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

X L Wang; M Y Tian; N Zhang; H Gao; H Z Tan

doi:10.3760/cma.j.issn.0254-6450.2020.01.020

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

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.

[Article in Chinese]

Authors

X L Wang¹, M Y Tian¹, N Zhang², H Gao¹, H Z Tan¹

Affiliations

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.

PMID: 32062952
DOI: 10.3760/cma.j.issn.0254-6450.2020.01.020

Abstract
in English, Chinese

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.

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。.

Keywords: Generalized estimating equation; Propensity score; Sequential conditional mean model; Time-dependent covariate.

MeSH terms

Confounding Factors, Epidemiologic*
Environmental Exposure*
Humans
Models, Statistical*
Multivariate Analysis
Prospective Studies

Abstract in English, Chinese

MeSH terms

Abstract
in English, Chinese