| 王丹华,尤东方,黄丽红,赵杨.观察性研究中针对未测量混杂干扰的敏感性分析方法[J].Chinese journal of Epidemiology,2019,40(11):1470-1475 |
| 观察性研究中针对未测量混杂干扰的敏感性分析方法 |
| Sensitivity analysis method for unmeasured confounding interference in observational study |
| Received:February 27, 2019 |
| DOI：10.3760/cma.j.issn.0254-6450.2019.11.023 |
| KeyWord: 观察性研究 因果推断 未测量混杂因素 敏感性分析 |
| English Key Word: Observational study Causal inference Unmeasured confounding factor Sensitivity analysis |
| FundProject:国家自然科学基金（81872709，81903407）；江苏省高等学校自然科学研究重大项目（18KJA110004）；江苏省青蓝工程学科带头人；南京医科大学中青年学术带头人项目；江苏省预防医学优势学科；江苏省社会发展项目（BE2017749） |
| Author Name | Affiliation | E-mail | | Wang Danhua | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | | | You Dongfang | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Key Laboratory of Modern Toxicology, Ministry of Education, Nanjing Medical University, Nanjing 211166, China | | | Huang Lihong | Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China | | | Zhao Yang | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
Jiangsu Provincial Key Laboratory of Malignant Tumor Biomarkers and Prevention, Nanjing 211166, China
Collaborative Innovation Center for Individual Medicine in Cancer, Nanjing 211166, China
Key Laboratory of Biomedical Big Data, Nanjing Medical University, Nanjing 211166, China | zhaoyang@njmu.edu.cn |
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| Abstract: |
| 目的 介绍敏感性分析方法，并对不同方法进行探讨和比较。方法 通过模拟试验和实例比较混杂函数敏感性分析法和边界因子敏感性分析方法在观察性研究中校正未测量混杂因素准确性的差异。结果 模拟试验与实际例子研究结果均显示，当暴露（X）与结局（Y）之间存在未测量混杂情况下，混杂函数法和边界因子相比，在分析未测量混杂因素的效应至少达到多大强度才能导致观测效应值大小和方向彻底改变的问题上，混杂函数和边界因子分析结果相似。但混杂函数法在完全解释观测效应值时所需的混杂效应强度小于边界因子做出同样解释所需的混杂效应值。边界因子分析中设置两个参数，而混杂函数中只有一个参数，混杂函数法在分析计算过程中较边界因子法简便灵敏。结论 对于真实世界观察性研究数据，分析暴露（X）与结局（Y）之间的因果效应时，敏感性分析过程必不可少，从计算过程和结果解释上，混杂函数敏感性分析方法是一个值得推荐的方法。 |
| English Abstract: |
| Objective To introduce the methods for sensitivity analysis, discuss and compare the advantages and disadvantages of different methods. Methods The difference between confounding function method and bounding factor method in accuracy of identifying unmeasured confounding factors in observational studies through simulation trials and actual clinical data was compared. Results The results of simulation trials and actual clinical data showed that when there was unmeasured confounding between exposure (X) and outcome (Y), the results of confounding function and the bounding factor analysis were similar in terms of the effect of unmeasured confounding factor to lead to the complete change of the magnitude and direction of the observed effect value. However, the confounding function method needed smaller confounding effect to fully interpret the observed effect value than the bounding factor needed. In addition, the bounding factor method needed to analyze two confounding parameters, while only one parameter was needed in the confounding function method. The confounding function method was simpler and more sensitive than the bounding factor method. Conclusion For real-world observational data, the sensitivity analysis process is essential in analyzing the causal effects between exposure (X) and outcome (Y). In terms of the calculation process and result interpretation the sensitivity analysis method of confounding function is worth to recommend. |
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