Application of Cox and extended regression models on modeling the effect of time-updated exposures in cohort studies

VernacularTitle:Cox及其拓展模型在基于队列的依时暴露因素效应估计中的应用
Author: Zhenyu WANG ¹ ; Shuohua CHEN ; Xinyu ZHAO ; Yanhong WANG ; Shouling WU ; Li WANG
Author Information

1. 中国医学科学院基础医学研究所/北京协和医学院基础学院流行病学和卫生统计学系，北京　100005
Keywords: Cohort studies; Time-updated exposures; Cox proportional hazard model; Time-dependent confounding; Marginal structure model
From: Chinese Journal of Epidemiology 2020;41(6):957-961
CountryChina
Language:Chinese
Abstract: One of the characteristics on cohort studies is that exposures may change over time. The full use of information related to time-updated exposures, time-dependent covariates and their relationships to estimate the association between exposures and outcomes has become the hotspot of research. In this paper, the Kailuan cohort is used as an example to explore the association between fasting blood-glucose and hepatocellular carcinoma, based on different Cox regression models. Cox or time-dependent Cox regression models can be used to estimate the impact of exposure at baseline or on the time-updated exposures. When time-dependent confounders exist, marginal structure model is recommended. We also summarize the basic principles, conditions of applications, effect estimates, and results interpretation for each model, in this paper.