Abstract
吴诗蓝,周价,李逊,黄麟婷,张佳月,郭楚豪,龙斯思,谭红专.Parametric g-formula方法在因果分析中的应用[J].Chinese journal of Epidemiology,2019,40(10):1310-1313
Parametric g-formula方法在因果分析中的应用
Application of parametric g-formula in causal analysis
Received:January 27, 2019  
DOI:10.3760/cma.j.issn.0254-6450.2019.10.025
KeyWord: 时依性混杂  Parametric g-formula方法  因果分析
English Key Word: Time-varying confounding  Parametric g-formula method  Causal analysis
FundProject:国家自然科学基金(81773535)
Author NameAffiliationE-mail
Wu Shilan Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410008, China  
Zhou Jia Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410008, China  
Li Xun Institute of Pediatric Research, Hunan Children's Hospital, Changsha 410007, China  
Huang Linting Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410008, China  
Zhang Jiayue Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410008, China  
Guo Chuhao Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410008, China  
Long Sisi Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410008, China  
Tan Hongzhuan Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410008, China tanhz99@qq.com 
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Abstract:
      目前,传统的统计学方法在控制时依性混杂等方面存在局限,本研究详细介绍了一种可调整时依性混杂的分析方法——parametric g-formula,并举例说明了实施的具体步骤,为研究者处理长期观察性数据提供了新的参考。
English Abstract:
      At present, traditional methods on statistics have limitations in controlling time- varying confounding. This paper introduces an analysis method, parametric g-formula, which would adjust time-varying confounding, and also exemplifies the steps of its implementation for purpose to provide a new reference for researchers to deal with long-term observational data.
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