| 张淼,朱以敏,李亚欣,牟育彤,阚慧,范伟,戴江红,郑英杰.因果推断中研究人群的形成[J].Chinese journal of Epidemiology,2021,42(7):1292-1298 |
| 因果推断中研究人群的形成 |
| Formation of study population for causal inference |
| Received:June 12, 2020 |
| DOI：10.3760/cma.j.cn112338-20200612-00839 |
| KeyWord: 因果思维 研究人群 因果推断 横截面 纵向 研究设计 |
| English Key Word: Causal thinking Study population Causal inference Cross-section Longitudinal Research design |
| FundProject:国家自然科学基金（81373065，81773490）；国家重点研发计划（2017YFC1200203） |
| Author Name | Affiliation | E-mail | | Zhang Miao | Department of Epidemiology/Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning/Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China | | | Zhu Yimin | Department of Epidemiology/Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning/Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China | | | Li Yaxin | Department of Epidemiology/Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning/Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China | | | Mou Yutong | Department of Epidemiology/Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning/Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China | | | Kan Hui | Department of Epidemiology/Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning/Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China | | | Fan Wei | Department of Epidemiology/Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning/Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China | | | Dai Jianghong | Department of Epidemiology and Biostatistics, School of Public Health, Xinjiang Medical University, Urumqi 830011, China | | | Zheng Yingjie | Department of Epidemiology/Key Laboratory for Health Technology Assessment, National Commission of Health and Family Planning/Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China | zhengshmu@gmail.com |
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| Abstract: |
| 流行病学是对一定数量的人群进行特征描述和比较，并在此基础上进行因果推断。研究人群的形成是其第一步。本研究以观察性研究为例，首先定义个体截面和人群截面，并阐明其测量需满足的3个假设：属性真实值随时间保持不变，属性变量间互不干扰，个体间互不干扰；接着指出因果推断研究应以待定因（或暴露）的发生或状态开始的时间为标准进行统一；最后，基于人群截面的双重角色，提出人群的因果推断研究可分为2类：历史重建研究和探索未来研究，并初步梳理了研究设计框架、估计的效应及设计间的关系。从因果思维角度探讨研究人群的形成过程，可为明确因果推断研究设计类型奠定基础，选取合适的效应估计进行因果推断，值得深入研究。 |
| English Abstract: |
| Epidemiological analysis describes and compares the characteristics of a certain number of people to make causal inferences. The formation of the study population is always the first step. In this paper, we first define the concepts of cross-sections at both individual level and population level and introduce the three assumptions needed in the measurements in observational studies, i. e. the true values of the attributes are stable with time, the attribute variables are independent and the individuals are independent during the measuring process. We also determine that the causal inference research should be unified based on the time of the occurrence or beginning of a postulated cause, or exposure, should be in. Then, based on the dual roles of the population cross-section with causal thinking, we propose that research designs can be classified into two types with different characteristics:history reconstruction research and future exploration research. Finally, we briefly analyze the research design framework and the relationship between estimated effects and different designs. The discussion of the formation of a study population from the perspective of causal thinking can make a foundation for the classification of causal inference research design with appropriate effect parameters, which needs to be further studied. |
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