the interpretation of the results. For better supporting evidence-informed decision making in public health, future studies should be aware of the accuracy of parameters in Markov models and the transparency of the models and results, as well as complying with the relevant reporting standards. |
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