A sequential conditional mean model for assessing total effects of exposure in longitudinal data

VernacularTitle: 纵向数据中评估暴露总效应的序列条件平均模型
Author: Xiaolei WANG ¹ ; Mengyuan TIAN ¹ ; Na ZHANG ² ; Hong GAO ¹ ; Hongzhuan TAN ¹
Author Information

1. Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, China
2. 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
Publication Type:Journal Article
Keywords: Sequential conditional mean model; Time-dependent covariate; Propensity score; Generalized estimating equation
From: Chinese Journal of Epidemiology 2020;41(1):111-114
CountryChina
Language:Chinese
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.