文章摘要
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].中华流行病学杂志,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
收稿日期:2019-02-27  出版日期:2019-11-26
DOI:10.3760/cma.j.issn.0254-6450.2019.11.023
中文关键词: 观察性研究;因果推断;未测量混杂因素;敏感性分析
英文关键词: Observational study;Causal inference;Unmeasured confounding factor;Sensitivity analysis
基金项目: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)
作者单位E-mail
王丹华 南京医科大学公共卫生学院生物统计学系 211166  
尤东方 南京医科大学公共卫生学院生物统计学系 211166
南京医科大学现代毒理学教育部重点实验室 211166 
 
黄丽红 复旦大学附属中山医院生物统计室, 上海 200032  
赵杨 南京医科大学公共卫生学院生物统计学系 211166
江苏省恶性肿瘤生物标志物与防治重点实验室, 南京 211166
肿瘤个体化医学协同创新中心, 南京 211166
南京医科大学生物医学大数据重点实验室 211166 
zhaoyang@njmu.edu.cn 
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中文摘要:
      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。
英文摘要:
      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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