| 王天雷,牟育彤,阚慧,李亚欣,范伟,戴江红,郑英杰.基于大自然时间轴的测量时序分类法[J].Chinese journal of Epidemiology,2020,41(5):782-787 |
| 基于大自然时间轴的测量时序分类法 |
| A new classification of measured temporalities: based on the time axis in nature |
| Received:September 29, 2019 |
| DOI：10.3760/cma.j.cn112338-20190929-00711 |
| KeyWord: 因果思维 因果时序 测量时序 因果推断 流行病学 观察 实验 |
| English Key Word: Causal thinking Causal temporality Measured temporality Causal inference Epidemiology Observation Experiment |
| 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 | | Wang Tianlei | 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, | | | 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, | | | 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, | yjzheng@fudan.edu.cn |
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| Abstract: |
| 因果推断中，时序（或方向性）的概念尚未完全明确。本文从因果思维出发，以真实因和真实果的发生时间将大自然时间轴划分为3个时域和2个时点，从而锚定了因果推断只能实现于第3时域。测量时序可分为5种类型：跨第1和3时域纵向时序（实验性时序）、跨第2和3时域纵向时序、同时域纵向时序、同时域逆纵向时序和同时域横向时序（观察性时序）。这种分类法适用于首次或多次测量、及时和延后测量等所有测量策略。除了实验中真实因的测量（或干预措施）在其发生之前（第1时域）或观察和实验中真实因的测量在真实果发生之前（第2时域）的情形外，所有其他测量策略类似于历史重建或"考古"，测量时序的重要性次于测量的准确性。从研究设计应整合偏倚设计的观点来看，本文提出基于大自然时间轴的测量时序五分类法，概念清楚并将有助于判断研究过程中可能出现的偏倚，为正确进行因果推断研究奠定基础。 |
| English Abstract: |
| In causal inference, the concept of temporality (or directionality) has not been fully clarified. Starting from causal thinking, this paper divides the time axis in nature into three time domains and two time points by the occurrence timings of both a real cause and a real effect. This has anchored that causal inference can only be realized in the third domain. The measured temporalities can be divided into five types:cross-first-to-third-domain longitudinal (or experimental temporalities), cross-second-to-third-domain longitudinal, within-domain longitudinal, within-domain reversely longitudinal, and within-domain transversal (or observational temporalities). This new classification encompasses all measurement strategies, either for first or multiple measurements, or timely and delayed measurements. Except that the actual measurement for the cause occurs either before its occurrence (only in experiment) or within the second domain, all other measurements are similar to the act of historical reconstruction or "archaeology", where the importance of measured temporalities may be inferior to the accuracy of the measurements. From the point of view that research design should integrate bias design, this new classification for measured temporalities based on the time axis in Nature, which has a clear meaning and helps to judge the possible biases in the observation methods, provides a basis for correct causal inferences. |
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