[A sequential conditional mean model for assessing total effects of exposure in longitudinal data].
作者信息
Wang X L, Tian M Y, Zhang N, Gao H, Tan H Z
机构信息
Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China.
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.
出版信息
Zhonghua Liu Xing Bing Xue Za Zhi. 2020 Jan 10;41(1):111-114. doi: 10.3760/cma.j.issn.0254-6450.2020.01.020.
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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