| 孙一鑫,王淼,杨明芳,詹思延.树状扫描统计量用于药品和疫苗安全性监测的综述[J].Chinese journal of Epidemiology,2021,42(7):1286-1291 |
| 树状扫描统计量用于药品和疫苗安全性监测的综述 |
| Review on tree-based scan statistic in drug and vaccine safety monitoring |
| Received:November 03, 2020 |
| DOI：10.3760/cma.j.cn112338-20201103-01297 |
| KeyWord: 树状扫描统计量 药品安全性监测 不良反应 综述 |
| English Key Word: Tree-based scan statistic Drug safety monitoring Adverse event Review |
| FundProject: |
| Author Name | Affiliation | E-mail | | Sun Yixin | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Wang Miao | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Department of Epidemiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 100029, China | | | Yang Mingfang | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Zhan Siyan | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | siyan-zhan@bjmu.edu.cn |
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| Abstract: |
| 本综述系统梳理了国内外使用树状扫描统计量（TreeScan）方法开展的药物流行病学原始研究，总结TreeScan方法的发展与应用现状，阐述其方法学原理，为我国开展同类研究提供参考。通过系统检索英文文献数据库（Medline、Embase与Web of Science），最终纳入15项研究，其中9项为药物安全性监测研究，6项为疫苗安全性监测研究。研究中采用的TreeScan方法有3类：泊松概率模型、伯努利概率模型及树状时空扫描统计量模型。3类模型的差别主要体现在：在研究设计时是否预先设定了对照、是否考虑了暴露至出现不良事件间的风险期时长。部分研究比较了TreeScan方法与其他不良反应信号发掘方法的检出结果，或使用已知存在的不良反应，探讨了该方法对不良反应信号的检出能力。通过总结发现TreeScan是一种有效的药品安全性信号监测方法，国际上目前仍处于快速发展阶段，十分有必要推进TreeScan方法在我国药品、疫苗安全性监测和其他相关研究领域的探索与应用。 |
| English Abstract: |
| To summarize the development and application of tree-based scan statistic (TreeScan), explain the methodology and provide a reference for future use of this method by reviewing the original pharmacoepidemiological and vaccine studies using the TreeScan. Medline, Embase and Web of Science databases were used for the retrieval of eligible studies using keywords related to TreeScan. A total of 15 eligible studies were included, in which 9 studies explored the adverse events of drugs and 6 studies focused on the safety of vaccines. Three types of models (Poisson probability model, Bernoulli probability model and tree-temporal scan statistic model) of TreeScan were used. The major differences among the three models were 1) whether predefined control was used according to research question, 2) whether the time from exposure to onset of adverse events was considered. Several studies explored its ability by comparing with other methods for adverse event detection or by using known adverse events. This review shows that TreeScan is an effective method for the safety signal detection of drugs or vaccines, which develops rapidly and globally. It is very necessary to promote its use in drug safety monitoring and other related fields in China. |
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