| 郭金鑫,赵厚宇,詹思延.基于多中心数据库的观察性关联分析中残余混杂的控制与评估方法[J].Chinese journal of Epidemiology,2023,44(8):1296-1301 |
| 基于多中心数据库的观察性关联分析中残余混杂的控制与评估方法 |
| Methods for controlling and evaluating residual confounding in the association analysis of observational study with a multicenter database |
| Received:February 16, 2023 |
| DOI：10.3760/cma.j.cn112338-20230216-00083 |
| KeyWord: 多中心数据库 观察性研究 残余混杂 统计方法 |
| English Key Word: Multicenter database Observational study Residual confounding Statistical method |
| FundProject:国家自然科学基金（81973146） |
| Author Name | Affiliation | E-mail | | Guo Jinxin | Key Laboratory of Epidemiology of Major Diseases, Ministry of Education/Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Zhao Houyu | Key Laboratory of Epidemiology of Major Diseases, Ministry of Education/Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Zhan Siyan | Key Laboratory of Epidemiology of Major Diseases, Ministry of Education/Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, China
Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing 100871, China | siyan-zhan@bjmu.edu.cn |
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| Abstract: |
| 基于健康医疗大数据的观察性研究越来越受到关注,残余混杂的控制与评估是其中亟须解决的关键问题,本文总结了多中心场景下开展关联分析的残余混杂统计学调整和敏感性分析方法。基于个体水平数据,可由各分中心使用断点回归等多种方法调整残余混杂,然后加权合并得到效应估计值;基于Meta水平数据,可采用贝叶斯Meta分析的方法获得调整后的合并效应值,也可开展残余混杂的敏感性分析,计算E值、p(q)、T(r,q)和G(r,q)。上述方法应根据适用条件及优缺点进行合理选择,如利用分中心个体数据进行残余混杂调整,通常要求严格的研究设计,并面临较高的协调成本;贝叶斯Meta分析基于部分强假设;E值等敏感性分析结果仍需经过专业的判断,以评估残余混杂风险大小。因此,利用多中心数据库开展观察性关联分析时,残余混杂的控制与评估方法仍待进一步发展和完善。 |
| English Abstract: |
| The observational research based on big data in healthcare has attracted increasing attention, with the control and evaluation of residual confounding being the critical issue that needs to be solved urgently. This review summarized the methods for statistical adjustment and sensitivity analysis of residual confounding in the association analysis with a multicenter database. Based on individual-level data, the residual confounding can be adjusted in each subcenter using methods such as regression discontinuity design, while the pooled estimate can be obtained as a weighted average. Based on the center-level results, the Bayesian Meta-analysis method can adjust the pooled estimates. The sensitivity analysis of residual confounding can also be carried out using center-level data to calculate the E-value, p(q)、T(r,q) and G(r,q). The abovementioned methods should be selected reasonably according to the requirements for practical applications, advantages, and disadvantages. For example, the use of subcenter individual data for residual confounding adjustment usually needs strict study design and frequent coordination; the Bayesian Meta-analysis is based on some strong assumptions; the interpretation of the results in the sensitivity analysis, such as E-value requires professional judgment to assess the risk of residual confounding. Therefore, the methods for controlling and evaluating residual confounding in association analysis based on multicenter databases still need further development and improvement. |
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