文章摘要
何一宁,刘丽丽,蔡倩莹,赵耐青,郑英杰.研究设计时混杂控制策略的结构分类[J].中华流行病学杂志,2018,39(7):999-1002
研究设计时混杂控制策略的结构分类
A structural classification of strategies for confounding control in research design
投稿时间:2017-11-23  
DOI:10.3760/cma.j.issn.0254-6450.2018.07.025
中文关键词: 混杂;有向无环图;研究设计;因果关系
英文关键词: Confounding control;Directed Acyclic Graphs;Research designs;Causality
基金项目:国家自然科学基金(81373065,81773490);国家重点研发计划“生物技术关键技术研发”重点专项(2017YFC1200203);上海市第四轮公共卫生体系建设三年行动计划重点学科项目(15GWZK0202)
作者单位E-mail
何一宁 200032 上海, 复旦大学公共卫生学院卫生微生物学教研室
200032 上海, 复旦大学公共卫生学院流行病学教研室 
 
刘丽丽 200032 上海, 复旦大学公共卫生学院卫生微生物学教研室  
蔡倩莹 200032 上海, 复旦大学公共卫生学院卫生微生物学教研室  
赵耐青 200032 上海, 复旦大学公共卫生学院生物统计学教研室  
郑英杰 200032 上海, 复旦大学公共卫生学院卫生微生物学教研室
200032 上海, 复旦大学公共卫生学院公共卫生安全教育部重点实验室
200032 上海, 复旦大学公共卫生学院国家卫生和计划生育委员会卫生技术评估重点实验室 
yjzheng@shmu.edu.cn 
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中文摘要:
      混杂影响着人群因果关系的发生。依据混杂因素是否已知、可测量及已测量,可将其分为4类情形。基于有向无环图,对混杂的控制策略分为两类:①混杂路径打断法,又可分为单路径和双路径打断法,分别对应于暴露完全干预法、限制法和分层法;②混杂路径保留法,分别对应于暴露不完全干预法(工具变量设计或不完美的随机对照试验)、中间变量法和匹配法。其中,随机对照试验、工具变量设计或孟德尔随机化设计、中间变量分析可满足4类混杂的控制,而限制法、分层法和匹配法仅适用于已知、可测量并已测量的混杂。识别不同类型混杂的控制机制,有助于在研究设计阶段提出应对措施,是获得正确因果效应估计的前提。
英文摘要:
      Confounding affects the causal relation among the population. Depending on whether the confounders are known, measurable or measured, they can be divided into four categories. Based on Directed Acyclic Graphs, the strategies for confounding control can be classified as (1) the broken-confounding-path method, which can be further divided into single and dual broken paths, corresponding to exposure complete intervention, restriction and stratification, (2) and the reserved-confounding-path method, which can be further divided into incomplete exposure intervention (in instrumental variable design and non-perfect random control test), mediator method and matching method. Among them, random control test, instrumental variable design or Mendelian randomized design, mediator method can meet the requirements for controlling all four types of confounders, while the restriction, stratification and matching methods are only applicable to known, measurable and measured confounders. Identifying the mechanisms of confounding control is a prerequisite for obtaining correct causal effect estimates, which will be helpful in research design.
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