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Zhang M, Zhu YM, Li YX, Mou YT, Kan H, Fan W, Dai JH, Zheng YJ. [Formation of study population for causal inference]. Zhonghua Liu Xing Bing Xue Za Zhi 2021; 42:1292-1298. [PMID: 34814546 DOI: 10.3760/cma.j.cn112338-20200612-00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
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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Affiliation(s)
- M Zhang
- 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
| | - Y M Zhu
- 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
| | - Y X Li
- 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
| | - Y T Mou
- 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
| | - H Kan
- 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
| | - W Fan
- 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
| | - J H Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Xinjiang Medical University, Urumqi 830011, China
| | - Y J Zheng
- 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
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