A sequential conditional mean model for assessing total effects of exposure in longitudinal data
10.3760/cma.j.issn.0254-6450.2020.01.020
- 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.
