| 王振宇,陈朔华,赵欣宇,王艳红,吴寿岭,王丽.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[J].Chinese journal of Epidemiology,2020,41(6):957-961 |
| 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 |
| Application of Cox and extended regression models on modeling the effect of time-updated exposures in cohort studies |
| Received:January 19, 2020 |
| DOI：10.3760/cma.j.cn112338-20200119-00046 |
| KeyWord: 队列研究 暴露变化 Cox比例风险回归 时间依赖性混杂 边际结构模型 |
| English Key Word: Cohort studies Time-updated exposures Cox proportional hazard model Time-dependent confounding Marginal structure model |
| FundProject:中国医学科学院医学与健康科技创新工程项目（2016-I2M-3-001） |
| Author Name | Affiliation | E-mail | | Wang Zhenyu | Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China | | | Chen Shuohua | Kailuan General Hospital, Tangshan 063000, China | | | Zhao Xinyu | Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China | | | Wang Yanhong | Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China | | | Wu Shouling | Kailuan General Hospital, Tangshan 063000, China | drwusl@163.com | | Wang Li | Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005, China | liwang@ibms.pumc.edu.cn |
|
| Hits: 5241 |
| Download times: 1918 |
| Abstract: |
| 队列研究的特点之一是暴露因素会随时间而改变，如何充分利用暴露因素及其协变量的变化及其相互关系，从而获得更真实的暴露因素与结局关系是目前的研究热点。本研究以开滦队列为例，探讨基于基线暴露状态、随时间变化的暴露信息以及同时控制依时混杂因素时，如何利用Cox比例风险回归及其拓展模型，包括依时Cox回归及边际结构模型，探讨FPG与肝癌的关系，概述并比较了上述拓展模型的基本原理、应用条件、估计结果及结果解释。 |
| English 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. |
|
View Fulltext
Html FullText
View/Add Comment Download reader |
| Close |
|
|
|