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 ^{1
,

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. 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.