Abstract
王小磊,田梦圆,张娜,高红,谭红专.纵向数据中评估暴露总效应的序列条件平均模型[J].Chinese journal of Epidemiology,2020,41(1):111-114
纵向数据中评估暴露总效应的序列条件平均模型
A sequential conditional mean model for assessing total effects of exposure in longitudinal data
Received:June 19, 2019  
DOI:10.3760/cma.j.issn.0254-6450.2020.01.020
KeyWord: 序列条件平均模型  时依性协变量  倾向评分  广义估计方程
English Key Word: Sequential conditional mean model  Time-dependent covariate  Propensity score  Generalized estimating equation
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Author NameAffiliationE-mail
Wang Xiaolei Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China  
Tian Mengyuan Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China  
Zhang Na Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China
Hunan Provincial People's Hospital/the First Affiliated Hospital of Hunan Normal University, Changsha 410016, China 
 
Gao Hong Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China  
Tan Hongzhuan Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China tanhz99@qq.com 
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Abstract:
      在前瞻性队列研究中,经常需要对研究对象进行多次随访,其产生的多个观测值之间相互关联,常导致时依性混杂,这种情况下的数据一般不满足传统的多因素回归分析的应用条件。序列条件平均模型(SCMM)是一种可以处理时依性混杂的新方法。本文主要对SCMM的基本原理、步骤及特点进行概括。
English Abstract:
      In prospective cohort study, multi follow up is often necessary for study subjects, and the observed values are correlated with each other, usually resulting in time-dependent confounding. In this case, the data generally do not meet the application conditions of traditional multivariate regression analysis. Sequential conditional mean model (SCMM) is a new approach that can deal with time-dependent confounding. This paper mainly summarizes the basic theory, steps and characteristics of SCMM.
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